ECCV2026

Abstract:
Recent joint audio-visual diffusion models achieve remark-able generation quality but suffer from high latency due to their bidirec-tional attention dependencies, hindering real-time applications. We pro-pose OmniForcing, the first framework to distill an offline, dual-streambidirectional diffusion model into a high-fidelity streaming autoregressivegenerator. However, naively applying causal distillation to such dual-stream architectures triggers severe training instability, due to the ex-treme temporal asymmetry between modalities and the resulting tokensparsity. We address the inherent information density gap by introducingan Asymmetric Block-Causal Alignment with a zero-truncation GlobalPrefix that prevents multi-modal synchronization drift. The gradient ex-plosion caused by extreme audio token sparsity during the causal shift isfurther resolved through an Audio Sink Token mechanism equipped withan Identity RoPE constraint. Finally, a Joint Self-Forcing Distillationparadigm enables the model to dynamically self-correct cumulative cross-modal errors from exposure bias during long rollouts. Empowered by amodality-independent rolling KV-cache inference scheme, OmniForcingachieves state-of-the-art streaming generation at ∼25 FPS on a singleGPU, maintaining multi-modal synchronization and visual quality on parwith the bidirectional teacher. Project Page: https://omniforcing.com.

Abstract:
Empowered by large-scale training, vision-language models (VLMs) achieve strong image and video understanding, yet their ability to perform spatial reasoning in both static scenes and dynamic videos remains limited. Recent advances try to handle this limitation by injecting geometry tokens from pretrained 3D foundation models into VLMs. Nevertheless, we observe that naive token fusion followed by standard finetuning in this line of work often leaves such geometric cues underutilized for spatial reasoning, as VLMs tend to rely heavily on 2D visual cues. In this paper, we propose GeoSR, a framework designed to make geometry matter by encouraging VLMs to actively reason with geometry tokens. GeoSR introduces two key components: (1) Geometry-Unleashing Masking, which strategically masks portions of 2D vision tokens during training to weaken non-geometric shortcuts and force the model to consult geometry tokens for spatial reasoning; and (2) Geometry-Guided Fusion, a gated routing mechanism that adaptively amplifies geometry token contributions in regions where geometric evidence is critical. Together, these designs unleash the potential of geometry tokens for spatial reasoning tasks. Extensive experiments on both static and dynamic spatial reasoning benchmarks demonstrate that GeoSR consistently outperforms prior methods and establishes new state-of-the-art performance by effectively leveraging geometric information. The project page is available at https://suhzhang.github.io/GeoSR/.

Abstract:
The latent space of diffusion models fundamentally deter-mines their learning efficiency and generation quality. While recent ad-vances in the latent space have driven substantial progress in single-imagegeneration, the optimal latent space for novel view synthesis (NVS) re-mains largely unexplored. In particular, NVS requires geometrically con-sistent generation across viewpoints, but existing approaches typicallyoperate in a view-independent latent space. In this paper, we proposeGeometric Latent Diffusion (GLD), a framework that repurposesthe feature space of a geometric foundation model as the latent space formulti-view diffusion. We show that the features of the geometric founda-tion model not only support high-fidelity RGB reconstruction but alsoencode strong cross-view geometric correspondences, providing a well-suited latent space for NVS. Through experiments, GLD outperformsboth VAE and RAE on 2D image quality and 3D consistency metrics,accelerating training by more than 4.4× compared to the VAE latentspace. Notably, GLD remains competitive with state-of-the-art methodsthat leverage large-scale text-to-image pretraining, despite training itsdiffusion model from scratch without such generative pretraining.

Abstract:
Streaming 3D reconstruction aims to recover 3D information, such as camera poses and point clouds, from a video stream, which necessitates geometric accuracy, temporal consistency, and computational efficiency. Motivated by the principles of Simultaneous Localization and Mapping (SLAM), we introduce GCT, geometric context transformer, a feed-forward 3D foundation model for reconstructing scenes from streaming data. A defining aspect of GCT lies in its carefully designed attention mechanism, which integrates an anchor context, a pose-reference window, and a trajectory memory to address coordinate grounding, dense geometric cues, and long-range drift reduction, respectively. This design keeps the streaming state compact while retaining rich geometric context, enabling stable efficient inference at around 20 FPS on 518×378 resolution inputs over long sequences exceeding 10,000 frames. Extensive evaluations across a variety of benchmarks demonstrate that our approach achieves superior performance compared to both existing streaming and iterative optimization-based approaches.

Abstract:
Most recent advances in 3D generative modeling rely on diffusion or flow-matching formulations. We instead explore a fully autoregressive alternative and introduce GaussianGPT, a transformer-based model that directly generates 3D Gaussians via next-token prediction, thus facilitating full 3D scene generation. We first compress Gaussian primitives into a discrete latent grid using a sparse 3D convolutional autoencoder with vector quantization. The resulting tokens are serialized and modeled using a causal transformer with 3D rotary positional embedding, enabling sequential generation of spatial structure and appearance. Unlike diffusion-based methods that refine scenes holistically, our formulation constructs scenes step-by-step, naturally supporting completion, outpainting, controllable sampling via temperature, and flexible generation horizons. This formulation leverages the compositional inductive biases and scalability of autoregressive modeling while operating on explicit representations compatible with modern neural rendering pipelines, positioning autoregressive transformers as a complementary paradigm for controllable and context-aware 3D generation.

Abstract:
We introduce PRISM-VO, a novel pure optimization-basedsparse photometric visual odometry framework for focused plenopticcameras. The core of PRISM-VO is a novel photometric plenoptic bun-dle adjustment which jointly optimizes camera poses and inverse depthvalues of points in a sliding window. By combining geometric depth froma single plenoptic image with temporal multi-view constraints, PRISM-VO achieves accurate and drift-resilient motion estimation. Through ex-plicit modeling of the plenoptic projection, PRISM-VO provides reliablemetric-scale reconstructions, overcoming the scale ambiguity of monocu-lar SLAM algorithms. Importantly, our approach relies solely on a singleplenoptic sensor and avoids complex initialization, as depth priors arecomputed directly from plenoptic imaging.Experiments show that PRISM-VO outperforms the current state-of-the-art plenoptic visual odometry method on indoor and outdoor scenes.The proposed approach rivals other optimization- and learning-basedmethods while accurately and reliably recovering a metric scale of thescene.Project page: https://prism-vo.github.io/.

Abstract:
Monocular surface normal estimators trained on large-scale RGB-normal data often perform poorly in the edge cases of reflective, textureless, and dark surfaces. Polarization encodes surface orientation independently of texture and albedo, offering a physics-based complement for these cases. Existing polarization methods, however, require multi-view capture or specialized training data, limiting generalization. We introduce Poppy, a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time. Keeping backbone weights frozen, Poppy optimizes perpixel offsets to the input RGB and output normal along with a learned reflectance decomposition. A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal. Across seven benchmarks and three backbone architectures (diffusion, flow, and feed-forward), Poppy reduces mean angular error by 23–26% on synthetic data and 6–16% on real data. These results show that guiding learned RGB-based normal estimators with polarization cues at test time refines normals on challenging surfaces without retraining. An interactive demo and code are available at https://irnkim.github.io/poppy/.

Abstract:
Current multimodal large language models (MLLMs) havedemonstrated remarkable capabilities in short-form video understand-ing, yet translating long-form cinematic videos into detailed, temporallygrounded scripts remains a significant challenge. This paper introducesthe novel video-to-script (V2S) task, aiming to generate hierarchical,scene-by-scene scripts encompassing character actions, dialogues, expres-sions, and audio cues. To facilitate this, we construct a first-of-its-kindhuman-annotated benchmark and propose a temporally-aware hierarchi-cal evaluation framework. Furthermore, we present OmniScript, an 8B-parameter omni-modal (audio-visual) language model tailored for long-form narrative comprehension. OmniScript is trained via a progressivepipeline that leverages chain-of-thought supervised fine-tuning for plotand character reasoning, followed by reinforcement learning using tem-porally segmented rewards. Extensive experiments demonstrate that de-spite its parameter efficiency, OmniScript significantly outperforms largeropen-source models and achieves performance comparable to state-of-the-art proprietary models, including Gemini 3-Pro, in both temporallocalization and multi-field semantic accuracy. The code is available athttps://github.com/TencentARC/OmniScript.

Abstract:
Scene Coordinate Regression (SCR) methods are increasingly adopted for visual localization. In these approaches, the scene is implicitly encoded within a neural network that regresses a 3D world coordinate for each image pixel. Because the scene is represented only through the network parameters and not stored explicitly as images or maps, such methods are often assumed to be privacy-preserving. In this work, we show that this assumption is incorrect in practice. Specifically, we introduce a query-based attack that reconstructs the 3D geometry of the training environment from an SCR model under different levels of model access. To do so, we repeatedly query the model with batches of proxy images unrelated to the target scene to obtain dense pixel-wise 3D coordinates. Reliable points are identified through their stability under small input perturbations and can be further refined in a white-box setting. These stable points are accumulated across independent query batches to recover the scene geometry. From the recovered 3D representation, we also invert the network features to synthesize images from arbitrary viewpoints, revealing additional appearance information. Experiments on indoor and outdoor datasets demonstrate that substantial portions of training environments can be reconstructed with high geometric fidelity. Beyond geometry, we also recover an approximate color appearance, which exposes recognizable layout and potentially sensitive scene elements. This directly contradicts claims in the literature that SCR representations are privacy-preserving by design, and reveals a real risk when such systems are deployed in private or security-critical spaces. The project page is available here.

Abstract:
We introduce a controlled subspace intervention framework to investigate how self-supervised Vision Transformers (ViTs) encode dense geometric information. While linear probing is widely used to assess geometric representations, it treats features as a black box, failing to disentangle the underlying topology. To address this issue, we decompose the weights of converged linear probes to isolate the low-rank subspaces containing explicit geometric signals using Singular Value Decomposition (SVD). Our perspective yields three key insights: (1) Pre-training objectives determine how features are encoded. DINOv2 aligns spatial features for efficient linear extraction, while Masked Autoencoders (MAE) tend to disperse these signals, requiring a broader spatial context. (2) Explicit geometric representations are highly compressible, suggesting dense predictive heads could potentially be constrained to low-rank subspaces with minimal performance loss. (3) The layer-wise task affinity suggests that geometric precision peaks at intermediate layers before yielding to semantic abstraction in the final layers. By connecting internal encoding mechanics with downstream performance, these findings provide a basis for effective feature selection and lightweight decoder design. The source code is available at https://github.com/Zhou-Weichen/Geosubprobe.

Abstract:
Point cloud registration aligns multiple unposed point clouds into a common reference frame and is a core step for 3D reconstruction and robot localization when no initial pose guess is available. In this work, we cast point cloud registration as conditional generation: a learned, continuous point-wise velocity field transports noisy points to a registered scene, from which the pose of each view is recovered. Unlike prior methods that perform correspondence matching to estimate pairwise transformations and then optimize a pose graph for multi-view registration, our model directly generates the registered point cloud, yielding both efficiency and point-level global consistency. By scaling the training data and conducting test-time rigidity enforcement, our approach achieves state-of-the-art average performance on existing pairwise registration benchmarks and on our proposed cross-domain multi-view registration benchmark. The superior zero-shot performance on this benchmark demonstrates that our method generalizes across view counts, scene scales, and sensor modalities even with low overlap.

Abstract:
Latent diffusion models pair VAEs with diffusion backbones,and the structure of VAE latents strongly influences the difficulty of dif-fusion training. However, existing video VAEs typically focus on recon-struction fidelity, overlooking latent structure. We present a statisticalanalysis of video VAE latent spaces and identify two spectral propertiesessential for diffusion training: a channel-wise eigenspectrum dominatedby a few modes, and a spatio-temporal frequency spectrum biased towardlow frequencies. To induce these properties, we propose two lightweight,backbone-agnostic regularizers: Latent Masked Reconstruction and Lo-cal Correlation Regularization. Experiments show that our Spectral-Structured VAE (SSVAE) achieves a 3× speedup in text-to-video gen-eration convergence and a 10% gain in video reward, outperformingstrong open-source VAEs. Code is available at: https://github.com/zai-org/SSVAE.

Abstract:
When videos extend from hours to days, directly process-ing them end-to-end becomes impractical for current Multi-modal LargeLanguage Models (MLLMs). This ultra-long setting necessitates a two-stage paradigm: query-agnostic memory construction followed by retrieval-based inference. Prior work invests in complex memory construction topre-model high-level relations in videos, despite not knowing the down-stream query at build time. We instead prioritize high-recall retrievabil-ity during memory building, and defer query-specific, high-level relationcomposition to inference time. To this end, we propose MERIT (Multi-key Episodic Retrieval with Inference-time Temporal expansion), a sim-ple yet effective agentic framework for ultra-long video understanding.First, we formulate an episodic multi-key representation that enablesprecise retrieval of fine-grained memories through a simple key-matchingmechanism. Second, we introduce a neighbor filtering mechanism to cap-ture broader semantic context without the massive computational over-head of global memory construction. This is achieved by expanding thetemporal scope exclusively around the retrieved segments at inferencetime. By leveraging simple key-matching with this on-demand tempo-ral expansion, MERIT achieves state-of-the-art performance across threelong-video benchmarks: EgoLifeQA, LVBench, and Video-MME(Long).

Abstract:
Vision transformers (ViTs) have achieved remarkable gen-eralization across visual domains, yet little is known about how theyinternally represent the structure of the world. To address this gap, weuse Cross-Layer Transcoders (CLTs) to read concept circuits from ViTs:directed graphs whose nodes correspond to sparse, interpretable con-cepts and edges capture concept interactions across layers. Our methodyields two complementary views of model behavior. The global conceptcircuit is input-invariant and can be recovered directly from learnedcross-layer weights, exposing the reusable “world knowledge” encodedin the model. The instance concept circuit is input-dependent and iden-tifies the concepts and pathways actually used for a specific prediction,enabling faithful example-level explanations. We demonstrate the util-ity of concept circuits in three ways: (1) Automatic spurious correla-tion discovery: leveraging the statistics of our global concept circuitsto identify shortcut dependencies within the model. (2) Spurious cor-relation removal: intervening on the instance concept circuit to steerthe model towards correct predictions. Empirical results show that ourmethod outperforms existing counterparts by 11.0% on the Waterbirddataset. (3) Model comparison: contrasting the global concept circuits ofdifferent foundation models (e.g., CLIP vs. DINO) to reveal how super-vision paradigms shape representational structure. Our code is availableat https://github.com/deep-real/VisionCLT

Abstract:
Normalizing Flows (NFs) are powerful generative models ca-pable of exact density estimation and sampling. However, their strictinvertibility often forces the model to exhaust its capacity on low-levelpixel details, hindering the capture of high-level semantic structures.While Masked Image Modeling (MIM) has excelled in representationlearning, its integration into generative pipelines has remained largelymodular and disjointed. In this paper, we propose MIMFlow, a unifiedend-to-end framework that jointly optimizes latent semantics, pixel re-construction, and generative flow. By employing a VAE encoder to infersemantic latent from masked images, MIMFlow achieves a principleddecoupling of the generative task: the Normalizing Flow focuses on mod-eling a simplified, low-frequency semantic manifold, while a specializeddecoder handles high-frequency synthesis. This design effectively resolvesthe inherent capacity bottleneck of NFs, allowing the model to prioritizeglobal structural coherence over redundant noise. Empirical results onImageNet 256×256 show that MIMFlow-L reaches 71.3% linear prob-ing accuracy and an FID of 2.50. Despite using only 128 tokens (50%fewer than standard models), it yields a 32.8% performance gain oversimilar-scale NF baselines.

Abstract:
Contrastive vision-language models such as CLIP map se-mantically opposite phrases (e.g., “a dog” vs. “not a dog”) to nearly iden-tical embeddings, rendering them insensitive to negation. We attributethis failure to a phenomenon we call Representational Collapse: by track-ing compositional divergence and visual alignment across the CLIP textencoder, we show that middle layers build compositional syntax, but thefinal layers collapse this structure as visual alignment rises, producinga syntax-blind final representation. To recover the lost negation signalwithout altering pretrained weights, we propose PeakPatch, a lightweightpost-hoc correction system that intercepts the encoder at its composi-tional peak while keeping CLIP fully frozen. An Embedding CorrectionNetwork (ECN) uses cross-attention to extract a negation-specific sig-nal from the peak layer, anchored to a stable baseline, and predicts adeviation vector that re-injects the lost syntax into the final-layer em-bedding space. A complementary Score Correction Network (SCN) pre-dicts bounded scalar score offsets for discriminative tasks. Both modulesare trained jointly end-to-end while all CLIP parameters remain frozen,adding only 5.2M parameters (3.5% of the backbone) and preserving thestandard cosine similarity interface. On NegBench, PeakPatch achieves74.3% on COCO MCQ (+35.1 over CLIP, +17.8 over the best encoderfine-tuning method) and 65.5% on VOC MCQ, while outperforming allfine-tuning baselines on fully out-of-distribution negation retrieval de-spite training only 3.5% of the parameters. The corrected embeddingsalso transfer to text-to-image generation (+18.4 negation score) and gen-eralize across ViT-B/32, ViT-L/14, and SigLIP backbones.Project page: https://stevencylu.github.io/PeakPatch/

Abstract:
Vision–Language Models (VLMs) excel at reasoning in lin-guistic space but struggle with perceptual understanding that requiresdense visual perception, e.g., spatial reasoning and geometric aware-ness. This limitation stems from the fact that current VLMs have lim-ited mechanisms to capture dense visual information across spatial di-mensions. We introduce Chain-of-Visual-Thought (CoVT), a frameworkthat enables VLMs to reason not only with discrete text tokens but alsothrough continuous visual tokens—compact latent representations thatencode rich perceptual cues. With a small budget of roughly 20 tokens,CoVT distills knowledge from lightweight vision experts that capturecomplementary properties such as 2D appearance, 3D geometry, spatiallayout, and edge structure. During training, the VLM with CoVT au-toregressively predicts these visual tokens to reconstruct dense supervi-sion signals (e.g., depth, segmentation, edges, and DINO features). At in-ference, the model reasons directly in the continuous visual latent space,preserving efficiency while optionally decoding dense predictions for in-terpretability. Evaluated across more than ten diverse benchmarks, in-cluding CV-Bench, MME-RealWorld, MMVP, RealWorldQA, MMStar,WorldMedQA, and HRBench, integrating CoVT into strong VLMs suchas Qwen2.5-VL and LLaVA consistently improves performance by 3% to16% and demonstrates that compact continuous visual thinking enablesmore precise, grounded, and interpretable multimodal intelligence.

Abstract:
Pretrained Vision Transformers (ViTs) such as DINOv2 andMAE provide generic image features that can be applied to a varietyof downstream tasks such as retrieval, classification, and segmentation.However, such representations tend to focus on the most salient visualcues in the image, with no way to direct them toward less prominentconcepts of interest. In contrast, Multimodal LLMs can be guided withtextual prompts, but the resulting representations tend to be language-centric and lose their effectiveness for generic visual tasks. To addressthis, we introduce Steerable Visual Representations, a new class of vi-sual representations, whose global and local features can be steered withnatural language. While most vision-language models (e.g., CLIP) fusetext with visual features after encoding (late fusion), we inject text di-rectly into the layers of the visual encoder (early fusion) via lightweightcross-attention. We introduce benchmarks for measuring representationalsteerability, and demonstrate that our steerable visual features can focuson any desired object in an image while preserving the underlying rep-resentation quality. Our method also matches or outperforms dedicatedapproaches on anomaly detection and personalized object discrimination,exhibiting zero-shot generalization to out-of-distribution tasks.Project Website: jonaruthardt.github.io/project/SteerViTPrompt CLS AttentionSteerable Visual RepresentationsGoal: control what vision features encode New Pareto FrontierEval: retrieving images w/ prompted objects Previous SoTAUse: task-specific adaptationFig. 2: SteerViT produces high-quality visual representations that can besteered by text. Left: Traditional (non-steerable) representations like DINOv2 tendto focus on the dominant object in an image and retrieve images with the same object.SteerViT can adapt to a text prompt, enabling retrieval of images even with smallobjects of interest. Right: We compare SteerViT to prior work in terms of its ability toadapt to text (measured by text-guided image retrieval (cf. Sec. 4.1)) and the qualityof the visual representation (measured by the accuracy of linear probing for the CLSfeature and semantic segmentation for patch features). While models typically tradeoff steerability for representation quality, SteerViT preserves both. By modulating agating factor (Eq. (2)), SteerViT achieves a new Pareto frontier.

Abstract:
We introduce the Large Sparse Reconstruction Model tostudy how scaling transformer context windows affects feed-forward 3Dreconstruction. Although recent object-centric feed-forward methods pro-duce robust, high-quality reconstructions, they still lag behind dense-view optimization in recovering fine-grained texture and appearance. Weshow that expanding the context window—by substantially increasingthe number of active object and image tokens—narrows this gap andenables high-fidelity 3D object reconstruction and inverse rendering. Toscale effectively, we adapt native sparse attention [68] for 3D reconstruc-tion with three key contributions: (1) an efficient coarse-to-fine pipelinethat focuses computation on informative regions by predicting sparsehigh-resolution residuals; (2) a 3D-aware spatial routing mechanism thatestablishes accurate 2D-3D correspondences using explicit geometric dis-tances rather than standard attention scores; and (3) a custom block-aware sequence-parallel strategy with an All-gather-KV protocol to bal-ance dynamic, sparse workloads across GPUs. As a result, LSRM handles20× more object tokens and >2× more image tokens than prior state-of-the-art (SOTA) methods. Extensive evaluations on standard novel-viewsynthesis benchmarks show substantial gains over the current SOTA,yielding >2.4 dB higher PSNR and >40% lower LPIPS. Furthermore,when extending LSRM to inverse rendering, qualitative and quantitativeevaluations on widely used benchmarks demonstrate consistent improve-ments in texture and geometry details, achieving an LPIPS that matchesor exceeds that of SOTA dense-view optimization methods. Code andmodel weights are available on our project page.

Abstract:
Wavefront sensing involves estimating the phase and intensity of light, enabling a wide range of imaging applications, from adaptive optics and astronomy to biomedical imaging. Since conventional image sensors can only measure the spatial intensity distribution, phase retrieval arises as the central problem in wavefront sensing. Conventional interferometric approaches like phase-shifting interferometry (PSI) can recover phase information, but they rely on a stable reference beam that is difficult to realize in practical settings. To overcome this limitation, we propose a novel self-reference framework that relies on interference between shifted copies of the incoming wave; this results in pairwise phase differences between shifted pixels. We formulate an analytical solution for the complete phase retrieval based on the propagation of these differences across a connected graph. Furthermore, we provide a theoretical analysis of optimal measurement patterns, proving that co-prime shifts guarantee a connected graph and bound worst-case error accumulation, yielding a provably robust method. Extensive simulations demonstrate that complete phase profiles can be recovered from as few as eight shifted measurements, outperforming several existing approaches. Finally, we validate our framework using a hardware prototype, demonstrating real experiments for optical phase profile recovery, auto-refocusing, and imaging through scattering media.

Abstract:
Transformers have become a common foundation across deep learning, yet 3D scene understanding still relies on specialized backbones with strong domain priors. This keeps the field isolated from the broader Transformer ecosystem, limiting the transfer of research advances from other domains and the benefits of increasingly optimized software and hardware stacks. To bridge this gap, we propose the Volume Transformer (Volt), which adapts the vanilla Transformer encoder to 3D scenes with minimal modifications. Specifically, Volt partitions 3D scenes into volumetric patch tokens, processes them with full global self-attention, and injects positional information through 3D rotary positional embeddings (RoPE). Our initial experiments reveal that naively training Volt on standard 3D benchmarks leads to poor generalization, highlighting the limited scale of current 3D supervision. To overcome this, we introduce a data-efficient training recipe based on strong 3D augmentations, regularization, and distillation from a convolutional teacher, making Volt competitive with state-of-the-art methods. We then scale supervision through joint training on multiple datasets and show that Volt benefits more from increased scale than domain-specific 3D backbones, achieving state-of-the-art results across indoor and outdoor semantic segmentation benchmarks. Finally, we use Volt as a drop-in backbone in a standard 3D instance segmentation pipeline, where it also achieves new state-of-theart results, highlighting its potential as a simple, scalable, and generalpurpose backbone for 3D scene understanding.

Abstract:
Traditional glass-based optics are typically optimized for nar-row spectral bands, such as the visible (400–700nm) or shortwave infrared(1000–1800nm). While the emergence of VIS-SWIR sensors (400–1700nm)offers transformative potential, refractive optics struggle to focus thisentire range simultaneously. Mirrors represent a promising achromaticalternative; however, they are often sidelined by field curvature, and off-axis aberrations. This paper introduces Computational Mirrors, aframework that enables high-resolution, full-field-of-view imaging acrossthe complete VIS-SWIR spectrum using a single sensor. Our method isbuilt on the observation that distinct regions of the field of view reachfocus at varying distances from the mirror. By capturing a minimal fo-cal stack (2–4 images), we utilize a computational backend to recovera sharp, all-in-focus image. A key contribution of this paper is Seidel-Conv, a novel, physics-inspired, spatially-varying point spread function(PSF) model designed to accurately characterize and correct the off-axisaberrations inherent in simple concave mirrors. We demonstrate the ef-ficacy of our approach using a first-of-its-kind 50mm F/1 optical systemequipped with a VIS-SWIR sensor. Our system produces sharp imagesacross RGB, NIR, and SWIR wavelengths without requiring refocusing,revealing material details invisible within individual spectral bands. Wefurther validate the scalability of our approach with a 100mm F/2 systemoptimized for long-range imaging.

Abstract:
We present TriFlow, a new generative approach for produc-ing compact 3D meshes with artist-like triangle topology directly frominput geometry conditions such as signed distance fields. Our key insightis to represent mesh topology as a nearest-vertex vector field (NVF) de-fined over the surface, where each point encodes its association to thenearest triangle vertex in the local barycentric frame. We train a latentflow-matching model to synthesize this field, enabling topology genera-tion conditioned on the input geometry. To extract a coherent mesh, wecluster surface regions using the generated NVF and guide a constrainedquadric error metric mesh simplification with topology-aware optimiza-tion. This yields output meshes that closely match the input geometrywhile exhibiting structured, artist-like connectivity. Experiments demon-strate that TriFlow achieves stronger generalization and significantlyimproved topology quality compared to state-of-the-art learning-basedapproaches, alongside 90% lower Chamfer Distance and an 8× speedup.

Abstract:
Differentiable rendering of planar rational splines remains largely underexplored, despite their widespread use in vector graphics and design. Existing differentiable vector renderers primarily focus on Bézier curves and rely on analytic rasterization, which can suffer from gradient instability and limited flexibility. We propose NURBS Splatting, a unified framework that represents planar rational curves as continuous Gaussian fields. By sampling Gaussians along the curve parameter domain and inside closed regions, rendering is reformulated as a smooth accumulation process with stable gradients. Our method naturally supports long splines, rational weights, non-uniform knots, and closed-region filling. We demonstrate its effectiveness in calligraphy reconstruction, vectorization frameworks, and long-spline image abstraction, showing improved stability and reconstruction quality over existing approaches.

Abstract:
Accurate yaw estimation is a bottleneck in cross-view lo-calization between ground view and Bird’s Eye View (BEV). Existingmethods couple yaw with translation and rely on height or projectionassumptions that degrade under large yaw ambiguity. We disentangleyaw from location accuracy and introduce LAYS, a radially invariantline-consensus voting method. By exploiting the radial invariance of ourformulation, we achieve sub-degree yaw precision via 3D voting over allcandidate poses, while eliminating the need for accurate location. Ourkey observation is that a ground-image column matched to BEV pixelsinduces the same yaw across all camera positions along the radial direc-tion of the pixels. LAYS matches BEV pixels to ground columns usingfeature similarity and accumulates the induced yaw votes into discrete3D bins, where correct correspondences along the radial line concentrateinto a sharp peak for the correct yaw. Experiments on Mapillary, Ford,KITTI, and VIGOR show significant gains under unknown yaw, particu-larly for normal FoV with unknown yaw (+28∼45%p), and using LAYSas a yaw prior improves downstream 3-DoF localization.

Abstract:
We introduce FEEL (Force-Enhanced Egocentric Learning), the first large-scale dataset pairing force measurements gathered from custom piezoresistive gloves with egocentric video. Our gloves enable scalable data collection, and FEEL contains approximately 2 million force-synchronized frames of natural unscripted manipulation in kitchen environments, with ∼45% of frames involving hand-object contact. Because force is the underlying cause that drives physical interaction, it is a critical primitive for physical action understanding. We demonstrate the utility of force for physical action understanding through application of FEEL to two families of tasks: (1) contact understanding, where we jointly perform temporal contact segmentation and pixel-level contacted object segmentation; and, (2) action representation learning, where force prediction serves as a self-supervised pretraining objective for video backbones. We achieve state-of-the-art temporal contact segmentation results and competitive pixel-level segmentation results without any need for manual contacted object segmentation annotations. Furthermore we demonstrate that action representation learning with FEEL improves transfer performance on action understanding tasks without any manual labels over EPIC-Kitchens, SomethingSomething-V2, EgoExo4D and Meccano.

Abstract:
3D Gaussian Splatting has recently revolutionised novel viewsynthesis as well as many other 3D vision methods and applications.Drawing inspiration from this representation, we now rethink texturesto overcome the main issues of UV mapping while considerably lower-ing their memory footprint. Heat Kernel Textures (HKTex) eliminateUV unwrapping as well as their persistent issues of wasted UV space,seams, distortions, vertex-duplication, and varying resolution. Groundedin discrete Riemannian geometry and intrinsically defined on any man-ifold surface discretised as a triangular mesh, HKTex uses anisotropicheat kernels as geodesic equivalents to Gaussians. Like our kernels, alsothe optimisation of their position and the adaptive densification strate-gies were redefined to operate on the surface of the object to be tex-tureised. Our novel representation is also fully integrated with a physi-cally based renderer and can be optimised either from existing texturesor multi-view images. Our project page and code are available at circle-group.github.io/research/HeatKernelTextures.

Abstract:
Depth from Defocus (DfD) is the task of estimating a densemetric depth map from a focus stack. Unlike previous works overfittingto a certain dataset, this paper focuses on the challenging and practi-cal setting of zero-shot generalization. We first propose a new real-worldDfD benchmark ZEDD, which contains 8.3× more scenes and signifi-cantly higher quality images and ground-truth depth maps comparedto previous benchmarks. We also design a novel network architecturenamed FOSSA. FOSSA is a Transformer-based architecture with noveldesigns tailored to the DfD task. The key contribution is a stack atten-tion layer with a focus distance embedding, allowing efficient informa-tion exchange across the focus stack. Finally, we develop a new trainingdata pipeline allowing us to utilize existing large-scale RGBD datasetsto generate synthetic focus stacks. Experiment results on ZEDD andother benchmarks show a significant improvement over the baselines,reducing errors by up to 55.7%. The ZEDD benchmark is released athttps://zedd.cs.princeton.edu. The code and checkpoints are re-leased at https://github.com/princeton-vl/FOSSA.Input: Focus stack and corresponding focus distancesFocus Distance = 0.8m Focus Distance = 1.6m Focus Distance = 3.1m Focus Distance = 8.1mDFF-FV 𝛿! = 0.71 MoGe-2 𝛿! = 0.45 𝛿! = 0.98Existing depth from defocus Monocular depth models suffer Ours Ground Truthmodels are not generalizable from scale ambiguityEqual contribution.

Abstract:
We propose a remeshing-enhanced method for generativelydeforming shapes with visual losses. It is intuitive that sufficiently dras-tic deformations of a mesh without changing its triangulation can easilycompromise element quality, even if such large geometry changes maybe semantically desired. Shape deformation methods could thus bene-fit from changing the triangulation; however, this is not done by mostgenerative, text-based, visually-supervised mesh deformation methods.Remeshing is a discrete operation, proven to be especially challengingto couple with the notoriously noisy supervision signal provided by vi-sual losses. We propose a vertex-based deformation optimization quantitycapable of large deformations and robustness to such noise; we period-ically remesh using an isotropic remesher that interpolates and carriesforward the deformation optimization state. This enables continuous,geometry-informed progress in coarse-to-fine addition of resolution. Theresulting shapes’ triangulations fit their optimized geometry and haveneat isotropic elements. Further, our method is localizable, able to grownew features on a base shape with expressive detail, leaving the rest un-changed. We showcase the effectiveness of our method on a variety ofshapes and prompts, both local and global deformations, and demon-strate its superior visual quality and triangle efficiency. Our project pageis at https://threedle.github.io/radmesh.

Abstract:
Gait patterns play a critical role in human identification andhealthcare analytics, yet current progress remains constrained by small,narrowly designed models that fail to scale or generalize. Building a uni-fied gait foundation model requires addressing two longstanding barriers:(a) Scalability – Why have gait models historically failed to follow em-pirical scaling trends? (b) Generalization – Can one model serve thediverse gait tasks that have traditionally been studied in isolation? Weintroduce FoundationGait, the first scalable, self-supervised pretrain-ing framework for vision-based gait understanding. Its largest versionhas nearly 0.13 billion parameters and is pretrained on 12 public gaitdatasets comprising over 2 million walking sequences. Extensive exper-iments demonstrate that FoundationGait, with or without fine-tuning,performs robustly across a wide spectrum of gait datasets, conditions,tasks (e.g., human identification, scoliosis screening, depression predic-tion, and attribute estimation), and even input modality. Notably, itachieves 48.0% self-supervised rank-1 accuracy on the challenging in-the-wild Gait3D dataset (1,000 test subjects) and 64.5% on the largestin-the-lab OU-MVLP dataset (5,000+ test subjects), setting a new mile-stone in robust gait recognition. These results establish FoundationGaitas a strong and versatile foundation for future gait research. All codeand models: https://github.com/ShiqiYu/OpenGait.

Abstract:
While existing feed-forward Gaussian splatting models of-fer computational efficiency and can generalize to sparse view settings,their performance is fundamentally constrained by relying on a singleforward pass for inference. We propose ReSplat, a feed-forward recurrentGaussian splatting model that iteratively refines 3D Gaussians withoutexplicitly computing gradients. Our key insight is that the Gaussiansplatting rendering error serves as a rich feedback signal, guiding therecurrent network to learn effective Gaussian updates. This feedbacksignal naturally adapts to unseen data distributions at test time, enablingrobust generalization across datasets, view counts, and image resolutions.To initialize the recurrent process, we introduce a compact reconstructionmodel that operates in a 16× subsampled space, producing 16× fewerGaussians than previous per-pixel Gaussian models. This substantiallyreduces computational overhead and allows for efficient Gaussian updates.Extensive experiments across varying number of input views (2, 8, 16, 32),resolutions (256×256 to 540×960), and datasets (DL3DV, RealEstate10K,and ACID) demonstrate that our method achieves state-of-the-art perfor-mance while significantly reducing the number of Gaussians and improvingthe rendering speed. Our project page is at haofeixu.github.io/resplat.

Abstract:
Local feature matching has long been a fundamental component of 3D vision systems such as Structure-from-Motion (SfM), yet progress has lagged behind the rapid advances of modern data-driven approaches. The newer approaches, such as feed-forward reconstruction models, have benefited extensively from scaling dataset sizes, whereas local feature matching models are still only trained on a few mid-sized datasets. In this paper, we revisit local feature matching from a datadriven perspective. In our approach, which we call LoMa, we combine large and diverse data mixtures, modern training recipes, scaled model capacity, and scaled compute, resulting in remarkable gains in performance. Since current standard benchmarks mainly rely on collecting sparse views from successful 3D reconstructions, the evaluation of progress in feature matching has been limited to relatively easy image pairs. To address the resulting saturation of benchmarks, we collect 1000 highly challenging image pairs from internet data into a new dataset called HardMatch. Ground truth correspondences for HardMatch are obtained via manual annotation by the authors. In our extensive benchmarking suite, we find that LoMa makes outstanding progress across the board, outperforming the state-of-the-art method ALIKED+LightGlue by +18.6 mAA on HardMatch, +29.5 mAA on WxBS, +21.4 (1m, 10→) on InLoc, +24.2 AUC on RUBIK, and +12.4 mAA on IMC 2022. We release our code and models publicly at https://github.com/davnords/LoMa.

Abstract:
Embodied manipulation requires accurate 3D understanding of objects and their spatial relations to plan and execute contactrich actions. While large-scale 3D foundation models provide strong priors, their computational cost incurs prohibitive latency for real-time control. We propose Real-time 3D-aware Policy (R3DP), which integrates powerful 3D priors into manipulation policies without sacrificing real-time performance. A core innovation of R3DP is the asynchronous fast–slow collaboration module, which sophisticatedly integrates largescale 3D model’s priors into the policy without compromising real-time performance. The system queries the pre-trained slow system (VGGT) only on sparse key frames, while simultaneously employing a lightweight Temporal Feature Prediction Network (TFPNet) to predict features for all intermediate frames. By leveraging historical data to exploit temporal correlations, TFPNet explicitly improves task success rates through consistent feature estimation. Additionally, we introduce a Multi-View Feature Fuser (MVFF) that aggregates features across views by explicitly incorporating camera intrinsics and extrinsics. R3DP offers a plug-and-play solution for integrating 3D foundation models into realtime inference systems. We evaluate R3DP against multiple baselines across different visual configurations. R3DP effectively harnesses largescale 3D priors to achieve superior results, outperforming single-view and multi-view DP by 32.9% and 51.4% in average success rates. Furthermore, by decoupling heavy 3D understanding from policy execution, R3DP achieves a 44.8% reduction in inference time compared to a naive DP+VGGT integration. Project page and code are available at: https://dazazh.github.io/r3dp-project-page/

Abstract:
Reconstructing 3D geometry from streaming video requirescontinuous inference under bounded resources. Recent geometric foun-dation models achieve impressive reconstruction quality through all-to-all attention, yet their quadratic cost confines them to short, offline se-quences. Causal-attention variants such as StreamVGGT enable single-pass streaming but accumulate an ever-growing KV cache, exhaustingGPU memory within hundreds of frames and precluding the long-horizondeployment that motivates streaming inference in the first place. Wepresent OVGGT, a training-free framework that bounds both memoryand compute to a fixed budget regardless of sequence length. Our ap-proach combines Self-Selective Caching, which leverages FFN residualmagnitudes to compress the KV cache while remaining fully compatiblewith FlashAttention, with Dynamic Anchor Protection, which shieldscoordinate-critical tokens from eviction to suppress geometric drift overextended trajectories. Extensive experiments on indoor, outdoor, andultra-long datasets show that OVGGT processes arbitrarily long videoswithin a constant VRAM envelope while achieving state-of-the-art 3D ge-ometric accuracy. The code is available at github.com/VAISR/OVGGT.

Abstract:
Rapid advancements in image generation have led models likeGPT-Image and Qwen-Image to excel in text-to-image consistency andworld knowledge. However, they still struggle with photorealism, oftenproducing "fake" images laden with obvious AI artifacts, such as overlysmooth skin or unnatural oily sheens. To recapture the original goal of"indistinguishable-from-reality" generation, we propose RealGen, a photo-realistic text-to-image framework. RealGen integrates an LLM componentfor prompt optimization and a diffusion model for realistic image genera-tion. Inspired by adversarial generation, RealGen introduces a "DetectorReward" mechanism, which quantifies artifacts and assesses realism usingboth semantic and feature-level synthetic image detectors. We leveragethis reward with the GRPO algorithm to optimize the entire generationpipeline, significantly enhancing image realism and detail. Furthermore,we propose RealBench, an automated evaluation benchmark employingDetector-Scoring and Arena-Scoring. It enables human-free photorealismassessment, yielding results that are more accurate and aligned with realuser experience. Experiments show that RealGen produces photorealisticimages with enhanced realism, detail, and aesthetics.

Abstract:
Multimodal large language models (MLLMs) have achieved impressiveprogress in vision-language reasoning, yet their ability to understand temporallyunfolding narratives in videos remains largely underexplored. Narrative under-standing requires more than recognizing isolated events: models must maintaincoherent representations of who is doing what, when, and where across scene tran-sitions and temporal gaps. We introduce NARRATIVE T RACK, the first benchmarkto evaluate narrative understanding in MLLMs through fine-grained entity-centricreasoning. Unlike existing benchmarks limited to short clips or coarse scene-levelsemantics, we decompose videos into constituent entities and evaluate modelsusing a Compositional Reasoning Progression (CRP), a structured frameworkthat progressively increases narrative complexity across three dimensions: en-tity existence, entity changes, and entity ambiguity. This progression requiresmodels to move beyond local perception to reasoning about entities’ temporalpersistence, state changes, and fine-grained perceptual disambiguation. To enablescalable benchmark construction, we develop a fully automated entity-centricpipeline that extracts temporally grounded entity representations and providesthe foundation for CRP. Evaluations of state-of-the-art MLLMs reveal that ex-isting models struggle to maintain coherent entity representations under visualtransitions and temporal dynamics. Open-source general-purpose MLLMs exhibitstrong perceptual grounding but weak temporal continuity, while video-specializedMLLMs capture temporal context yet frequently hallucinate entities’ contexts.These findings uncover a fundamental trade-off between perceptual grounding andtemporal reasoning, indicating that narrative understanding emerges only fromtheir integration. NARRATIVE T RACK provides the first systematic framework todiagnose and advance temporally grounded narrative comprehension in MLLMs.

Abstract:
We present RiO-DETR: DETR for Real-time OrientedObject Detection, the ﬁrst real-time oriented detection transformer tothe best of our knowledge. Adapting DETR to oriented bounding boxes(OBBs) poses three challenges: semantics-dependent orientation, angleperiodicity that breaks standard Euclidean reﬁnement, and an enlargedsearch space that slows convergence. RiO-DETR resolves these issueswith task-native designs while preserving real-time eﬃciency. First, wepropose Content-Driven Angle Estimation by decoupling angle from po-sitional queries, together with Rotation-Rectiﬁed Orthogonal Attentionto capture complementary cues for reliable orientation. Second, Decou-pled Periodic Reﬁnement combines bounded coarse-to-ﬁne updates witha Shortest-Path Periodic Loss for stable learning across angular seams.Third, Oriented Dense O2O injects angular diversity into dense super-vision to speed up angle convergence at no extra cost. Extensive ex-periments on DOTA-1.0, DIOR-R, and FAIR-1M-2.0 demonstrate RiO-DETR establishes a new speed–accuracy trade-oﬀ for real-time orienteddetection. Code is available at https://github.com/RicePasteM/RiO-DETR.

Abstract:
End-to-end transformer architectures have driven significant progress in multi-object tracking by unifying detection and association into a single, heuristic-free framework. Despite these benefits, poor detection performance and the inherent conflict between detection and association in a joint architecture remain critical concerns. Recent approaches aim to mitigate these issues by employing advanced denoising or label assignment strategies, or by incorporating detection priors from external object detectors. In this paper, we propose SelfMOTR, a simple yet highly effective detector-free alternative that decouples proposal discovery from association using self-generated internal detection priors. Through extensive analysis and ablation studies, we show that end-to-end transformer trackers with joint detection–association decoding retain substantial hidden detection capacity, and we provide a practical detector-free mechanism for leveraging it. To shed light on these joint decoding dynamics, we draw inspiration from attention sink analyses in large language models, leveraging Track Attention Mass to show that standard generic queries exhibit unbalanced attention, frequently struggling to weigh track context against novel object discovery. SelfMOTR achieves highly competitive performance in complex, dynamic environments, yielding 69.2 HOTA on DanceTrack and leading with 71.1 HOTA on the Bird Flock Tracking (BFT) dataset. Project page: https://medem23.github.io/SM.

Abstract:
In visual object tracking, SAM2 stands out in deformation adaptability and distinguishing similar objects due to its pixel-level masks, whereas traditional discriminative trackers have advantages under occlusion and motion blur thanks to global semantic feature matching. To leverage this complementarity, we propose TR-MoE, a Temporal Reliability-aware Mixture-of-Experts framework. Unlike conventional fusion strategies that rely on static or per-frame confidence, we reframe tracker fusion as a time-series modeling problem. Specifically, we introduce a Mamba-based Trajectory Refiner (MTR) that processes the historical sequence of tracker states—including relative deviations and consistency scores—to capture the temporal evolution of error regimes. This enables the model to adaptively allocate fusion weights among experts based on historical temporal context. Furthermore, to achieve precise geometric correction, we design a KAN-enhanced Disentangled Head. Leveraging Kolmogorov–Arnold Networks, this module factorizes the refinement into independent directional offsets, dynamic scale magnitudes, and fusion weights. Extensive experiments show that TR-MoE effectively adapts to varying failure patterns, significantly boosting robustness and establishing new state-of-the-art performance. The source code is available at https://github.com/Avant1v/TR-MoE.

Abstract:
Unified multimodal models target joint understanding, rea-soning, and generation, but current image editing benchmarks are largelyconfined to natural images and shallow commonsense reasoning, offeringlimited assessment of this capability under structured, domain-specificconstraints. In this work, we introduce GRADE, the first benchmarkto assess discipline-informed knowledge and reasoning in image editing.GRADE comprises 520 carefully curated samples across 10 academicdomains, spanning from natural science to social science. To supportrigorous evaluation, we propose a multi-dimensional evaluation proto-col that jointly assesses Discipline Reasoning, Visual Consistency, andLogical Readability. Extensive experiments on 20 state-of-the-art open-source and closed-source models reveal substantial limitations in currentmodels under implicit, knowledge-intensive editing settings, leading tolarge performance gaps. Beyond quantitative scores, we conduct rigor-ous analyses and ablations to expose model shortcomings and identifythe constraints within disciplinary editing. Together, GRADE pinpointskey directions for the future development of unified multimodal models,advancing the research on discipline-informed image editing and reason-ing. Our benchmark and evaluation code are publicly released.

Abstract:
Language-aligned vision foundation models perform strongly across diverse downstream tasks. Yet, their learned representations remain opaque, making interpreting their decision-making difficult. Recent work decompose these representations into human-interpretable concepts, but provide poor spatial grounding and are limited to image classification tasks. In this work, we propose CFM, a language-aligned concept foundation model for vision that provides fine-grained concepts, which are human-interpretable and spatially grounded in the input image. When paired with a foundation model with strong semantic representations, we get explanations for any of its downstream tasks. Examining local co-occurrence dependencies of concepts allows us to define concept relationships through which we improve concept naming and obtain richer explanations. On benchmark data, we show that CFM provides performance on classification, segmentation, and captioning that is competitive with opaque foundation models while providing fine-grained, high quality concept-based explanations. Code at https://github.com/kawi19/CFM

Abstract:
Camera-controlled video diffusion models (VDMs) have re-cently emerged as powerful world models, enabling users to explore 3Dscenes through flexible, user-defined camera trajectories. Nevertheless,current VDMs typically rely on implicit spatiotemporal representationswithout explicit 3D geometric grounding. Such geometry-agnostic mod-eling often leads to issues including geometrically implausible struc-tures and cross-view spatial inconsistencies. To alleviate this, we presentDreamWorld, a new recipe of world model that novelly bridges the strongspatial structure priors of 3D foundation models with the high-fidelitygenerative capabilities of video diffusion models for geometry-consistent3D scene generation. Specifically, given the input image and camera tra-jectory, DreamWorld first learns a geometry video diffusion model topredict compact geometry features for the target novel views, function-ing as explicit structure pivots to reflect the underlying 3D spatial layout.To achieve this, we introduce a distillation paradigm that transfers high-level structural knowledge from a pretrained 3D foundation model to thediffusion model, thereby enabling it to produce geometrically consistentand spatially coherent features. Conditioned on such geometry features,another appearance video diffusion model is then utilized to synthesizethe final video, ensuring improved geometric plausibility and cross-viewconsistency while maintaining high visual fidelity. Extensive experimentsdemonstrate that DreamWorld outperforms existing methods in visualquality, 3D consistency, and camera controllability. Our project page isavailable at https://yanghb22-fdu.github.io/DreamWorld.

Abstract:
Applying 3D Gaussian Splatting (3DGS) to uncontrolled, in-the-wild environments remains challenging due to transient distractors.Existing approaches typically mask these distractors and suppress theirlosses during training. However, this zero-masking strategy leaves suchregions unsupervised, leading to per-view overfitting and causing severefloaters in novel views. To resolve this, it is essential to re-establish densemulti-view constraints by recovering the missing background informa-tion. In light of this, we propose PriSplat, a novel framework designed topropagate reliable multi-view information to restore these missing regionswith high geometric integrity. Specifically, we repurpose a large-scaleview synthesis prior into a 3D-aware inpainting engine, adapted throughmask-aware fast-weight updates to prevent distractor leakage into thescene memory. To ensure the fidelity of this restoration, we introducea geometry-aware support view selection algorithm based on informa-tion density and spatio-angular constraints. Ultimately, these synergisticadvancements yield 3D-consistent pseudo-ground truth from masked re-gions, establishing the dense supervision necessary to eliminate artifacts.Extensive experiments show that our method outperforms state-of-the-art baselines in both synthesis quality and multi-view consistency. Thecode is available at https://github.com/yun-seo/PriSplat.git.

Abstract:
Estimating 3D geometry in underwater environments presentsunique challenges due to light attenuation, scattering, and the absenceof large-scale, high-quality 3D annotations. Pioneering methods rely onmassive dense annotations that are impractical in underwater settings.In this paper, we propose Wat3R, a cross-domain semi-supervised learn-ing framework designed to adapt feed-forward 3D reconstruction modelsfrom air to underwater scenes. Uniquely, our method eliminates the needfor any annotated underwater data following a teacher-student architec-ture, that learns robust geometry representations merely on abundantunlabeled real underwater video footage. We also design a cross-viewconsistency loss that leverages geometric cues from other views to com-pensate for the information degradation in the current view caused bywater attenuation and scattering. Furthermore, considering the lack ofcomprehensive evaluation benchmarks, we construct Water3D, a di-verse dataset covering various water bodies and underwater scenarios, de-signed for geometric task evaluation. Experimental results demonstratethat Wat3R outperforms current state-of-the-art methods in underwatermulti-view depth estimation and point cloud reconstruction. The datasetand code are available at https://github.com/LSXI7/Wat3R .

Abstract:
Vector graphics are prized for their resolution independence,compact storage, and direct editability, making differentiable optimiza-tion of their parametric primitives an attractive goal. Yet classical ras-terization is discontinuous with respect to geometry, and existing reme-dies that smooth the forward pass demand increasingly elaborate heuris-tics as scene complexity grows. We trace this fragility to a gradientseesaw: design choices that improve forward geometric exactness cansystematically degrade the induced gradient signal, and vice versa. Tonavigate this tension we introduce CubicSplat, a differentiable vectorrasterizer that replaces Bézier closest-point solvers with uniform poly-line surrogates whose geometric error is bounded at O(S −2 ). The re-sulting static computation graph yields well-conditioned gradients byconstruction, while a compositing-derived visibility mechanism prunesdegenerate primitives without auxiliary regularization. On DIV2K andKodak benchmarks CubicSplat achieves state-of-the-art reconstructionquality with over 2 dB PSNR gain in the closed-fill setting, while train-ing up to 4× faster than prior methods. The code is available at https://github.com/CubicSplat/repo

Abstract:
Most end-to-end autonomous driving methods rely solely oninstantaneous sensor observations, limiting them to reactive behaviorwithout the anticipatory foresight human drivers employ through priorexperience. We introduce geospatial visual priors, street-level visual con-text anchored to the intended driving route, providing visual-spatialforesight independent of real-time sensors. We propose a memory aug-mentation module featuring a dual-memory architecture and an adap-tive memory gate, which can be easily integrated into existing end-to-end approaches. This design pairs a contextual memory for retrievedpriors with a persistent fallback memory, and dynamically regulatesthe influence of memories based on current state compatibility. Eval-uated on the NAVSIM-v2 benchmark, our approach consistently im-proves performance across diverse end-to-end baselines. Furthermore,because these priors are independent of onboard sensors, our method in-herently improves robustness against sensor corruption, while the dual-memory design ensures safe fallback when the retrieved priors them-selves become unreliable. Our project page is available at https://ori-mrg.github.io/PriorEye.

Abstract:
Dense feature matching aims to estimate all correspondencesbetween two images of a 3D scene and has recently been established asthe gold standard due to its high accuracy and robustness. However,existing dense matchers still fail or perform poorly for many hard real-world scenarios, and high-precision models are often slow, limiting theirapplicability. In this paper, we attack these weaknesses on a wide frontthrough a series of systematic improvements that together yield a sig-nificantly better model. In particular, we construct a novel matchingarchitecture and loss, which, combined with a curated diverse trainingdistribution, enables our model to solve many complex matching tasks.We further make training faster through a decoupled two-stage matching-then-refinement pipeline, and at the same time, significantly reduce re-finement memory usage through a custom CUDA kernel. Finally, weleverage the recent DINOv3 foundation model along with multiple otherinsights to make the model more robust and unbiased. In our extensiveset of experiments, we show that the resulting novel matcher sets a newstate-of-the-art, being significantly more accurate than its predecessors.ω

Abstract:
Recent advances in Virtual Try-On (VTON) and VirtualTry-Off (VTOFF) have greatly improved photo-realistic fashion synthesisand garment reconstruction. However, existing datasets remain static,lacking instruction-driven editing for controllable and interactive fash-ion generation. In this work, we introduce the Dress Editing Dataset(Dress-ED), the first large-scale benchmark that unifies VTON, VTOFF,and text-guided garment editing within a single framework. Each sam-ple in Dress-ED includes an in-shop garment image, the correspondingperson image wearing the garment, their edited counterparts, and anatural-language instruction of the desired modification. Built througha fully automated multimodal pipeline that integrates MLLM-basedgarment understanding, diffusion-based editing, and LLM-guided ver-ification, Dress-ED comprises over 146k verified quadruplets spanningthree garment categories and seven edit types, including both appear-ance (e.g., color, pattern, material) and structural (e.g., sleeve length,neckline) modifications. Based on this benchmark, we further propose aunified multimodal diffusion framework that jointly reasons over linguisticinstructions and visual garment cues, serving as a strong baseline forinstruction-driven VTON and VTOFF.

Abstract:
While personalized image generation has achieved remark-able progress, multi-reference image generation (MRIG) remains a chal-lenging task. Most existing benchmarks fail to adequately evaluate com-plex MRIG scenarios, hindering further progress in this area. To bet-ter assess model performance on complex MRIG tasks, we introduceOmniRef-Bench, a benchmark that covers complex combinations of ref-erence image types and a large number of reference images. Evaluationson OmniRef-Bench show that mainstream open-source models strugglein complex MRIG scenarios, and their performance deteriorates signifi-cantly as the number of mixed-type reference images increases. To ad-dress this issue, we propose DyRef, a two-stage training framework.In the first stage, supervised fine-tuning equips the model with the ba-sic capability to handle complex MRIG tasks. In the second stage, weintroduce Difficulty-aware Advantage Reweighting (DAR) and Discrim-inative Reward Scaling (DRS). DAR dynamically adjusts the optimiza-tion objective to improve performance when handling a large numberof mixed-type reference images. DRS enlarges intra-group reward differ-ences for more effective policy optimization. Experiments demonstratethat DyRef significantly improves the performance of open-source modelson OmniRef-Bench and single-image editing benchmarks, demonstratingthe effectiveness and generalization capability of our approach. Our codeis available at https://github.com/Weistrass/DyRef.

Abstract:
Autoregressive (AR)-Di x001B usion hybrid paradigms combine AR'sstructured semantic modeling with di x001B usion's high- x001C delity synthesis, yetsu x001B er from a dual speed bottleneck: the sequential AR stage and theiterative multi-step denoising of the di x001B usion vision decode stage. Exist-ing methods address each in isolation without a uni x001C ed principle design.We observe that the per-position prediction entropy of continuous-spaceAR models naturally encodes spatially varying generation uncertainty,which simultaneously governing draft prediction quality in the AR stageand re x001D ecting the corrective e x001B ort required by vision decoding stage,which is not fully explored before. Since entropy is inherently tied toboth bottlenecks, it serves as a natural unifying signal for joint accel-eration. In this work, we propose Drift-AR, which leverages entropysignal to accelerate both stages: 1) for AR acceleration, we introduceEntropy-Informed Speculative Decoding that align draft x0015 target entropydistributions via a causal-normalized entropy loss, resolving the entropymismatch that causes excessive draft rejection; 2) for visual decoder ac-celeration, we reinterpret entropy as the physical variance of the initialstate for an anti-symmetric drifting x001C eld x0016 high-entropy positions acti-vate stronger drift toward the data manifold while low-entropy positionsyield vanishing drift x0016 enabling single-step (1-NFE) decoding without it-erative denoising or distillation. Moreover, both stages share the sameentropy signal, which is computed once with no extra cost. Experimentson MAR, TransDi x001B , and NextStep-1 demonstrate 3.8 x0015 5.5× speedup withgenuine 1-NFE decoding, matching or surpassing original quality. Codeis available at https://github.com/aSleepyTree/Drift-AR.

Abstract:
Diffusion-based video super-resolution (VSR) methods deliver strong perceptual quality but are often unsuitable for latency-sensitive scenarios due to reliance on future frames and expensive multi-step denoising. We propose Stream-DiffVSR, a causally conditioned diffusion framework for efficient online VSR. Operating strictly on past frames, Stream-DiffVSR integrates a four-step distilled denoiser for fast inference, an Auto-regressive Temporal Guidance (ARTG) module that injects motion-aligned cues during latent denoising, and a lightweight temporalaware decoder with a Temporal Processor Module (TPM) to enhance detail and temporal coherence. Unlike chunk-wise streaming inference, our strictly frame-by-frame causal design avoids sequence-level waiting, substantially reducing time-to-first-frame and end-to-end latency. Stream-DiffVSR processes 720p frames in 0.328 seconds on an RTX 4090 and consistently outperforms prior diffusion-based baselines. Compared with the online state-of-the-art TMP, it improves perceptual quality (LPIPS +0.095). Compared with prior diffusion-based VSR methods such as MGLD-VSR [104], it reduces per-frame runtime by over 130×. Moreover, Stream-DiffVSR substantially lowers time-to-firstframe for diffusion-based VSR, reducing initial delay from over 4600 seconds to 0.328 seconds, making diffusion-based VSR markedly more practical for low-latency online and streaming deployment. Project page: https://jamichss.github.io/stream-diffvsr-project-page/

Abstract:
How can we teach large multimodal models (LMMs) newskills without erasing prior abilities? We study sequential fine-tuningon five target skills while monitoring general ability on eight held-outbenchmarks across three model families. Surprisingly, we find that per-formance lost on held-out tasks after fine-tuning on one skill can partlyrecover when the model is subsequently tuned on a different skill. Wetrace this behavior to a measurable shift in the output token distribu-tion, manifested through a simple counting-bias probe that shows theshift co-varies with forgetting. Guided by this insight, we identify twosimple, robust tuning recipes that learn strongly while limiting drift: (i)updating only the self-attention projection layers (SA Proj., ∆ learning+24.9 / ∆ held-out forgetting -0.6), and (ii) updating only the MLPGate&Up while freezing the Down projection (+30.5 / -2.1). Both sub-stantially outperform full-LLM tuning (+31.8 / -23.3) in the learning–forgetting trade-off. We also compare against common forgetting mitiga-tion methods—Learning without Forgetting (LwF), LoRA, Mixture-of-Experts, and weight-space interpolation (WiSE-FT)—and find that ourselective tuning recipes match or exceed their learning–stability balancewhile remaining simpler, requiring no replay, auxiliary parameters, orper-stage tuning. These results hold across LLaVA-OneVision, LLaVA-NeXT, and Qwen2.5-VL, confirming that the key to teaching LMMs newskills without forgetting lies in controlling output distribution shift bychoosing which components to tune. Code will be made available.

Abstract:
Reconstructing 4D animals from monocular videos is chal-lenging due to large inter-species variation, complex articulations, andthe lack of reliable templates. Existing approaches typically rely on eitherstrict category-specific priors that restrict generalization, or unconstrainedgenerative models that sacrifice input fidelity. To bridge this gap, wepresent a progressive test-time optimization framework built on 3D Gaus-sian Splatting for high-fidelity 4D animal reconstruction from a singlevideo. Our key insight is that a coarse shape prior suffices when coupledwith a progressive strategy that disentangles articulated pose from non-rigid deformation. Specifically, we employ a symmetry-aware temporalencoding that exploits bilateral cues while absorbing camera estimationdrift and a part-conditioned deformation mechanism guided by learn-able part anchors and a learnable skinning field. Extensive experimentsdemonstrate that our approach generalizes robustly across diverse species,achieving superior geometric accuracy, temporal consistency, and visualfidelity compared to existing baselines, even under severe prior mismatch.Project page: https://syl-322.github.io/ReWild4D/

Abstract:
Relative position embedding has become a standard mech-anism for encoding positional information in Transformers. However,existing formulations are typically limited to a fixed geometric space,namely 1D sequences or regular 2D/3D grids, which restricts their ap-plicability to many computer vision tasks that require geometric rea-soning across camera views or between 2D and 3D spaces. To addressthis limitation, we propose URoPE, a universal extension of RotaryPosition Embedding (RoPE) to cross-view or cross-dimensional geomet-ric spaces. For each key/value image patch, URoPE samples 3D pointsalong the corresponding camera ray at predefined depth anchors andprojects them into the query image plane. Standard 2D RoPE can thenbe applied using the projected pixel coordinates. URoPE is a parameter-free and intrinsics-aware relative position embedding that is invariant tothe choice of global coordinate systems, while remaining fully compatiblewith existing RoPE-optimized attention kernels. We evaluate URoPE asa plug-in positional encoding for transformer architectures across a di-verse set of tasks, including novel view synthesis, 3D object detection,object tracking, and depth estimation, covering 2D–2D, 2D–3D, and tem-poral scenarios. Experiments show that URoPE consistently improvesthe performance of transformer-based models across all tasks, demon-strating its effectiveness and generality for geometric reasoning. Our codeis available on our project website: https://urope-pe.github.io/.

Abstract:
We present Edit3r , a feed-forward framework that recon-structs and edits a 3D scene in a single pass. Unlike prior approachesthat require per-scene optimization, Edit3r directly predicts instruction-aligned 3D Gaussians from unposed sparse-view images and a text prompt.A key challenge in training 3D editing models is the lack of view-consistentedited images for supervision, due to the stochastic nature of 2D imageediting. We address this by reformulating editing as a recoloring pretexttask, and show that a model trained on simple recoloring transfers effec-tively to 3D-consistent editing at inference time. Specifically, we develop(i) a SAM2-based recoloring pipeline that generates one recolored ref-erence view along with reliable, cross-view-consistent supervision views,and (ii) an asymmetric input strategy that pairs the recolored refer-ence view with raw auxiliary views, encouraging the network to fuse andalign disparate observations. At inference, it supports diverse 2D editorsby resolving cross-view conflicts and yielding coherent 3D edits, withoutrequiring multi-view edited supervision during training. For large-scalequantitative evaluation, we introduce DL3DV-Edit-Bench. Compre-hensive quantitative and qualitative results show that Edit3r achievessuperior semantic alignment and enhanced 3D consistency compared torecent baselines, while operating at significantly higher inference speed,making it promising for real-time 3D editing applications.

Abstract:
Accurate reconstruction and tracking of dynamic human facesfrom image sequences is challenging because non-rigid deformations, ex-pression changes, and viewpoint variations occur simultaneously, creat-ing significant ambiguity in geometry and correspondence estimation. Wepresent a unified method for high-fidelity 4D facial reconstruction basedon canonical facial point prediction, a representation that assigns eachpixel a normalized facial coordinate in a shared canonical space. Thisformulation transforms dense tracking and dynamic reconstruction intoa canonical reconstruction problem, enabling temporally consistent ge-ometry and reliable correspondences within a single feed-forward model.By jointly predicting depth and canonical coordinates, our method en-ables accurate depth estimation, temporally stable reconstruction, dense3D geometry, and robust facial point tracking within a single architec-ture. We implement this formulation using a transformer-based modelthat jointly predicts depth and canonical facial coordinates, trained us-ing multi-view geometry data that non-rigidly warps into the canonicalspace. Extensive experiments on image and video benchmarks demon-strate state-of-the-art performance across reconstruction and trackingtasks, achieving approximately 3× lower correspondence error and fasterinference than prior dynamic reconstruction methods, while improvingdepth accuracy by 16%. These results highlight canonical facial point pre-diction as an effective foundation for unified feed-forward 4D facial recon-struction. Website: https://kocasariumut.github.io/FaceAnything/

Abstract:
The Visual Geometry Grounded Transformer (VGGT) enables unified feed-forward 3D reconstruction from multi-view images. However, deploying such a high-performance model may expose critical security vulnerabilities. Traditional adversarial perturbations require costly per-scene optimization, while Universal Adversarial Perturbations (UAPs) rely on a single static pattern and fail to effectively attack VGGT. To address these limitations, we propose MVAP-G, a multiview adversarial perturbation generator that produces imperceptible consistent perturbations across multiple views in a single feed-forward pass. To ensure perturbation consistency across diverse scenes, we design a cross-view adversarial alignment mechanism to process multi-view images. Experiments demonstrate that MVAP-G significantly degrades VGGT performance without iterative optimization during inference. This work pioneers multi-view adversarial attacks on 3D foundation models, uncovering severe vulnerabilities and underscoring the urgent need for robust 3D vision systems. The code is available at https://github.com/ qsong2001/mvap-g.

Abstract:
Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy metrics or qualitative inspection rather than measuring semantic correspondence. We present a human-grounded evaluation framework that quantifies alignment between SAE latents and human-annotated concepts, without requiring user studies, and validate this matching through targeted attribute perturbations. To enable this intervention-style evaluation in vision, we construct synCUB and synCOCO, synthetic benchmarks of paired images that differ in exactly one attribute. We introduce Fully-Binary Matching Pursuit (FBMP), a coalition-based matching procedure that supports many-to-one mappings between SAE latents and annotated concepts, and consistently outperforms one-to-one baselines. For functional validation, we propose a Targeted Attribute Perturbation Alignment Score (TAPAScore), which tests whether matched concepts respond selectively and in the expected direction under targeted image-level attribute perturbations. Under sanity checks, our matching and TAPAScore are the only evaluated metrics that reliably distinguish trained SAEs from untrained ones. Across SAEs trained on CLIP and DINOv2 embeddings, we find that increased overcompleteness can reduce perturbation alignment, indicating a reduction in interpretability. Our evaluation framework suggests that moderate dictionary sizes provide the best trade-off, yielding the most interpretable SAEs. Code and datasets are available at https://github.com/JonasKlotz/sae-concept-eval.

Abstract:
Generative world models hold immense promise as scalablesimulators for autonomous systems, particularly for synthesizing rare butsafety-critical multi-agent interactions, such as vehicle collisions. How-ever, current evaluation paradigms index heavily on visual fidelity and se-mantic alignment, leaving a critical blind spot: they cannot reliably quan-tify whether generated dynamics actually obey the fundamental physicallaws required for reliable simulation. Assessing this physical plausibilityis inherently difficult due to a lack of physical metrics and the challengeof extracting metric-scale kinematics from uncalibrated video rollouts.To bridge this gap, we introduce CrashTwin, a physics-grounded eval-uation framework designed to stress-test the physical trustworthiness ofworld models. CrashTwin couples a diverse dataset of multi-agent colli-sion scenarios, comprising 25K controllable synthetic and 12K in-the-wildreal-world collision sequences with a novel calibration-free reconstruc-tion pipeline, enabling the recovery of 3D physical attributes directlyfrom world model rollouts. We propose a diagnostic suite that systemat-ically evaluates three dimensions: spatio-temporal consistency, momen-tum and kinetic energy conservation, and world-dynamics integrity. Ex-tensive benchmarking of state-of-the-art models reveals a crucial insight:high perceptual quality frequently masks severe physical violations dur-ing complex interactions. By quantitatively exposing these failure modes,CrashTwin provides a vital diagnostic tool for developing physicallygrounded world models capable of reliable real-world simulation. Codeand dataset are available at: https://github.com/phai-lab/CrashTwin.

Abstract:
Self-supervised novel view synthesis (NVS) remains challenging to scale, despite the abundance of video data, largely due to the brittleness of training on realistic videos and the hard-to-predict scaling behavior of multi-network system designs. We introduce RayDer, a unified, feed-forward transformer that consolidates camera estimation, scene reconstruction, and rendering into a single backbone, turning self-supervised NVS into a well-posed single-model scaling problem. A minimal dynamic state, treated as a nuisance factor, absorbs time-varying content and enables stable training on unconstrained real-world video. Importantly, RayDer keeps static-scene NVS as its target task: dynamic content is leveraged purely as scalable supervision, not reconstructed as in dynamicscene (4D) NVS. Across multiple model sizes and orders of magnitude in data, RayDer exhibits clean power-law scaling with data and compute, and outperforms static-scene data mixtures. On a large number of benchmarks, RayDer achieves strong zero-shot open-set performance competitive with state-of-the-art supervised approaches. Project Page: https://compvis.github.io/rayder.

Abstract:
Measuring structured object understanding in vision founda-tion models remains challenging due to inconsistent evaluation protocolsand limited part-level supervision. Semantic correspondence (SC) eval-uates this capability by testing whether object parts can be matchedacross instances and categories under large variations in appearance,viewpoint, and geometry. To enable a systematic SC evaluation, we in-troduce SOCO, a new benchmark for Semantic Object Correspondencethat introduces a taxonomy of correspondence types and provides con-sistent, functionally meaningful keypoint annotations across 100 cate-gories and over 1M correspondence pairs. In addition, SOCO includeskeypoint language descriptions, enabling the evaluation of large vision-language models (LVLMs) and their fine-grained part-level understand-ing. Comprehensive experiments reveal that (i) vision foundation back-bones encode strong semantic structure but transfer correspondencespoorly across related categories and only partially capture object-partposition, (ii) LVLMs are stronger at text-prompted part localizationthan at visual-reference cross-image matching, exposing a gap betweenlanguage-grounded localization and fine-grained visual correspondence,and (iii) correspondence performance predicts dense downstream tasks—segmentation, tracking, 3D pose estimation, and 3D detection—morestrongly than ImageNet classification. Together, these findings positionSOCO as a benchmark for structured, part-level representation qualityin vision and multimodal foundation models.

Abstract:
Recent radiance field methods represent scenes with 2D prim-itives that offer surface alignment and efficient rasterization, from Gaus-sian disks to triangles, yet all rely on convex boundaries: curved and con-cave structures demand excessive primitives. We introduce DeformableTriangle Splatting, which augments each triangle with K control pointsper edge, each parameterized by a single learnable scalar displacementthat shifts the boundary inward or outward, enabling non-convex shaperepresentation while preserving the three base vertices that define the 3Dplane. To render these non-convex primitives differentiably, we designa rasterization pipeline in the triangle’s barycentric coordinate space,ensuring view-consistent rendering. A winding number test determineswhether each pixel lies inside the deformed primitive, and a windowfunction controlled by two learnable parameters, sharpness and cornersmoothness, together with a per-primitive scalar opacity, produces thesmooth opacity transition from interior to boundary. Validation is donein a variety of real-world scenes, outperforming recent works based onnon-volumetric primitives in terms of visual quality and versatility whilestill achieving competitive rendering efficiency.Project page: orioljim1.github.io/detris

Abstract:
Video-based remote physiological measurement (RPM) ishighly accessible but remains fragile under varying illumination, skintones, and motion. Radio frequency (RF) radar is largely invariant to il-lumination and appearance, providing complementary cardio-respiratorymicro-motion cues; however, requiring radar at inference is often imprac-tical due to its limited ubiquity and deployment overhead. We proposeRPM-Distill, a physiology-guided cross-modal distillation frameworkthat leverages synchronized radar only during training while retainingvideo-only inference. Our key observation is that although RGB and RFwaveforms differ in sensing physics and time-domain morphology, theyshare similar latent periodic rhythm in the frequency domain. We thusdistill physiology-structured spectral evidence to improve robustness, vialosses that (i) anchor the fundamental peak, (ii) match the off-peak back-ground distribution, and (iii) preserve spectral morphology and sharp-ness. To avoid negative transfer under sample-level teacher quality andalignment uncertainty, a spectral policy network predicts sample-leveldistillation gates and component weights from the student–teacher spec-tral relation map, learned with a meta bilevel objective on a small labeledvalidation split. Through extensive experiments in challenging conditionsand cross-dataset settings, RPM-Distill reduces MAE by up to 81% andimproves correlation by up to 21% over the best video-only baseline.Code is at https://github.com/WJULYW/RPM-Distill.

Abstract:
World models enable agents to reason about future outcomesand learn policies from their knowledge of state transition, but existingapproaches primarily focus on reconstructing future observations or fea-tures, which introduces unnecessary complexity and limits their effec-tiveness for decision making. In this work, we propose a compatibilityprediction Latent World Model (LWM) for robot navigation that pre-dicts action-conditioned latent feature compatibility rather than recon-structing observations. Our key insight is that spatial proximity corre-lates with latent feature similarity, enabling action consequences to beevaluated directly in latent space. To support counterfactual training,our model leverages action sequences sampled across trajectories andlearns to predict which sequences lead closer to the goal. Furthermore,we demonstrate how the learned world model can supervise policy learn-ing from unlabeled video data and further improve policies through re-inforcement learning entirely within the world model. This imagination-driven framework eliminates the need for action annotations and addi-tional environment interaction. Extensive experiments on multiple real-world robot navigation datasets show that our approach significantlyoutperforms prior world model and imitation learning methods in pre-diction accuracy, policy learning, and real-world navigation performance.The code, pretrained models, and additional materials are available athttps://wzm206.github.io/latent-world-model-nav/.

Abstract:
Lane topology reasoning aims to construct a lane graph fromonboard sensor observations. Existing methods follow a detection and as-sociation paradigm that treats each lane instance independently, leadingto geometric inconsistency at connected endpoints and incomplete graphsdue to visual occlusions. To address these issues, we propose TopoGPT,a generative framework that learns the geometry prior from typical lanegraph structures through autoregressive sequence modeling. Specifically,we construct a large-scale map dataset comprising 3.3M scenes. For eachlane graph, a lane tokenizer serializes it into discrete tokens, while a scenecontext encoder converts it into a rasterized image and extracts globalfeatures as scene tokens. We pre-train an autoregressive lane sequencetransformer via scene-conditioned next-token prediction, endowing themodel with the geometry prior over lane graph structures. Building uponthis prior, a perception adapter aligns BEV features from multi-view im-ages with the pre-trained scene condition, transferring the learned geom-etry prior to sensor-based lane graph prediction. On the OpenLane-V2benchmark, TopoGPT outperforms existing methods by an average of+6.4 on lane-level and +11.6 on point-level metrics, and produces geo-metrically consistent and structurally complete lane graphs. Our projectpage is available at https://buaa-colalab.github.io/topogpt_page.

Abstract:
Few-step video generation has been significantly advancedby consistency distillation. However, the performance of consistency-distilled models often degrades as more sampling steps are allocatedat test time, limiting their effectiveness for any-step video diffusion.We argue that this limitation arises because consistency distillation re-places the original probability-flow ODE trajectory with a consistency-sampling trajectory, weakening the desirable test-time scaling behaviorof ODE sampling. To address this limitation, we introduce AnyFlow,the first any-step video diffusion distillation framework based on flowmaps. Instead of distilling a model for only a few fixed sampling steps,AnyFlow optimizes the full ODE sampling trajectory. To this end, weshift the distillation target from endpoint consistency mapping (zt → z0 )to flow-map transition learning (zt → zr ) over arbitrary time inter-vals. We further propose Flow Map Backward Simulation, whichdecomposes a full Euler rollout into shortcut flow-map transitions, en-abling efficient on-policy distillation that reduces test-time errors (i.e.,discretization error in few-step sampling and exposure bias in causalgeneration). Extensive experiments across both bidirectional and causalarchitectures, at scales ranging from 1.3B to 14B parameters, demon-strate that AnyFlow achieves performance comparable to or better thanconsistency-based counterparts in the few-step regime, while support-ing flexible and scalable sampling under varying step budgets. Code isreleased at https://github.com/NVLabs/AnyFlow.

Abstract:
Recent text-guided image editing (TIE) models have achievedremarkable progress, while many edited images still suffer from issuessuch as artifacts, unexpected editings, unaesthetic contents. Althoughsome benchmarks and methods have been proposed for evaluating editedimages, scalable evaluation models are still lacking, which limits the de-velopment of human feedback reward models for image editing. To ad-dress the challenges, we first introduce EditHF-1M, a million-scale im-age editing dataset with over 29M human preference pairs and 148Khuman mean opinion ratings, both evaluated from three dimensions,i.e., visual quality, instruction alignment, and attribute preservation.Based on EditHF-1M, we propose EditHF, a multimodal large lan-guage model (MLLM) based evaluation model, to provide human-alignedfeedback from image editing. Finally, we introduce EditHF-Reward,which utilizes EditHF as a reward signal to optimize the text-guidedimage editing models through reinforcement learning. Extensive experi-ments show that EditHF achieves superior alignment with human prefer-ences and demonstrates strong generalization on other datasets. Further-more, we fine-tune the Qwen-Image-Edit using EditHF-Reward, achiev-ing significant performance improvements, which demonstrates the abil-ity of EditHF to serve as a reward model to scale-up the image editing.Both the dataset and code will be released in our GitHub repository:https://github.com/IntMeGroup/EditHF.

Abstract:
While 3D Vision-Language Models (3D VLMs) have demon-strated remarkable spatial reasoning capabilities, they suffer from mas-sive visual token counts that create severe computational bottlenecksduring inference. Existing token pruning methods primarily rely on di-versity-based selection, discarding similar tokens to maximize dispersion.However, in 3D environments, this approach frequently drops represen-tative prototype tokens in favor of outliers, breaking the multi-view con-sistencies and geometric structures essential for spatial reasoning. In thispaper, we propose a paradigm shift for 3D VLM token pruning: frommaximizing diversity to preserving visual evidence coverage. We intro-duce CoverPrune, a training-free framework that formulates inference-time token pruning as an Optimal Transport (OT) problem. To over-come the intractable combinatorial subset selection inherent in this for-mulation, we design the Feature-Spatial-Temporal (FST) transport costand target capacity, along with an efficient Spatial-Guided Greedy Selec-tion (SGS) algorithm to approximate the OT objective. Furthermore, wepropose CoverPrune-Lite, an accelerated variant utilizing spatially struc-tured local matching for minimal overhead. Extensive experiments acrossmultiple 3D visual-spatial reasoning benchmarks demonstrate that ourmethods achieve state-of-the-art token efficiency, maintaining robust rea-soning performance even under highly aggressive pruning budgets. Visitour project website at https://github.com/Brucess/CoverPrune.

Abstract:
Semi-supervised crowd analysis is a prominent area of re-search, as unlabeled data are typically abundant and inexpensive toobtain. However, traditional point-based annotations constrain perfor-mance because individual regions are inherently ambiguous, and con-sequently, learning fine-grained structural semantics from sparse anno-tations remains an unresolved challenge. In this paper, we first pro-pose an Exclusion-Constrained Dual-Prompt SAM (EDP-SAM), basedon our Nearest Neighbor Exclusion Circle (NNEC) constraint, to gener-ate mask supervision for current datasets. With the aim of segmentingindividuals in dense scenes, we then propose Exclusivity-Guided MaskLearning (XMask), which enforces spatial separation through a discrim-inative mask objective. Gaussian smoothing and a differentiable centersampling strategy are utilized to improve feature continuity and train-ing stability. Building on XMask, we present a semi-supervised crowdcounting framework that uses instance mask priors as pseudo-labels,which contain richer shape information than traditional point cues. Ex-tensive experiments on the ShanghaiTech A, UCF-QNRF, and JHU++datasets (using 5%, 10%, and 40% labeled data) verify that our end-to-end model achieves state-of-the-art semi-supervised segmentation andcounting performance, effectively bridging the gap between counting andinstance segmentation within a unified framework. Code can be found athttps://github.com/JoyceeH0127/ECCV2026XMask.

Abstract:
Implicit Neural Representations (INRs) have been provensuccessful in encoding continuous signals through coordinate-based net-works, yet facing a spectral dilemma: periodic activations capture finedetails but act as all-pass filters that memorise noise, while spatiallycompact activations regularise effectively but suffer from low-frequencybias. Existing attempts to resolve this trade-off introduce computationaloverhead or tuning frailty. We propose to model each neuron’s activationas the steady-state response of a sinusoidally-forced damped harmonicoscillator, whose amplitude naturally governs the network’s spectral se-lectivity during training. By jointly optimising the oscillator parametersalongside the network weights, our method adapts to the target signal’sspectral content without explicit regularisation. Initialised in the stop-band, the network exhibits a coarse-to-fine learning curriculum that pro-gressively expands its spectral gate, capturing low-frequency structuresfirst and high-frequency details only when justified by the reconstructionobjective. Comprehensive experiments show that our approach consis-tently achieves state-of-the-art or competitive results against establishedINRs, while requiring no task-specific tuning of any hyperparameters.Project Page available at https://alex-costanzino.github.io/fdho/.

Abstract:
Audio-driven avatar interaction demands real-time, stream-ing, and in x001C nite-length generation x0016 capabilities fundamentally at oddswith the sequential denoising and long-horizon drift of current di x001B usionmodels. We present Live Avatar, an algorithm-system co-designed frame-work that addresses both challenges for a 14-billion-parameter di x001B usionmodel. On the algorithm side, a two-stage pipeline distills a pre-trained bidirectional model into a causal, few-step streaming one whoseKV cache stores noisy rather than clean history. This lossy condition-ing, counter-intuitively, suppresses error accumulation and enables mod-els trained on short clips to generalize stably beyond 10,000 seconds.On the system side, this noisy conditioning also breaks the sequen-tial sampling bottleneck, allowing Timestep-forcing Pipeline Parallelism(TPP) to assign each GPU a x001C xed denoising timestep, converting the se-quential di x001B usion chain into an asynchronous spatial pipeline that simul-taneously boosts throughput and improves temporal consistency. LiveAvatar achieves 45 FPS with a TTFF of 1.21 s on 5 H100 GPUs, andto our knowledge is the x001C rst to enable practical real-time streaming ofa 14B di x001B usion model for in x001C nite-length avatar generation. Our projectpage is at https://liveavatar.github.io/.

Abstract:
Music-driven dance video generation aims to synthesize expressive human motion that is temporally aligned with music while maintaining high visual fidelity. Despite recent progress, existing methods still struggle to generate dance videos that simultaneously exhibit expressive motion and high visual quality. This limitation primarily arises from two factors: (1) Dataset. The lack of large-scale and high-quality datasets and effective data collection pipelines specifically tailored for dance video generation; and (2) Method. The absence of principled framework-level solutions for effectively integrating music as a complementary conditioning signal into the Video Generation Foundation Models. To address the dataset limitation, we introduce CIPE-Dance, a large-scale Internetsourced dance video dataset, equipped with Choreograph Informed text annotations and constructed via a Progressive Expert pipeline. To the best of our knowledge, CIPE-Dance is the largest dataset for dance video generation to date, comprising 300k high-quality clips (over 400 hours) and covering diverse dancers, environments, and dance genres. To overcome the method limitation, we propose OmniDance, a frameworklevel recipe for integrating music into a TI2V foundation model without sacrificing its original controllability or visual fidelity. Motivated by the complementary roles of text (low-frequency semantics) and music (highfrequency temporal dynamics), OmniDance co-designs a depth-aware specialization model architecture, an anchored easy-to-hard curriculum learning strategy, and modality-specialized time-dependent CFG strategy, achieving unified TI2V/MI2V/MTI2V generation. Extensive experiments on the CIPE-Dance dataset demonstrate that OmniDance achieves state-of-the-art performance across TI2V, MI2V, and MTI2V tasks, while exhibiting robust multimodal integration capability. Project is available at https://github.com/AMAP-ML/OmniDance.

Abstract:
Multimodal large language models (MLLMs) have rapidlyadvanced video understanding, achieving strong zero-shot and few-shotrecognition across standard benchmarks. Yet their ability to deny anaction by recognizing when an activity is not happening despite strongcontextual cues remains largely unexplored. We introduce UCF101-AD, a large-scale benchmark consisting of paired Action-Presence andAction-Denial clips, designed to evaluate this capacity for denial. Eachnegative video in UCF101-AD preserves the same contextual and mo-tion cues (persons, objects, locations) as its positive counterpart, butthe defining action itself is explicitly absent. Evaluating 20 state-of-the-art MLLMs reveals a consistent failure: models that exceed 85% accu-racy on the positive action classes collapse below 50% on its action-denial counterpart, indicating a strong inclination to affirm plausibleactions rather than verify that they truly occur. This exposes a criti-cal blind spot in modern video understanding: the inability to reasoncausally about whether a motion actually happens. To probe this is-sue, we explore a causal graph formulation, CausalAct, which expressesscene structure through natural-language prompts linking context, in-teraction, and motion. Incorporating such causal cues substantially re-duces false positives, demonstrating that denial is a learnable reason-ing skill. UCF101-AD provides a new lens for diagnosing and improv-ing causal reasoning in multimodal models. Dataset and relevant code:https://github.com/raiyaan-abdullah/Learn-to-Deny.

Abstract:
Vision-language models (VLMs) achieve strong performanceon spatial reasoning benchmarks, yet it remains unclear whether this re-flects structured 3D understanding or reliance on statistical shortcuts innatural images. We introduce a representation-level analysis frameworkthat constructs minimal contrastive pairs to measure how spatial axes areorganized and disentangled within VLM embeddings. Our analysis acrossmultiple model families reveals a consistent vertical-distance entangle-ment: models conflate vertical image position with distance, mirroringthe perspective bias of natural photographs. This bias produces a signif-icant accuracy gap between perspective-consistent and counter-heuristicexamples, and intensifies under data scaling even as overall benchmarkaccuracy improves. We further show that models with similar benchmarkscores can exhibit different internal representations, and that these dif-ferences predict accuracy and robustness across diverse spatial reasoningbenchmarks. To isolate this bias from evaluation-set skew, we introduceSpatialTunnel, a synthetic benchmark designed to expose spatial short-cut biases by removing common correlations present in natural images.Experiments suggest that the entanglement is model-intrinsic, and thatmodels with well-separated spatial axes exhibit greater robustness, indi-cating that well-structured spatial representations lead to more reliablespatial reasoning across diverse benchmarks. Code and benchmark areavailable on the project website.

Abstract:
Event cameras, also known as neuromorphic cameras, havegained significant attention in recent years due to their high tempo-ral resolution, high dynamic range, and low power consumption. Whilemany studies and datasets in neuromorphic vision have focused on auto-motive and drone applications, human-centric daily-life scenarios remainlargely underrepresented, despite their importance for developing andbenchmarking event-based perception systems. Moreover, the few ex-isting event-based human activity datasets are typically recorded withscripted human actions, limiting their ability to capture natural hu-man behaviors. In this paper, we introduce EventKitchen, a large-scalestereo event camera benchmark dataset of human cooking activities inthe kitchen. EventKitchen is egocentrically collected from 10 participantsin 13 diverse kitchens, where the participants wear a helmet with multiplesensors and naturally perform cooking activities, without any scriptedactions. EventKitchen comprises 5.5 hours of stereo event recordingswith synchronized RGB, depth, and IMU data. We provide human an-notations for 10,762 action segments and 13,482 bounding boxes. Wetrain baseline models on EventKitchen to perform multiple event-basedtasks, including action recognition, object detection, and stereo depthestimation. By capturing natural, real-world human activities, Event-Kitchen establishes a challenging benchmark for neuromorphic visionbeyond autonomous driving. The dataset and toolkit are available athttps://chengmingf.github.io/EventKitchen.github.io/

Abstract:
In this paper, we explore spatial-aware humanoid whole-body manipulation task. Compared with tabletop settings, this taskposes two key challenges: 1) Spatial understanding is challenging in com-plex 3D environments with diverse spatial relations. 2) Action generationis difficult to generalize, as limited and costly real-robot data restrictsdata-driven models generalization. To address these challenges, we pro-pose a generalizable humanoid loco-manipulation framework that lever-ages the spatial perception and action generation capabilities of multi-agent large models. Specifically, our framework includes two components:Active Spatial Brain for active spatial perception and decision-making,and Generalizable Action Cerebellum for executable robot action genera-tion. The first component actively perceives the spatial scene and makesdecisions on task planning and subtask decomposition. The second com-ponent generates executable robot actions based on the decisions madeby the first module without needs of task-specific real robot data. Tobenchmark our framework, we design a set of spatial manipulation tasksfrom two perspectives: evaluating spatial perception and understand-ing, and assessing real-robot task performance. The results demonstratestrong performance on both aspects across diverse tasks and environ-ments.

Abstract:
Existing instruction-based video editing datasets commonlyfocus on single-task appearance editing, failing to meet the complex cre-ative demands of real-world scenarios. To bridge this gap, we presentGoku, a large-scale dataset featuring 2 million high-quality, instruction-aligned video editing pairs, which is the first to extend task boundariesfrom basic appearance editing to multi-task and structural manipulations(e.g., precise control of subject movement). To tackle the data synthesischallenges inherent in these complex tasks, we design an efficient datasynthesis pipeline that decomposes complex edits into controllable sub-problems and introduce a progressive filtering system for data reliabil-ity throughout the whole process. Furthermore, we explore the optimalnetwork structures on Goku, and propose Goku-Edit. To deeply com-prehend complex editing instructions, Goku-Edit leverages an MLLMas its text encoder and adopts a decoupled dual-branch design: a dedi-cated mask branch handles structural control, freeing the main branchfor appearance rendering. A comprehensive video editing benchmark,Goku-Bench, is also proposed with 1,000 human-verified test cases and7 novel editing-specific metrics. Evaluated on Goku-Bench, Goku-Editobtains up to +8% improvement on other open-source models in termsof instruction following.

Abstract:
We introduce VSeek, an agentic framework that transformslong-video question answering (LVQA) from a passive, single-pass per-ception task into a multi-turn retrieval process. VSeek utilizes a naturallanguage-driven search to identify relevant context within long videosand is post-trained with reinforcement learning (RL) to jointly formulatetargeted search queries and reason over retrieved clips for LVQA. WhileRL post-training has revolutionized reasoning in symbolic domains suchas mathematics and code, its application to long-video understandingremains hindered by a lack of verified rewards. To ensure that the re-trieved context is relevant, we propose a novel neuro-symbolic approachthat bridges open-ended natural language with discrete visual verifica-tion. Specifically, complex user queries are compiled into formal tempo-ral logic specifications for systematically decomposing natural languagequestions into a definitive checklist of required atomic visual primitives,such as key objects and activities, along with their temporal ordering.These systematically derived grounding events provide the critical feed-back signal for RL post-training, enabling dense, verifiable rewards basedon the successful retrieval of these specific visual elements rather thanrelying entirely on outcome-only answer accuracy. By explicitly optimiz-ing for this verifiable evidence-seeking behavior, VSeek improves Pass@1scores by up to 8% and Pass@4 scores by 15% on long-video understand-ing benchmarks compared to base models. We open-source our code athttps://utaustin-swarmlab.github.io/VSeek.

Abstract:
Deep learning models have become the standard tool for doc-ument rectification and illumination correction, yet their performance isfundamentally bound by their training data. For nearly a decade, the com-munity has heavily relied on Doc3D, a pioneering but increasingly limiteddocument unwarping dataset in terms of scale and quality. To address thisbottleneck, we introduce SyntheticDoc, a massive, high-quality datasetdesigned to push the boundaries of document unwarping. SyntheticDocis composed of 1,000,000 high-resolution procedurally generated trainingsamples, alongside extensive validation and test sets. Each sample ispaired with rich, pixel-perfect annotations, including UV maps, normalmaps, albedo and shading. To ensure physical accuracy and photorealism,the paper geometries are generated via a physics-based simulator andrendered using a path tracer. To demonstrate the benefit of our dataset,we train a simple baseline model on SyntheticDoc and report on itsperformance in comparison to state-of-the-art methods on both documentunwarping and illumination correction tasks. Our dataset is available athttps://igl.ethz.ch/projects/SyntheticDoc/ and the code used to generateit at https://github.com/tanguymagne/SyntheticDoc.

Abstract:
Text-based person anomaly search (TPAS) refers to the taskof retrieving people exhibiting normal or anomalous behaviors from nat-ural language descriptions. Existing TPAS models often learn a singlejoint embedding where appearance, action, and background informationare entangled, causing over-reliance on identity cues, poor alignment foraction-centric queries, and limited semantic connection between actionsand places where they occur. To address these issues, we propose Se-mantic Decoupled Alignment (SeDA), a disentangled vision–languageretrieval framework that explicitly factorizes both visual and textualrepresentations into appearance, action, and background components.SeDA introduces Semantic Token Projection, which derives three se-mantic queries from the global [CLS] token, softly aggregates modal-ity tokens relevant to each factor, and recomposes the resulting factortokens into a compact retrieval embedding. To enforce factor-specific se-mantics, we decompose each caption into appearance/action/backgroundsub-captions and supervise the corresponding tokens with a Feature De-coupling Loss, combined with contrastive and image–text matching ob-jectives. On the Person Anomaly Benchmark (1M pairs), SeDA achieves86.45% R@1 (+1.52 over SOTA), improves average multi-weather R@1by +2.43, and gains +3.74 R@1 under out-of-distribution evaluation.https://github.com/ErgastiAlex/SeDA

Abstract:
Supervised learning for image segmentation typically requires spatially aligned image and label sets. When images and labels originate from different sources, the pairing may be misaligned, which can significantly deteriorate the performance of the learned models. This is especially common in remote sensing, when aerial or satellite images are co-registered with labels from another source (e.g., OpenStreetMap). In this work, we propose a novel approach for training on misaligned labels, where we simultaneously learn the label alignment. Our align and segment (AnS) approach builds on the spatial transformer module to transform the misaligned labels using an affine transformation to provide a better learning target for a canonical semantic segmentation network. We prevent shortcut learning of misaligned labels in these semantic segmentation networks through a self-supervised regularization loss and show that it is complementary to data augmentation, especially for systematically misaligned training data. A decisive characteristic of our AnS approach is that it learns without requiring any “golden” labels. We experimentally show on both synthetic and real-world data from different cities that our approach enables high-quality building segmentation and precise label-image alignment at the same time. Code and derived datasets are available at https://github.com/venkanna37/align-and-segment.

Abstract:
Video Face Swapping (VFS) requires seamlessly injecting asource identity into a target video while meticulously preserving the orig-inal pose, expression, lighting, background, and dynamic information.Existing methods struggle to maintain identity similarity and attributepreservation while preserving temporal consistency. To address the chal-lenge, we propose a comprehensive framework to seamlessly transfer thesuperiority of Image Face Swapping (IFS) to the video domain. Wefirst introduce a novel data pipeline SyncID-Pipe that pre-trains anIdentity-Anchored Video Synthesizer and combines it with IFS modelsto construct bidirectional ID quadruplets for explicit supervision. Build-ing upon paired data, we propose a powerful Diffusion Transformer-basedframework OmniFace, employing a core Modality-Aware Conditioningmodule to discriminatively inject multi-model conditions. Meanwhile,we propose a Synthetic-to-Real Training mechanism and an Identity-Coherence Reward Weighting strategy to enhance visual realism andidentity consistency under challenging scenarios. To address the issue oflimited benchmarks, we introduce IDBench-V, a comprehensive bench-mark encompassing diverse scenes. Extensive experiments demonstrateOmniFace outperforms state-of-the-art methods and further exhibits ex-ceptional versatility, which can be seamlessly adapted to various swap-related tasks.

Abstract:
We propose a multimodal, physically grounded approach formetric-scale amodal object reconstruction and pose estimation undersevere hand occlusion. Unlike prior occlusion-aware 3D generation meth-ods that rely only on vision, we leverage physical interaction signals:proprioception provides the posed hand geometry, and multi-contacttouch constrains where the object surface must lie, reducing ambigu-ity in occluded regions. We represent object structure as a pose-aware,camera-aligned signed distance field (SDF) and learn a compact latentspace with a Structure-VAE. In this latent space, we train a condi-tional flow-matching diffusion model, pretraining on vision-only imagesand finetuning on occluded manipulation scenes while conditioning onvisible RGB evidence, occluder/visibility masks, the hand latent rep-resentation, and tactile information. Crucially, we incorporate physics-based objectives and differentiable decoder-guidance during finetuningand inference to reduce hand–object interpenetration and to align thereconstructed surface with contact observations. Because our methodproduces a metric, physically consistent structure estimate, it integratesnaturally into existing two-stage reconstruction pipelines, where a down-stream module refines geometry and predicts appearance. Simulation ex-periments show that adding proprioception and touch substantially im-proves completion under occlusion and yields physically plausible recon-structions at correct real-world scale compared to vision-only baselines;we further validate transfer by deploying the model on a real humanoidrobot with an end-effector different from those used during training. Seehttps://github.com/hsp-iit/physical-generative-reconstruction

Abstract:
Image-based high-throughput plant phenotyping leveragesinstance-level understanding of individual leaves growth over experimen-tal time, yet existing datasets lack temporal depth and annotation con-sistency to jointly benchmark segmentation, tracking, and growth stageclassification. We introduce PhenoLeaf-TS, a time-series dataset of 17,082top-down RGB images spanning 21 Arabidopsis thaliana genotypes, to-talling 318 plant replicates, each annotated with colour-coded leaf in-stance masks that maintain consistent identity throughout the growthsequence. We define three benchmark computer vision tasks with stan-dardised protocols and evaluate 21 distinct models: 9 instance segmenta-tion architectures, 6 multi-object trackers, and 6 classification architec-tures. Mask R-CNN with ResNet-50 achieves 73.2 mAP for leaf segmen-tation, ByteTrack reaches 84.1% MOTA for tracking individual leaves insequences, and Swin-T achieves 91.7% accuracy for growth stage classifi-cation. Cross-dataset experiments on CVPPP and Komatsuna show thatfine-tuning from PhenoLeaf-TS weights yields up to +51 mAP over zero-shot transfer. The dataset, the evaluation code, and the trained modelsare publicly accessible at https://pisyntor.github.io/PhenoLeaf-TS.

Abstract:
Current text-to-video models can make individual frames look convincing while still getting simple interactions wrong: objects move before contact, an intended action is skipped, a placed object keeps drifting, or a support relation breaks. Our starting point is that standard frame-first denoising updates every latent region at every step, even when the prompt implies that only a local interaction should be active. We introduce Event-Driven Video Generation (EVD), a small DiT-compatible intervention that gives the sampler an explicit event signal. A lightweight head predicts token-level event activity; training losses tie that activity to latent state change; and event-gated sampling, with hysteresis and an early-step schedule, applies the update field mainly where an interaction is forming. On EVD-Bench, EVD improves human preference and VBench dynamics for state persistence, spatial accuracy, support relations, and contact stability, while keeping appearance quality comparable to the base model. The results suggest that a modest amount of event structure can correct several interaction failures that otherwise remain hidden behind good frame-level appearance. Project webpage: https://evd-project-website.pages.dev

Abstract:
Image-goal navigation is a fundamental capability for em-bodied AI, yet its practical deployment is strained by strong prior as-sumptions. Existing methods predominantly rely on temporally orderedvideo streams or auxiliary sensors (e.g., depth, LiDAR) to maintain spa-tial consistency. These sequential and multimodal dependencies severelyrestrict scalability, especially when deploying robots using crowd-sourcedor pre-recorded unordered image collections. When temporal priors areremoved, current methods struggle with severe perceptual aliasing, noisyassociations, and catastrophic mapping failures. To address this under-explored challenge, we propose Unordered Landmark Visual Navigation(ULVN), a unified RGB-only framework free from temporal and odo-metric priors. ULVN systematically mitigates error accumulation by in-tegrating mapping, localization, and planning. Specifically, it constructsa robust 2D topological map directly from unstructured images via cali-brated geometric verification and maximum spanning forest refinement.For closed-loop execution, ULVN abandons sequential heuristics, utiliz-ing a graph-based belief propagation filter with entropy-adaptive fusionfor global localization and dynamic subgoal planning. Extensive experi-ments in simulation and real-world deployments demonstrate that ULVNsignificantly outperforms state-of-the-art methods.

Abstract:
Rolling shutter (RS) cameras are widely used in consumerdevices, but their row-wise exposure causes distortions under motion,making geometric 3D vision problems dependent on both camera in-trinsics and readout time ratio. Existing RS calibration methods relyon calibration targets or specialised hardware, limiting their use in un-constrained settings. We present the first self -calibration method forRS cameras that directly estimates camera intrinsics and the readouttime ratio from image sequences, without requiring calibration targets.The method is implemented as a self-calibrating bundle adjustment(BA), which critically depends on the RS imaging model. We com-bine two known complementary models. The first formulates RS imag-ing as continuous-time trajectory estimation under a row-wise pose rep-resentation. The second interprets RS images as temporally distortedglobal shutter (GS) images and requires to estimate correction fields.The combination is non-trivial and results in a unified dual-projectionmodel, in which each 3D point is simultaneously constrained at both row-dependent and reference timestamps along a shared continuous trajec-tory, enforcing stronger geometric and temporal consistency. Extensivesimulations analyse the applicability of several implementations undervarying conditions, and real data experiments demonstrate the accuracy,robustness, and practical effectiveness of the proposed approach.

Abstract:
Background-oriented schlieren (BOS) tomography reconstructs3D flow density fields from refraction-induced distortions observed fromone or more viewpoints. Capturing high-speed flows with frame-basedBOS typically requires high-speed cameras and bright illumination tocompensate for short exposure times. In this paper, we propose an event-based BOS tomography approach that reconstructs time-varying 4D den-sity fields from event streams. Leveraging the high temporal resolutionand high dynamic range of event cameras, the proposed approach enableshigh-speed airflow reconstruction under ambient lighting. We representthe spatiotemporal density field as a neural implicit field and render BOSobservations via refractive ray tracing. We utilize physics-informed regu-larization to improve reconstruction under sparse views. Experiments onsimulated and real data in single-view and orthogonal dual-view setupsdemonstrate accurate reconstructions.

Abstract:
Person Re-Identification (ReID) faces severe challenges frommodality discrepancy and clothing variation in long-term surveillancescenario. While existing studies have made significant progress in ei-ther Visible-Infrared ReID (VI-ReID) or Clothing-Change ReID (CC-ReID), real-world surveillance system often face both challenges simul-taneously. To address this overlooked yet realistic problem, we definea new task, termed Cross-Modality Clothing-Change Re-Identification(CMCC-ReID), which targets pedestrian matching across variations inboth modality and clothing. To advance research in this direction, weconstruct a new benchmark SYSU-CMCC, where each identity is cap-tured in both visible and infrared domains with distinct outfits, reflectingthe dual heterogeneity of long-term surveillance. To tackle CMCC-ReID,we propose a Progressive Identity Alignment Network (PIA) that pro-gressively mitigates the issues of clothing variation and modality dis-crepancy. Specifically, a Dual-Branch Disentangling Learning (DBDL)module separates identity-related cues from clothing-related factors toachieve clothing-agnostic representation, and a Bi-Directional PrototypeLearning (BPL) module performs intra-modality and inter-modality con-trast in the embedding space to bridge the modality gap while furthersuppressing clothing interference. Extensive experiments on the SYSU-CMCC dataset demonstrate that PIA establishes a strong baseline forthis new task and significantly outperforms existing methods.

Abstract:
Camera pose estimation from image streams is a critical com-ponent of spatial world models that integrate perception into planningand decision-making. Nearly all Visual Odometry (VO) and SimultaneousLocalization and Mapping (SLAM) systems have focused on datasetscontaining raw, uncompressed videos. Many working systems insteaduse ubiquitous hardware units to efficiently compress and decode videostreams, saving orders of magnitude in storage and bandwidth. However,this lossy compression introduces visual artifacts that hinder the per-formance of traditional tracking systems. We present VOCA, a causalstereo visual-odometry method that exploits codec information to im-prove tracking performance. We achieve state-of-the-art performance oncausal VO for relative trajectory error, efficiency, and absolute trajec-tory error on compressed streams. This work highlights the potential ofleveraging widely available video codec information for vision tasks.

Abstract:
While modern multimodal models integrate vision with lan-guage, audio, or touch, olfaction remains largely unexplored due to thelack of paired visuo-olfactory data. We introduce SmellNet-V, a scal-able visuo-olfactory dataset built on the insight that odor identity islargely invariant to visual transformations within a semantic category.This allows us to synthetically pair smell-only samples with semanticallyaligned in-the-wild web images, converting a unimodal olfactory datasetinto a cross-modal benchmark without costly co-collection. Building onthis dataset, we propose See & Sniff, a self-supervised framework thatlearns joint visuo–olfactory representations via dense local alignment andnaturally produces smell saliency maps for spatial grounding of odorsources. We further introduce pixel-level smell localization task and abenchmark for evaluation. Our method surpasses smell-only baselines by7% in smell classification from smell alone and generalizes to cross-modalretrieval and smell localization, establishing visuo-olfactory learning asa new direction in multimodal perception.

Abstract:
The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts. Handcrafted templates and Large Language Model (LLM)-generated descriptions not only make predictions more interpretable, but also enable reuse of the same prompts across heterogeneous VLMs. Recent works construct taskadapted text prompts with a small number of labeled images. However, existing few-shot text prompting methods do not explicitly focus on misclassified examples during prompt construction, leading to only marginal improvements even as more shots become available. To fully exploit few-shot supervision, we propose Text Prompt Boosting (TPB), an AdaBoost-inspired framework that treats each text-prompt-based classifier as a weak learner and sequentially aggregates them into a strong ensemble by explicitly targeting hard, misclassified examples. Extensive experiments show that TPB preserves task-intrinsic, model-agnostic cues in text space, enabling robust cross-model transfer. Across eleven classification benchmarks, TPB improves accuracy on the source model and preserves shot-driven gains when transferred to larger, more capable VLMs, where existing methods struggle to sustain such improvements.

Abstract:
Test-time scaling is a paradigm where large models use ad-ditional compute at inference to achieve better performance, withoutchanging model weights. While it has been widely studied for Large Lan-guage Models (LLMs), its applicability to Large Vision-Language Mod-els (LVLMs) remains less explored and analyzed, with limited analysis ofwhether, when, and to what extent these approaches transfer to LVLMs.In this work, we ask a simple but fundamental question: can conven-tional test-time scaling methods developed for LLMs be directly appliedto LVLMs? We present the first comprehensive study of test-time scal-ing for LVLMs, spanning multiple models and model sizes, nine test-timescaling methods, and six diverse benchmarks. Our main findings is that1) different from previous findings, small, well-performing models benefitthe most from test-time scaling, enabling performance improvements ofup to around 30%, reaching large models performance, and often out-performing them, 2) LVLMs lose focus when given more compute thannecessary, and 3) Visual information is encoded early in the reasoningchain, after which the chain is dominated by text-only reasoning andthe contribution of image tokens drops significantly. Finally, we also pro-vide a global and fine-grained analysis on the quality and informationsufficiency of the reasoning chains produced. Overall, our findings andanalysis provide practical guidance and insights into LVLMs and theirdeployment in research and industry.

Abstract:
Polarization cues benefit applications such as material de-tection and de-reflection, yet acquiring them typically requires dedicatedhardware. This motivates us to estimate the linear polarization from asingle RGB image. However, the task is inherently ill-posed, with theAngle of Polarization (AoP) becoming particularly unstable in weak-polarization regions, where the polarimetric signal is overwhelmed bynoise, leading to erratic angle estimates. To address these limitations, wepropose GenPolar, a Stokes-informed diffusion framework grounded inthe Mueller formalism from an intensity observation. Specifically, Gen-Polar predicts channel-wise linear Stokes components (S1 , S2 ) from inten-sity S0 , from which degree of linear polarization (DoLP) and AoP are an-alytically derived; AoP is further supervised with an observability-awareloss. In addition, to enable efficient and high-fidelity inference, we adopta two-stage training strategy. Firstly, a multi-step conditional diffusionmodel is trained with a physics-based loss. Subsequently, we distill it intoa one-step generator, which further supports stable Low-Rank Adapta-tion (LoRA) of the VAE encoder to mitigate domain-specific autoen-coding bias. Extensive experiments across rotating-polarizer, division-of-focal-plane, and hybrid datasets demonstrate that GenPolar achievesstate-of-the-art performance in both DoLP fidelity and AoP stability.Crucially, these improvements translate to significant and consistent gainsin downstream applications, including material detection and de-reflection.

Abstract:
Polarimetric imaging enables advanced vision applicationssuch as normal estimation and de-reflection by capturing unique surface-material interactions. However, existing applications (alternatively calleddownstream tasks) rely on datasets constructed by naïvely regroupingraw measurements from division-of-focal-plane sensors—where pixels ofthe same polarization angle are extracted and aligned into sparse im-ages without proper demosaicking. This reconstruction strategy resultsin suboptimal, incomplete targets that limit downstream performance.Moreover, current demosaicking methods are task-agnostic, optimizingonly for photometric fidelity rather than utility in downstream tasks.Towards this end, we propose PolarAPP, the first framework to jointlyoptimize demosaicking and its downstream tasks. PolarAPP introducesa feature alignment mechanism that semantically aligns the representa-tions of demosaicking and downstream networks via meta-learning, guid-ing the reconstruction to be task-aware. It further employs an equivalentimaging constraint for demosaicking training, enabling direct regressionto physically meaningful outputs without relying on rearranged data.Finally, a task-refinement stage fine-tunes the task network using thestable demosaicking front-end to further enhance accuracy. Extensiveexperimental results demonstrate that PolarAPP outperforms existingmethods in both demosaicking quality and downstream performance.Code is available here.

Abstract:
End-to-end autonomous driving in urban environments re-quires robust decision-making under partial observability and complexmulti-agent interactions. Severe occlusions and dense traffic at inter-sections limit the perception capability of single-agent systems, moti-vating recent efforts on Vehicle-to-Infrastructure (V2I) cooperation forperception and planning. However, existing evaluation protocols face afundamental trade-off: open-loop evaluation fails to capture error accu-mulation and recovery from deviations, while closed-loop evaluation iscostly, difficult to scale, and often relies on simulated environments thatmay suffer from domain gaps. To bridge this gap, we propose VIPS, abenchmark for cooperative autonomous driving in V2I settings based onpseudo-simulation. VIPS extends pseudo-simulation by integrating ve-hicle and infrastructure observations. This enables scalable yet realisticevaluation of robustness and error propagation without full simulation.We further present CoS-V2X, a cooperative planning framework basedon sparse representations. CoS-V2X models vehicle–infrastructure inter-actions using compact features for efficient communication and robustdecision-making under heterogeneous observations. Code and dataset areavailable at https://vips2026.github.io.

Abstract:
Training-free controllable motion generation has attracted growing interest for enabling flexible constraint enforcement without constraint-specific training. However, existing training-free methods require constraints to be continuous objective-based with differentiable losses, while many real-world requirements are criterion-based and provide only discontinuous, sparse, or even black-box feedback. In this paper, we propose Motion-Inference-as-Control (MIC), the first training-free motion generation framework that handles both continuous objective-based and criterion-based motion constraints under a shared mechanism. The key idea is to cast diffusion-based motion generation as a stochastic control problem. This perspective not only provides principled and practically effective step-wise control laws that support criterion-based constraints without requiring differentiability and naturally accommodate objectivebased constraints as a special case, but also motivates a control-oriented constraint coordination mechanism that adaptively balances and reconciles motion constraints during generation. Experiments across diverse constraint settings demonstrate the effectiveness of our framework.

Abstract:
Rapid advances in diffusion models have raised concernsabout privacy, harmful content, and copyright violations. Concept era-sure methods aim to address these risks by removing undesirable con-cepts from pretrained models without full retraining. However, existingapproaches often degrade non-target generation quality through heuristicstrength scaling (e.g., classifier-free guidance or negative prompt scaling),leading to uncontrolled and excessive semantic drift. To address this, wepropose Push–Pull Attentional Anchoring (PPAA), a mechanism in thecross-attention space that displaces target representations while anchor-ing the scale of erasure via bounded cosine-similarity constraints. Byusing a relative similarity ratio instead of an absolute difference, ouradaptive threshold represents a meaningful percentage of semantic re-tention, facilitating a single value to be applied uniformly across layers.We conduct extensive experiments across multiple concept categories, in-cluding celebrities, artistic styles, nudity, and intellectual property. Ourmethod achieves favorable trade-offs against prior approaches on StableDiffusion v1.4 and demonstrates its applicability to modern architecturessuch as FLUX.

Abstract:
While Omni-modal Large Language Models have made strides in joint sensory processing, they fundamentally struggle with a cornerstone of human interaction: deciphering complex, multi-person conversational dynamics to accurately answer “Who said what and when.” Current models suffer from an “illusion of competence”—they exploit visual biases in conventional benchmarks to bypass genuine cross-modal alignment, while relying on sparse, low-frame-rate visual sampling that destroys crucial high-frequency dynamics like lip movements. To address this limitation, we introduce Visual-Registered Speaker Diarization and Recognition (VR-SDR) and the HumanOmni-Speaker Benchmark. By strictly eliminating visual shortcuts, this rigorous paradigm demands true end-to-end spatio-temporal identity binding using only natural language queries. To overcome the underlying architectural perception gap, we propose HumanOmni-Speaker, powered by a Visual Delta Encoder. By sampling raw video at 25 fps and explicitly compressing inter-frame motion residuals into just 6 tokens per frame, it captures fine-grained visemes and speaker trajectories without triggering a catastrophic token explosion. Ultimately, HumanOmni-Speaker demonstrates strong multimodal synergy, natively enabling end-to-end lip-reading and high-precision spatial localization without intrusive cropping, and achieving superior performance across a wide spectrum of speaker-centric tasks.

Abstract:
We introduce CameraAnything, the first unified frame-work for camera controlled video editing that enables joint control ofboth intrinsic and extrinsic camera parameters. Existing approaches ei-ther rely on expensive 3D reconstruction to achieve full camera function-ality or restrict editing to extrinsic parameter manipulation. Moreover,the coupled influence of intrinsic and extrinsic parameters on video ap-pearance makes disentangled modeling particularly challenging. To ad-dress this, we adopt per-pixel Plücker ray injection alongside resolution-aware 3D RoPE in self-attention, building both camera conditioning andspatial positional encoding on the target latent to jointly control cam-era position, focal length, and native resolution editing without croppingor outpainting. To overcome the scarcity of paired training data, wefurther develop a scalable synthetic pipeline that constructs diverse dy-namic scenes through structured multi-camera recording and generatessynchronized videos with varied camera configurations. With a tailoredorthogonal training strategy, CameraAnything enables expressive videoreshooting with arbitrary viewpoint control, focal length adjustment, res-olution adaptation, and multi-shot transitions within a single generationprocess, offering strong practical value for cinematic video editing andcross-platform content adaptation in video production.

Abstract:
Extracting an explicit surface that exactly matches the zerolevel set of a neural signed distance function (SDF) remains challenging. Sampling-based isosurfacing methods such as Marching Cubes introduce discretization error. In contrast, continuous piecewise affine (CPWA) analytic approaches typically require plain ReLU MLPs, which limits the ability to learn high-frequency SDFs in practice. We present TetraSDF, an analytic isosurface extraction framework for SDFs that retains the expressiveness of grid-based encoders while enabling exact zero-level set extraction, by representing the SDF with a ReLU MLP composed with a multi-resolution tetrahedral positional encoder. Our positional encoder’s barycentric interpolation preserves a global CPWA structure, allowing us to track ReLU linear regions within an encoder-induced polyhedral complex. We further introduce a fixed analytic input preconditioner derived from the encoder’s metric to reduce directional bias, thereby stabilizing training. Across multiple benchmarks, TetraSDF matches or surpasses existing grid-based encoders in SDF reconstruction accuracy, while faithfully recovering the network’s zero-level set as a triangle mesh.

Abstract:
Pixel-aligned Gaussian splatting enables efficient and gen-eralizable novel-view synthesis. However, high-resolution rendering facesa critical trade-off where increasing input resolution improves detail atthe expense of quadratically rising network computational cost. Con-versely, maintaining low-resolution inputs stabilizes this cost but resultsin insufficient Gaussian density and artifacts. To address this, we proposeSubSplat, which introduces Sub-pixel Gaussian Reparameterizer(SPGR)to subdivide primary Gaussians into fine-grained primitives, restoringstructural density directly from low-resolution features. We further en-hance the reparameterization quality through feature aggregation, whicheffectively captures high-frequency details across multiple views. Experi-ments on RealEstate10K and ACID demonstrate that SubSplat achieveshigh-fidelity rendering with superior efficiency. Our results validate thatthe proposed framework successfully resolves the trade-off between repa-rameterization fidelity and network computational cost inherent in pixel-aligned Gaussian Splatting.

Abstract:
Feed-forward 3D reconstruction provides an efficient paradigmfor scene modeling from image sequences. Scaling these models to largemonocular scenarios are constrained by excessive GPU memory foot-print, degraded local geometry, and long-term trajectory drift. Exist-ing chunk-based optimization strategies provide limited geometric con-straints and fail to maintain global consistency over extended trajecto-ries. We present a unified framework for stable and scalable feed-forward3D reconstruction from long monocular sequences. Our approach buildson coarse-to-fine trajectory alignment augmented by lightweight geomet-ric prior injection. Distilling monocular geometric cues into the feed-forward backbone via LoRA adaptation improves depth accuracy on finestructures while preserving inference efficiency. We introduce a hybrid-weight sparse ray-field optimization that leverages high-frequency geo-metric features to guide local point-cloud refinement and enforce con-sistent inter-frame ray constraints. Unlike prior chunk-based methods,this establishes strong cross-frame geometric coupling while maintainingscalability. Finally, an efficient trajectory stitching strategy with jointray-error optimization explicitly reduces accumulated drift. Extensiveexperiments show that our approach achieves competitive trajectory ac-curacy compared with representative SLAM systems, while maintainingglobally consistent 3D reconstruction in large-scale scenarios.

Abstract:
We study positional encodings for multi-view transformersthat process tokens from a set of posed input images, and seek a mech-anism that encodes patches uniquely, allows SE(3)-invariant attentionwith multi-frequency similarity, and can adapt to the geometry of theunderlying 3D scene. We find that prior (absolute or relative) encod-ing schemes for multi-view attention do not meet these desiderata, andpresent RayRoPE to address this gap. RayRoPE represents patch po-sitions based on associated rays and computes query-frame projectivecoordinates to ensure SE(3) invariance. To adapt to scene geometry,RayRoPE predicts (without direct supervision) a per-token depth to ob-tain its position along the corresponding ray, while also modeling uncer-tainty and analytically computing the expected positional encoding. Wevalidate our method on the tasks of novel-view synthesis, stereo depthestimation, and feed-forward 3DGS reconstruction. While remaining ef-ficient, RayRoPE consistently improves over alternate position encodingschemes (e.g. 24% relative improvement on LPIPS in RE10K).

Abstract:
We perceive our surrounding environments with an active fo-cus, paying more attention to regions of interest, such as the shelf labelsin a grocery store or a family photo on the wall. When it comes to scenereconstruction, this human perception trait calls for spatially varyingdegrees of detail ready for closer inspection in critical regions, prefer-ably reconstructed on demand as users shift their focus. While recentapproaches in 3D Gaussian Splatting (3DGS) can achieve fast, general-izable scene reconstruction from sparse views, their uniform resolutionoutput leads to high computational costs, making them unscalable tohigh-resolution training. As a result, they cannot leverage available imagecaptures at their original high resolution for detail reconstruction. Per-scene optimization methods reconstruct finer details with heuristic-basedadaptive density control, yet require dense observations and lengthy of-fline optimization. To bridge the gap between the prohibitive cost of high-resolution holistic reconstructions and the user needs for localized finedetails, we propose the problem of localized high-resolution reconstruc-tion through on-demand generalizable Gaussian densification. Given aninitial low-resolution 3DGS reconstruction, the goal is to learn a gener-alizable network that densifies the reconstruction to capture fine detailsin a user-specified local region of interest (RoI), based on sparse high-resolution observations of the RoI. This formulation avoids the high costand redundancy of uniformly high-resolution reconstructions and enablesthe full leverage of high-resolution observations in critical regions. To ad-dress the problem, we propose GaussianLens, a feed-forward densifica-tion framework that fuses multi-modal information from the initial 3DGSand multi-view images. We further propose a pixel-guided densificationmechanism that effectively captures details under significant resolutionincreases. Experiments demonstrate our method’s superior performancein local high-fidelity detail reconstruction and strong scalability to im-ages of up to 1024 × 1024 resolution.

Abstract:
Interactive simulators have become powerful tools for train-ing embodied agents and generating synthetic visual data, but existingphotorealistic simulators suffer from limited generality, programmabil-ity, and rendering speed. We address these limitations by introducingSPEAR: A Simulator for Photorealistic Embodied AI Research. At itscore, SPEAR is a Python library that can connect to, and program-matically control, any Unreal Engine (UE) application via a modularplugin architecture. SPEAR exposes over 14K unique UE functions toPython, representing an order-of-magnitude increase in programmablefunctionality over existing UE-based simulators. Additionally, a singleSPEAR instance can render 1920×1080 photorealistic beauty images di-rectly into a user’s NumPy array at 73 frames per second – an order ofmagnitude faster than existing UE plugins – while also providing groundtruth image modalities that are not available in any existing UE-basedsimulator (e.g., a non-diffuse intrinsic image decomposition, material IDs,and physically based shading parameters). Finally, SPEAR introduces anexpressive high-level programming model that enables users to specifycomplex graphs of UE work with arbitrary data dependencies amongwork items, and to execute these graphs deterministically within a sin-gle UE frame. We demonstrate the utility of SPEAR through a diversecollection of example applications: controlling multiple embodied agentswith distinct action spaces (e.g., humans, cars, and robots) across sev-eral in-the-wild UE projects; rendering photorealistic city-scale environ-ments; manipulating UE’s procedural content generation systems; ren-dering synchronized multi-view images of detailed human faces; coordi-nating an interactive co-simulation with the MuJoCo physics simulator;and editing scenes with natural language via an AI coding assistant.

Abstract:
We propose Vecchia Kernel Surface Reconstruction (VKSR),an accurate implicit surface reconstruction method that efficiently scalesrecent kernel-based techniques to large point clouds with millions ofpoints. While existing (global) kernel methods work well in a sparsesetting, due to low-rank approximations, performance degrades quicklywhen presented with dense point clouds sampled from surfaces with highgeometric complexity or large-scale inputs with millions of points. Toovercome this limitation and inspired by the Gaussian Process litera-ture, VKSR uses Vecchia’s approximation instead of low-rank approx-imations, which naturally shifts computation from a global to a locallevel and allows reconstructing 14M+ points in minutes. VKSR achievesstate-of-the-art results on several challenging datasets while retainingkernel methods’ favorable properties when reconstructing sparse inputs.

Abstract:
Standard pipelines for physics-ready 3D reconstruction relyon a decoupled two-stage paradigm: extracting surface geometry followedby an error-prone tetrahedralization process. While recent Lagrangianmethods like TetSphere Splatting attempt to bypass this by directlyoptimizing volumetric primitives, their homeomorphic constraints pre-vent topology-adaptive optimization. Consequently, they produce dis-joint tetrahedra rather than a single connected mesh, rendering thestructures unsuitable for further physical simulations. To address this,we propose a topology-adaptive framework for holistic tetrahedral meshreconstruction through end-to-end topological and geometric optimiza-tion. First, by coupling Gaussian spheres to tetrahedral elements andleveraging edge connections, we estimate a continuous opacity field fordifferentiable element pruning. Next, jointly minimizing mesh smoothingenergy and multi-view Gaussian rendering error drives alternating geo-metric refinement while preserving topological adaptivity. Consequently,our approach effectively constructs a unified and topologically coherenttetrahedral mesh. Extensive experiments demonstrate that our methodoutperforms state-of-the-art techniques by achieving superior geometricaccuracy and producing coherent, single-connected tetrahedral meshes,thereby effectively bypassing the error-prone conventional tetrahedraliza-tion step for reconstructed surface meshes and streamlining downstreamphysical simulation.

Abstract:
Existing methods for Visual Document Question Answering(DocVQA) often predict answers without explicit intermediate analysisor produce ungrounded intermediate steps. The few approaches whichemploy grounding produce rectangular bounding boxes which fail to ac-curately represent complex structures such as pie-chart wedges, flowchartnodes, and curved text. We present DoCoG, a mask-based multi-typegrounded step-wise Chain-of-Thought architecture for Document QA.The model supports multi-type grounding, i.e., it localizes both textualcontent and fine-grained graphical elements such as legends, pie slices,and flowchart nodes. It generates step-wise intermediate outputs and fi-nal answers grounded through precise segmentation masks, enabling con-sistent evidence alignment. DoCoG integrates a Vision-Language Modelwith a promptable segmenter via a novel Grounding Interaction Module(GIM). During decoding, special [GND] tokens trigger mask prediction,linking each step and the final answer to supporting evidence. GIM re-fines [GND] tokens during decoding to improve spatial consistency andalignment. We introduce DoCoG-QA, a large-scale diverse dataset com-prising 325K documents and 1.5M step-wise grounded QA pairs. Wefurther release DoCoG-PQA with 20K preference pairs to improve align-ment using Direct Preference Optimization (DPO), and DoCoG-Bench,a human-verified benchmark. Experiments show that DoCoG achievesstate-of-the-art performance across multiple benchmarks, improving an-swer accuracy and grounding precision.

Abstract:
Panoramic images capture holistic environments, yet retriev-ing them using fine-grained, localized textual descriptions remains a fun-damental challenge. Through empirical analysis, we reveal that modernVision-Language Models (VLMs) suffer from severe semantic dilutionwhen processing panoramic inputs. By compressing an information-densepanorama into a single global embedding, VLMs inevitably submerge lo-cal details within vast backgrounds, restricting retrieval to coarse scene-level matching. To overcome this bottleneck, we propose PanoRec, amulti-granularity panoramic retrieval framework built on a spatially-structured sequence modeling paradigm. Specifically, PanoRec serializesdistortion-free cubemap faces and a downsampled global panorama along-side spatial anchor tokens into a unified sequence. This enables the ef-ficient extraction of decoupled local and global representations within asingle forward pass. To effectively supervise this multi-granularity featurespace, we formulate a joint spatial InfoNCE objective. For local match-ing, we adopt a MaxSim routing strategy that dynamically aligns eachquery with its most relevant cubemap face. Crucially, this strategy notonly suppresses background noise during inference but also inherentlyintroduces a powerful hard spatial negative mining mechanism duringtraining. Extensive experiments demonstrate that PanoRec achieves im-pressive performance across multiple scenarios, effectively unifying holis-tic scene-level retrieval with fine-grained spatial discrimination.

Abstract:
Pose-agnostic Anomaly Detection (PAD) remains challeng-ing as anomalies can appear under arbitrary viewpoints, requiring meth-ods to handle significant pose variations. Existing approaches rely oncomplex 3D reconstruction, which are computationally expensive and re-quire extensive multi-view data. We propose PADFormer, a novel image-space approach that leverages Vision Transformer (ViT) to directly re-construct anomaly-free versions of query images while preserving poseinformation. Our key insight is to adapt cross-view masked reconstruc-tion for anomaly detection through training exclusively on normal data,combined with dynamic patch selection and spatial alignment mecha-nisms that enable effective learning from sparse reference views undersignificant pose variations. During inference, we perform multiple for-ward passes with different masking patterns to generate an ensembleof anomaly-free reconstructions, ensuring comprehensive coverage of thequery image. Anomalies are detected by comparing these reconstructionswith the query image. PADFormer achieves state-of-the-art results on thePAD benchmark while maintaining comparable performance on classicfew-shot anomaly detection (FSAD) tasks, demonstrating superior effi-ciency and generalization without requiring 3D reconstruction.

Abstract:
The massive volume and privacy-sensitive nature of visualdata have made federated learning (FL) a preferred paradigm for train-ing vision models across distributed data sources. However, during train-ing, FL repeatedly shares the in-progress model with randomly selectedparticipants. This paper investigates an overlooked yet practical threatarising from this sharing process: leaked intermediate models can be ex-ploited by adversaries to craft adversarial examples that compromise thefinal deployed model. Although directly using an intermediate model,especially one leaked early in training, as a surrogate yields only mod-erate attack gains, it can serve as an anchor for anticipating subsequenttraining dynamics. Based on this insight, we propose Proteus, a modelleakage-induced adversarial attack that leverages a leaked model to iden-tify vulnerabilities that persist throughout training, thereby generatingadversarial examples that remain effective against the final deployedmodel. For the first time, we show that models exposed well before con-vergence can already pose substantial risks to the final model, even if itundergoes hundreds of additional training rounds after leakage. Exten-sive experiments across diverse datasets, neural architectures, and FLconfigurations confirm the severity of this threat. Proteus exploits in-herent model leakage in FL and improves the attack success rate from59.75% when directly using the leaked model to 85.40%, even when leak-age occurs after only 30% of the total training process.

Abstract:
Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes. The cueconflict benchmark has been influential in probing shape-texture preference and in motivating the insight that stronger, human-like shape bias is often associated with improved in-domain performance. However, we find that the current stylization-based instantiation can yield unstable and ambiguous bias estimates. Specifically, stylization may not reliably instantiate perceptually valid and separable cues nor control their relative informativeness, ratio-based bias can obscure absolute cue sensitivity, and restricting evaluation to preselected classes can distort model predictions by ignoring the full decision space. Together, these factors can confound preference with cue validity, cue balance, and recognizability artifacts. We introduce REFINED-BIAS, an integrated dataset and evaluation framework for reliable and interpretable shape–texture bias diagnosis. REFINED-BIAS constructs balanced, humanand modelrecognizable cue pairs using explicit definitions of shape and texture, and measures cuespecific sensitivity over the full label space via a ranking-based metric, enabling fairer cross-model comparisons. Across diverse training regimes and architectures, REFINED-BIAS enables fairer cross-model comparison, more faithful diagnosis of shape and texture biases, and clearer empirical conclusions, resolving inconsistencies that prior cue-conflict evaluations could not reliably disambiguate. Our code is available at REFINED-BIAS.

Abstract:
While recent generative models produce high-fidelity videos,they struggle with the complex narrative control required for coherentmulti-shot audio-visual generation. Existing methods suffer from tem-poral misalignment, limited controllability, and incomplete scripting. Inthis paper, we propose MAVIN, the first framework for multi-shot audio-visual generation with customized narrative control. To resolve tempo-ral misalignment, we propose boundary-aware attention, which lever-ages hierarchical captions and boundary-aware token routing to renderaudio-visual elements within their respective temporal boundaries. Toimprove the controllability for multi-subject scenarios, we propose ID-aware propagation, utilizing identity embeddings and an identity-awaremask to bind specific identities to consistent visual appearances and vocaltimbres. To provide comprehensive audio-visual narratives, we present amulti-agent scripting pipeline to transform free-form user inputs into hi-erarchical captions. Furthermore, we construct MAVINSet, a multi-shotaudio-visual dataset for robust training and evaluation. Extensive exper-iments demonstrate that MAVIN achieves state-of-the-art performance,opening up a new avenue for integrating generative models into profes-sional filmmaking workflows.

Abstract:
Current expressive avatar systems rely heavily on visual cuesand often fail when faces are occluded or emotions remain internal. Wepresent Mind-to-Face, the first framework to decode non-invasive elec-troencephalogram (EEG) signals directly into high-fidelity facial expres-sions. We build a dual-modality recording setup that captures synchro-nized EEG and multi-view facial video during emotion-eliciting stimuli,providing precise supervision for neural-to-visual learning. Our modeluses a CNN-Transformer encoder to map EEG signals into dense 3Dposition maps that sample over 65k vertices, capturing fine-scale geom-etry and subtle emotional dynamics, and renders them through a mod-ified 3D Gaussian Splatting pipeline for photorealistic, view-consistentresults. Extensive evaluations show that EEG alone can reliably predictdynamic, subject-specific facial expressions, including subtle emotionalresponses, demonstrating that neural signals contain far richer affectiveand geometric information than previously assumed. Mind-to-Face es-tablishes a new paradigm for neural-driven avatars, enabling personal-ized, emotion-aware telepresence and cognitive interaction in immersive

Abstract:
What if a world simulation model could render not an imag-ined environment but a city that actually exists? Prior generative worldmodels synthesize visually plausible yet artificial environments by imag-ining all content. We present Seoul World Model (SWM), a city-scale world model grounded in the real city of Seoul. SWM anchors au-toregressive video generation through retrieval-augmented conditioningon nearby street-view images. However, this design introduces severalchallenges, including temporal misalignment between retrieved referencesand the dynamic target scene, limited trajectory diversity and data spar-sity from vehicle-mounted captures at sparse intervals. We address thesechallenges through cross-temporal pairing, a large-scale synthetic datasetenabling diverse camera trajectories, and a view interpolation pipelinethat synthesizes coherent training videos from sparse street-view images.We further introduce a Virtual Lookahead Sink to stabilize long-horizongeneration by continuously re-grounding each chunk to a retrieved im-age at a future location. We evaluate SWM against recent video worldmodels across three cities: Seoul, Busan, and Ann Arbor. SWM out-performs existing methods in generating spatially faithful, temporallyconsistent, long-horizon videos grounded in actual urban environmentsover trajectories reaching hundreds of meters, while supporting diversecamera movements and text-prompted scenario variations.

Abstract:
Grounding has become a fundamental capability of vision-language models (VLMs). Most existing VLMs point by generating coor-dinates as part of their text output, which requires learning a complicatedcoordinate system and results in a high token count. Instead, we pro-pose a more intuitive pointing mechanism that directly selects the visualtokens that contain the target concept. Our model generates a specialpointing token that cross-attends to the input image or video tokens andselects the appropriate one. To make this model more fine-grained, we fol-low these pointing tokens with an additional special token that selects afine-grained subpatch within the initially selected region, and then a thirdtoken that specifies a location within that subpatch. We further showthat performance improves by generating points sequentially in a consis-tent order, encoding the relative position of the previously selected point,and including a special no-more-points class when selecting visual to-kens. Using this method, we set a new state-of-the-art on image pointing(70.7% on PointBench), set a new state-of-the-art for fully open modelson GUI pointing (61.1% on ScreenSpotPro), substantially improve VLMvideo tracking (62.5 on J &F vs 56.7 for Molmo2 on Molmo2Track), andimprove video pointing (59.1% human preference win rate vs. Molmo2).We additionally show that our method significantly improves learningefficiency and discuss the qualitative differences that emerge with thisdesign change. Our model weights, new datasets, and source code areavailable at https://allenai.org/blog/molmopoint.

Abstract:
Vision-Language Action (VLAs) models promise to extendthe remarkable success of vision-language models (VLMs) to robotics. Yet,unlike VLMs in the vision-language domain, VLAs for robotics requirefinetuning to contend with varying physical factors like robot embodiment,environment characteristics, and spatial relationships of each task. Wepropose leveraging few-shot demonstrations of tasks to better capture thisphysical variability. Overfitting to the trained task is yet another problemof existing methods. Inspired by functional specificity, we hypothesizethat finetuning only task-specific sparse model representations is bothmore effective and better retains pretrained model generality. In thiswork, we introduce Robotic Steering, a finetuning approach groundedin mechanistic interpretability that leverages few-shot demonstrations toidentify and selectively finetune task-specific attention heads aligned withthe physical, visual, and linguistic requirements of robotic tasks. Throughcomprehensive on-robot evaluations with a Franka Emika robot arm, wedemonstrate that Robotic Steering outperforms LoRA while achievingsuperior robustness under task variation, reduced computational cost,and enhanced interpretability for adapting VLAs to diverse robotic tasks.

Abstract:
Novel view synthesis requires strong 3D geometric consis-tency and the ability to generate visually coherent images across di-verse viewpoints. While recent camera-controlled video diffusion modelsshow promising results, they often suffer from geometric distortions andlimited camera controllability. To overcome these challenges, we intro-duce GeoNVS, a geometry-grounded novel-view synthesizer that en-hances both geometric fidelity and camera controllability through ex-plicit 3D geometric guidance. Our key innovation is the Gaussian Splat-ting Feature Adapter (GS-Adapter), which lifts input-view diffusion fea-tures into 3D Gaussian representations, renders geometry-constrainednovel-view features, and adaptively fuses them with diffusion features tocorrect geometrically inconsistent representations. Unlike prior methodsthat inject geometry at the input level, GS-Adapter operates in featurespace, avoiding view-dependent color noise that degrades structural con-sistency. Its plug-and-play design enables zero-shot compatibility withdiverse feed-forward geometry models without additional training, andcan be adapted to other video diffusion backbones. Experiments across 9scenes and 18 settings demonstrate state-of-the-art performance, achiev-ing 11.3% and 14.9% improvements over SEVA and CameraCtrl, withup to 2× reduction in translation error and 7× in Chamfer Distance.

Abstract:
Primitive-based methods such as 3D Gaussian Splatting haverecently become the state-of-the-art for novel-view synthesis and relatedreconstruction tasks. Compared to neural fields, these representationsare more flexible, adaptive, and scale better to large scenes. However,the limited expressivity of individual primitives makes modeling high-frequency detail challenging. We introduce Neural Harmonic Textures, aneural representation approach that anchors latent feature vectors on avirtual scaffold surrounding each primitive. These features are interpo-lated within the primitive at ray intersection points. Inspired by Fourieranalysis, we apply periodic activations to the interpolated features, turn-ing alpha blending into a weighted sum of harmonic components. Theresulting signal is then decoded in a single deferred pass using a smallneural network, significantly reducing computational cost. Neural Har-monic Textures yield state-of-the-art results in real-time novel view syn-thesis while bridging the gap between primitive- and neural-field-basedreconstruction. Our method integrates seamlessly into existing primitive-based pipelines such as 3DGUT, Triangle Splatting, and 2DGS. We fur-ther demonstrate its generality with applications to 2D image fitting andsemantic reconstruction.

Abstract:
We present TriO, a multi-modal unsupervised world modelthat predicts 4D occupancy, obstacle segmentation, flow and LiDAR.In contrast to prior work, TriO utilizes three distinct sensor modali-ties (camera, LiDAR, and RADAR) as both inputs and sources of self-supervision, eliminating the need for additional human annotations. Thanksto its novel supervision, the model is able to segment any occupancyfrom the drivable surface, overcoming the limitations of existing open-set methods in handling long-tail objects. TriO achieves state-of-the-artresults in multiple 3D and 4D tasks, including occupancy, flow, and Li-DAR prediction, as well as zero-shot road obstacle segmentation acrossmultiple datasets such as Argoverse 2, and Spotting the Unexpected.Fig. 1: We present TriO, an unsupervised tri-modal occupancy world model designedto perceive and forecast anything that could be considered path-blocking.

Abstract:
Cellular vehicle-to-everything (C-V2X) enables cooperativeperception, prediction, and planning beyond the field of view of individualagents. However, existing datasets often overlook the complexities of real-world deployment, such as limited communication bandwidth and its dy-namics, heterogeneous sensing modalities, and scalability beyond a singlecooperative partner. In this paper, we introduce CooperScene, a high-fidelity cooperative autonomy dataset with real-world C-V2X communica-tion characterization. The dataset is organized into diverse scenes, includ-ing intersections, highway ramps, and parking lots. These scenes involvethree connected and autonomous vehicles (CAVs) and one infrastructureroadside unit (RSU), all equipped with multi-modal sensors and commer-cial off-the-shelf C-V2X communication radios. All scenes are annotatedwith globally consistent 3D labels at 10 Hz, totaling 344K objects across59K frames, underpinned by tight sensor- and agent-synchronization,centimeter-level localization and spatial alignment, precise cross-modalitycalibration, and 3GPP-standard-compliant C-V2X communication. Coop-erScene establishes a rigorous benchmark for evaluating multi-agentscaling and actual performance in real-world deployable settings. Projectwebsite for data and benchmark: https://cisl.ucr.edu/CooperScene.

Abstract:
While Diffusion Large Language Models (DLLMs) have demon-strated remarkable capabilities in multi-modal generation, performingprecise, training-free image editing remains an open challenge. Unlikecontinuous diffusion models, the discrete tokenization inherent in DLLMshinders the application of standard noise inversion techniques, often lead-ing to structural degradation during editing. In this paper, we introduceGIDE (Grounded Inversion for DLLM Image Editing), a unified frame-work designed to bridge this gap. GIDE incorporates a novel DiscreteNoise Inversion mechanism that accurately captures latent noise pat-terns within the discrete token space, ensuring high-fidelity reconstruc-tion. We then decompose the editing pipeline into grounding, inver-sion, and refinement stages. This design enables GIDE supportingvarious editing instructions (text, point and box) and operations whilestrictly preserving the unedited background. Furthermore, to overcomethe limitations of existing single-step evaluation protocols, we introduceGIDE-Bench, a rigorous benchmark comprising 805 compositional edit-ing scenarios guided by diverse multi-modal inputs. Extensive exper-iments on GIDE-Bench demonstrate that GIDE significantly outper-forms prior training-free methods, improving Semantic Correctness by51.83% and Perceptual Quality by 50.39%. Additional evaluations onImgEdit-Bench confirm its broad applicability, demonstrating consistentgains over trained baselines and yielding photorealistic consistency onpar with leading models.3 .

Abstract:
Aerial–Ground Person Re-Identification (AGPReID) matches identities across unmanned aerial vehicles (UAVs) and ground cameras but suffers from extreme viewpoint gaps. Most existing methods rely on coarse binary aerial/ground labels and rigid orthogonality to disentangle view and identity features. We identify an important limitation of prior methods: the oversimplified binary-label design cannot fully capture continuous aerial viewpoint shifts. Moreover, rigid orthogonality constraints may further suppress identity cues. To address these issues, we propose Query-guided View-aware Adaptive Modulation (QVAM) for identity-preserving cross-view matching. Specifically, a View-aware Decoder (VAD) distills fine-grained viewpoint cues from patch tokens using learnable view queries. Guided by these cues, Adaptive Feature Modulation (AFM) predicts query-conditioned masks to suppress view-biased responses while preserving identity discrimination. A Cross-View Prototype Alignment (CVPA) loss further aligns modulated features at batch and memory levels with dual-view memory banks. Extensive experiments on AG-ReID, AG-ReIDv2, and CARGO show that QVAM achieves stateof-the-art performance, improving the previous best results by +10.26% Rank-1/+10.83% mAP on CARGO-ALL and +2.62% Rank-1/+2.37% mAP on AG-ReID A→G. The code is available at https://github.com/ Sakuraandroxy/QVAM.

Abstract:
Continual learning suffers from two forms of catastrophic forgetting: encoder drift, where visual representations shift as the model adapts to new tasks, and classifier overwriting, where gradient-based updates distort decision boundaries for previously learned classes. We present a unified exemplar-free framework that tackles both effects at the encoder and classifier levels. For the encoder, multi-anchor distillation maintains consistency with multiple historical encoder states and effectively bounds cumulative feature drift along the task sequence. For the classifier, a text-guided analytic classifier computes weights in closed form from incrementally accumulated statistics, completely bypassing gradient-based optimization. To further enhance the classifier, we incorporate a text prior that anchors classifier weights near CLIP’s text embeddings to inject cross-modal semantic knowledge, and propose text subspace regularization that encourages the classifier to learn directions orthogonal to the text embedding subspace, capturing discriminative visual patterns that text semantics alone cannot represent. Experiments on CIFAR-100, ImageNet-100, ImageNet-R, and CUB-200 show state-ofthe-art performance, including gains of +2.7% on CIFAR-100 and +3.5% on ImageNet-100 over prior methods. The code will be included in the supplementary material.

Abstract:
We present GenLCA, a diffusion-based generative modelfor generating and editing photorealistic full-body avatars from text andimage inputs. The generated avatars are faithful to the inputs, whilesupporting high-fidelity facial and full-body animations. The core ideais a novel paradigm that enables training a full-body 3D diffusion modelfrom partially observable 2D data, allowing the training dataset to scaleto millions of real-world videos. This scalability contributes to the supe-rior photorealism and generalizability of GenLCA. Specifically, we scaleup the dataset by repurposing a pretrained feed-forward avatar recon-struction model as an animatable 3D tokenizer, which encodes unstruc-tured video frames into structured 3D tokens. However, most real-worldvideos only provide partial observations of body parts, resulting in ex-cessive blurring or transparency artifacts in the 3D tokens. To addressthis, we propose a novel visibility-aware diffusion training strategy thatreplaces invalid regions with learnable tokens and computes losses onlyover valid regions. We then train a flow-based diffusion model on the to-ken dataset, inherently maintaining the photorealism and animatabilityprovided by the pretrained avatar reconstruction model. Our approacheffectively enables the use of large-scale real-world video data to traina diffusion model natively in 3D. We demonstrate the efficacy of ourmethod through diverse and high-fidelity generation and editing results,outperforming existing solutions by a large margin.

Abstract:
This paper focuses on a challenging setting of simultaneouslymodeling geometry and appearance of hand-object interaction sceneswithout any object priors. We follow the trend of dynamic 3D GaussianSplatting based methods, and address several significant challenges. Tomodel complex hand-object interaction with mutual occlusion and edgeblur, we present interaction-aware hand-object Gaussians with newlyintroduced optimizable parameters aiming to adopt piecewise linear hy-pothesis for clearer structural representation. Moreover, considering thecomplementarity and tightness of hand shape and object shape duringinteraction dynamics, we incorporate hand information into object de-formation field, constructing interaction-aware dynamic fields to modelflexible motions. To further address difficulties in the optimization pro-cess, we propose a progressive strategy that handles dynamic regions andstatic background step by step. Correspondingly, explicit regularizationsare designed to stabilize the hand-object representations for smooth mo-tion transition, physical interaction reality, and coherent lighting. Exper-iments show that our approach surpasses existing dynamic 3D-GS-basedmethods and achieves state-of-the-art performance in reconstructing dy-namic hand-object interaction.

Abstract:
Vision-Language Models (VLMs) incur substantial computational overhead and inference latency due to the large number of visual tokens introduced by high-resolution image and video inputs. Existing parameter-free token compression methods typically rely on token selection or merging, yet they risk discarding substantial visual information or distorting the original representation distribution, resulting in pronounced performance degradation at high compression ratios. In response, we aim to explore a more effective and efficient visual token compression strategy, with a promising direction in the frequency domain. Motivated by the success of frequency-domain transforms in image compression (e.g., JPEG), we systematically analyze the frequency redundancy in visual representations and uncover a nonuniform distribution of semantic information across frequency bands. Building upon this, we introduce Fourier Compressor, an effective, parameter-free, and highly generalizable module that removes redundancy from visual representations within the frequency domain. Implemented via FFT with O(n2 logn) complexity and no additional parameters, Fourier Compressor introduces negligible computational overhead while preserving semantic fidelity. Extensive experiments on imagebased benchmarks demonstrate that our method achieves a favorable performance-efficiency trade-off, retaining over 96% of the original accuracy while reducing inference FLOPs by up to 83.8% and boosting generation speed by 31.2%. It consistently outperforms existing parameterfree methods and even surpasses some parameterized approaches. Importantly, Fourier Compressor generalizes consistently across both LLaVA and Qwen-VL architectures, and further extends to video understanding tasks, highlighting its practical applicability for efficient VLMs.

Abstract:
We introduce EmbedCopilot-Bench, a multimodal bench-mark designed to evaluate large vision-language models (LVLMs) as as-sistants for embedded development. Compared with pure software pro-gramming, embedded programming inherently involves hardware, mak-ing it well-suited to benefit from LVLMs’ visual capabilities. However, tothe best of our knowledge, no existing benchmark comprehensively eval-uates how effectively LVLMs can serve as embedded copilots. Our bench-mark is developed to fill this gap. Built on real-world embedded develop-ment videos, we constructed 216 annotated multimodal QA triplets span-ning heterogeneous hardware platforms and peripheral modules, cov-ering hardware operation, software configuration, and code generationtasks. To assess model performance, we pair a rubric-driven LLM-as-a-Judge protocol with execution-centric evaluation in Wokwi platformand hardware-in-the-loop Execution Success Rate (ESR), jointly cap-turing semantic quality and functional correctness. Experiments on arange of closed- and open-source LVLMs show that visual context sig-nificantly boosts performance, especially for hardware-related tasks, butthat models remain far from reliable embedded copilots. ESR analysesand a pin-localization case study reveal frequent near-miss failures causedby brittle grounding and limited structured reasoning over board layouts.EmbedCopilot-Bench provides a challenging, realistic testbed for futuremethods that more tightly integrate perception, circuit understanding,and executable code synthesis for real-world embedded development.

Abstract:
Transformer architectures have achieved strong performancein image denoising, but their computational and memory costs remainhigh. We revisit self-attention (SA) from a Bayesian perspective and showthat standard SA mainly exploits first-order inter-patch statistics, resem-bling a learnable non-local averaging scheme. However, this formulationneglects second-order intra-patch statistics, which are important for cap-turing local pixel dependencies. To address this, we introduce a BayesianSA formulation that jointly models first-order inter-patch and second-order intra-patch statistics. This leads to a lightweight denoising Trans-former, termed NLformer, featuring a dual-branch attention design andan efficient feed-forward module. Experiments on several benchmarksshow that NLformer outperforms existing lightweight denoising networksand substantially narrows the gap to full-size Transformer-based denois-ers while maintaining low model complexity.

Abstract:
The development of 0.1◦ global weather forecasting modelsbased on machine learning (ML) is constrained by the limited availabil-ity of high-resolution data, as decades of reanalysis are only availableat 0.25◦ resolution. While existing approaches fine-tune 0.25◦ forecastmodels on limited 0.1◦ samples, we show that this transfer is hinderedby the irreversible information loss inherent in coarse-resolution fore-casting. Therefore, we propose BaguanHR, a framework that shiftsthe focus from transferring models to transferring data. We first showthat super-resolution (SR) has lower conditional entropy and input am-plification than forecasting, making it a more robust vehicle for reso-lution transfer. By leveraging this advantage through variable-wise SR,we synthesize extensive 0.1◦ data from ERA5. BaguanHR’s performanceon the synthetic-plus-real dataset exceeds both ML-based methods andIFS-HRES, achieving superior performance across over 85% of the leadtimes within 72 hours. Furthermore, our findings highlight a power-lawscaling effect, as a twofold increase in data reduces RMSE by 4.6% for 72-hour forecasting and 4.9% for 120-hour forecasting. Our results demon-strate that scaling high-resolution ML-based forecasting is primarily adata bottleneck, and that variable-wise super-resolution provides a sim-ple yet general solution to unlock long coarse-resolution reanalyses forhigh-resolution training.

Abstract:
Multi-View Pedestrian Detection (MVPD) aims to detectpedestrians in the form of a bird’s eye view map from multi-view images.Recent MVPD methods adopt a unified framework that projects 2D im-age features into a 3D world space and aggregates them into a singlefeature. Although they are effective, they struggle to generalize to un-seen camera configurations during training due to two main issues. First,they are difficult to capture accurate visual geometry across views in un-seen camera configurations. Second, they make detection models highlydependent on distortion patterns during training arising from their im-age feature projection. To address these, we leverage a visual geometricfoundation model and propose MV2GF. This foundation model has ex-hibited strong generalization in capturing visual geometry across viewsand predicting accurate 3D attributes in diverse camera configurations.MV2GF fuses task-specific features with general-purpose geometric fea-tures extracted by the foundation model to effectively capture the visualgeometry even in unseen camera configurations. Furthermore, MV2GFprojects each pixel in the image features to an appropriate 3D locationusing 3D pointmaps predicted by the foundation model, preventing thedetection model from depending on distortion patterns during training.Our experiments demonstrate the effectiveness of leveraging a visual geo-metric foundation model for MVPD and that MV2GF generalizes betterthan existing methods.

Abstract:
Unified Multimodal Models (UMMs) have emerged as a critical direction for general-purpose multimodal intelligence, integrating understanding and generation into a single framework. However, existing UMMs face prominent challenges: (1) the inherent learning conflicts between visual understanding and generation tasks, leading to suboptimal modeling in both tasks; (2) different understanding and generation visual spaces impeding scalability; (3) over-reliance on task-specific data that neglects the duality of text-image understanding and generation. To address these challenges, we propose UniDDT, which leverages a Noisy ViT encoder along with a LLM to unify semantic encoding for visual generation and understanding tasks, while employing a separate diffusion decoder to decouple diffusion decoding from text decoding. With this Noisy ViT encoder, UniDDT is able to leverage the latent space as a unified visual representation, enabling seamless compatibility between understanding and generation tasks. Thus, the scalability within the generation tasks and the semantic expressiveness within understanding tasks can be balanced. Also, we construct dual data structures from the same image-text pairs, fostering interdependence between the generation and understanding data to exploit their inherent duality. Extensive experiments demonstrate that UniDDT achieves effective unification of multimodal understanding and generation with enhanced semantic consistency and scalability. For visual generation tasks, our UniDDT achieves 0.86 GenEval score and 86.9 DPG overall score. For multimodal understanding tasks, our UniDDT achieves 1699.5 score on MME benchmark and 76.5 overall score on SEEDbench.

Abstract:
Concept Bottleneck Models (CBMs) promise transparent rea-soning by predicting through human-interpretable concepts, yet theireffectiveness fundamentally depends on how well visual and textual rep-resentations are aligned or matched. Existing vision–language CBMs of-ten rely on pre-aligned encoders or global cosine similarity, which ob-scures fine-grained concept localization and fails to reflect true seman-tic geometry. In this work, we rethink concept alignment as dynamiccross-modal transport process instead of static projection and proposeOptimal Transport Flow Concept Bottleneck Model (OTF-CBM). Itfirst learns a data-driven semantic cost via Inverse Optimal Transport tomeasure cross-modal distances, and then performs unbalanced optimal-transport-based flow matching to model semantic transitions betweenvisual patches and textual concepts. With velocity-based concept activa-tion, OTF-CBM captures interpretable geometric relations without ODEintegration. Experiments further show that OTF-CBM achieves greatclassification accuracy and concept faithfulness, offering a new geomet-ric and dynamical perspective for interpretable cross-modal reasoning.Our code can be found at github.com/ChenyangZhang00/OTF-CBM.

Abstract:
Deep neural networks have shown remarkable performance across diverse domains, but their substantial model size and computational requirements hinder deployment in memoryand computationconstrained environments. Despite progress in pruning, existing methods primarily target inter-filter redundancy and leave significant spatial redundancy within convolutional kernels unaddressed. The resulting models retain many correlated spatial patterns, limiting achievable efficiency gains. We propose Top-K Pruning (TKP), a pruning framework that extends sparsity to the spatial dimension by retaining only the most informative positions within each convolutional kernel. TKP introduces a two-stage procedure: an auxiliary regularization phase that concentrates information into dominant spatial locations, followed by deterministic Top-K selection that yields semi-structured sparsity. This transforms dense convolutions into efficient selective-sampling operations with strictly bounded computational cost. Across diverse architectures, including CNNs, vision transformers, vision-language models, and a large language model, TKP consistently achieves strong accuracy–efficiency trade-offs. On CIFAR and ImageNet models, TKP matches or exceeds the accuracy of prior structured pruning methods while delivering up to 8.9× theoretical FLOP reduction. TKP remains robust under quantization-aware training, achieving 16–17× compression with minimal accuracy loss on ResNet–18 and VGG–19. Moreover, TKP generalizes to BLIP-Base and LLaMA-2-7B, outperforming state-of-the-art pruning baselines. These results highlight TKP as a simple and effective approach for removing spatial redundancy in modern vision and largescale models.

Abstract:
Multi-view human mesh recovery (HMR) is broadly deployed in diverse domains where high accuracy and strong generalization are essential. Existing approaches can be broadly grouped into geometry-based and learning-based methods. However, geometry-based methods (e.g., triangulation) rely on cumbersome camera calibration, while learningbased approaches often generalize poorly to unseen camera con(cid:28)gurations due to the lack of multi-view training data, limiting their performance in real-world scenarios. To enable calibration-free reconstruction that generalizes to arbitrary camera setups, we propose a training-free framework that leverages pretrained single-view HMR models as strong priors, eliminating the need for multi-view training data. Our method (cid:28)rst constructs a robust and consistent multi-view initialization from single-view predictions, and then re(cid:28)nes it via test-time optimization guided by multi-view consistency and anatomical constraints. Extensive experiments demonstrate state-of-the-art performance on standard benchmarks, surpassing multi-view models trained with explicit multiview supervision. • •

Abstract:
We propose Self-Augmented Residual 3D Gaussian Splat-ting, a novel framework for stabilizing uncertainty quantification andenhancing uncertainty-aware supervision in Next-Best-View selection foractive scene reconstruction. To efficiently estimate scene coverage, SA-ResGS generates geometry-consistent Self-Augmented point clouds (SA-Points) via triangulation between observed training views and rasterizedextrapolated views. To address the lack of learning signals in underrep-resented regions within sparse, wide-baseline settings, we introduce thefirst skip-connection-inspired residual learning strategy tailored for 3DGS.This mechanism amplifies gradient flow to weakly contributing, high-uncertainty Gaussians. Our contributions are threefold: (1) a physicallygrounded, diversified view selection strategy; (2) an uncertainty-awareresidual supervision scheme that improves gradient flow and learningstability; and (3) implicitly debiased uncertainty quantification resultingfrom constrained view selection and residual supervision. Experiments onNeRF Synthetic, Mip-NeRF 360, and challenging extended benchmarkfrom Deep Blending and Tanks and Temples demonstrate that SA-ResGSconsistently outperforms state-of-the-art competing methods in bothreconstruction quality and view selection robustness.

Abstract:
Traditional in-camera colorimetric mapping relies on corre-lated color temperature (CCT)–based interpolation between pre-calibratedtransforms optimized for Planckian illuminants such as CIE A and D65.However, modern lighting technologies such as LEDs can deviate sub-stantially from the Planckian locus, exposing the limitations of rely-ing on conventional one-dimensional CCT for illumination characteriza-tion. This paper demonstrates that transitioning from 1D CCT (on thePlanckian locus) to a 2D chromaticity space (off the Planckian locus) im-proves colorimetric accuracy across various mapping approaches. In addi-tion, we replace conventional CCT interpolation with a lightweight multi-layer perceptron (MLP) that leverages 2D chromaticity features for ro-bust colorimetric mapping under non-Planckian illuminants. A lightbox-based calibration procedure incorporating representative LED sourcesis used to train our MLP. Validated across diverse LED lighting, ourmethod reduces angular reproduction error by 22% on average in LED-lit scenes, maintains backward compatibility with traditional illuminants,accommodates multi-illuminant scenes, and supports real-time in-cameradeployment with negligible additional computational cost. Code and datacan be found on the project webpage: ccmmlp.github.io

Abstract:
Spatial graphs provide a lightweight and elegant represen-tation of curvilinear anatomical structures such as blood vessels, lungairways, and neuronal networks. Accurately modeling these graphs iscrucial in clinical and (bio-)medical research. However, the high spatialresolution of large networks drastically increases their complexity, result-ing in significant computational challenges. In this work, we aim to tacklethese challenges by proposing VesselTok, a framework that approachesspatially dense graphs from a parametric shape perspective to learn la-tent representations (tokens). VesselTok leverages centerline points witha pseudo radius to effectively encode tubular geometry. Specifically, welearn a novel latent representation conditioned on centerline points to en-code neural implicit representations of vessel-like, tubular structures. Wedemonstrate VesselTok’s performance across diverse anatomies, includ-ing lung airways, lung vessels, and brain vessels, highlighting its ability torobustly encode complex topologies. To prove the effectiveness of Vessel-Tok’s learned latent representations, we show that they (i) generalize tounseen anatomies, (ii) support generative modeling of plausible anatom-ical graphs, and (iii) transfer effectively to downstream inverse problems,such as link prediction.

Abstract:
We present a dense prediction system for ice cloud geom-etry retrieval from extremely sparse supervision, where active satellitesensors (EarthCARE radar/lidar) provide accurate but spatially narrowtraining labels (∼1.6% of pixels) and passive imagers (VIIRS, VisibleInfrared Imaging Radiometer Suite) observe the full globe. A ConvNex-tUNet trained on co-located tracks predicts eight targets at every pixelwith calibrated 90% prediction intervals using conformalized quantileregression (CQR). The best configuration, a five-member quantile en-semble, achieves R2= 0.742 with prediction quality constant regardlessof distance to the supervision track, confirming that the model learnsa per-pixel spectral retrieval rather than interpolating from nearby la-bels. Calibration is robust across latitudes and cloud types, with deepconvective clouds as the main failure mode. Deployed globally, the sys-tem produces 609M predictions from one day of VIIRS data in 2.4 hourson a single GPU, enabling dense 3D cloud characterization at planetaryscale.1

Abstract:
Human motion forecasters are increasingly accurate and fast, but reliable deployment requires uncertainty estimates that are structured, calibrated, and efficient. Bayesian and ensemble-based uncertainty estimates often require repeated stochastic inference [15,26], while conformal calibration alone does not provide an epistemic signal or preserve trajectory covariance structure [14,50]. We introduce SPARC (SinglePass Adaptive Risk Calibration), a Bayesian–conformal uncertainty layer for motion forecasting. A deterministic MLP backbone predicts the future mean, and a conjugate Bayesian last layer converts time-domain feature leverage into an analytic horizon-wise epistemic scale κ (x). This t scale inflates a graph-temporal Gaussian covariance without changing its correlation structure, and split conformal calibration produces 95% marginal prediction tubes with finite-sample validity under exchangeability. The key interface is the structured factorization κ (x)Σ (x), t str,t which injects feature-space epistemic uncertainty into trajectory densities without Monte Carlo sampling. Across nine dataset/protocol blocks and deterministic, multimodal, and calibration baselines, SPARC ranks first on NLL and on the combined MPJPE+NLL criterion while retaining competitive point accuracy and efficient calibrated tubes. Ranking windows by κ separates high-error cases, making the scale usable as a lightweight risk monitor.

Abstract:
Fine-grained Cross-View Localization (CVL) estimates theprecise position and orientation of a ground-level image by aligningit with geo-referenced aerial imagery, offering a scalable alternative toGlobal Navigation Satellite Systems (GNSS) in challenging urban envi-ronments. Existing datasets rely on data collected with high-end sensorsuites, which inherently limit image diversity and scalability. While in-the-wild images are abundant, their noisy geo-tags make them unsuit-able for reliable evaluation. To bridge this gap, we introduce OpenCVL,a large-scale, diverse, and open dataset containing 617,388 ground-aerialimage pairs spanning 41 cities across four European countries. All imagesare sourced from permissive platforms, ensuring long-term accessibilityand supporting open and reproducible research. The training set com-bines images captured with high-end sensors with diverse in-the-wild im-agery. We further develop a data curation framework that filters and cor-rects pose annotations to construct reliable in-the-wild evaluation data.In addition, OpenCVL includes dedicated cross-area and snowy test setsto assess generalization and robustness. Experiments with a state-of-the-art CVL model on OpenCVL show that incorporating noisy in-the-wilddata consistently improves performance on clean test sets, suggesting apromising direction for scaling CVL with diverse real-world imagery.

Abstract:
Recent advances in single image-to-3D generation have en-abled high-quality asset synthesis, yet extending these capabilities toindoor scene generation remains challenging. Existing methods focus onasset-level generation while neglecting the structural layout, which isessential for downstream applications and serves as the spatial anchorfor grounding assets. However, a single image with a limited field ofview lacks the spatial coverage to recover a coherent global layout. Tothis end, we use a 360° image represented in equirectangular projec-tion (ERP) and propose InSpace, a structure-aware framework for 3Dindoor scene generation. InSpace comprises three stages: (1) estimat-ing partial scene geometry as spatial priors, (2) generating coarse scene †Work done during an internship at NAVER LABS. Co-corresponding authorsArtifactsMisplacementMisplacementERP Image ERP ImageInput Result Floating Input Result ResultResultSceneGen SAM3D InSpace (Ours)(a) Current Single Image to 3D Scene Generation (b) ERP Image to 3D Scene GenerationFig. 2: (a) Existing single-image methods generate individual assets without structurallayout, causing floating, misplacement, and artifacts. (b) InSpace uses an ERP imageto generate complete indoor scenes with structural layout and well-grounded assets.structure with view-selective cross-attention, and (3) producing detailedlayout and asset geometry with textures through a global-local hybridattention, using flow matching. We also propose ERP-FRONT, a pairedERP-Image-to-3D indoor scene dataset based on 3D-FRONT. Exper-iments show that InSpace generates complete 3D indoor scenes withstructural layout, along with separate textured assets from a single ERPimage, achieving strong performance across 3D and 2D metrics.

Abstract:
Inertial measurement units (IMUs) are now standard in mostconsumer devices, such as smartphones, drones, and extended reality(XR) headsets. By fusing visual and inertial data, localization systemsgain significantly in speed and robustness compared to vision-only orIMU-only approaches. However, traditional pose estimation methods failto utilize the local geometric information embedded in feature descriptorslike SIFT. Recent work has proved the advantages of leveraging this in-formation for relative and absolute pose estimation, but its application topartially calibrated absolute pose estimation remains unexplored. In thispaper, we derive novel constraints for joint estimation of absolute poseand focal length, making use of a gravity vector obtained from IMU dataand the feature-induced local geometry, which we use to construct twoefficient solvers: UP1PfAC, that operates given a single affine correspon-dence and UP2PfORI, which requires two orientation-covariant features.Unlike traditional, semi-calibrated absolute pose methods requiring fourpoint correspondences, our solvers benefit from fewer samples and lowercomputational cost, simplifying robust estimation in modern RANSAC-like frameworks. We evaluate the proposed solvers against the state-of-the-art on large-scale public datasets and demonstrate that our methodachieves fast and accurate localization and focal length estimation.

Abstract:
Reinforcement learning with verifiable rewards (RLVR) and Rubrics as Rewards (RaR) have driven strong gains in domains with clear correctness signals and even in subjective domains by synthesizing evaluation criteria from ideal reference answers. But many real-world tasks admit multiple valid outputs and lack the single ideal answer that rubric generation depends on. We identify this reference-free setting as a gap in current post-training methods and propose Implicit Error Counting (IEC) to fill it. Instead of checking what a response gets right against a rubric, IEC enumerates what it gets wrong, applying severity-weighted scores across task-relevant axes and converting them into calibrated per-aspect rewards. We show that naïve explicit enumeration is too noisy for stable optimization, and that two design choices: implicit score emission and group calibration are necessary to make error counting a reliable reward. As a case study, we validate IEC on virtual try-on (VTO), a domain that is simultaneously too constrained for holistic scoring and too permissive for rubric-based evaluation: subtle garment errors are unacceptable, yet many output variations are correct. We introduce Cascaded Error Counting (CEC) as an evaluation metric, which tracks human preferences well (60% top-1 vs. 30% others), and curate Mismatch-DressCode (MDressBench), a benchmark with maximal attribute mismatch to stress-test reward designs. On MDressBench, IEC outperforms RaR across all metrics (CEC: 5.31 vs. 5.60 on flat references; 5.20 vs. 5.53 on non-flat). On VITON-HD and DressCode, IEC matches or surpasses six baselines on 6 of 8 perceptual metrics. These results suggest that when ideal answers are unavailable, counting errors provide a stronger signal than constructing rubrics.

Abstract:
Instruction-based image editing aims to modify specific con-tent within existing images according to user-provided instructions whilepreserving non-target regions. Beyond traditional object- and style-centricmanipulation, text-centric image editing focuses on modifying, translating,or rearranging textual elements embedded within images. However, exist-ing leading models often struggle to execute complex text editing precisely,frequently producing blurry or hallucinated characters. We attribute thesefailures primarily to the lack of specialized training paradigms tailoredfor text-centric editing, as well as the absence of large-scale datasetsand standardized benchmarks necessary for a closed-loop training andevaluation system. To address these limitations, we present WeEdit, a sys-tematic solution encompassing a scalable data construction pipeline, twobenchmarks, and a tailored two-stage training strategy. Specifically, wepropose a novel HTML-based automatic editing pipeline, which generates330K training pairs covering diverse editing operations and 15 languages,accompanied by standardized bilingual and multilingual benchmarks forcomprehensive evaluation. On the algorithmic side, we employ glyph-guided supervised fine-tuning to inject explicit spatial and content priors,followed by a multi-objective reinforcement learning stage to align genera-tion with instruction adherence, text clarity, and background preservation.Extensive experiments demonstrate that WeEdit outperforms previousopen-source models by a clear margin across diverse editing operations.

Abstract:
Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities. However, existing research rarely extends this paradigm to the tactile domain, where both object-level semantics and sensor-level configurations jointly determine the meaning of touch. To address this gap, we propose UniTac, the first UMM designed for tactile understanding and generation. UniTac models the tactile process as a transition from noncontact to contact, capturing the physical interaction between sensors and objects through a dual-level representation that encodes both sensor and object attributes. For tactile understanding, UniTac introduces two tasks, object property description and sensor identification, to enhance reasoning over physical and cross-sensor information. For tactile generation, we design a two-stage training paradigm consisting of reconstruction and alignment, together with a sensor-prior-based sampling strategy that simulates realistic tactile contact. Trained on large-scale multi-sensor datasets, UniTac achieves state-of-the-art performance in tactile understanding and generates realistic tactile signals across sensors.

Abstract:
Modern generators faithfully model macroscopic semantics,producing synthetic images that appear highly realistic. Consequently,decisive forensic cues reside in subtle non-semantic visual discrepancies.To reveal these cues, we revisit AIGI detection from a geometric perspec-tive and identify an architecture-agnostic signature. Specifically, mod-ern generators exhibit low-rank collapse (i.e., rank degeneracy) in thesemantic-residual orthogonal subspace while largely preserving the dom-inant semantic direction. This structural flattening consistently emergesduring the final decoding stage, forming a shared bottleneck across di-verse generator architectures. Motivated by this signature, we proposeLoRC, a framework that decouples semantic dominance to capture thecollapsed residual geometry induced by the generative decoding bottle-neck. Our method improves accuracy by an average of 7.0% across mul-tiple benchmarks and achieves 97.0% accuracy on 39 unseen generators.These results demonstrate strong cross-model generalization and robust-ness, making LoRC a reliable approach for AIGI detection in complexreal-world environments.

Abstract:
A faithful 3D world representation should account for layeredgeometry, where a single camera ray may contain multiple visible and ge-ometrically valid surfaces. Monocular depth estimation, however, reducesthis structure to one scalar depth per pixel. Transparent scenes makethis ambiguity measurable: the same ray can pass through foregroundglass and observe the background, turning the supervised target into aconvention of annotation, data, and training rather than a scene-intrinsictruth. A learned predictor exposes this convention as its depth-layerpreference. We introduce MultiDepth-3k (MD-3k), a sparse two-layer or-dinal benchmark for measuring depth-layer preference and multi-layerspatial relationship accuracy (ML-SRA). On MD-3k, leading depth foun-dation models exhibit diverse layer preferences under standard RGBinput, showing that the same layered geometry can be resolved differentlyacross models. We further find that Laplacian Visual Prompting (LVP),a training-free spectral input transformation, can substantially changethe reported layer for certain frozen models. The strongest RGB/LVPpair, DAv2-L, reaches 75.5% ML-SRA. These results suggest that depthfoundation models may express complementary geometric hypotheses thatstandard RGB inference leaves unexpressed. We invite the community torethink depth supervision and evaluation through an ambiguity-awarelens, where multiple valid 3D interpretations are treated as geometricstructure to be measured, preserved, and expressed.

Abstract:
Vision-language models (VLMs) have recently shown remark-able capabilities in visual understanding and generation, but remainvulnerable to adversarial manipulations of visual content. Prior object-hiding attacks primarily rely on suppressing or blocking region-specificrepresentations, often creating semantic gaps that inadvertently inducehallucination, where models invent plausible but incorrect objects. In thiswork, we demonstrate that hallucination arises not from object absenceper se, but from semantic discontinuity introduced by such suppression-based attacks. We propose a new class of background-consistent objectconcealment attacks, which hide target objects by re-encoding their vi-sual representations to be statistically and semantically consistent withsurrounding background regions. Crucially, our approach preserves tokenstructure and attention flow, avoiding representational voids that trig-ger hallucination. We present a pixel-level optimization framework thatenforces background-consistent re-encoding across multiple transformerlayers while preserving global scene semantics. Extensive experimentson state-of-the-art vision-language models show that our method effec-tively conceals target objects while preserving up to 86% of non-targetobjects and reducing grounded hallucination by up to 3× compared toattention-suppression-based attacks. Qualitative results further confirmthat our approach maintains scene coherence and avoids spurious ob-ject insertion. Our findings highlight semantic continuity as a key factorin hallucination behavior and introduce a new direction for adversarialanalysis of generative multimodal models.

Abstract:
Scene flow represents the 3D cartesian motion of points inthe world and can be used for applications such as navigation in dy-namic environments, human-robot interaction or non-rigid motion anal-ysis, among others. Despite the potential benefits of event cameras formotion estimation tasks, due to their high temporal resolution and low-latency, not many works have yet addressed their use for scene flowestimation. Progress may be limited by the unconventional data addingcomplexity to established processing pipelines, but also due to the lackof event camera datasets with scene flow ground truth. With the aimof filling this gap, we present E-MOTION: a large and versatile datasetrecorded with high-resolution event cameras suitable for depth, opticalflow and scene flow estimation. E-MOTION features a number of inde-pendent moving objects for which ground truth poses and segmentationmasks are also available. We release a total of 43 sequences with denseground truth maps and poses at 200 Hz. The dataset is available at:https://emotion.hds.utc.fr/

Abstract:
Urban-scale Visual Place Recognition (VPR) aims to identify the geographic location of a query image by matching it against a geo-tagged database. While recent methods achieve impressive performance, they overlook a serious long-tailed problem hidden in urban-scale datasets, which biases the model towards locations with abundant images and ignores less-visited areas, causing models to systematically favor frequently photographed locations while failing in sparsely covered areas. In this paper, we systematically characterize this imbalance challenge and propose Distribution-Aware Place Recognition (DAPR), a model-agnostic plug-in framework that rebalances gradient contributions across head and tail classes. Additionally, within classification-retrieval pipelines, DAPR applies a multi-scale distance search mechanism to compute per-class distributional compactness, providing complementary gains at the retrieval stage. On the large-scale SF-XL benchmark, our framework outperforms the previous classification-retrieval baseline by 18.3% on test set v1, and 6.7% on test set v2. As a plug-in module, it achieves consistent improvements across representative VPR methods on SF-XL, MSLS, and Pitts30k, demonstrating broad generalizability across different methods and benchmarks.

Abstract:
Attention sinks are defined as tokens that attract dispro-portionate attention. While these have been studied in single modalitytransformers, their cross-modal impact in Large Vision-Language Mod-els (LVLM) remains largely unexplored: are they redundant artifactsor essential global priors? This paper first categorizes visual sinks intotwo distinct categories: ViT-emerged sinks (V-sinks), which propagatefrom the vision encoder, and LLM-emerged sinks (L-sinks), which arisewithin deep LLM layers. Based on the new definition, our analysis re-veals a fundamental performance trade-off: while sinks effectively encodeglobal scene-level priors, their dominance can suppress the fine-grainedvisual evidence required for local perception. Furthermore, we identifyspecific functional layers where modulating these sinks most significantlyimpacts downstream performance. To leverage these insights, we proposeLayer-wise Sink Gating (LSG), a lightweight, plug-and-play module thatdynamically scales the attention contributions of V-sink and the restvisual tokens. LSG is trained via standard next-token prediction, re-quiring no task-specific supervision while keeping the LVLM backbonefrozen. In most layers, LSG yields improvements on representative mul-timodal benchmarks, effectively balancing global reasoning and preciselocal evidence.1

Abstract:
Spin plays a crucial role in many ball sports due to its effecton the trajectory of the ball. Vision-based estimation of the ball’s spinduring a game with conventional cameras is challenging due to the ball’ssmall size, high speed, and fast rotation. To address these challenges, wepropose an event-based active vision system that can track unmodifiedballs and measure their spin in real time. The system consists of an eventcamera for its high temporal resolution and minimal motion blur, high-speed pan/tilt galvanometer mirrors to keep the ball in the field of view,and a low-latency focus-tunable telephoto lens to increase the spatial res-olution on the ball and keep it in focus. To track the ball, we use a hybridapproach that combines 2D event-based detection for centering and 3Dpositions from a ball localization system for re-initialization. For high-accuracy spin estimation, we propose an offline method that performscontrast maximization on the sphere (s-CMax). This method achievesstate-of-the-art accuracy on static balls across multiple sports (table ten-nis, baseball, tennis, and golf), with mean magnitude and axis errors of1.2% and 1.5 degrees, respectively. We then develop a low-latency onlinemethod for table tennis as a case study in real-time applications. Thismethod uses an uncertainty-aware convolutional neural network trainedon pseudo-ground-truth spin labels from the offline approach, combinedwith a GPU-accelerated batch implementation of contrast maximizationfor refinement. We demonstrate reliable tracking and spin estimationwith a three-view setup during professional table tennis matches, withhigh accuracy (8.8% magnitude and 6.4 degrees axis mismatch w.r.t. theoffline method), 3 ms latency, and 750 Hz throughput.

Abstract:
Video fundamentally intertwines two crucial axes: the dynamic content of a scene and the camera motion through which it is observed. However, existing generation models often entangle these factors, limiting independent control. In this work, we introduce OmniCamera, a unified framework designed to explicitly disentangle and command these two dimensions. This compositional approach enables flexible video generation by allowing arbitrary pairings of camera and content conditions, unlocking unprecedented creative control. To overcome the fundamental challenges of modality conflict and data scarcity inherent in such a system, we present two key innovations. First, we construct OmniCAM, a novel hybrid dataset combining curated real-world videos with synthetic data that provides diverse paired examples for robust multi-task learning. Second, we propose a Dual-level Curriculum Co-Training strategy that mitigates modality interference and synergistically learns from diverse data sources. This strategy operates on two levels: first, it progressively introduces control modalities by difficulties (condition-level), and second, trains for precise control on synthetic data before adapting to real data for photorealism (data-level). As a result, OmniCamera achieves state-of-the-art performance, enabling flexible control for complex camera movements while maintaining superior visual quality.

Abstract:
Accurate anatomical structure segmentation in medical im-age is essential for diagnosis and treatment planning. While recent inter-active segmentation foundation models enhance generalization throughlarge-scale multimodal pretraining, they still depend on precise promptsand can fail in underrepresented clinical contexts (e.g., small organs-at-risk). We present AtlasSegFM, an atlas-guided framework that cus-tomizes off-the-shelf foundation models to new clinical contexts with asingle annotated example. AtlasSegFM 1) performs atlas-query regis-tration to generate context-aware prompts, 2) refines the segmentationwith a frozen foundation model, and 3) applies a lightweight adaptivefusion module to combine atlas priors with foundation-model inputs andpredictions. Extensive experiments on six public and in-house datasetsacross radiotherapy and vascular scenarios show consistent gains, withthe largest improvements on small and delicate structures. AtlasSegFMprovides a lightweight, deployable solution for one-shot customization ofsegmentation foundation models in real-world clinical workflows.

Abstract:
Multiple small object tracking (MSOT) is critical for ap-plications such as anti-UAV systems and security surveillance, yet tra-ditional frame-based cameras struggle to track fast-moving small ob-jects in complex environments due to their low frame rates and lim-ited dynamic range. Event cameras, with their ability to continuouslyrecord subtle brightness changes, can naturally overcome these limita-tions. However, most existing event-based tracking methods follow a“convert-then-detect-and-track” pipeline. This pipeline sacrifices the in-herent high temporal resolution of event data, leading to fragmentedtrajectories of fast-moving objects. Moreover, it introduces significantbackground redundancy during framing, which reduces computational ef-ficiency. To handle these issues, we introduce “instance segmentationas tracking”, a novel paradigm that formulates event-based MSOT asan instance segmentation task in the spatio-temporal dimension. Follow-ing this paradigm, we first design Ev-ISNet, which leverages 3D sparseconvolutions to extract per-voxel features while simultaneously predict-ing object confidence and motion direction. Then we construct an eventgraph and progressively cluster trajectory instances by predicting edge-wise instance affinities. By leveraging intra-graph and inter-graph associ-ation modules, our method achieves highly efficient streaming inference.To address the lack of large-scale benchmarks for event-based MSOT,we build EV-UAV-Track, a comprehensive dataset featuring per-eventinstance-level annotations. Extensive experiments demonstrate that Ev-ISNet consistently outperforms state-of-the-art MSOT methods, achiev-ing over 30% improvement in MOTA score compared to frame-basedtracking methods.

Abstract:
Human motion capture from head-mounted devices (HMDs)offers a scalable way to acquire real-world human motion and interac-tion data, which is crucial for applications in embodied AI and VR/AR.Existing approaches focus on either egocentric body tracking, estimat-ing the motion of the subject wearing the device, or exocentric track-ing, capturing the movements of people in the wearer’s surroundings. Sofar, these two paradigms have largely been explored in isolation. In thispaper, we propose a novel distributed framework that jointly leveragesego- and exocentric multi-modal signals for human motion estimationfrom HMDs. Unlike traditional motion capture systems requiring bulkymulti-camera setups or obtrusive mocap suits, our approach, EgoExo-MoCap, is as simple as two (or more) people, each wearing a pair ofsmart glasses. The method leverages head (plus potentially wrist) track-ing signals for accurate estimation of global motion in the 3D world andcombines context-aware image features based on DINOv3 to achieve ro-bustness in the presence of noise and occlusions. Extensive experimentson two in-the-wild datasets show that our approach can robustly recon-struct motion even in challenging scenarios.

Abstract:
Computed tomography report generation (CTRG) automatesradiology reporting to reduce clinical workload and facilitate patient care.Recent efforts in applying the rapidly developing large language mod-els (LLMs) have advanced the field; yet, they still face a fundamentalchallenge due to the large volume of 3D data: effectively reducing highfeature redundancy and computational burden while simultaneously ex-tracting information-rich representations. To address this challenge, thiswork presents a novel CTRG framework that fully imitates the coarse-to-fine visual search pipeline practiced by radiologists. Our frameworkfirst employs a ViT-based image-text alignment architecture to extracta global token and local patch tokens for each anatomical structure, en-hanced by a mask-guided sparse negative-entropy loss. An abnormality-proposal LLM then processes the global token and clinical metadata topropose a shortlist of candidate abnormalities. This shortlist promptsan abnormality-prompted local token filter (AP-LTF) to select the mostinformative patch tokens, effectively reducing redundancy while preserv-ing critical information. Finally, a report-generation LLM takes in theglobal token, selected local tokens, and clinical metadata to composea full report. During training, ground-truth abnormalities are prefixedto the reference report to enhance awareness of clinic-relevant findings,and group relative policy optimization (GRPO) aligns the abnormality-proposal and report-generation LLMs for collaborative efficacy. Exper-imental results on two public CTRG datasets demonstrate the supe-rior performance of our framework compared to existing state-of-the-artFig. 1: Comparison of token compression strategies for CTRG. (a) 3D spatial pooling[2, 8] achieves high compression rate but incurs significant information loss. (b) Mask-guided volume cropping [12, 22] yields region-specific tokens but omits intra-regionredundancy. (c) [26] selects the top K tokens with the largest attention weights toregion-specific queries, yet may be subject to dominant “sink tokens” [14, 29]. (d) Ourmethod first proposes a shortlist of candidate abnormalities using only a single globaltoken per structure and clinical metadata. Then, an abnormality-prompted local tokenfilter (AP-LTF) combines the shortlist, a learned score, and the attention weight for acomprehensive token selection, thereby preventing sink tokens from dominating.methods in terms of clinical efficacy, RaTEScore, and GREEN scores.Ablation studies further validate the effectiveness of its novel design.

Abstract:
Higher-order ODE solvers are widely believed to improvesample quality in diffusion and flow-based generative models at a fixednumber of function evaluations (NFE). We show that this assumptioncan fail: at practical step sizes, higher-order methods can systematicallyunderperform first-order Euler. The mechanism is a multiplicative in-teraction between discretization and learning error. Applying backwarderror analysis to a learned field fˆ = f +ε reveals interaction terms hk ∇k εthat dominate classical truncation error at practical NFEs in diffusionand flow models, causing a solver of nominal order p to behave as lowerorder—pre-asymptotic order collapse. Our analysis yields a predictivecrossover step size h⋆ characterizing when higher order helps or hurts.Validation on toy manifolds and pretrained diffusion and flow modelsconfirms the predicted hierarchy: Euler is best at low NFE, Heun over-takes at moderate budgets, and RK4 can surpass Heun at higher NFE.Building on this theory, we propose an adaptive sampler that dynami-cally selects solver order based on the local roughness score R = h/h⋆ ,estimated at zero extra NFE, and often improves sample quality acrossmodels and datasets.

Abstract:
The ability to render scenes at adjustable fidelity from a single model, known as level of detail (LoD), is crucial for practical deployment of 3D Gaussian Splatting (3DGS). Existing discrete LoD methods expose only a limited set of operating points, while concurrent continuous LoD approaches enable smoother scaling but often suffer noticeable quality degradation at full capacity, making LoD a costly design decision. We introduce Matryoshka Gaussian Splatting (MGS), a training framework that enables continuous LoD for standard 3DGS pipelines without sacrificing full-capacity rendering quality. MGS learns a single ordered set of Gaussians such that rendering any prefix, the first k splats, produces a coherent reconstruction whose fidelity improves smoothly with increasing budget. Our key idea is stochastic budget training: each iteration samples a random splat budget and optimises both the corresponding prefix and the full set. This strategy requires only two forward passes and introduces no architectural modifications. Experiments across four benchmarks and six baselines show that MGS matches the full-capacity performance of its backbone while enabling a continuous quality–speed trade-off from a single model. Extensive ablations on ordering strategies, training objectives, and model capacity further validate the designs.

Abstract:
Monocular depth estimation foundation models, such as the Depth Anything series, have achieved remarkable performance across diverse domains. However, they still suffer from critical failures under adverse weather conditions, such as fog, rain, snow, or at night. To address this, we present Weather-Conditioned Depth Anything (DA-W), a framework that explicitly disentangles style from content for weatherrobust depth estimation. Specifically, we introduce a Style Filter trained on a curated mix of real and synthetic degradation datasets to extract content-independent, degradation-aware weather embeddings. This style embedding is then injected into the Depth Anything backbone using a parameter-efficient, zero-initialized adapter. Such a lightweight modulation allows a single unified model to robustly adapt to diverse conditions—including fog, rain, snow, and low-light—while avoiding catastrophic forgetting of its core generalization abilities in normal conditions. We train the adapter using a pseudo-label distillation and alignment strategy. Our comprehensive experiments demonstrate that our proposed DA-W achieves state-of-the-art robust depth estimation, improving AbsRel by an average of 3.7% on our curated weather benchmarks, while matching or slightly outperforming performance on standard clean benchmarks.

Abstract:
Di!usion models have emerged as a dominant paradigm ingenerative modeling, enabling high-fidelity sampling from complex datadistributions. Despite impressive capabilities, controlling di!usion mod-els to produce outputs aligned with user intent remains an open chal-lenge, especially when balancing global coherence with local precision.Existing control mechanisms vary in the granularity of their conditioningsignals. For example, textual prompts guide generation globally throughhigh-level semantics, while ControlNet-like approaches secure precise lo-cal structure via dense conditions. In this work, we introduce Histogram-constrained Image Generation (HIG), a novel control mechanism thatfalls into the middle ground of control granularity. Our framework en-forces user-specified distributional constraints (e.g., color histograms orlatent token distributions) during the generation process with exact pre-cision. We model such control as an optimal transport (OT) problemand apply explicit guidance transformations during sampling, therebydriving the di!usion trajectory to align with the desired histogram. Wedemonstrate the versatility of HIG across diverse applications, includingconstrained generation via color/latent histograms and high-capacity in-formation embedding through histogram-level encoding. Our findings un-derscore the promise of distributional control, a flexible and interpretablecontrol scheme that is fully compatible with existing control mechanisms,diversifying the hybrid strategies for controllable image generation. Ourproject page is available at: https://maps-research.github.io/hig/.

Abstract:
Concept Bottleneck Models (CBMs) provide interpretabilityby routing predictions through a layer of human-understandable con-cepts. However, defining an optimal concept set for a specific datasetremains an open challenge. Existing approaches rely on expensive ex-pert annotations or LLM-generated lists based solely on class names.Even “open-vocabulary” variants typically depend on static concept sets,which restrict discovery and introduce label bias. Furthermore, tradi-tional CBMs often suffer from information leakage, where unmodeledvisual features bypass the bottleneck and compromise the integrity ofthe explanations. To overcome these limitations, we propose CaptionBottleneck Models (CaBM), a framework that circumvents the need forpredefined concept sets by replacing rigid concept layers with free-formnatural language. By representing images via LMM-generated captionsand training a classifier strictly on this text, CaBM ensures a leakage-freearchitecture by construction. Additionally, by analyzing the text classi-fier post-training, CaBM autonomously discovers high-quality, dataset-specific concepts. Our results across fine- and coarse-grained benchmarksdemonstrate that CaBM achieves competitive accuracy while preservinginterpretability without the constraints of external dictionaries or manuallabeling. Our code is available at https://github.com/bariscagliyan/CaptionBottleneckModels.

Abstract:
Geolocation encoders, which map geographic coordinates tolearned representations, are emerging as an effective means of capturingvisual and non-visual characteristics from a latitude-longitude pair alone.However, existing approaches project coordinates onto fixed bases (e.g.,spherical harmonics), allocating representational capacity uniformly anddevoting equal resources to the open ocean and to a developing city. Weintroduce Tessellating the Earth (TTE), a location encoder built fromlearnable Spherical Voronoi partitions that concentrates representationalcapacity where it is needed in a fully differentiable, end-to-end manner.Each Voronoi site carries its own embedding and migrates during train-ing toward discriminative areas. To bridge the gap between local spatialstructure and global semantic understanding, we introduce global seman-tic tokens: a set of shared learnable concept tokens that distill semanticknowledge from the satellite imagery into a compact vocabulary the lo-cation encoder can reference at inference, enabling geographically distantsites covering similar environments to share semantics. TTE sets a newstate of the art for location encoders across a suite of geospatial classifi-cation and regression tasks, and achieves the strongest results when usedas a geographic prior for fine-grained species classification on iNaturalist-2018. Code, and weights are available at https://github.com/mvrl/TTE.

Abstract:
Recent Gaussian Splatting SLAM (GS-SLAM) methods alignrendered and observed images through photometric and geometric resid-uals to achieve high-quality reconstruction and rendering. However, thesesignals weaken in low-texture and repetitive environments, which leadsto floaters, inconsistent geometry, and drift. Existing systems mitigatedrift through loop closure or global layout assumptions, but these mech-anisms are opportunistic or lack persistent structural entities that pro-vide reliable constraints throughout mapping. We introduce a structure-aware GS-SLAM framework that models planar structures as persistentPlanar Gaussian Instances (PGIs) within a 3D Gaussian map. EachPGI represents a planar surface associated with Gaussian primitives andmaintains a consistent identity across frames. This representation pro-motes planes from transient observations to map entities and convertsplanar structures into stable geometric constraints. Building on PGIs,we propose structure closure, which estimates relative camera pose frommultiple non-parallel planes without requiring trajectory revisits. Wefurther integrate PGIs into a joint optimization that combines photo-metric–depth supervision with plane-instance consistency to improvemulti-view geometric coherence and suppress floater growth. Experi-ments on public benchmarks show that the proposed system achievesstate-of-the-art performance in camera tracking, dense reconstruction,and novel-view rendering, with strong robustness in scenes with largetextureless surfaces and repetitive layouts. Our code is publicly availableat https://github.com/yanyan-li/StructureGS-SLAM.

Abstract:
X-ray computed tomography (CT) reconstruction is inher-ently an ill-posed inverse problem, particularly with sparse-view pro-jections. Unlike traditional frame-based sensors that acquire projectionsat fixed intervals, neuromorphic event cameras operate asynchronouslyat the pixel level and trigger events only when the log-intensity changeexceeds a threshold. This asynchronous sensing naturally captures high-frequency angular variations during object rotation, providing comple-mentary information between sparsely sampled projections. However,the application of event cameras to CT reconstruction remains largelyunexplored. To bridge this gap, we introduce a Neuromorphic X-rayCT framework. Specifically, we propose an Event-enhanced Neural At-tenuation Field (ENAF) that models the volumetric attenuation fieldby jointly leveraging sparse projections and event streams. To supportand validate this framework, we construct synthetic neuromorphic CTdatasets and acquire a real-world dataset. Experiments on both syn-thetic and real-world datasets demonstrate that ENAF consistently out-performs state-of-the-art frame-only methods while maintaining train-ing efficiency. To the best of our knowledge, this work presents thefirst application of neuromorphic sensing to X-ray CT and establishesthe feasibility of event-enhanced sparse-view CT reconstruction. Thecode and datasets are available at https://wanghongjian98.github.io/projects/neuroxct/.

Abstract:
This paper addresses the problem of omnidirectional videointerpolation, which plays an essential role in applications such as vir-tual reality and immersive video enhancement. Existing video interpo-lation methods are not well-suited for spherical videos, as they havedifficulty handling severe distortions close to the poles. To address thisissue, we propose SVI360, a dual-branch framework that combines theimage frame and its rotated orthogonal view to deal with these dis-tortions. The core methodological aspect of the approach is to rein-force equivariance of the flow displacements between the original andorthogonal views to improve intermediate frame prediction. Experimentsshow that our method outperforms state-of-the-art approaches in inter-polation quality while maintaining accurate optical flow in four differ-ent public benchmarks. Code and pre-trained models are available at:https://icb-vision-ai.github.io/video360_interpolation/

Abstract:
This paper tackles the challenge of simultaneously manipu-lating multiple objects in image editing—a scenario that extends beyondthe capabilities of existing methods focused on single-object manipula-tion. Our approach SIMON efficiently handles complex rearrangementssuch as object shuffling, achieving higher visual quality at both image andregion levels, while reducing inference time. To this end, we propose threekey modules: a content-aware attention mechanism for region-adaptivefocus, a multi-object energy guidance strategy for subtask-specific con-sistency, and a latent initialization technique for artifact suppression.Together, these components enhance visual coherence across inpainting,object relocation, and background preservation subtasks. To evaluate ourframework, we curate 1,000 images from the 3D-FUTURE dataset, eachcontaining 2 to 5 target objects. For every object, we annotate a newtarget location, ensuring that the resulting layouts are physically plausi-ble and semantically meaningful. We conduct comparative experimentsagainst three state-of-the-art methods, using both image-level, region-level, and instance-level metrics. Both quantitative results and humananalysis confirm that our framework delivers significant improvementsin both visual quality and computational efficiency. We further validateour design through extensive ablation studies with diverse architecturesand applications. The code and annotated dataset are publicly avail-able at https://github.com/yijichar/SIMON-SImultaneous-Multi-Object-Navigation.

Abstract:
Single-photon LiDAR (SP-LiDAR) simulators face a dilemma:fast but inaccurate Poisson models or accurate but prohibitively slow se-quential models. This paper breaks that compromise. We present a sim-ulator that achieves both fidelity and speed by focusing on the critical,yet overlooked, component of simulation: the photon count statistics. Ourkey contribution is a Markov-renewal process (MRP) formulation that,for the first time, analytically predicts the distribution of registered pho-ton counts under dead time. To make this MRP model computationallytractable, we introduce a spectral truncation rule that efficiently com-putes the complex covariance statistics. By proving the shift-invarianceof the process, we extend this per-pixel model to full histogram cubegeneration via a precomputed lookup table. Our method generates 3Dcubes indistinguishable from the sequential gold standard, yet is ordersof magnitude faster. This finally enables large-scale, physically faithfuldata generation for learning-based SP-LiDAR reconstruction. Projectpage: https://mars-splidar.github.io/.

Abstract:
Flow matching (FM) trains a time-dependent vector field that transports samples from a simple prior to a complex data distribution. However, for high-dimensional images, each training sample supervises only a single trajectory and intermediate point, yielding an extremely sparse and high-variance training signal. This under-constrained supervision can cause flow collapse, where the learned dynamics memorize specific source–target pairings, mapping diverse inputs to overly similar outputs, failing to generalize. We introduce Posterior Augmented Flow Matching (PAFM), a theoretically grounded generalization of FM that replaces single-target supervision with an expectation over an approximate posterior of valid target completions for a given intermediate state and condition. PAFM factorizes this intractable posterior into (i) the likelihood of the intermediate under a hypothesized endpoint and (ii) the prior probability of that endpoint under the condition, and uses an importance sampling scheme to construct a mixture over multiple candidate targets. We prove that PAFM yields an unbiased estimator of the original FM objective while substantially reducing gradient variance during training by aggregating information from many plausible continuation trajectories per intermediate. Finally, we show that PAFM improves over FM by up to 3.4 FID50K across different model scales (SiT-B/2 and SiT-XL/2), different architectures (SiT and MMDiT), and in both class and text conditioned benchmarks (ImageNet and CC12M), with a negligible increase in the compute overhead. Code: https://github.com/gstoica27/PAFM.git.

Abstract:
Training-free image editing with diffusion models is highlydesirable yet is complex and remains a significant challenge. While recentoptimisation-based methods achieve strong zero-shot edits from text,they still struggle to preserve identity and capture intricate details, suchas facial structure, surface texture, or object-specific geometry, that ex-ist below the level of linguistic abstraction. To address this fundamentalgap, we propose Concept Distillation Sampling (CDS). To the best of ourknowledge, we are the first to introduce a unified, training-free frameworkfor target-less, multi-concept image editing.CDS overcomes this linguistic bottleneck of previous methods by anchor-ing the editing process in the certainty of pretrained LoRA adapters.We integrate a highly stable distillation backbone (featuring orderedtimesteps, regularisation, and negative-prompt guidance) with a noveldynamic weighting mechanism. This approach enables the compositionand control of multiple visual concepts directly within the diffusion pro-cess, utilising spatially-aware priors from pretrained LoRA adapters with-out causing concept clashing. Our method preserves instance conceptidentity without requiring reference samples of the desired edit. Extensivequantitative and qualitative evaluations demonstrate that CDS estab-lishes a new state-of-the-art over existing training-free editing and multi-LoRA composition methods on the InstructPix2Pix and ComposLoRAbenchmarks. Project Page: https://nickyfot.github.io/cds/.

Abstract:
Cross-view object geo-localization (CVOGL) aims to deter-mine the location of a specific object in high-resolution satellite imagerygiven a query image with a point prompt. Existing approaches treatCVOGL as a one-shot detection process, directly regressing object loca-tions from cross-view information aggregation, but they are vulnerableto feature noise and lack mechanisms for error correction. In this paper,we propose ReCOT, a Recurrent Cross-view Object geo-localizationTransformer, which models CVOGL as a recurrent localization process.ReCOT introduces a set of learnable tokens that encode task-specificintent from the query image and prompt embeddings, and iteratively at-tend to the reference features to refine the predicted location. To enhancethis recurrent process, we incorporate two complementary modules: (1) aSAM-based knowledge distillation strategy that transfers segmentationpriors from the Segment Anything Model (SAM) to provide clearer se-mantic guidance without additional inference cost, and (2) a ReferenceFeature Enhancement Module (RFEM) that introduces hierarchical at-tention to emphasize object-relevant regions in the reference features.Extensive experiments on CVOGL benchmarks demonstrate that Re-COT achieves state-of-the-art (SOTA) performance while significantlyreducing parameters compared to previous SOTA approaches. Our codeis available at https://github.com/Temperature-ai/ReCOT.git.

Abstract:
Industrial anomaly inspection faces a major challenge dueto the lack of real-world anomaly samples. While generative models areused to create anomaly data, existing methods still struggle when han-dling small-scale anomalies. This failure occurs because extreme down-sampling in diffusion models causes the information of small anomaliesto be lost in the latent space. To address this, we introduce UniScale,a unified training and inference framework for high-fidelity industrialanomaly generation across arbitrary scales. During training, we intro-duce an Error-Suppressed Multi-Scale Training (EMT) strategy, whichenables the model to learn the rich location-aware textures of anomalies,while suppressing upsampling-induced interpolation errors in texture ac-quisition, ensuring the model is capable of learning small-scale anomalies,while remaining effective for regular scale anomalies. For inference, wepropose Generation-then-Fusion Denoising. It decouples anomaly genera-tion from background integration, preventing small anomalies from beingoverwhelmed. Extensive experiments demonstrate that our method out-performs state-of-the-art competitors in both anomaly generation qualityand downstream detection performance. It achieves a relative IS(a) im-provement of 45.86% (from 1.81 to 2.64) on VisA and 37.70% (from 1.22to 1.68) on MVTec AD 2, while also improving the downstream pixel-level IoU by 4.22% on VisA and AUROC by 6.55% on MVTec AD 2.Code is available at https://github.com/HUST-SLOW/UniScale.

Abstract:
As AI systems are being integrated more rapidly into di-verse and complex real-world environments, the ability to perform holis-tic reasoning over an implicit query and an image to localize a target isbecoming increasingly important. However, recent reasoning segmenta-tion methods fail to sufficiently elicit the visual reasoning capabilities ofthe base model. In this work, we present Segment Anything Reasoner(StAR), a comprehensive framework that refines the design space frommultiple perspectives—including parameter-tuning scheme, reward func-tions, learning strategies and answer format—and achieves substantialimprovements over recent baselines. In addition, for the first time, wesuccessfully introduce parallel test-time scaling to the segmentation task,pushing the performance boundary even further. To eXtend the scopeand depth of reasoning covered by existing benchmark, we also con-struct the ReasonSeg-X, which compactly defines reasoning types and in-cludes samples that require deeper reasoning. Leveraging this dataset, wetrain StAR with a rollout-expanded selective-tuning approach to activatethe base model’s latent reasoning capabilities, and establish a rigorousbenchmark for systematic, fine-grained evaluation of advanced methods.With only 5k training samples, StAR achieves significant gains over itsbase counterparts across extensive benchmarks, demonstrating that ourmethod effectively brings dormant reasoning competence to the surface.

Abstract:
Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detection (SFOD) methods rely on heavyweight architectures that prioritize accuracy alone. We show this trade-off is unnecessary: building on YOLOv10, an NMSfree dual-head detector, we achieve state-of-the-art adaptation accuracy while being faster and more compact. We observe that directly applying vanilla mean-teacher self-training to dual-head detectors leads to suboptimal adaptation performance due to two key factors. First, simple pseudo-label generation strategies, such as using a single head or directly combining high-confidence predictions from both heads, yield suboptimal supervision under domain-shift. We propose DHF (Dual-Head Pseudo-Label Fusion) which selectively admits one-to-one (O2O) and one-to-many (O2M) head predictions, preserving precision and recovering missed objects. Second, we observe domain-shift collapses multi-scale feature discriminability. We propose the use of our MARD (Multi-scale Adaptive Representation Diversification) loss which mitigates this by enforcing detection-aware variance and covariance constraints on multiscale feature maps. Both modules are training-time only, leaving inference unchanged. Across domain-shift benchmarks, our method, RTSFOD yields 1.4 to 3.5% mAP gains, 1.3× higher throughput, with ∼2× fewer parameters than prior state-of-the-art SFOD methods, thus advancing the Pareto frontier of the speed-accuracy-model size trade-off. We report main results with YOLOv10, and demonstrate generalizability with additional YOLOand DETR-based dual-head detectors. Code is available here: https://github.com/Sairam13001/RT-SFOD/.

Abstract:
Real-time understanding of continuous video streams is es-sential for interactive assistants and multimodal agents operating in dy-namic environments. However, most existing video reasoning approachesfollow a batch paradigm that defers reasoning until the full video con-text is observed, resulting in high latency and growing computationalcost that are incompatible with streaming scenarios. In this paper, weintroduce ThinkStream, a framework for streaming video reasoningbased on a Watch–Think–Speak paradigm that enables models toincrementally update their understanding as new video observations ar-rive. At each step, the model performs a short reasoning update and de-cides whether sufficient evidence has accumulated to produce a response.To support long-horizon streaming, we propose Reasoning-CompressedStreaming Memory (RCSM), which treats intermediate reasoning tracesas compact semantic memory that replaces outdated visual tokens whilepreserving essential context. We further train the model using a Stream-ing Reinforcement Learning with Verifiable Rewards scheme that alignsincremental reasoning and response timing with the requirements ofstreaming interaction. Experiments on multiple streaming video bench-marks show that ThinkStream significantly outperforms existing onlinevideo models while maintaining low latency and memory usage. Code,models and data are available at https://github.com/CASIA- IVA-Lab/ThinkStream.

Abstract:
Action chunking has recently emerged as a standard prac-tice in flow-based Vision-Language-Action (VLA) models. However, thee!ect and choice of the execution horizon—the number of actions tobe executed from each predicted chunk—remains underexplored. In thiswork, we first show that varying the execution horizon leads to sub-stantial performance deviations, with performance initially improvingand then declining as the horizon increases. To uncover the reasons, weanalyze the cross- and self-attention weights in flow-based VLAs and re-veal two key phenomena: (i) intra-chunk actions attend invariantly tovision–language tokens, limiting adaptability to environmental changes;and (ii) the initial and terminal action tokens serve as stable anchors,forming latent centers around which intermediate actions are organized.Motivated by these insights, we interpret action self-attention weights asa proxy for the model’s predictive limit and propose AutoHorizon, thefirst test-time method that dynamically estimates the execution horizonfor each predicted action chunk to adapt to changing perceptual condi-tions. Across simulated and real-world robotic manipulation tasks, Au-toHorizon is performant, incurs negligible computational overhead, andgeneralizes across diverse tasks and flow-based models. Demonstrationvideos are available at this project page.

Abstract:
Vision-Language-Action (VLA) policies fine-tuned with RLand frozen VLM reward evaluators can produce observations that lookcorrect without physically completing the task—a phenomenon we for-malize as stage hallucination. To suppress it, we propose EvoVLA, aself-supervised framework with three synergistic modules: Stage-AlignedReward (SAR) penalizes near-miss states via counterfactual hard nega-tives in CLIP-based scoring; Pose-Based Object Exploration (POE)grounds curiosity in relative gripper–object pose; and Long-HorizonMemory uses selective context retrieval with gated fusion to stabi-lize credit assignment. We also introduce Discoverse-L, a long-horizonbenchmark (3 tasks, 18–74 stages) with a formal Hallucination Rate met-ric. Under matched backbones and budgets, EvoVLA achieves 69.2% suc-cess (+10.2 over OpenVLA-OFT), 1.5× sample efficiency, and reduceshallucination from 38.5% to 14.8%; real-robot deployment attains 54.6%(+11.0) across four tasks, confirming Sim2Real transfer.

Abstract:
We propose Ref-GeNVS, a training-free, reflection-awaremethod for generative novel view synthesis (NVS) in mirror scenes. Ex-isting multi-view diffusion models often fail to recognize the mirror in thescene and cannot exploit reflected content for scene generation. To fix thisissue without additional training, our key idea is to treat a mirror imageas two complementary views. From input images, we estimate the mirrorplane and reflect camera poses to form virtual views. Based on this vir-tual view setup, we propose a two-stage generation method consisting ofMirror-gated attention and Reflection injection, which enables reflection-consistent NVS by explicitly leveraging reflection relationships in a multi-view diffusion model. Ref-GeNVS inherits the strong generalizability ofthe multi-view diffusion backbone, while it does not require finetuning.On synthetic and real scenes including mirrors, Ref-GeNVS outperformsrecent generative NVS methods by generating reflection-consistent andcontextually coherent novel views, revealing scene structure visible onlythrough mirrors. Project page: https://kim-geonu.github.io/Ref-GeNVS/

Abstract:
Video diffusion models generate high-quality and diverse worlds;however, individual frames often lack 3D consistency across the outputsequence, which makes the reconstruction of 3D worlds difficult. To thisend, we propose a new method that handles these inconsistencies bynon-rigidly aligning the video frames into a globally-consistent coordi-nate frame that produces sharp and detailed pointcloud reconstructions.First, a geometric foundation model lifts each frame into a pixel-wise 3Dpointcloud, which contains unaligned surfaces due to these inconsisten-cies. We then propose a tailored non-rigid iterative frame-to-model ICPto obtain an initial alignment across all frames, followed by a global opti-mization that further sharpens the pointcloud. Finally, we leverage thispointcloud as initialization for 3D reconstruction and propose a novelinverse deformation rendering loss to create high quality and explorable3D environments from inconsistent views. We demonstrate that our 3Dscenes achieve higher quality than baselines, effectively turning videomodels into 3D-consistent world generators.

Abstract:
We introduce LEGO for advanced open-vocabulary scene un-derstanding. Beyond basic concept recognition, its core innovation lies incapturing the intrinsic semantic hierarchies within the scene, such as the"flowerpot → bouquet → bud → petal" lineage. While foundation mod-els like SAM can identify multi-granular structures in 2D, their partitionsare strictly perspective-bound and lack cross-view consensus. LEGO self-adaptively re-grades volatile multi-view SAM granularities into a unified,3D-consistent hierarchy. This provides precise supervision for the struc-turally coherent, multi-level segmentation of 3D scenes. By groundingthese segments with CLIP embeddings, LEGO recovers open-vocabularysemantic logic across hierarchical levels. Furthermore, by incorporatingspatial relationships, we elevate these segments into level-wise languagescene graphs, effectively empowering Large Language Models to performcomplex, context-aware spatial reasoning and precise visual grounding.Experimental results demonstrate that LEGO establishes new state-of-the-art performance across both promptable and open-vocabulary 3Dsegmentation benchmarks, exhibiting advanced hierarchical scene de-composition and context-aware spatial reasoning. Project page: https://pz0826.github.io/LEGO-Webpage/

Abstract:
Inferring physical properties such as mass, stiffness, and elas-ticity from a single image is essential for simulation and embodied AI, yetmost existing approaches rely on multi-view reconstruction or physics-based supervision. We introduce SiPhy, a unified framework for single-image physical property reasoning that aligns 3D-aware visual cues, depthwith language-based material knowledge. From one RGB image, SiPhysamples pseudo-voxel points, extracts CLIP features, and grounds themto material candidates proposed by an VLM. A part-based contrastiveaggregator enforces region consistency, while a heaviness-aware refine-ment improves thickness and volume estimation for dense objects.Across ABO-500, MVImgNet-100, and PhysXNet-100, SiPhy achievesstate-of-the-art single-image performance, surpassing multi-view recon-struction methods by improving mass MnRE by up to 93% (vs. PUGS),reducing density MAE by 35.5% (vs. NeRF2Physics), and loweringYoung’s modulus error by 23.5%. We further validate SiPhy on realhand–object interaction datasets, demonstrating its potential as a dataannotation engine for physical understanding from single-view imagery.Codes are available at https://github.com/DominoAI-Lab/SiPhy-ECCV-2026.

Abstract:
When a real-world scene is captured by a smartphone cam-era and viewed on its screen, the displayed image often differs noticeablyfrom the original scene in color, brightness, and contrast. This gap per-sists despite substantial advances in both modern cameras and displays.A key reason is that most pipelines factor the high-dimensional capture-to-display process into two separately calibrated camera and displaystages, and then connect them through low-dimensional color transforms,leading to information bottlenecks and inevitable error accumulation. Toaddress this systemic challenge, we propose Color Pass-Through, anend-to-end learned framework that operates directly on captured images.Our key insight is to treat the camera and display as a coupled systemrather than calibrating them in isolation. Coupling the camera and dis-play yields two practical advantages: (1) it brings the entire real-worldscenes to the display via end-to-end optimization, and (2) it allows effi-cient one-step calibration for each distinct observer via complete capture-to-display path. We validate Color Pass-Through using both digi-tal and human observers. Compared with representative baselines, ourmethod achieves an average gain of +2.0 points on a 5-point user-studyand more than 2× improvement on quantitative metrics, demonstratingimproved reproduction of the perceived color of the original scene. Seeproject page: https://lyricccco.github.io/color-pass-through/

Abstract:
Accurately forecasting human trajectories from an egocen-tric perspective plays a central role in applications such as humanoidrobotics, wearable sensing systems, and assistive navigation. However,progress in this direction remains limited due to the scarcity of egocen-tric trajectory datasets collected in real-world environments. Address-ing this need, we introduce EgoTraj, an egocentric multimodal opendataset recorded using Meta Quest Pro (MQPro). EgoTraj contains 75sequences of human navigation collected from multiple MQPro wearersin real-world urban environments. Each recording provides synchronizedRGB video along with ground-truth data, including 6-degree-of-freedomhead poses, per-frame 3D gaze vectors, and scene annotations. To thebest of our knowledge, EgoTraj differs from typical egocentric trajectorydatasets by capturing long-horizon, self-chosen pedestrian navigation onconsumer AR headset across urban routes with broad participant diver-sity. We benchmark several state-of-the-art trajectory prediction modelsand ablate the contributions of gaze, scene, and motion cues. The resultshighlight the utility of EgoTraj for AR-based perception, navigation, andassistive systems. The EgoTraj dataset, code, and EgoViz Dashboard arepublicly available at https://github.com/yehiahmad/EgoTraj.

Abstract:
Vision-and-Language Navigation (VLN) requires an embod-ied agent to ground complex natural-language instructions into long-horizon navigation in unseen environments. While Vision-Language Mod-els (VLMs) offer strong 2D semantic understanding, current VLN sys-tems remain constrained by limited spatial perception, 2D–3D repre-sentation mismatch, and monocular scale ambiguity. In this paper, wepropose AgentVLN, a novel and efficient embodied navigation frameworkthat can be deployed on edge computing platforms. We formulate VLNas a Partially Observable Semi-Markov Decision Process (POSMDP)and introduce a VLM-as-Brain paradigm that decouples high-levelsemantic reasoning from perception and planning via a plug-and-playskill library. To resolve multi-level representation inconsistency, we de-sign a cross-space representation mapping that projects perception-layer3D topological waypoints into the image plane, yielding pixel-alignedvisual prompts for the VLM. Building on this bridge, we integrate acontext-aware self-correction and active exploration strategy to recoverfrom occlusions and suppress error accumulation over long trajectories.To further address the spatial ambiguity of instructions in unstructuredenvironments, we propose a Query-Driven Perceptual Chain-of-Thought(QD-PCoT) scheme, enabling the agent to actively query and acquiregeometric depth information according to task demands. Finally, we con-struct AgentVLN-Instruct, a large-scale instruction-tuning dataset withdynamic stage routing conditioned on target visibility. Extensive experi-ments show that AgentVLN consistently outperforms prior state-of-the-art (SOTA) methods on long-horizon VLN benchmarks, offering a prac-tical paradigm for lightweight deployment of next-generation embodiednavigation models. Code: https://github.com/Allenxinn/AgentVLN.

Abstract:
Recently, vision foundation models (VFMs) have demonstrated strong power for versatile downstream task analysis. This paper starts from the problem of how to efficiently deploy VFMs at the cloud to support various user requests with arbitrary multi-task combinations that end in any scalable fashion. To achieve feature data transmission between frontend-cloud, traditional feature coding employs the direct compression-reconstruction paradigm. However, this can result in potential redundancy: (1) task-irrelevant information, and (2) repeated coding of cross-task shared knowledge, leading to an inflexible and redundant scheme. To this end, we propose a novel feature coding paradigm, termed reconfigurable multi-task feature compression, which aims to efficiently and adaptively compress the intermediate features to support the requested targeted tasks. Correspondingly, we propose a unified Reconfigurable Feature Compression framework, RFC, by feature factorization and recomposition. Specifically, the original feature is first factorized into multiple task-specific features with light-weight adapters. Then to efficiently compress these separate features, we consolidate them in a task-conditional auto-regressive manner, leveraging previously encoded task features as hyperprior conditions to reduce the redundancy of shared information in the current task feature. At the cloud side, a task-attentive recomposition module is further developed to fulfill the multi-task inference power within a single forward pass. Finally, we construct a comprehensive benchmark of reconfigurable feature compression to verify the effectiveness of RFC. Our project page can be found at https://jhang2020.github.io/Projects/RFC/RFC.html.

Abstract:
We address multiview self-calibration from image correspon-dences when camera intrinsic parameters vary across views. While Kruppaequations constrain an implicit matrix encoding of the camera intrinsics(called the dual image of the absolute conic; DIAC), practical calibrationpriors are expressed directly in intrinsic-parameter space. We formulatesuch priors algebraically and map them into DIAC space, yielding explicitconstraints that integrate directly with Kruppa relations. Unlike priorself-calibration methods that focus on specific varying-intrinsic regimes(e.g. shared focal length, fixed aspect, or zero skew), we provide a unifiedalgebraic framework that handles arbitrary intrinsic priors expressed aspolynomial constraints in the space of intrinsics, yielding a more flex-ible formulation of varying-intrinsic self-calibration. Since Kruppa sys-tems are projective and often algebraically dependent, we algorithmicallyconstruct locally independent square subsystems via Jacobian analysis toassess solvability and algebraic complexity under varying intrinsics. Ex-periments demonstrate parity with a few previous classical approachesin shared-focal settings as well as shared-principal-point settings andenable stable estimation for varying-intrinsic configurations previouslyconsidered unsolved, validated on synthetic and real dynamic-intrinsicsequences. Code is available at https://github.com/Rowing0914/Self-Calibration-Varying-Intrinsics.

Abstract:
Estimating room layouts from multi-view imagery is a coretask for indoor scene understanding. Existing methods are typically lim-ited either by poor generalization to new datasets or restrictive geometricassumptions of the room shape or camera configuration. Most also es-timate rooms independently, failing to exploit shared building structuresuch as dominant directions, ground plane or ceiling height.We propose PolyLayout, a multi-room layout estimation method that pa-rameterizes room layouts as Manhattan 3D polygons and optimizes themjointly across multiple rooms. The optimization objective is predicted bya neural network on top of robust pre-trained visual features and trainedend-to-end with supervision only on output room layouts. At the sametime, camera projection and polygon updates remain explicit and model-based. This separation between learned scoring and geometry improvesgeneralization to new datasets and camera parameters. During optimiza-tion, PolyLayout adaptively refines the polygon topology through iter-ative wall split and merge operations while jointly utilizing structuralcues across rooms. We introduce two new multi-view multi-room layoutbenchmarks by providing layout annotations to existing datasets, andexperiments show that PolyLayout outperforms prior approaches, bothin terms of accuracy and robustness.Project page: https://ghanning.github.io/PolyLayout

Abstract:
While text-guided image editing has made remarkable progress,it remains limited in structural portrait retouching. Textual descriptionsstruggle to convey fine-grained changes to facial features and body pro-portions. To address this gap, we introduce Exemplar-Based PortraitPhoto Retouching, where the model is given an exemplar pair and taskedwith inferring and applying the same retouching operations to a newquery image. Existing exemplar-based editing methods primarily focuson tasks with pronounced visual transformations. In contrast, structuralportrait retouching involves extremely delicate and localized modifica-tions, making accurate extraction and transfer of these edits challeng-ing. To tackle this, we propose MirrorPPR, a novel framework specifi-cally designed to capture and transfer subtle structural retouching oper-ations. Our method uses a Retouching Operation Extractor to capturethe subtle differences from the exemplar pair. The extracted represen-tations are then injected into a pre-trained Diffusion Transformer (DiT)through a connector and Low-Rank Adaptation (LoRA) modules. Fur-thermore, constructing perfectly aligned cross-identity training pairs isseverely hindered by operation misalignment. To overcome this, we pro-pose an advanced data self-augmentation paradigm that ensures strictlyaligned retouching operations. To alleviate data scarcity and supportthis novel task, we introduce MirrorPPR47M, a large-scale dataset withover 47 million retouched pairs. By structuring the dataset into simu-lated and professional subsets, we enable progressive curriculum learningto smoothly optimize the network. Extensive experiments demonstratethat MirrorPPR significantly outperforms existing baselines in both re-touching quality and identity preservation. The project page is availableat https://sjtu-deng-lab.github.io/MirrorPPR.

Abstract:
Realistic virtual try-on (VTON) concerns not only faithful rendering of garment details but also coordination of the style. Prior art typically pursues the former, but neglects a key factor that shapes the holistic style—garment fit. Garment fit delineates how a garment aligns with the body of a wearer and is a fundamental element in fashion design. In this work, we introduce fit-aware VTON and present FitControler, a learnable plug-in that can seamlessly integrate into modern VTON models to enable customized fit control. To achieve this, we highlight two challenges: i) how to delineate layouts of different fits and ii) how to render the garment that matches the layout. FitControler first features a fitaware layout generator to redraw the body-garment layout conditioned on a set of delicately processed garment-agnostic representations, and a multi-scale fit injector is then used to deliver layout cues to enable layoutdriven VTON. In particular, we build a fit-aware VTON dataset termed Fit4Men, including 13,000 body-garment pairs of different fits, covering both tops and bottoms, and featuring varying camera distances and body poses. Two fit consistency metrics are also introduced to assess the fitness of generations. Extensive experiments show that FitControler can work with various VTON models and achieve accurate fit control. Code and data are released at https://github.com/tiny-smart/FitControler.

Abstract:
Image-to-image (I2I) translation has achieved strong resultsin tasks like human relighting and driving scene translation using la-tent diffusion models (LDMs). However, compact LDMs often strug-gle to preserve fine-grained structures because the encoder compresseshigh-resolution inputs into a spatially downsampled latent space. Toaddress this issue, we propose a simple saliency-guided warp–unwarpframework that reallocates spatial representation toward salient regionsbefore encoding, enabling better preservation of structural details with-out increasing latent resolution. The warped image is processed by theoriginal diffusion model and then mapped back via an inverse warp.In addition, we propose a simple and efficient outpainting-based syn-thetic data generation pipeline to produce high-quality paired data forimage relighting. Our method is model-agnostic, requires no architec-tural modification, and introduces negligible computational overhead.Experiments on human relighting, driving scene relighting, and transla-tion demonstrate improved structural preservation, lighting faithfulness,and image quality, with our framework extending naturally to video viaframe-by-frame application with good temporal stability. Project Web-page: https://shenzheng2000.github.io/WarpI2I.github.io/

Abstract:
Retrieval-Augmented Text-to-Motion (RAG-T2M) modelshave demonstrated superior performance over conventional T2M ap-proaches, particularly in handling uncommon and complex textual de-scriptions by leveraging external motion knowledge. Despite these gains,existing RAG-T2M models remain limited by two closely related factors:coarse-grained text-motion retrieval that overlooks the hierarchical struc-ture of human motion, and underexplored mechanisms for effectivelyfusing retrieved information into the generative process. In this work,we present ReMoMask, a structure-aware RAG framework for text-to-motion generation that addresses these limitations. To improve retrieval,we propose Hierarchical Bidirectional Momentum (HBM) contrastivelearning, which employs dual objectives to jointly align global motionsemantics and fine-grained part-level features with text. To bridge thegap between structured retrieval and generation, we introduce TopologyStructured Masking (TSM), a training strategy that adaptively masksmotion tokens based on semantic relevance, forcing the model to learnrobust part-level grounding. Furthermore, we design Semantic Spatial-Temporal Attention (SSTA), a topology-aware fusion module that inte-grates retrieved knowledge via an asymmetric attention mechanism. Ex-tensive experiments on HumanML3D, KIT-ML, and SnapMoGen demon-strate that ReMoMask consistently outperforms prior methods on bothtext-motion retrieval and text-to-motion generation benchmarks.

Abstract:
Can we recover the 3D poses of multiple people using onlysound? This paper presents the first attempt to estimate multi-person3D poses solely from acoustic signals. Estimating the poses of multipleindividuals using acoustic signals is inherently challenging due to the su-perposition of motion-dependent signal variations. Unlike single-personscenarios, the presence of multiple subjects leads to overlapping acousticsignatures, making it difficult to attribute specific signal changes to anindividual’s pose. Furthermore, the complexity is compounded by inter-person reflections, which introduce intricate propagation delays that ob-scure the temporal motion-acoustic relationship. To address these issues,we propose SoundMHPE (Sound-based Multi-person Human Pose Es-timator), a novel encoder-decoder framework consisting of two key com-ponents. First, the Acoustic Multi-scale Encoder captures diverse tem-poral and fine-grained frequency features to isolate subtle acoustic sig-natures from complex, overlapping signals. Second, the Temporal PoseDecoder employs an attention mechanism to disentangle multi-personinformation across successive frames. By jointly accounting for tempo-ral dynamics and inter-person dependencies, this component preciselyreconstructs frame-wise individual poses. To validate our approach, weconstructed the 6-hour Acoustic Multi-person Pose (AMP) dataset con-sisting of 432K synchronized frames of multi-person pose and acous-tic data, and demonstrated that our SoundMHPE outperforms baselinemodels. Project page: https://oumi03.github.io/sound-mhpe/

Abstract:
Segmenting 3D assets into their constituent parts is crucialfor enhancing 3D understanding, facilitating model reuse, and supportingvarious applications such as part generation. However, current methodsface limitations such as poor robustness when dealing with complex ob-jects and cannot fully automate the process. In this paper, we propose anative 3D point-promptable part segmentation model termed P3 -SAM,designed to fully automate the segmentation of any 3D objects into com-ponents. Inspired by SAM, P3 -SAM consists of a feature extractor, mul-tiple segmentation heads, and an IoU predictor, enabling interactive seg-mentation for users. We also propose an algorithm to automatically selectand merge masks predicted by our model for part instance segmentation.Our model is trained on a newly built dataset containing nearly 3.7 mil-lion models with reasonable segmentation labels. Comparisons show thatour method achieves precise segmentation results and strong robustnesson any complex objects, attaining state-of-the-art performance. Our codewill be released soon.

Abstract:
Video personalization aims to generate personalized videos by preserving the motion and background of a reference video while transferring the subject appearance from a given subject image. Such video personalization has achieved tremendous progress alongside recent advancements in video diffusion transformers (DiT). Existing DiT-based approaches predominantly rely on training-based methods that require auxiliary control signals, while independent control over motion, subject appearance, and background remains challenging due to the entangled nature of the unified spatiotemporal attention mechanism. In this paper, we uncover a key structural insight that the spatio-temporal attention matrix of DiT exhibits an inherent functional decomposition—intra-frame blocks primarily encode spatial information, while inter-frame blocks encode motion dynamics. Building on this observation, we introduce SA-V2V, a novel training-free video personalization framework that disentangles and enables independent control over these visual attributes. We propose two guidance approaches: (1) For motion guidance, we present stochastic temporal injection, which leverages inter-frame attention maps to precisely control dynamic trajectories.(2) For appearance guidance, we propose a Target-aware Feature Guidance to enable independent control over subject and background appearance by modulating the synthesized features with masked attention features from the subject image and the reference video, respectively. Extensive experiments demonstrate that the proposed SA-V2V significantly outperforms existing approaches in motion fidelity, subject adherence, and background preservation. Our code is available at https://parksoobin1219.github.io/SA-V2V/

Abstract:
Controllable video generation remains challenging due tothe di"culty of specifying precise multi-object interactions using textprompts or motion-control inputs that primarily constrain pixel move-ment. In practice, trajectory-based control often requires users to drawaccurate tracks for multiple objects, which scales poorly with scene com-plexity and becomes ambiguous under occlusion or overlap. To enableflexible yet precise multi-subject control, we introduce GraphVid, agraph-conditioned image-to-video generation model that enables inter-active control through structured interaction graphs. We further cu-rate GraphVid-Bench, a large-scale interaction-centric video datasetwith structured relational annotations to enable training of interaction-aware video generation models. Despite using substantially less trainingdata and fewer trainable parameters than prior motion-control methods,GraphVid delivers strong controllability and video quality. Comparedwith Motion-I2V, GraphVid reduces FID by up to 39.9% and FVD by37.6%, while improving PSNR (9.87→15.98) and SSIM (0.38→0.61). Ourresults highlight the potential of structured semantic interfaces as a pow-erful paradigm for controllable video generation.PLAN Lab https://plan-lab.github.io/graphvid

Abstract:
Weather synthesis aims to add weather effects to input videos while preserving scene identity, structure, and motion. The key limitation of existing methods is the lack of diversity in weather appearance and effective control over weather dynamics (e.g., temporal evolution and particle motion). Most approaches rely on text prompts, which are inherently underspecified and often fail to produce detailed weather characteristics. Additionally, general-purpose video editors optimized for clean and aesthetic outputs tend to suppress heavy weather phenomena, making dense particle effects difficult to generate. To address these, we propose a Semantic-Aware, Physics-Informed, and Geometry-Grounded framework that steers an off-the-shelf video editor to synthesize diverse global appearances and detailed particle dynamics. We factorize the synthesis into three conditional signals, so that each provides a distinct and stable source of guidance: semantics specifies what the weather should look like, dynamics governs how it evolves over time, and geometry determines where it should appear in the scene. Specifically, we introduce (1) semantic-aware appearance anchoring to establish the target appearance from scene semantics and user input; (2) physicsinformed dynamic simulation to generate particle effects by simulating a Gaussian-represented particle field under gravity, wind, and turbulence; and (3) geometry-grounded video synthesis to align the simulated particles with target scene geometry and synthesize the final video. Experiments demonstrate that our method produces diverse, physically and visually realistic weather effects. Furthermore, we show that our synthesized data significantly improves the robustness of autonomous driving semantic segmentation under adverse weather conditions. See our project page at https://jumponthemoon.github.io/w-crafter/.

Abstract:
It is natural for us to walk and talk simultaneously. This paper tackles the challenge of replicating such natural behaviors in 3D avatar motion generation driven by concurrent multi-modal inputs, e.g., a text description “a man is walking” alongside a speech audio. Existing methods, constrained by the scarcity of aligned multi-modal data, typically combine motions from individual modalities sequentially or through weighted sum. However, they often result in mismatched or unrealistic movements. To overcome these limitations, we propose MOCO, a novel diffusion-based framework capable of processing multiple simultaneous inputs—including speech audio, text descriptions, and trajectory data—to generate coherent and lifelike motions without requiring aligned multimodal data. Our key innovation lies in decoupling the motion generation process. During each denoising step, the diffusion model independently generates motions for each modality from the input noise and assembles the body parts according to predefined spatial rules. The resulting combined motion is then diffused and serves as the input noise for the subsequent denoising step. This iterative approach enables each modality to refine its contribution within the context of the overall motion, progressively harmonizing movements across modalities. Consequently, the generated motions become increasingly natural and fluid with each iteration, achieving coherent and synchronized behaviors. We evaluate our approach using a purpose-built multi-modal benchmark. Experimental results demonstrate that MOCO outperforms existing baselines, advancing the field of multi-modal motion generation for 3D avatars. The code will be released on https://feifeifeiliu.github.io/MOCO.

Abstract:
Modeling the cognitive and experiential factors of humannavigation is central to deepening our understanding of human–environmentinteraction and to enabling safe social navigation and effective assis-tive wayfinding. Most existing methods focus on forecasting motionsin fully observed scenes and often neglect human factors that capturehow people feel and respond to space. To address this gap, we proposeEgoCogNav, a multimodal egocentric navigation framework that jointlyforecasts perceived path uncertainty, trajectories and head motion fromegocentric video, gaze, and motion history. To facilitate research in thefield, we introduce the Cognition-aware Egocentric Navigation (CEN)dataset consisting of 6 hours real-world egocentric recordings capturingdiverse navigation behaviors in real-world scenarios. Experiments showthat EgoCogNav learns the perceived uncertainty that strongly correlateswith human-like behaviors such as scanning, hesitation, and backtrack-ing while improving trajectory and head-motion forecasting on held-outnavigation recordings. Project page: https://calvinzqiu.github.io/egocognav-project/

Abstract:
Contrastive Language-Image Pre-training (CLIP) relies onVision Transformers whose attention mechanism is susceptible to spu-rious correlations and scales quadratically with resolution. To addressthese limitations, we present CLIMP, the first fully Mamba-based con-trastive vision-language model that replaces both the vision and text en-coders with state-space architectures. VMamba’s cross-scan mechanismcaptures spatial inductive biases that reduce reliance on spurious correla-tions, producing an embedding space with tighter cross-modal alignmentand lower hubness - geometric properties that translate to superior re-trieval and out-of-distribution robustness, surpassing even CLIP-ViT-Btrained on a dataset 167× larger on ImageNet-O. CLIMP naturally sup-ports variable input resolutions without positional encoding interpolationor specialized training, achieving up to 6.6% higher retrieval accuracyat 16× training resolution while using 5× less memory and 1.8× fewerFLOPs. Mamba’s autoregressive nature further enables processing of ar-bitrarily long text, overcoming CLIP’s fixed 77-token context limitationfor dense captioning retrieval. Our scaling experiments across model sizesand dataset sizes show consistent, unsaturated improvements - indicat-ing that CLIMP’s architectural advantages are not limited by trainingscale. These results demonstrate that Mamba is a compelling alternativeto Transformers for vision-language pre-training. The code and modelsare publicly available at https://github.com/NimrodShabtay/CLIMP

Abstract:
With the emergence of various pre-trained vision and language models, computer vision is shifting from narrow-domain to opendomain recognition. The construction of a more powerful yet general keypoint detection (GKD) model to support diverse tasks has become increasingly important in the field. To this end, we firstly present a largescale unified keypoint dataset called MegaKPT. The dataset is composed of over 1.3 million diverse object instances from twenty-nine existing datasets, and enjoys high-quality unified annotations with keypoint text descriptions. Based on MegaKPT, we develop GKDT, a simple, flexible and powerful DINOv3 based Transformer model for General Keypoint Detection. Our GKDT supports visual prompts, text prompts, or both. To enhance model training, we also propose a suite of useful strategies such as mix-modal prompted training and dynamic importance sampling. By testing over 22 test sets with seen or unseen objects, our single GKDT model shows strong performance and generality in detecting keypoints on broad categories, with most categories over 90% PCK@0.1 accuracy, offering high practical applicability to real-world problems. The dataset, models, and codes will be released at https://github.com/AlanLuSun/General-Keypoint-Detection.

Abstract:
Multimodal embedding spaces in models like CLIP enablepowerful capabilities such as semantic similarity retrieval and cross-modalzero-shot classification. These embeddings compress high-level seman-tics into a single vector, which comes at the cost of primarily express-ing a dominant semantics like main object while suppressing other im-portant attributes such as camera angle or color tone. We propose atext-conditioned transformation of visual embeddings that makes suchattributes explicitly accessible. Given a natural language description ofan attribute category (e.g., “color” or “art style”), a network generates anaffine transformation that emphasizes the specified attribute. Condition-ing on text enables it to learn many attributes simultaneously, accessingthem at inference time through an intuitive interface. The network istrained to align transformed embeddings with the frozen latent space,enabling retrieval using existing large-scale embeddings without any re-encoding. When applied to a full set, the same mechanism transforms thelatent space for attribute disentanglement tasks such as multi-clustering.By operating directly in latent space, our method provides a unifiedand efficient framework for controlling embedding spaces, demonstratingstate-of-the-art performance across both attribute-based retrieval andmulti-attribute organization tasks with near-zero inference cost.

Abstract:
Reconstructing high-fidelity surfaces of dynamic scenes re-mains a critical challenge. While current methods excel at novel viewsynthesis, they often struggle to recover accurate and stable geometry,particularly under large non-rigid deformations. This results in noisymeshes that are unsuitable for downstream simulation or editing. In thiswork, we introduce a new paradigm for dynamic surface reconstructionbased on a parametric Signed Distance Function (p-SDF). Our key in-sight is to generalize static SDF fields—where each spatial point stores aconstant value—into time-dependent parametric curves where each curvemodels a temporally evolving SDF trajectory. This parametric SDF rep-resentation provides a principled way to capture complex temporal varia-tions, naturally enforcing smoothness and continuity in shape dynamics.At each timestamp, a static SDF field can be queried from p-SDF andconverted into an explicit surface mesh via differentiable iso-surfacing.By rendering these meshes with a physically based differentiable ren-derer, we optimize the underlying parametric curves end-to-end against2D image observations. Our framework produces high-fidelity surfacesand inherently disentangles geometry, material, and lighting from multi-view videos. It robustly reconstructs geometry under large-scale motionsand resolves appearance ambiguities caused by lighting and occlusions.Experiments on both synthetic and real-world scenes demonstrate thatour method achieves state-of-the-art geometric accuracy. The projectpage for p-SDF is available at https://timchonggao.github.io/psdf.

Abstract:
Neural Radiance Fields achieve high-fidelity scene representation but suffer from costly training and rendering, while 3D Gaussian splatting offers real-time performance with strong empirical results. Recently, solutions that harness the best of both worlds by using Gaussians as proxies to guide neural field evaluations, still suffer from significant computational inefficiencies. They typically rely on stochastic volumetric sampling to aggregate features, which severely limits rendering performance. To address this issue, a novel framework named IRIS (Intersection-aware Ray-based Implicit Editable Scenes) is introduced as a method designed for efficient and interactive scene editing. To overcome the limitations of standard ray marching, an analytical sampling strategy is employed that precisely identifies interaction points between rays and scene primitives, effectively eliminating empty space processing. Furthermore, to address the computational bottleneck of spatial neighbor lookups, a continuous feature aggregation mechanism is introduced that operates directly along the ray. By interpolating latent attributes from sorted intersections, costly 3D searches are bypassed, ensuring geometric consistency, enabling high-fidelity, real-time rendering, and flexible shape editing.

Abstract:
Recently, 3D Gaussian Splatting (3DGS) greatly acceleratedmesh extraction from posed images due to its explicit representation andfast software rasterization. While the addition of geometric losses andother priors has improved the accuracy of extracted surfaces, mesh ex-traction remains difficult in scenes with abundant view-dependent effects.To resolve the resulting ambiguities, prior works rely on multi-view tech-niques, iterative mesh extraction, or large pre-trained models, sacrificingthe inherent efficiency of 3DGS. In this work, we present a simple and ef-ficient alternative by introducing a self-supervised confidence frameworkto 3DGS: within this framework, learnable confidence values dynam-ically balance photometric and geometric supervision. Extending ourconfidence-driven formulation, we introduce losses which penalize per-primitive color and normal variance and demonstrate their benefits tosurface extraction. Finally, we complement the above with an improvedappearance model, by decoupling the individual terms of the D-SSIMloss. Our final approach delivers state-of-the-art results for unboundedmeshes while remaining highly efficient.

Abstract:
Monocular 3D scene reconstruction has recently seen signif-icant progress. Powered by the modern neural architectures and large-scale data, recent methods achieve high performance in depth estimationfrom a single image. Meanwhile, reconstructing and decomposing com-mon scenes into individual 3D objects remains a hard challenge due tothe large variety of objects, frequent occlusions and complex object rela-tions. Notably, beyond shape and pose estimation of individual objects,applications in robotics and animation require physically-plausible scenereconstruction where objects obey physical principles of non-penetrationand realistic contacts. In this work we advance object-level scene re-construction along two directions. First, we introduce MessyKitchens,a new dataset with real-world scenes featuring cluttered environmentsand providing high-fidelity object-level ground truth in terms of 3D ob-ject shapes, poses and accurate object contacts. Second, we build onthe recent SAM 3D approach for single-object reconstruction and ex-tend it with Multi-Object Decoder (MOD) for joint object-level scenereconstruction. To validate our contributions, we demonstrate MessyK-itchens to significantly improve previous datasets in registration accuracyand inter-object penetration. We also compare our multi-object recon-struction approach on three datasets and demonstrate consistent andsignificant improvements of MOD over the state of the art. Our newbenchmark, code and pre-trained models are publicly available on ourproject website: https://messykitchens.github.io/.

Abstract:
We tackle fine-grained indoor localization from natural lan-guage: given a free-form description of one’s surroundings, estimate theobserver’s 2D position and heading within a known 3D environment.Language queries are lightweight, privacy-preserving, and need no cam-era – yet prior work stops at coarse scene retrieval and cannot resolve anintra-scene pose. We close this gap with LangLoc, a three-stage pipelinethat (i) retrieves the correct scene via a dual-branch GATv2 encoderwith CLIP semantic features, surpassing the previous best by 8 per-centage points in Top-1 recall; (ii) estimates position and heading byscoring a dense floor grid through ray-cast object visibility, reaching amedian error of 0.95 m; and (iii) resolves residual ambiguity through aBayesian dialog module that asks targeted yes/no questions and updatesa pose posterior until the location is pinpointed. To support this task wecontribute a benchmark of 13,000+ pose-indexed natural-language de-scriptions over 1,300+ indoor 3D scans. Code and data will be released.Project page: https://rzninvo.github.io/Lang-Loc/.

Abstract:
Rotary positional embeddings (RoPE) are widely used in diffusion transformers (DiTs) to encode spatial relationships, yet their behavior with mixed-resolution tokens remains underexplored. A natural approach is to rescale token positions from different resolutions into a unified coordinate system before attention, but we show this fails. Our analysis shows that with RoPE, the attention similarity score is a highly structured and periodic function of token distance, so rescaling distances across resolutions moves token pairs to different regions of this periodic function, leading to incorrect attention scores. Motivated by this, we introduce Phase-Aligned Mixed-Resolution Attention (PMA), a trainingfree mechanism that stabilizes mixed-resolution attention. PMA modifies the RoPE position mapping to enforce a consistent positional scale for every query–key pair, ensuring that relative distances are evaluated under a single reference scale. To further improve local coherence near resolution transitions, we incorporate a lightweight boundary refinement module that softly exchanges features across adjacent scales. Experiments on image and video diffusion models validate our analysis and demonstrate consistent improvements in visual fidelity and computational efficiency. Project page: https://hao-yu-wu.github.io/mixed_res/.

Abstract:
Universal medical image segmentation seeks to use a singlefoundational model to handle diverse tasks across multiple imaging modal-ities. However, existing approaches often rely heavily on manual visualprompts or retrieved reference images, which limits their automationand robustness. In addition, naive joint training across modalities oftenfails to address large domain shifts. To address these limitations, wepropose Concept-to-Pixel (C2P), a novel prompt-free universal seg-mentation framework. C2P explicitly separates anatomical knowledge intotwo components: Geometric and Semantic representations. It leveragesMultimodal Large Language Models (MLLMs) to distill abstract, high-level medical concepts into learnable Semantic Tokens and introducesexplicitly supervised Geometric Tokens to enforce universal physical andstructural constraints. These disentangled tokens interact deeply withimage features to generate input-specific dynamic kernels for precisemask prediction. Furthermore, we introduce a Geometry-Aware InferenceConsensus mechanism, which utilizes the model’s predicted geometricconstraints to assess prediction reliability and suppress outliers. Extensiveexperiments and analysis on a unified benchmark comprising eight diversedatasets across seven modalities demonstrate the significant superiorityof our jointly trained approach, compared to universe- or single-modelapproaches. Remarkably, our unified model demonstrates strong general-ization, achieving impressive results not only on zero-shot tasks involvingunseen cases but also in cross-modal transfers across similar tasks. Codeis available at: https://github.com/Yundi218/Concept-to-Pixel.

Abstract:
Accurate estimation of large displacement optical flow re-mains a critical challenge. Existing methods typically rely on iterativelocal search or/and domain-specific fine-tuning, which severely limits theirperformance in large displacement and zero-shot generalization scenarios.To overcome this, we introduce MegaFlow, a simple yet powerful model forzero-shot large displacement optical flow. Rather than relying on highlycomplex, task-specific architectural designs, MegaFlow adapts powerfulpre-trained vision priors to produce temporally consistent motion fields.In particular, we formulate flow estimation as a global matching problemby leveraging pre-trained global Vision Transformer features, which natu-rally captures large displacements. This is followed by a few lightweightiterative refinement to further improve the sub-pixel accuracy. Extensiveexperiments demonstrate that MegaFlow achieves state-of-the-art zero-shot performance across multiple optical flow benchmarks. Moreover, ourmodel also delivers highly competitive zero-shot performance on long-range point tracking benchmarks, demonstrating its robust transferabilityand suggesting a unified paradigm for generalizable motion estimation.Project Page: https://kristen-z.github.io/projects/megaflow/.

Abstract:
Recent generative approaches to geometry estimation adaptpretrained image diffusion models and treat the task as image-conditionedgeneration. Leveraging off-the-shelf image diffusion models, they either(i) train task-specific geometry models (for depth and surface normal es-timation) independently, losing the opportunity of exploring the intrinsiccorrelation of these geometric targets, or (ii) jointly fine-tune modifiedimage diffusion backbones (e.g., altered self-attention), which typicallydemands substantial labeled data. To overcome these limitations in aprincipled fashion, we repurpose pretrained video generative models as aunified and data-efficient framework for geometry estimation, formulatedinnovatively as a next-frames prediction task. Our method, GeoNeXt,inherits naturally structured knowledge and richer priors from the videomodel, while further adapting them for joint modeling of images and ge-ometry targets (

Abstract:
Spatial visual perception is a fundamental requirement in physical-world applications like autonomous driving and robotic manipulation, driven by the need to interact with 3D environments. Capturing pixel-aligned metric depth using RGB-D cameras would be the most viable way, yet it usually faces obstacles posed by hardware limitations and challenging imaging conditions, especially in the presence of specular or texture-less surfaces. In this work, we argue that the inaccuracies from depth sensors can be viewed as “masked” signals that inherently reflect underlying geometric ambiguities. Building on this motivation, we present MDM, a depth completion model which leverages visual context to refine depth maps through masked depth modeling and incorporates an automated data curation pipeline for scalable training. It is encouraging to see that our model outperforms top-tier RGB-D cameras in terms of both depth precision and pixel coverage. Experimental results on a range of downstream tasks further suggest that MDM offers an aligned latent representation across RGB and depth modalities. Code, checkpoints, and 3M RGB-depth pairs (including 2M real data and 1M simulated data): https://github.com/robbyant/lingbot-depth.

Abstract:
Generative world models for autonomous driving (AD) are ofgreat value in applications such as data augmentation, closed-loop sim-ulation, and safety-critical scenario evaluation. Unlike the widely stud-ied image modality, in this work we explore generative world modelsfor LiDAR data. Existing generation methods for LiDAR predominantlyfocus on single frame generation or lack the capacity for interactive sim-ulation, while existing prediction approaches require multiple frames ofhistorical input and can only deterministically predict multiple framesat once. Both paradigms fail to support long-horizon interactive gen-eration. To this end, we introduce LaGen, which, to the best of ourknowledge is the first autoregressive framework capable of generatinglong-horizon LiDAR scenes in a frame-by-frame, interactive manner. La-Gen is able to take a single-frame input as a starting point and effectivelyutilize bounding box information as conditions to generate high-fidelity4D scene. In addition, we introduce a scene decoupling estimation moduleto enhance the model’s interactive generation capability for object-levelcontent, as well as a noise modulation module to mitigate error accumu-lation during long-horizon generation. We extensively evaluate LaGen’sperformance in controlled data generation and long-horizon scene genera-tion on the nuScenes dataset. The experimental results demonstrate thatLaGen achieves state-of-the-art performance, especially on later frames.The code is publicly available at: https://github.com/szzhou88/LaGen.

Abstract:
We propose TacFiLM, a lightweight modality-fusion ap-proach that integrates visual-tactile signals into vision-language-action(VLA) models. While advances in VLAs have introduced robot policiesthat are both generalizable and semantically grounded, these modelsmainly rely on vision-based perception. Vision alone, however, cannotcapture the complex interaction dynamics that occur during contact-richmanipulation, including contact forces, surface friction, compliance, andshear. While recent attempts to integrate tactile signals into VLA modelsoften increase complexity through token concatenation or large-scale pre-training, the heavy computational demands of behaviour models necessi-tate lightweight fusion strategies. To address these challenges, TacFiLMoutlines a post-training finetuning approach that conditions intermedi-ate visual features on pretrained tactile representations using feature-wise linear modulation (FiLM). Experimental results on insertion anddrawer opening tasks demonstrate consistent improvements in successrate, direct task performance, completion time, and force stability acrossboth in-distribution and out-of-distribution tasks. Together, these resultssupport our method as an effective approach to integrating tactile sig-nals into VLA models, improving contact-rich manipulation behaviours.Project page: https://charliem7.github.io/projects/TacFilm/

Abstract:
Ternary transformers offer extreme memory and compute ef-ficiency, but existing low-bit LoRA-based methods cannot directly fine-tune ternary weights. Current approaches either require dequantization,restoring low-bit base weights to higher precision to merge with adap-tation weight, or update only quantization parameters, preventing amerged model that remains ternary. We propose ternary multiplica-tive adaptation, which represents discrete updates of ternary weightssuch as sign flips or zeroing through a low-rank Kronecker factoriza-tion into two small ternary matrices applied element-wise to ternaryweights. This design is parameter-efficient and expressive, preserves theternary domain, and supports direct merging without dequantization.Experiments on six models across language and vision, including ternar-ized LLaMA-3 1B and 3B and a ternary ViT-B/16, demonstrate thatour method recovers much of the performance lost to quantization andoutperforms strong low-bit and ternary baselines. Code is available athttps://github.com/alexmanoo/ternary_adaptation.

Abstract:
Diffusion Transformers (DiTs) have emerged as a dominantarchitecture for high-quality text-to-image generation, yet their scaleposes challenges for efficient deployment. While truncated singular valuedecomposition (SVD) is a principled tool for parameter reduction, evi-dence from large language models (LLMs) suggests that naive low-rankapproximation can cause catastrophic failure. In contrast, we find thattruncated SVD in DiTs produces smooth degradation even under sub-stantial global compression, with redundancy distributed across projec-tion matrices throughout the whole network rather than concentratedin a few transformer blocks. Building on these insights, we introduceSVDtrunc, a two-step block-level compression scheme, first allocatingranks across blocks and compressing the least important ones via trun-cated SVD under a global parameter budget, and then fine-tuning allblocks with modular knowledge distillation and a rectified-flow objec-tive. We apply SVDtrunc to FLUX.dev across compression levels rangingfrom 40–90 % of the original parameter count. Across three benchmarks,GenEval, HPSv2, and DPG, we outperform all competing approaches.Notably, and in contrast to prior work, we retain near-full performanceat 68 % and remain competitive even at 57 % of the original parameterbudget. Furthermore, we show that SVDtrunc complements step distil-lation and achieves strong results even without fine-tuning, positioningit as a practical continuation of efficiency improvements beyond diffusionstep reduction for large-scale generative models.

Abstract:
Real-world deployment of text-to-image diffusion models re-quires continual concept removal as new privacy, copyright, or safetyobligations arise over time. Existing unlearning methods, however, aredesigned for single-step deletion and collapse after only 3–5 sequentialapplications. We trace this instability to two compounding factors: (i)coarse mapping targets that cause degradation to accumulate unnec-essarily across steps, and (ii) the absence of local protection for se-mantically neighboring concepts, whose shared internal representationsmake them the first to suffer collateral damage. Because this damageis strongest in the local semantic neighborhood of the forget concept,global replay alone cannot prevent it. Building on this analysis, we pro-pose Locality-Aware Continual Unlearning (LACU), a frameworkwith two complementary mechanisms. Locality-Aware Target Selec-tion chooses, for each forget prompt, the context-preserving mappingprompt that the diffusion model itself treats as most similar to the orig-inal prompt, measured by score-prediction distance (how differently themodel denoises the same noisy image under two text conditions), ensur-ing each update is as small and targeted as possible. Locality-AwareReplay uses the same metric to identify the retain concepts closest to theforget concept in the model’s own representation and replays them as alocal functional regularizer, directly shielding the most vulnerable neigh-borhood. Combined with teacher-student distillation and lightweight ℓ2parameter regularization, LACU maintains stable unlearning over 10 se-quential steps, preserving significantly higher related retention (RRacc )and general retention (GRacc ) than recent baselines. The code is availableat https://github.com/SonyResearch/LACU.

Abstract:
Progress in tasks like 3D reconstruction and tracking of dynamic scenes from monocular video is constrained by the scarcity of high-quality datasets with dense, complete, and accurate geometric annotations. To address this limitation, we introduce Syn4D, a multiview synthetic dataset of dynamic scenes that includes ground-truth camera motion, depth maps, dense tracking, and parametric human pose annotations. A key feature of Syn4D is the ability to unproject any pixel into 3D to any time and to any camera. We conduct extensive evaluations across multiple downstream tasks to demonstrate the utility and e!ectiveness of the proposed dataset, including 4D scene reconstruction, 3D point tracking, geometry-aware camera retargeting, and human pose estimation. The experimental results highlight Syn4D’s potential to facilitate research in dynamic scene understanding and spatiotemporal modeling.

Abstract:
Correspondence learning aims to estimate accurate geomet-ric model parameters from the data with severe outliers and is crucial formany computer vision tasks. While recent deep correspondence learningmethods have substantially improved inlier-outlier identification, most ofthem still rely on a differentiable weighted least squares (WLS) estima-tor to recover the final model parameters. This strategy typically treatscorrespondences as conditionally independent observations and there-fore ignores the structured dependencies among inliers. In real-worldscenarios, inliers are often sampled from geometric manifolds and tendto appear in spatially dense clusters, especially in texture-rich regions.As a result, WLS may be dominated by dense inlier groups, leadingto a poorly-conditioned estimation problem that is vulnerable to noiseor degenerate configurations. To address this bottleneck, we propose astructure-aware estimator, which explicitly models inlier correlations viaa graph Laplacian matrix and integrates this prior into a maximum-likelihood framework. Our proposed estimator is fully differentiable, ar-chitecturally lightweight, and can seamlessly replace the standard WLSestimator in existing correspondence learning pipelines. Extensive exper-iments on multiple benchmarks demonstrate consistent improvementswhile introducing a modest increase in computational cost. The code ispublicly available at: https://github.com/Tianyu-Yan/SAE

Abstract:
Anticipating human motion from an egocentric perspectiveis fundamental for proactive assistance in AR/VR, human-robot collab-oration, and embodied AI. While recent works incorporate language asa semantic prior to reduce the ill-posed nature of egocentric forecasting,they largely neglect the 3D spatial and semantic context that governshow motion unfolds, and treat pose and language prediction as separateinference streams. We introduce Ego3DLM, built on two core principles:accurate motion forecasting requires explicit spatial and semantic un-derstanding of the 3D environment, and pose and language must be pre-dicted holistically in a single pass, since motion is inherently tied to thesemantic interpretation of actions being performed. Given three-pointtracking, 3D scene features, and egocentric video, Ego3DLM simultane-ously decodes past pose, future pose, past narration, and future narrationin a single autoregressive pass, grounding predicted poses and descrip-tions in one another to enforce cross-modal and temporal consistency. Weadopt a three-stage training scheme: (1) spatial-semantic scene aware-ness pretraining; (2) holistic instruction tuning over all four outputs ina single pass; and (3) GRPO-based reinforcement finetuning with intra-and inter-modal rewards that directly optimize pose-language fidelity.Experiments on the Nymeria benchmark demonstrate that Ego3DLMachieves state-of-the-art performance across future motion prediction,past motion tracking, and motion description, showing that 3D scenegrounding and holistic cross-modal prediction yield physically plausibleand semantically coherent motion forecasts. The project page is availableat https://jaewoo97.github.io/Ego3DLM/.

Abstract:
Cross-view geo-localization matches ground-level observationsagainst geo-tagged satellite imagery. Recent methods show that sequen-tial queries such as video clips yield richer spatiotemporal cues thansingle images, yet they overlook a complementary sequential modality:route descriptions—which capture the same trajectory at a higher levelof abstraction and are often the only input available (e.g., a user direct-ing an autonomous vehicle to a pickup point). To bridge this gap, weintroduce SeqGeo-VL, a dataset of ∼39K video–text–satellite triplets,and TrajLoc, a unified framework capable of processing both video clipsand route descriptions. By leveraging both dense visual and abstract lin-guistic semantics, TrajLoc enables these modalities to mutually reinforcecross-view matching. We further propose TrajMod, a lightweight mod-ule that conditions query embeddings on trajectory geometry, yieldingspatially-aware representations. Experiments show that TrajLoc achievessubstantial gains over state-of-the-art methods on both video and textgeo-localization. Code, model weights, and the dataset are released athttps://humblegamer.github.io/trajloc/.

Abstract:
Multi-view computer vision pipelines typically rely on accu-rate sparse keypoints and robust descriptors. While incorporating linefeatures has shown clear benefits for matching and pose estimation, exist-ing point-line approaches remain inefficient: they detect points and linesseparately, use increasingly heavy networks, and depend on CPU-boundheuristics that hinder real-time performance. We introduce a Unified Effi-cient Points and Lines (UPAL) feature extractor that jointly extracts key-points, line segments, and feature descriptors within a single lightweightarchitecture. A shared backbone provides common representations thatfeed different branches for point and line features. Line segments are recov-ered through an accelerated post-processing stage, an enhanced and highlyefficient variant of the LSD algorithm. UPAL matches or exceeds state-of-the-art performance in both point and line applications while significantlyreducing computational cost, achieving, for instance, a 4× speedup and10× smaller memory footprint over the ALIKED + DeepLSD pipeline.Code is publicly available at https://github.com/francois141/upal.

Abstract:
Image relighting modifies illumination while preserving non-lighting content such as identity and geometry. Existing diffusion-basedmethods often suffer from unstable illumination changes or inconsistentcontent preservation under complex lighting, as they lack an explicitmechanism to learn feature transformations between images. We refor-mulate relighting as an illumination feature transport problem and in-troduce Consistent Feature Transport (CFT), a training principle thatexplicitly enforces illumination-consistent transport between source andtarget image distributions. Built upon rectified flow, CFT jointly modelsnoise-to-image generation and illumination-consistent source-to-targettransport through trajectory-level supervision. This dual-transport for-mulation encourages isolation of illumination-specific variations whilepreserving content-aligned features. To support complex lighting scenar-ios, we construct a large-scale portrait relighting dataset with diverserelighting effects. Experiments show consistent improvements over ex-isting state-of-the-art relighting approaches and demonstrate that CFTcan generalize to other editing tasks, including style transfer. Code isavailable at https://github.com/Dixin-Lab/CFT.

Abstract:
Reward models are central to post-training and test-timeoptimization for visual generative models, yet existing approaches typi-cally rely on either large-scale pairwise preference annotations (hundredsof thousands to millions of labeled pairs) or heavily engineered multi-metric scoring pipelines, both of which are costly and labor-intensive. Wepresent DisRM, a discriminative reward modeling framework that avoidsthe need for pairwise annotations by training a lightweight discriminatorto distinguish model-generated outputs from a small set of preferred rep-resentative samples, called Preference Proxy Data (PPD). As the rewardmodel is repeatedly re-trained to distinguish outputs from the updatedgenerator from fixed PPD samples, DisRM naturally supports iterative,multi-round co-refinement with the generator. Across evaluations of im-age quality, safety alignment, and video generation, DisRM achieves com-petitive and often better performance than methods trained with up to1 million annotated preference pairs, while using only 500 unlabeled tar-get samples in image-quality setting, and generalizes across Best-of-Nselection, SFT, and DPO.

Abstract:
Recent advancements in reinforcement learning, particularlythrough Group Relative Policy Optimization (GRPO), have significantlyimproved multimodal large language models for complex reasoning tasks.However, two critical limitations persist: 1) they often produce unfo-cused, verbose reasoning chains that obscure salient spatiotemporal cues,and 2) binary rewarding fails to account for partially correct answers,resulting in high reward variance and ine!cient learning. In this pa-per, we propose TW-GRPO, a novel framework that enhances visualreasoning with focused thinking and dense reward granularity. Specifi-cally, we employ a token weighting mechanism that prioritizes tokenswith high informational density (estimated by intra-group informationentropy), suppressing redundant tokens like generic reasoning prefixes.Furthermore, we reformulate RL training by shifting from single-choice tomulti-choice QA tasks, where soft rewards enable finer-grained gradientestimation by distinguishing partial correctness. Additionally, we proposequestion-answer inversion, a data augmentation strategy to generatediverse multi-choice samples from existing benchmarks. Experimentsdemonstrate superior performance on several video reasoning and un-derstanding benchmarks. Notably, under identical training settings, ourmethod significantly outperforms the Video-R1, achieving an averageimprovement of 2.6% across four comparable benchmarks, e.g., +3.8%on MVBench, +2.4% on TempCompass, and +2.3% on VideoMME. Ourcodes are available at https://github.com/longmalongma/TW-GRPO.

Abstract:
We introduce PPTArena, a benchmark for PowerPoint edit-ing that evaluates how agents modify real slides from natural-languageinstructions. Unlike benchmarks that rely on image-PDF renderings ortext-to-slide generation, PPTArena features 100 decks with over 1,300human-curated edits across 2,125 slides, spanning text, charts, anima-tions, and professional master styles. Each edit pairs a ground-truthdeck with a target rubric and is scored by two Vision-Language Model(VLM) judges: one rates instruction following from structural diffs, theother visual quality from slide images. On top of this benchmark, wepresent PPTPilot, a structure-aware agent that plans semantic edit se-quences, routes between programmatic tools and deterministic XMLoperations, and verifies each result in an iterative plan-edit-check loop.PPTPilot outperforms strong VLM-based agents by more than 10 per-centage points on compound, layout-sensitive, and cross-slide edits, withlarge gains in visual fidelity and deck-wide consistency. Despite this, allagents still struggle on long-horizon, document-scale tasks, underscoringhow hard reliable PowerPoint editing remains. We publicly release ourcode: https://github.com/michaelofengend/PPTArena.

Abstract:
Distribution Matching Distillation (DMD) facilitates efficientinference by distilling multi-step diffusion models into few-step variants.Concurrently, Reinforcement Learning (RL) has emerged as a vital toolfor aligning generative models with human preferences. While both repre-sent critical post-training stages for large-scale diffusion models, existingstudies typically treat them as independent, sequential processes, leav-ing a systematic framework for their unification largely unexplored. Inthis work, we demonstrate that jointly optimizing these two objectivesyields mutual benefits: RL enables more preference-aware and control-lable distillation rather than uniformly compressing the full data dis-tribution, while DMD serves as an effective regularizer to mitigate re-ward hacking during RL training. Building on these insights, we proposeDMDR, a unified framework that incorporates Reward-Tilted Distribu-tion Matching optimization alongside two dynamic distillation trainingstrategies in the initial stage, followed by the joint DMD and RL opti-mization in the second stage. Extensive experiments demonstrate thatDMDR achieves state-of-the-art visual quality and prompt adherenceamong few-step generation methods, even surpassing the performance ofits multi-step teacher model.

Abstract:
Pre-trained flow-based models excel at synthesizing complexscenes, yet lack a direct mechanism for disentangling and customizingtheir underlying concepts from one-shot real-world sources. To demystifythis process, we first introduce a novel differential probing technique toisolate and analyze the influence of individual concept tokens on the ve-locity field over time. This investigation yields a critical insight: the gen-erative process is not monolithic but unfolds in three distinct stages. Aninitial Blueprint Stage establishes low-frequency structure, followed bya pivotal Instantiation Stage where content concepts emerge with peakintensity and become naturally disentangled, creating an optimal windowfor manipulation. A final concept-insensitive Refinement Stage thensynthesizes fine-grained details. Guided by this discovery, we proposeConceptWeaver, a framework for one-shot concept disentanglement.ConceptWeaver learns concept-specific semantic offsets from a single ref-erence image using a stage-aware optimization strategy that aligns withthe three-stage framework. These learned offsets are then deployed dur-ing inference via our novel ConceptWeaver Guidance (CWG) mechanism,1 ∗Equal contribution; † Project Lead; ‡ Co-corresponding author.which strategically injects them at the appropriate generative stage. Ex-tensive experiments validate that ConceptWeaver enables high-fidelity,compositional synthesis and editing, demonstrating that understandingand leveraging the intrinsic, staged nature of flow models is key to un-locking precise, multi-granularity content manipulation. We will releaseour code at https://github.com/JasperChennn/ConceptWeaver.

Abstract:
Existing diffusion-based methods have recently made signif-icant progress in image dehazing. However, they typically neglect thephysics of haze formation and reconstruct clean images from pure Gaus-sian noise, thereby limiting their restoration potential. To address thisissue, we propose Haze-Noise Diffusion (HNDiff), a novel diffusion frame-work that embeds the atmospheric scattering model as an inductive bias.By grounding diffusion in physical principles, HNDiff ensures that therestoration aligns more closely with underlying mechanisms of haze for-mation. In its forward process, we introduce joint haze-noise diffusionwith a haze-aware noise scheduler, which progressively adds both hazeand noise to an image. Essentially, the scheduler adapts noise levels ac-cording to haze density, meaning that regions with heavier haze receivestronger noise injection to encourage content generation, while clearer re-gions receive lighter noise to better preserve details, which directly linksthe forward degradation process with the physics of haze. In the reverseprocess, we then derive a physically consistent dehazing-denoising pro-cess that simultaneously removes haze and noise to restore a clean imagein a manner aligned with the forward degradation process. To furtherenhance practicality, we propose Latent HNDiff, which compiles cleanlatent priors that can be seamlessly integrated into existing dehazing net-works to boost performance. Extensive experiments show that our worksignificantly improves leading dehazing backbones and achieves state-of-the-art results on benchmark datasets. The project page is available athttps://jin-ting-he.github.io/HNDiff/.

Abstract:
We present SD3.5-Fla.sh, an efficient few-step distillation framework that brings high-quality image generation to accessible consumer devices. Our approach distills computationally prohibitive rectified flow models through a reformulated distribution matching objective tailored specifically for few-step generation. We introduce two key innovations: “timestep sharing” to reduce gradient noise and “split-timestep fine-tuning” to improve prompt alignment. Combined with comprehensive pipeline optimizations like text encoder restructuring and specialized quantization, our system enables both rapid generation and memory-efficient deployment across different hardware configurations. This democratizes access across the full spectrum of devices, from mobile phones to desktop computers. Through extensive evaluation including large-scale user studies, we demonstrate that SD3.5-Flash consistently outperforms existing few-step methods, making advanced generative AI truly accessible for practical deployment.

Abstract:
Diffusion models have seen widespread adoption for text-driven human motion generation and related tasks due to their impres-sive generative capabilities and flexibility. However, current motion dif-fusion models face two major limitations: a representational gap causedby pre-trained text encoders that lack motion-specific information, anderror propagation during the iterative denoising process. This paper in-troduces Reconstruction-Anchored Diffusion Model (RAM) to addressthese challenges. First, RAM leverages a motion latent space as interme-diate supervision for text-to-motion generation. To this end, RAM co-trains a motion reconstruction branch with two key objective functions:self-regularization to enhance the discrimination of the motion space andmotion-centric latent alignment to enable accurate mapping from textto the motion latent space. Second, we propose Reconstructive ErrorGuidance (REG), a testing-stage guidance mechanism that exploits themotion diffusion model’s inherent self-correction ability to mitigate errorpropagation. At each denoising step, REG uses the motion reconstruc-tion branch to reconstruct the previous estimate, reproducing the priorerror patterns. By amplifying the residual between the current predic-tion and the reconstructed estimate, REG highlights the improvementsin the current prediction. Extensive experiments demonstrate that RAMachieves significant improvements and state-of-the-art performance. Ourcode will be released on https://feifeifeiliu.github.io/RAM .

Abstract:
Leveraging Multimodal Large Language Models (MLLMs)via contrastive learning has become a mainstream paradigm for improv-ing the performance of Universal Multimodal Retrieval (UMR). However,previous works have ignored the grain blindness when adapting thecontrastive paradigm into retrieval tasks. Grain blindness refers to thetendency of the model to overlook grain-level information contained inthe query, which is crucial for effectively handling complex queries. Thisstems from contrastive learning treating samples as a binary classifica-tion (positive/negative), while ignoring the different information carriedby each negative sample. To address this, we argue that negatives shouldbe treated differently according to their similarity to the positive sample,enabling the model to learn distinct grain information from each nega-tive. In this paper, we introduce a simple but effective framework, calledELVA, a novel rule-based RL framework that mitigates grain blindnessthrough ranking-driven MLLMs. 1) Instead of relying on reward mod-els, we extend Reinforcement Learning with Verifiable Rewards (RLVR)to retrieval tasks, allowing the model to explore new ranking behav-iors without explicit ranking labels. 2) By utilizing rule-based rewards,our approach jointly optimizes the ranking of negative samples whileenlarging the similarity gap between positive and negative. To more pre-cisely measure grain blindness, we further introduce MRBench, a newbenchmark specifically designed for multi-grain query scenarios. ELVAachieves state-of-the-art results across standard retrieval benchmarks,and its notable 13.1% improvement on MRBench further demonstratesits effectiveness in alleviating grain blindness. Our code is available athttps://github.com/SeerRay-Lab/ELVA.

Abstract:
Vision-as-inverse-graphics, the concept of reconstructing im-ages into editable programs, remains challenging for Vision-LanguageModels (VLMs), which inherently lack fine-grained spatial grounding inone-shot settings. To address this, we introduce VIGA (Vision-as-Inverse-Graphics Agent), an interleaved multimodal reasoning framework wheresymbolic logic and visual perception actively cross-verify each other. VIGAoperates through a tightly coupled code-render-inspect loop: synthesizingsymbolic programs, projecting them into visual states, and inspectingdiscrepancies to guide iterative edits. Equipped with high-level semanticskills and an evolving multimodal memory, VIGA sustains evidence-basedmodifications over long horizons. This training-free, task-agnostic frame-work seamlessly supports 2D document generation, 3D reconstruction,multi-step 3D editing, and 4D physical interaction. Finally, we intro-duce BlenderBench, a challenging visual-to-code benchmark. Empirically,VIGA substantially improves accuracy compared with one-shot base-lines in BlenderGym (35.32% ), SlideBench (117.17% ) and our proposedBlenderBench (124.70% ).

Abstract:
Accurately segmenting objects without any manual anno-tations remains one of the core challenges in computer vision. In thiswork, we introduce Selfment, a fully self-supervised framework that seg-ments foreground objects directly from raw images without human labels,pretrained segmentation models, or any post-processing. Selfment firstconstructs patch-level affinity graphs from self-supervised features and ap-plies NCut to obtain an initial coarse foreground-background separation.We then introduce Iterative Patch Optimization (IPO), a feature-space refinement procedure that progressively enforces spatial coherenceand semantic consistency through iterative patch clustering. The refinedmasks are subsequently used as supervisory signals to train a lightweightsegmentation head with contrastive and region-consistency objectives,allowing the model to learn stable and transferable object representations.Despite its simplicity and complete absence of manual supervision, Self-ment sets new state-of-the-art (SoTA) results across multiple benchmarks.It achieves substantial improvements on Fmax over previous unsupervisedsaliency detection methods on ECSSD (+4.0%), HKUIS (+4.6%), andPASCAL-S (+5.7%). Moreover, without any additional fine-tuning, Self-ment demonstrates remarkable zero-shot generalization to camouflagedobject detection tasks (e.g., .910 S m on CHAMELEON and .792 \protect \mathcal F x0008 eta ^\omega on CAMO), outperforming all existing unsupervised approaches andeven rivaling the SoTA fully supervised methods. Codes and weights areavailable at: https://geshang777.github.io/Selfment/.

Abstract:
Recent advances in self-supervised visual representation learning have demonstrated the effectiveness of predictive latent-space objectives for learning transferable features. In particular, Image-based JointEmbedding Predictive Architecture (I-JEPA) learns representations by predicting latent embeddings of masked target regions from visible context. However, it predicts target regions in parallel and all at once, lacking ability to order predictions meaningfully. Inspired by human visual perception, which attends selectively and progressively from primary to secondary cues, we propose DSeq-JEPA, a Discriminative Sequential Joint-Embedding Predictive Architecture that bridges latent predictive and autoregressive self-supervised learning. Specifically, DSeq-JEPA integrates a discriminatively ordered sequential process with JEPA-style learning objective. This is achieved by (i) identifying primary discriminative regions using an attention-derived saliency map that serves as a proxy for visual importance, and (ii) predicting subsequent regions in discriminative order, inducing a curriculum-like semantic progression from primary to secondary cues in pre-training. Extensive experiments across tasks – image classification (ImageNet), fine-grained visual categorization (iNaturalist21, CUB, Stanford Cars), detection/segmentation (MS-COCO, ADE20K), and low-level reasoning (CLEVR) – show that DSeq-JEPA consistently learns more discriminative and generalizable representations compared to I-JEPA variants.

Abstract:
Continual Generalized Category Discovery (C-GCD) aimsto incrementally identify novel categories from sequential unlabeled datawhile preserving recognition of known classes, which is an essential ca-pability for open-world visual learning. A major bottleneck lies in am-biguous unlabeled samples that cannot be confidently assigned to knownclasses nor reliably grouped as novel ones, making pseudo-labeling brit-tle and often biasing learning toward familiar categories. In this work,we introduce Virtual Category-Guided Continual Generalized CategoryDiscovery by adapting Virtual Category Learning (VCL) to the contin-ual setting. Our method identifies uncertain samples and assigns them totemporary virtual categories, enabling safe and informative learning fromunlabeled streams without injecting noisy labels, while improving unla-beled data utilization and mitigating prediction bias. To further stabilizediscovery across sessions and enhance class separation, we augment VCLwith Expanded Neighborhood Contrastive Learning (ENCL), which ex-ploits extended neighborhood relations and an adaptive margin to learnmore discriminative and well-separated representations for both old andemerging classes. Extensive experiments on CIFAR-100, Tiny ImageNet,and ImageNet-100 demonstrate that our approach consistently outper-forms state-of-the-art methods, establishing a scalable and effective so-lution for C-GCD. Code is on: https://github.com/Mrxjh105/VC-CGCD

Abstract:
Compositional Zero-Shot Learning (CZSL) requires learningto compose primitive concepts from seen data and generalize to unseenattribute–object pairs. Existing methods typically disentangle visual fea-tures into isolated primitive components and align them with their cor-responding text concepts or full compositions. However, they often suf-fer from ambiguous vision–language alignment and lack mechanisms toexplicitly capture cross-primitive semantic interactions, ultimately hin-dering generalization to unseen compositions. To overcome these chal-lenges, we introduce Prototype-Conditioned Imagination (PCI), a two-stage framework. In the first stage, Bidirectional Alignment and Disen-tanglement (BAD) couples cross-modal attention with entropic optimal-transport regularization to effectively align visual and textual represen-tations while enforcing global disentanglement of attribute and objectprimitives, thereby mitigating ambiguous vision-language matching. Inthe second stage, Prototype-driven Compositional Modulation (PCM)explicitly models how attribute semantics modulate object features tosynthesize compositional representations, making cross-primitive rela-tions explicit and substantially improving recognition of unseen compo-sitions. Evaluated under both closed-world and open-world protocols onfour popular benchmarks, our approach achieves state-of-the-art perfor-mance, demonstrating superior generalization capabilities to unseen com-positions. Our code is available at https://github.com/YFan-Z1/PCI.

Abstract:
Clothes-Changing Person Re-Identification (CC-ReID) aimsto match pedestrians across non-overlapping camera views despite vari-ations in clothes. However, current research is severely constrained bytwo main shortcomings: existing datasets lack comprehensive diversityand semantic annotations, and current methods fail to globally modeland eliminate identity-irrelevant interfering factors, severely limiting CC-ReID generalization in real-world scenarios. To address these limitations,we propose a unified framework for generalizable CC-ReID. First, weintroduce CGCC, a Comprehensive dataset for Generalizable CC-ReIDfeaturing extensive diversity and fine-grained semantic annotations, com-prising 4,101 identities and 217,248 images. Additionally, we introduceText-Guided Identity Refinement (TGIR), a novel framework that uti-lizes text descriptions to construct an identity-irrelevant subspace, andemploys Singular Value Decomposition (SVD) to holistically extract andeliminate environmental and clothes interference. Together, CGCC andTGIR form a mutually reinforcing closed loop, where the diverse dataand structured annotations of the former provide the essential founda-tion for the semantic disentanglement of the latter. Extensive exper-iments demonstrate that the comprehensive diversity of CGCC effec-tively improves model generalizability, and TGIR precisely extracts ro-bust and generalizable identity representations. Dataset is available athttps://github.com/zhi-time/CGCC.

Abstract:
Animal Re-Identification (Animal ReID) is crucial for eco-logical monitoring in the wild, yet remains challenging due to the fine-grained and diverse visual features of animals, including intricate tex-tures, distinctive patterns, and various pose or form representations.While current Animal ReID methods mostly rely on holistic global fea-tures, they frequently overfit to the surrounding environment rather thancapturing localised, identity-relevant cues. Existing pattern-based meth-ods primarily focus on global generalised signals that are sensitive tobackground interventions and often fail to highlight identity evidencefor individual discrimination. Additionally, these methods are mainlyconstrained to the visual space only and lack pattern-based semanticinteractions from the multimodal space. To address these challenges,we introduce a Region-Aware Multimodal Interleaving (RAMI) frame-work, which formulates Animal ReID as a visual-textual semantic in-teraction over informative, localised regions. Specifically, RAMI projectssegmentation-derived region tokens into a shared token space and inter-leaves them with semantic tokens within a Transformer-based module tofacilitate dense cross-modal interactions. Given the lack of region-leveldata, we design a simple pipeline to produce biologically representativeregions from segmented animals. To the best of our knowledge, we are thefirst to formulate multimodal interleaving tailored for fine-grained dis-criminative tasks in Vision-Language Models (VLMs). Experiments showthat RAMI outperforms state-of-the-art methods across both benchmarkand in-the-wild datasets, with an average mAP gain of up to 18.7%. Ourcode is available at https://github.com/ML-4-SocialGood/RAMI.git.

Abstract:
Understanding person-level bi-manual interactions requiresnot only detecting hands, but also identifying which two hands belongto the same person and what each hand interacts with. Existing hand–object interaction methods are mostly hand-centric: they treat each handas an independent instance, which can lead to ambiguous ownership inmulti-person scenes.We propose a person-centric formulation in which a single query pre-dicts a structured output for one person, including the human box,body pose, hand boxes and states, and interaction targets. We intro-duce part-aware deformable attention to allocate attention acrosshuman, hand, and pose-specific reference regions, enabling one query tocapture the full person structure. We further unify detection and inter-action reasoning with a hand-to-query relationship matrix, whereeach hand selects its interaction target from the detected query set plusa learnable off token, directly recovering the target’s box and class with-out separate object regression.We build a COCO-based dataset with person-centric bi-manual interac-tion annotations and define structured metrics for evaluating hand statesand complete hand–object tuples. Experiments with a transformer-baseddetector show that our formulation improves person-level bi-manual in-teraction parsing and provides an effective unified framework for jointdetection, pose estimation, and hand reasoning.Project page: https://lgecto-ail-vil.github.io/SingleQuery-BHOI/

Abstract:
Concept Bottleneck Models (CBMs) are a class of interpretable AI models that enable concept-level control over the decisionmaking process, allowing humans to directly intervene in model predictions. As a result, CBMs have been widely adopted in tasks requiring interpretability and controllability. Existing research has demonstrated that human-model interaction can signi(cid:28)cantly enhance CBM performance. However, in practical applications, each intervention requires manual human e(cid:27)ort and explicit data provision. Therefore, a central challenge is how to improve model performance while minimizing intervention e(cid:27)ort. In this paper, we show that trained CBM parameters encode usable concept-correlation structure. We leverage this structure during interaction by propagating user feedback through the shared inputto-concept pathway, thereby adjusting non-intervened concepts without training an additional realignment model. Experimental results demonstrate that our method improves low-intervention performance across multiple models and datasets. Compared with all tested baselines, our approach improves early-intervention performance by approximately 9%; compared with Concept Realignment, it improves AUC@10 and AUC@20 by 3.90% and 2.88%, respectively, while avoiding extra training. Code is available at https://github.com/LMBTough/CI. • •

Abstract:
Parametric body models provide the structural basis formany human-centric tasks, yet existing models often rely on costly 3Dscans and learned shape spaces that are proprietary and demographi-cally narrow. We introduce Anny, a simple, fully differentiable, and scan-free human body model grounded in anthropometric knowledge from theMakeHuman community. Anny defines a continuous, interpretable shapespace, where phenotype parameters (e.g. gender, age, height, weight)control blendshapes spanning a wide range of human forms—across ages(from infants to elders), body types, and proportions. Calibrated usingWHO population statistics, Anny provides realistic and demographicallygrounded human shape variation within a single unified model. We re-lease the Anny body model and its code under the Apache 2.0 license.Thanks to its openness and semantic control, Anny serves as a versa-tile foundation for 3D human modeling—supporting millimeter-accuratescan fitting, controlled synthetic data generation, and Human Mesh Re-covery (HMR). We further introduce Anny-One, a collection of 780kphotorealistic images generated with Anny, showing that despite its sim-plicity, HMR models trained with Anny can match the performance ofthose trained with scan-based body models.

Abstract:
Self-supervised neural garment simulation has become pop-ular due to its computational efficiency, good visual realism, and noreliance on training data. However, existing methods greatly simplifythe mechanical properties of fabrics, ignoring persistent wrinkles causedby plasticity. Although this simplification allows for modeling of purelyelastic material and simple training via energy minimization, the lackof believable wrinkles adversely affects the visual realism. Therefore, weintroduce the first self-supervised neural garment simulator that explic-itly models persistent wrinkles. This is accomplished through a novelphysics-inspired loss function, which turns learning into a moving energyminimization problem to mimic plasticity. However, this requires learn-ing to use a changing loss function, which causes difficulties in trainingbecause the loss function changes during optimization. To this end, wepropose a new physics-inspired curriculum learning scheme where thetarget material for learning gradually changes from pure elasticity toelasto-plasticity, allowing the loss function and the learnable parametersto jointly converge. Through a comprehensive evaluation, we show thatfor the first time, self-supervised learning models can generate naturalpersistent wrinkles, outperforming existing methods on a variety of gar-ments, body shapes, and body motions, according to a range of metrics.Our code is publicly available at https://github.com/realcrane/EPNet

Abstract:
We present RefVFX, a new framework that transfers complextemporal effects from a reference video onto a target video or image ina feed-forward manner. While existing methods excel at prompt-basedor keyframe-conditioned editing, they struggle with dynamic temporaleffects such as dynamic lighting changes or character transformations,which are difficult to describe via text or static conditions. Transferringa video effect is challenging, as the model must integrate the new tem-poral dynamics with the input video’s existing motion and appearance.To address this, we introduce a large-scale dataset of triplets, where eachtriplet consists of a reference effect video, an input image or video, and acorresponding output video depicting the transferred effect. Creating thisdata is non-trivial, especially the video-to-video effect triplets, which donot exist naturally. To generate these, we propose a scalable automatedpipeline that creates high-quality paired videos designed to preserve theinput’s motion and structure while transforming it based on some fixed,repeatable effect. We then augment this data with image-to-video effectsderived from LoRA adapters and code-based temporal effects generatedthrough programmatic composition. Building on our new dataset, wetrain our reference-conditioned model using recent text-to-video back-bones. Experimental results demonstrate that RefVFX produces visuallyconsistent and temporally coherent edits, generalizes across unseen effectcategories, and outperforms prompt-only baselines in both quantitativemetrics and human preference. See our website at this URL

Abstract:
Editing portrait videos is a challenging task that requires flexible yet precise control over a wide range of modifications, such as appearance changes, expression edits, or the addition of objects. The key difficulty lies in preserving the subject’s original temporal behavior, demanding that every edited frame remains precisely synchronized with the corresponding source frame. We present Sync-LoRA, a method for editing portrait videos that achieves high-quality visual modifications while maintaining frame-accurate synchronization and identity consistency. Our approach uses an image-to-video diffusion model, where the edit is defined by modifying the first frame and then propagated to the entire sequence. To enable accurate synchronization, we train an in-context LoRA using paired videos that depict identical motion trajectories but differ in appearance. These pairs are automatically generated and curated through a synchronization-based filtering process that selects only the most temporally aligned examples for training. This training setup teaches the model to combine motion cues from the source video with the visual changes introduced in the edited first frame. Trained on a compact, highly curated set of synchronized human portraits, SyncLoRA generalizes to unseen identities and diverse edits (e.g., modifying appearance, adding objects, or changing backgrounds), robustly handling variations in pose and expression. Our results demonstrate high visual fidelity and strong temporal coherence, achieving a robust balance between edit fidelity and precise motion preservation. The project page is available at http://sagipolaczek.github.io/Sync-LoRA.

Abstract:
The inherent complexity of video understanding makes it dif-ficult to determine whether Video-LLM benchmark performance stemsfrom visual perception, linguistic reasoning, or knowledge priors. Whilemany benchmarks have emerged to assess high-level reasoning, sharedcriteria for evaluating video understanding remain largely overlooked.Instead of introducing yet another benchmark, we take a step back tore-examine the criteria for evaluating video understanding. In this work,we introduce Video-Oasis, a sustainable diagnostic suite for systemati-cally auditing existing video understanding benchmarks. This audit re-veals that 55% of existing benchmark samples are solvable without visualinput or temporal context. After filtering these shortcuts, the remainingvideo-native challenges expose a substantial capability gap: state-of-the-art models perform only marginally above random guessing. Building onthese findings, we use the distilled challenges as a testbed to investigatewhich algorithmic design choices contribute to robust video understand-ing. We hope our work provides a practical foundation for constructingrigorous video benchmarks and evaluating future Video-LLMs. Code isavailable at https://github.com/sejong-rcv/Video-Oasis.

Abstract:
Do Video-LLMs have consistent temporal understandingwhen videos capture the same event from different viewpoints? To studythis question, we introduce EgoExo-Con(sistency), a benchmark of syn-chronized egocentric and exocentric video pairs with human-refinedqueries that ensure all concepts are visible in both viewpoints. EgoExo-Con emphasizes two temporal understanding tasks: Temporal Verificationand Temporal Grounding. It evaluates not only correctness but consis-tency across viewpoints. Our analysis reveals two critical limitations ofexisting Video-LLMs: (1) models often fail to maintain consistency, withresults far worse than their single-view performances. (2) When naivelyfinetuned with synchronized videos of both viewpoints, the models showimproved consistency but often underperform those trained on a singleview. For improvements, we propose View-GRPO, a novel reinforcementlearning framework that effectively strengthens view-specific temporalreasoning while encouraging consistent comprehension across viewpoints.Our method demonstrates its superior temporal understanding capabili-ties, especially for improving cross-view consistency. All resources havebeen made available at EgoExo-Con.

Abstract:
Composed visual retrieval (CVR) aims to locate a targetimage or video that reflects a user’s modification of a reference visualinput. Prior works typically design separate models for images and videos,resulting in fragmented paradigms, limited reasoning diversity, and pooradaptability to diverse user intents. We introduce OCTOPUS, a novelmulti-agentic assistant for tool-integrated progressive self-improvementand user-friendly synergistic compositional retrieval. OCTOPUS featuresthree cooperative agents – a Perceiver, a Creator, and a Retriever – thatcollectively emulate a human-like process of perception, imagination, andreflection. The Perceiver interprets composed queries, formulates seman-tic instructions, and refines them through self-reflection. The Creatoremploys visual imagination and object-level reasoning tools to generatediverse textual and visual proxies that capture missing or ambiguoussemantics. The Retriever performs bidirectional retrieval and applies anevaluation mechanism to ensure alignment between results and user intent.Comprehensive experiments on five benchmarks demonstrate that OCTO-PUS consistently outperforms both training-free and supervised baselines.The code is available at https://github.com/zbzzbzzbz/OCTOPUS.

Abstract:
LLM agents often fail in long-horizon tasks due to com-pounding simulation errors. We propose ProAct, a two-stage frameworkfor internalizing foresight. First, Grounded LookAhead Distillation (GLAD) performs supervised fine-tuning (SFT) on search-derived trajectories,compressing complex search trees into concise causal reasoning chains.This allows agents to learn lookahead logic without inference-time com-putational overhead. Second, we introduce Monte-Carlo Critic (MC-Critic), a plug-and-play auxiliary value estimator for policy-gradient al-gorithms (PPO/GRPO). By using lightweight rollouts to provide low-variance value signals, MC-Critic enables stable optimization without ex-pensive model-based approximations. Experiments on stochastic (2048)and deterministic (Sokoban) tasks show ProAct significantly boosts plan-ning accuracy. Notably, a 4B model trained with ProAct outperformsopen-source baselines and rivals state-of-the-art closed-source models,demonstrating strong generalization to unseen environments. The codesand models are available at https://github.com/GreatX3/ProAct.

Abstract:
While modern 3D reconstruction excels at modeling objectgeometry and appearance, it largely ignores the rich acoustic cues re-vealed through physical interaction. Object impact sounds convey mate-rial, stiffness, and structural properties that complement vision, yet exist-ing impact sound modeling approaches either rely on expensive physics-based simulation or require large datasets to generalize in a purely data-driven manner. We introduce Audio-Visual Modal Sound Field (AV-MSF), a novel object-level acoustic representation reconstructed frommulti-view images and only a few impact sound recordings. AV-MSFbuilds on 3D Gaussian Splatting integrated with dense 3D visual featureto provide a strong geometry-aware prior, and represents the impactsound field using compact, physically meaningful modal parameters, en-abling robust few-shot reconstruction. Experiments on two real-worlddatasets show that AV-MSF achieves state-of-the-art impact sound ren-dering, outperforming both physics-based and data-driven baselines. Fur-thermore, we demonstrate downstream applications enabled by our rep-resentation, including contact localization and object sound editing.

Abstract:
Our objective is to build an embedding model that capturesthe nuanced relationship between a search query and candidate videos.We cover three aspects of nuanced retrieval: (i) temporal, (ii) negation,and (iii) multimodal. For temporal nuance, we consider chiral actionsthat need distinguishing between temporally opposite actions like “open-ing a door” vs. “closing a door”. For negation, we consider queries withnegators such as “not”, “none” that allow a user to specify what theydo not want. For multimodal nuance, we consider the task of composedretrieval where the query comprises a video along with a text edit in-struction. The goal is to develop a unified embedding model that handlessuch nuances effectively. To that end, we repurpose a Multimodal LargeLanguage Model (MLLM) trained to generate text into an embeddingmodel. We fine-tune it with a contrastive loss on text alone with carefullysampled hard negatives that instill the desired nuances in the learned em-bedding space. Despite the text-only training, our method achieves stateof the art performance on all benchmarks for nuanced video retrieval. Wealso analyze how this improvement is achieved, and show that text-onlytraining reduces the modality gap between text and video embeddingsleading to better organization of the embedding space.

Abstract:
The softmax activation in multihead attention (MHA) is thede facto standard for attention-based models in visual perception tasks.However, standard softmax can produce noisy attention patterns thatdilute relevant features and degrade its performance. In this paper, wepropose Denoising Attention or DnA, in which, first, a positive queryidentifies which image features belong to the correct class, and a neg-ative query identifies closely associated but irrelevant image features.DnA then projects these interactions into two distinct subspaces withlarger principal angles, promoting subspace separation and improved dis-criminability. Using a ViT-B backbone, our proposed DnA achieves anabsolute gain of 0.8% on ImageNet-1K compared to the baseline. Wefurther show improvements across multiple visual understanding tasks,including video understanding with video transformers (1.8%) and videoLLMs (0.5%). Our extensive empirical analyses justify the design choicesinvolving two interacting subspaces and the denoising effect of DnA. Thecode is publicly available at https://github.com/rjccv/DnA.

Abstract:
Backdoor attacks, which induce Convolutional Neural Net-works (CNNs) to behave maliciously when a predefined trigger is present,pose serious security risks. While such threats also extend to VisionTransformers (ViTs), previous studies have suggested that existing back-door attacks remain highly effective on ViTs and can evade commondefense mechanisms—often without significantly compromising accuracy.In this paper, we revisit this claim and demonstrate that such con-clusions are overly optimistic, largely due to inadequate adaptation ofCNN-based defenses to ViTs. We show that, with proper adjustments,existing backdoor attacks can in fact be effectively mitigated. More-over, we propose a more robust attack strategy: by introducing slightperturbations to the trigger, existing attacks can be made significantlymore resistant to various defenses. We hope that our findings—bothon the defensibility of current attacks with correct adaptations and theproposed enhanced attack—will inspire deeper investigation into thebackdoor robustness of Vision Transformers. Our code is available athttps://github.com/PKU-ML/ViT_backdoor.

Abstract:
Embodied Vision-Language Models (VLMs) have demon-strated impressive performance and generalization in robotics, partic-ularly within Vision-Language-Action frameworks. However, a signifi-cant gap remains between the high-level semantic focus of standardtext-guided pre-training paradigms and the low-level spatial and phys-ical knowledge critical for execution in embodied environments. In thispaper, we introduce GEM, a Generative-supervised Embodied vision-language Model designed to bridge this divide. We propose integratinga depth map generation task directly into the VLM pre-training phase.By training this generative objective jointly with the main model, weobserve substantial improvements in embodied intelligence, significantlyenhancing both semantic understanding and physical operation capa-bilities. To support this paradigm, we curate and release GEM-4M, acomprehensive large-scale dataset featuring a mixture of grounding, rea-soning, and planning data paired with high-quality depth supervision.Extensive experiments demonstrate that GEM achieves state-of-the-artresults across diverse embodied benchmarks. Furthermore, our deployedaction model, GEM-VLA, exhibits vastly superior task execution abilitiesin both simulation environments and real-world evaluations. Code, mod-els, and datasets are available at https://zhaorw02.github.io/GEM/.

Abstract:
Vision-Language-Action (VLA) models aim to unify per-ception, language understanding, and action generation, offering strongcross-task and cross-scene generalization with broad impact on embodiedAI. However, current VLA models often lack explicit step-by-step rea-soning, instead emitting final actions without considering affordance con-straints or geometric relations. Their post-training pipelines also rarelyreinforce reasoning quality, relying primarily on supervised fine-tuningwith weak reward design. To address these challenges, we present VLA-R1, a reasoning-enhanced VLA that integrates Reinforcement Learningfrom Verifiable Rewards (RLVR) with Group Relative Policy Optimiza-tion (GRPO) to systematically optimize both reasoning and execution.Specifically, we design an RLVR-based post-training strategy with ver-ifiable rewards for region alignment, trajectory consistency, and outputformatting, thereby strengthening reasoning robustness and executionaccuracy. Moreover, we develop VLA-CoT-13K, a high-quality datasetthat provides chain-of-thought supervision explicitly aligned with affor-dance and trajectory annotations. Furthermore, extensive evaluations onin-domain, out-of-domain, simulation, and real-robot platforms demon-strate that VLA-R1 achieves superior generalization and real-world per-formance compared to prior VLA methods. We plan to release the model,code, and dataset following the publication of this work.

Abstract:
Grounding natural-language instructions into executable con-tinuous control remains a core challenge for quadruped vision-language-action (VLA) systems due to the gap between high-level semantic reason-ing and low-level locomotion actuation. Existing approaches often relyon implicit reasoning or purely behavioral supervision, leading to unsta-ble long-horizon grounding and limited robustness in real-world deploy-ment. To address these issues, we present MobileVLA-R1, a unifiedquadruped VLA framework that explicitly aligns hierarchical reason-ing with continuous control. Our method introduces a multi-granularitychain-of-thought (CoT) supervision scheme for embodied trajectories, in-stantiated in MobileVLA-CoT, together with a two-stage training paradigmthat combines supervised CoT alignment and reinforcement learning forreasoning-to-control consistency. We conduct comprehensive evaluationson VLN and quadruped VLA benchmarks, including VLN-CE, QUARD,and real-world Unitree Go2 deployment. MobileVLA-R1 consistently out-performs strong baselines, achieving about 5% gains on key metrics whiledemonstrating deployment-time closed-loop execution under a fixed hy-brid inference setup.

Abstract:
EgoExo proficiency estimation aims to assess action qual-ity by integrating fine-grained motion cues from egocentric (1st-person)views with spatial context from multiple exocentric (3rd-person) views.Simply adding more exocentric views degrades EgoExo performance, asredundant or noisy perspectives dilute useful motion cues. Our anal-ysis identifies two key causes: (1) Multiview redundancy — Fromthe data perspective, certain views provide limited or noisy informa-tion, diluting discriminative cues; (2) Overfitting — From the featureperspective, conventional fusion increases representational complexity,causing the model to memorise view-specific patterns rather than learngeneralisable representations. To address these issues, we propose twocomplementary modules. AdaMVS adaptively identifies and fuses themost informative view tokens under weak supervision from the data per-spective, while VIB-GB combines Gradient Blending and VariationalInformation Bottleneck regularisation from the feature perspective tocompress redundant signals and suppress overfitting during training.Experiments on EgoExo-4D and EgoExo-Fitness demonstrate that ourmethod learns both which view to look at and how to fuse them,achieving new state-of-the-art results. Our source code is available athttps://github.com/dx199771/AdaMVS

Abstract:
In-context learning (ICL) enables large vision-language models (LVLMs) to perform tasks by following patterns in context examples, yet its potential to amplify societal biases remains underexplored. We systematically investigate how ICL influences gender bias in LVLMs through VL-BICLE, an evaluation framework comprising six ICL settings, three tasks, and four datasets. Our experiments on six LVLMs reveal that gendered ICL demonstrations act as a directional force, shifting model bias toward the demonstrated gender through a cross-gender mechanism that disproportionately degrades performance on the opposite gender. This effect appears in image captioning and pronoun prediction but not in visual question answering, indicating that gendered ICL influences bias only when the task output involves gendered language. Similarity-based retrieval methods inherit the training pool’s gender imbalance and offer no debiasing advantage, while standard quality metrics remain blind to these bias shifts. To mitigate this bias, we replace real in-context images with synthetic ones from stable diffusion models while keeping captions unchanged. This simple intervention reduces gender bias without degrading caption quality. The code can be found at: https://github.com/mathfather/Gender-Bias-in-VL-ICL.

Abstract:
In this paper, we challenge the conventional notion in 3DGS-SLAM that rendering quality is the primary determinant of trackingaccuracy. We argue that, compared to solely pursuing a perfect scenerepresentation, it is more critical to enhance the robustness of the ras-terization process against parameter errors to ensure stable camera posetracking. To address this challenge, we propose a novel approach thatleverages a smooth kernel strategy to enhance the robustness of 3DGS-based SLAM. Unlike conventional methods that focus solely on minimiz-ing rendering error, our core insight is to make the rasterization processmore resilient to imperfections in the 3DGS parameters. We hypothesizethat by allowing each Gaussian to influence a smoother, wider distribu-tion of pixels during rendering, we can mitigate the detrimental effectsof parameter noise from outlier Gaussians. This approach intentionallyintroduces a controlled blur to the rendered image, which acts as a reg-ularization term, stabilizing the subsequent pose optimization. While acomplete redesign of the rasterization pipeline is an ideal solution, wepropose a practical and effective alternative that is readily integratedinto existing 3DGS frameworks. Our method, termed Corrective BlurryKNN (CB-KNN), adaptively modifies the RGB values and locations ofthe K-nearest neighboring Gaussians within a local region. This dynamicadjustment generates a smoother local rendering, reducing the impactof erroneous GS parameters on the overall image. Experimental resultsdemonstrate that our approach, while maintaining the overall quality ofthe scene reconstruction (mapping), significantly improves the robust-ness and accuracy of camera pose tracking.https://github.com/xju-zsh/Robust-3DGS-based-SLAM-via-Adaptive-Kernel-Smoothing.git

Abstract:
Existing 3D relighting methods operate through either ex-plicit material decomposition, diffusion-based view-space generation, ora combination of both, requiring full recomputation for each new light-ing condition. We observe that recent latent scene representations, whichencode multi-view images into a set of compact tokens with no fixed phys-ical semantics, open up a novel design space for relighting. We presentLumiTokens, a framework that formulates 3D relighting as a directtransformation on latent scene tokens, without explicit 3D representa-tions, rendering equations, or physics-based decomposition. Our modelintroduces a Scene Token Editor that processes scene tokens jointly withlight-ray tokens through self-attention, producing updated tokens thatcan be decoded into multi-view-consistent relit images. To support di-verse lighting types through a unified interface, all lighting signals, in-cluding environment maps, point lights, and area lights, are parameter-ized as Plücker ray tokens, enabling native 3D user interaction with arepresentation that carries no explicit spatial structure. Crucially, thisdesign supports progressive relighting: because the editor’s output re-mains in the same latent space as its input, a user can incrementallybuild up illumination one light source at a time, with each edit compos-ing in token space. Experiments demonstrate that LumiTokens achievescomparable or superior relighting quality to other methods and supportsprogressive, composable lighting edits.

Abstract:
Reconstructing physics-based 3D assets—geometry, mate-rials, and illumination—from multi-view images is a core problem incomputer graphics and vision, and a prerequisite for realistic relightingand editing. Physics-based inverse rendering offers an accurate image-formation model, but is severely underconstrained: without strong pri-ors, illumination is baked into materials, and reconstructions generalizepoorly to novel views and lighting. Data-driven diffusion models, in con-trast, predict visually plausible materials, yet their predictions rarelysatisfy the rendering equation and are not directly usable for physics-based rendering. We bridge these two paradigms rather than replacingeither. Our key idea is to treat the predictions of a state-of-the-art dif-fusion model not as target material values but as a similarity kernel foroptimization: we introduce a regularization loss that penalizes deviationsin the optimized material over surface regions where the diffusion predic-tions are near-constant, while leaving the optimization free to match theinput images. Built on this regularizer, our end-to-end pipeline jointlyreconstructs geometry, materials, and illumination, yielding high-qualityassets that drop into standard rendering pipelines and relight faithfully.On the Synthetic4Relight, Stanford-ORB, and DTC-Synthetic datasets,our method significantly outperforms state-of-the-art baselines in bothreconstruction accuracy and relighting quality.

Abstract:
Despite advances in indoor scene generation, synthesizingcoherent building exteriors consistent with generated interiors remainslargely unexplored. Existing methods can generate floor plans and walllayouts but typically stop at a structural shell, lacking stylistically con-sistent facades and roofs. Completing these exteriors is challenging be-cause the footprint, wall geometry, and opening semantics must remainfixed—constraints that unconstrained generative models often violate.We introduce ShellMaker, a language-guided exterior completion frame-work that operates under these structural constraints. Given a build-ing scaffold and a text style prompt, ShellMaker generates a completeexterior mesh with PBR materials by combining parametric roof gen-eration, LLM-based part-aware prompt refinement, joint wall–roof ma-terial retrieval, and geometry-aware assembly. Operating on a format-agnostic scaffold representation, ShellMaker generalizes to indoor gener-ators, CityGML, and CAD inputs, while maintaining structural consis-tency and improving architectural coherence over retrieval and uncon-strained generative baselines. The project page is available at https://ruiqixu37.github.io/ShellMaker_web/.

Abstract:
Producing 3D human representations from input views onthe fly is essential for immersive live streaming systems, where repre-sentation compactness is as critical as high fidelity given limited com-putational power and transmission bandwidth. Although recent feed-forward reconstruction methods achieve impressive quality through theview-centric prediction of 3D representations, they repeatedly encode thesame subject content across multiple views, leading to significant inter-view redundancy. Our key insight is to perform predictions directly in 3Dspace, enabling the network to learn and produce a highly compact rep-resentation. To this end, we propose PointSplat, a novel human-centricapproach that directly infers Gaussian primitives from an input pointset. The proposed method first estimates a coarse geometric proxy andperforms ray casting to prune redundant points and establish explicit2D–3D correspondences. Subsequently, it employs a Point-Image Trans-former to fuse appearance and geometry features, predicting Gaussianattributes in a single forward pass. This design restricts predictions toforeground regions of interest, substantially reducing the total numberof Gaussians while improving novel-view rendering quality. Extensiveexperiments demonstrate that PointSplat achieves higher efficiency andquality while exhibiting strong robustness to variations in view countand image resolution across multiple datasets.

Abstract:
In this paper, we focus on motion discrete tokenization, whichconverts raw motion into compact discrete tokens—a process proven cru-cial for efficient motion generation. In this paradigm, increasing the num-ber of tokens is a common approach to improving motion reconstructionquality, but more tokens make it more difficult for generative models tolearn. To maintain high reconstruction quality while reducing genera-tion complexity, we introduce Language-Guided Tokenization (LG-Tok)for efficient motion tokenization. LG-Tok aligns natural language withmotion at the tokenization stage, yielding compact, high-level seman-tic representations. This approach not only strengthens both tokeniza-tion and detokenization but also simplifies the learning of generativemodels. Furthermore, existing tokenizers predominantly adopt convo-lutional architectures, whose local receptive fields struggle to supportglobal language guidance. To this end, we propose a Transformer-basedTokenizer that leverages attention mechanisms to enable effective align-ment. Additionally, we design a language-drop scheme, in which lan-guage conditions are randomly removed during training. This schemeprevents shortcut learning over text and enables the detokenizer to sup-port language-free guidance. On three generation benchmarks, LG-Tokoutperforms state-of-the-art methods (e.g., achieving an FID score of0.057 vs. MARDM’s 0.114 on HumanML3D). LG-Tok-mini uses onlyhalf the tokens while maintaining competitive performance, validatingthe efficiency of our semantic representations. Code and checkpoints areavailable at https://eanson023.github.io/LG-Tok/.

Abstract:
Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings. To address this, we systematically explore perceptual optimization strategies for 3DGS by searching over a diverse set of distortion losses. We conduct the first-of-its-kind large-scale human subjective study on 3DGS, involving 39,320 pairwise ratings across several datasets and 3DGS frameworks. A regularized version of Wasserstein Distortion, which we call WD-R, emerges as the clear winner, excelling at recovering fine textures without incurring a higher splat count. WD-R is preferred by raters more than 2.3× over the original 3DGS loss, and 1.5× over the current best method Perceptual-GS. WD-R also consistently achieves state-of-the-art LPIPS, DISTS, and FID scores across various datasets, and generalizes across recent frameworks, such as Mip-Splatting and Scaffold-GS, where replacing the original loss with WD-R consistently enhances perceptual quality within a similar resource budget (number of splats for Mip-Splatting, model size for Scaffold-GS), and leads to reconstructions being preferred by human raters 1.8× and 3.6×, respectively. We also find that this carries over to the task of 3DGS scene compression, with ≈ 50% bitrate savings for comparable perceptual metric performance.

Abstract:
Recent geometric foundation models enable feed-forward in-ference for SLAM, but their predictions are strongly dependent on the in-put view set, which leads to geometric inconsistencies and trajectory driftwhen results are chained over long sequences. Online deployment furtherexposes a trade-off between the low latency of two-view tracking and theconstraint richness of multi-view inference. We introduce UniSim-SLAM,an integrated system that runs lightweight two-view keyframe trackingin the frontend and performs periodic multi-view submap refinementin the backend. To combine predictions defined in heterogeneous localcoordinates with inconsistent scales, we formulate a unified multi-levelfactor graph on Sim(3) that jointly optimizes global keyframe poses andsubmap poses. The graph integrates temporal view-to-view odometryedges, view-to-submap bridge edges with depth-statistics scale anchor-ing, and submap-to-submap tie and scale constraints to enforce consis-tent similarity relations across submaps. Experiments on TUM RGB-Dand 7-Scenes show that UniSim-SLAM achieves state-of-the-art accuracyin the uncalibrated setting, reducing trajectory error by 38.5% on TUMRGB-D and 45.9% on 7-Scenes compared to prior best results. Projectpage: https://vision3d-lab.github.io/unisim-slam/.

Abstract:
Scene reconstruction with 3D Gaussian Splatting (3DGS) has become common, however deployment remains painful as the uncompressed file sizes can be massive. Current 3DGS compression systems combine multiple strategies for file size reduction, which can obscure where gains come from and limit component reuse across training pipelines. To make the gains more transparent, we propose KISS-GS, a modular compression pipeline named after the principle of keeping things simple, designed to decouple compression entirely from training. Given a 3DGS scene reconstructed with vanilla 3DGS, we are able to reduce it through compaction by 15.7x using a combination of state-ofthe-art pruning schemes. Then we encode it into an image-based format designed for simple, ubiquitous decoding. With the SOG-XT format, we propose a novel extension to Self-Organizing Gaussians with two main contributions: (i) Self-organizing 2D Codebooks and (ii) Parallel Representative Assignment Smoothing (PRAS), which leverages the symmetry of quaternion and scale parameterizations to produce 2D attribute grids more amenable to encoding. This encoding reduces scene size by 6.6x. We show that optional encoding-aware fine-tuning yields a further 2.2x. Across standard 3DGS benchmarks, our simple and modular approach thus achieves a total of 85× to 319× reductions in the size of the scene over uncompressed vanilla 3DGS, setting new benchmarks for real-world scenes and surpassing tightly integrated methods in rate-distortion. Decoding relies solely on web-native image formats, and the modular design makes each stage easy to combine with future advances in reconstruction and compaction. Code and project page: https://fraunhoferhhi.github.io/KISS-GS/

Abstract:
While 3D Gaussian Splatting (3DGS) provides an efficientand explicit representation for novel view synthesis, enforcing stylisticcoherence across viewpoints remains challenging. Existing 3D stylizationmethods typically apply 2D feature-matching losses independently perrendered view, which leads to unstable style allocation, many-to-one fea-ture reuse, and limited cross-view consistency. We propose a capacity-controlled framework for multi-view stylization of 3DGS, grounded inoptimal transport. Specifically, we reformulate local style matching as asemi-balanced optimal transport problem. By introducing explicit column-capacity constraints with tunable strength, our formulation mitigatesmany-to-one matching and enables controllable allocation of style fea-tures. This transport-based objective provides a principled mechanismfor balancing feature coverage and stylistic diversity while maintainingstable correspondences across viewpoints. To further enhance cross-viewcoherence, we incorporate a novel cross-view matching guidance to con-strain correspondences between scene content and style patterns. In ad-dition, we introduce several geometric regularizations to enhance thevanilla 3DGS, thereby enabling optimized Gaussian primitives to rep-resent finer-grained textures during stylization. Extensive experimentsdemonstrate that our approach significantly improves multi-view stylis-tic consistency and produces stable, expressive 3D stylizations while pre-serving the core semantic structure of the scene.

Abstract:
Recent breakthroughs in 3D generation have enabled thesynthesis of high-fidelity individual assets. However, generating 3D com-positional objects from single images–particularly under occlusions–remainschallenging. Existing methods often degrade geometric details in hiddenregions and fail to preserve the underlying object-object spatial rela-tionships (OOR). We present a novel framework Interact3D designedto generate physically plausible interacting 3D compositional objects.Our approach first leverages advanced generative priors to curate high-quality individual assets with a unified 3D guidance scene. To physicallycompose these assets, we then introduce a robust two-stage compositionpipeline. Based on the 3D guidance scene, the primary object is anchoredthrough precise global-to-local geometric alignment (registration), whilesubsequent geometries are integrated using a differentiable Signed Dis-tance Field (SDF)-based optimization that explicitly penalizes geometryintersections. To reduce challenging collisions, we further deploy a closed-loop, agentic refinement strategy. A Vision-Language Model (VLM) au-tonomously analyzes multi-view renderings of the composed scene, for-mulates targeted corrective prompts, and guides an image editing mod-ule to iteratively self-correct the generation pipeline. Extensive exper-iments demonstrate that Interact3D successfully produces promisingcollision-aware compositions with improved geometric fidelity and con-sistent spatial relationships. The code and dataset will be available athttps://github.com/SII-Hui/Interact3D.

Abstract:
In this paper, we present a novel geometry-aware style trans-fer framework for 3D Gaussian splatting (3DGS) that simultaneouslytransfers appearance attributes and geometric structures. Unlike priorworks that primarily focus on color-based stylization and often over-look structural adaptation, our method explicitly incorporates geometryadaptation through a decoupled optimization scheme that alternatelyupdates color and geometry parameters. This strategy alleviates poten-tial interference between color and geometry updates, leading to stableand consistent scene-level geometry transformation. The decoupled opti-mization is enabled by the proposed geometry-aware contrastive featurematching (GCFM). GCFM integrates RGB, depth, and edge cues intoa contrastive objective and is employed in both optimization phases toeffectively transfer structural characteristics from style images to Gaus-sian primitives. Extensive experiments show that our approach achievessuperior performance in both qualitative fidelity and quantitative met-rics, significantly outperforming existing 3DGS-based stylization meth-ods. Our code is available at https://github.com/oweixx/gast.

Abstract:
We introduce a framework toward scalable, finer-grained object-level 3DGS compression. First, we propose a post-training method namedRecastGS to reorganize pretrained 3DGS into a layered representationand progressively distill cumulative submodels to improve rate–distortionefficiency. Leveraging multi-view SAM predictions from user click prompts,Gaussians are further partitioned into user-defined regions of interest(ROI), enabling region-adaptive quality control without retraining. Sec-ond, built upon this reorganized region-aware layered hierarchy, a feed-forward 3DGS compression method named LayeredCGS is proposed tocompress position using a lightweight point cloud codec and attributeswith a layer-wise context model to exploit cross-layer correlations. Ex-tensive experiments show that LayeredCGS achieves 35% BD-Rate gainover the existing feed-forward method FCGS. With progressive distil-lation in RecastGS enabled, our method further outperforms most per-scene optimization methods. Moreover, the proposed method supportsROI-aware compression and flexible bitstream truncation, achieving upto 2 dB higher ROI PSNR at comparable bitrates compared with theuniform quality allocation baseline while enabling low-latency previewand progressive quality refinement. The code will be released at https://github.com/RuixiangXue/ScalableGSC.

Abstract:
We consider the problem of regenerating 3D objects from2D images and initial 3D shapes. Most 3D generators operate in a one-shot fashion, converting text or images to a 3D object with limited con-trollability. We introduce instead MeshReGen, a 3D regenerator that isconditioned on an initial 3D shape. This conceptually simple formula-tion allows us to support numerous useful tasks, including 3D enhance-ment, reconstruction, and editing. MeshReGen uses a new condition-ing mechanism based on VecSet, which allows the regenerator to up-date or improve the input geometry with consistent fine-grained details.MeshReGen learns a widely applicable regeneration prior from o!-the-shelf 3D datasets via self-supervised pretext tasks and augmentations,without additional annotations. We evaluate both the geometric consis-tency and fine-grained quality of MeshReGen, achieving state-of-the-artperformance in controllable 3D generation across several tasks.Query pointsConditional ImagesCond.(a) Architecture Latents !! PE❌…3D Coarse Cond.DINO V2 OutputDiT blocks⟳ ×#Zero-InitVAE Proj. VAEEncoder C DecoderCross attnSelf attnMLP… … …RefinedLatents…Random(b) Applications LatentsCoarse Mesh Enhancement Partial Point Cloud Reconstruction Masked Mesh EditingFig. 2: Overview. MeshReGen takes both 2D image and initial 3D geometry as input,enabling explicit control over global geometry (e.g., pose, coarse shape) while improvingfine-grained details. The 3D condition is encoded as VecSet latents (z L ) that compactlyrepresent the global geometry. After taking the sum with the positional embeddings,these conditioning and random latents are di!used by a DiT into enhanced latents andthen decoded into a complete, high-quality 3D shape.

Abstract:
3D Gaussian Splat (3DGS) enables high-fidelity, real-timenovel view synthesis by representing scenes with large sets of anisotropicprimitives, but often requires millions of Splats, incurring significant stor-age and transmission costs. Most existing compression methods rely onGPU-intensive post-training optimization with calibrated images, limit-ing practical deployment. We introduce NanoGS, a training-free andlightweight framework for Gaussian Splat simplification. Instead of re-lying on image-based rendering supervision, NanoGS formulates sim-plification as local pairwise merging over a sparse spatial graph. Themethod approximates a pair of Gaussians with a single primitive usingmass preserved moment matching and evaluates merge quality througha principled merge cost between the original mixture and its approx-imation. By restricting merge candidates to local neighborhoods andselecting compatible pairs efficiently, NanoGS produces compact Gaus-sian representations while preserving scene structure and appearance.NanoGS operates directly on existing Gaussian Splat models, runs ef-ficiently on CPU, and preserves the standard 3DGS parameterization,enabling seamless integration with existing rendering pipelines. Experi-ments demonstrate that NanoGS substantially reduces primitive countwhile maintaining high rendering fidelity, providing an efficient and prac-tical solution for Gaussian Splat simplification. Our project website isavailable at https://saliteta.github.io/NanoGS/ .

Abstract:
Bird’s-eye view (BEV) perception fuses multi-camera imagesinto a unified top-down representation for autonomous driving. Despiterecent progress, state-of-the-art methods remain confined to closed-setscenarios, making them vulnerable to unpredictable real-world environ-ments. In this work, we introduce open-vocabulary BEV segmentation(OVBS), which leverages vision-language models (VLMs) to recognizecategories beyond the training set while maintaining precise BEV percep-tion and real-time efficiency. A key challenge in OVBS lies in the 3D geo-metric inconsistency inherent in the ill-posed lifting of 2D VLM semanticsinto BEV. To address this, we propose OVBEVSeg, a geometry-awareOVBS framework that enhances efficient Gaussian splatting (GS)-basedunprojection by leveraging robust 3D geometric constraints across threeprogressive stages: (1) 2D-to-BEV pseudo-labeling via reliable 3D pro-jection for OV generalization; (2) joint 2D–BEV per-scene optimizationwith BEV structural constraints for 3D geometric consistency; and (3)3D geometric distillation for online efficiency. On the nuScenes dataset,OVBEVSeg achieves state-of-the-art performance, outperforming closed-set methods by 15.3 mIoU on unseen categories. Remarkably, even withno novel-class ground-truth labels, it remains competitive with self- andsemi-supervised baselines trained with up to 40% of ground-truth anno-tations. Furthermore, it achieves 2.5× faster inference with only 0.22×the memory consumption of projection-based methods.

Abstract:
Cross-view geo-registration is the task of aligning a queryimage to a geodetically accurate reference (e.g., satellite image), so thateach query pixel maps to a real-world GPS coordinate. Most existingwork addresses the related problem of cross-view geo-localization, wherethe goal is typically to estimate the camera center using retrieval, classi-fication, matching, or regression. Because these approaches do not pro-vide dense supervision, they are poorly suited for learning and eval-uating pixel-wise alignment. We introduce SkyReg, a geometry-awaregeo-registration model that estimates the transformation between thequery and reference images by explicitly modeling the 3D scene ge-ometry. Applying this transformation to warp the query into the ref-erence frame yields pixel-wise geo-localization, without relying on 2Dpoint matches, and remains robust to occlusions and large viewpointchanges. To enable training and standardized evaluation, we release (i)SkyReg-Train, a large-scale dataset of drone–satellite images annotatedwith per-pixel GPS coordinates, depth maps, and camera parameters de-rived from LiDAR and structure-from-motion, and (ii) SkyReg-Bench, aheld-out benchmark of unseen Urban and Suburban scenes with the samedense annotations. SkyReg achieves state-of-the-art performance againststrong retrieval and homography baselines, demonstrating the value ofgeometry-aware models and dense geodetic benchmarks for cross-viewgeo-registration. Dataset available at https://parthpk.github.io/skyreg-webpage.

Abstract:
Streaming feed-forward 3D reconstruction enables real-timejoint estimation of scene geometry and camera poses from RGB images.However, without explicit dynamic reasoning, streaming models can beaffected by moving objects, causing artifacts and drift. In this work, wepropose RayMap3R, a training-free streaming framework for dynamicscene reconstruction. We observe that RayMap-based predictions exhibita static-scene bias, providing an internal cue for dynamic identification.Based on this observation, we construct a dual-branch inference schemethat identifies dynamic regions by contrasting RayMap and image pre-dictions, suppressing their interference during memory updates. We fur-ther introduce reset metric alignment and state-aware smoothing to pre-serve metric consistency and stabilize predicted trajectories. Our methodachieves state-of-the-art performance among streaming approaches ondynamic scene reconstruction across multiple benchmarks. The projectpage and code are available at https://raymap3r.github.io/.

Abstract:
3D reassembly is a fundamental geometric problem, and inrecent years it has increasingly been challenged by deep learning meth-ods rather than classical optimization. While learning approaches haveshown promising results, most still rely primarily on geometric features toassemble a whole from its parts. This reliance limits their effectiveness incases where geometric information is incomplete or degraded, such as forsmall or eroded fragments. Additionally, solutions do not impose physi-cal constraints that explicitly prevent overlapping assemblies. To addressthese limitations, we introduce E-M3RF, an equivariant multimodal 3Dreassembly framework that takes as input the point clouds, containingboth point positions and color of fractured fragments, and predicts thetransformations required to reassemble them, using SE(3) flow match-ing. Each fragment is represented by both geometric and color features:i) 3D point positions are encoded as rotation-consistent geometric fea-tures using a rotation-equivariant encoder, while ii) the colors at each3D point are encoded with a transformer. The two feature sets are thencombined to form a multimodal representation. We experimented on fivedatasets: two synthetic datasets, Breaking Bad and Fantastic Breaks,and three real-world cultural heritage datasets, RePAIR, Presious, andFRACTURA, demonstrating that E-M3RF on the RePAIR dataset (themost challenging one) reduces rotation error by 23.1% and translationerror by 13.2%, while Chamfer Distance decreases by 18.4% comparedto competing methods. Project page: https://em3rf.github.io/.

Abstract:
Recent advances in 3D feedforward reconstruction neural net-works have achieved remarkable success in dense reconstruction from imageswithout any camera parameters. Yet, equipping these models with robustsemantic understanding remains an open problem. Here we introduce anapproach that performs 3D reconstruction and 3D panoptic segmentationin a unified framework. We build on existing 3D reconstruction models andaugment them with a set-based mask decoder. The approach is jointly trainedwith a geometric and semantic loss, which are shown to be mutually beneficial.More precisely, the features are initialized from the geometric informationand then finetuned to capture jointly geometry and semantics. We demon-strate the generality of our approach by successfully applying our frameworkboth to online and all-to-all attention reconstruction backbones. Our methodachieves state-of-the-art performance in 3D panoptic segmentation acrossScanNet, ScanNet200, and ScanNet++ datasets. Ablation studies show thatsuch joint training of a unified model equips 3D feedforward reconstructionneural networks with panoptic segmentation and yields mutually beneficialimprovements.

Abstract:
Pretraining 3D encoders through alignment with ContrastiveLanguage–Image Pre-training (CLIP) has emerged as a promising direc-tion to learn generalizable representations for 3D scene understanding. Inthis paper, we propose UniScene3D, a transformer-based framework thatlearns unified 3D scene representations from multi-view RGB–Pointmapby leveraging the priors of a pretrained 2D foundation model. For robustRGB-Pointmap representation learning, we introduce novel cross-viewgeometric alignment and grounded view alignment to enforce geome-try and semantic consistency across views. Extensive low-shot and task-specific fine-tuning across viewpoint grounding, scene retrieval, sceneclassification, and 3D visual question answering achieves state-of-the-artperformance. These results establish UniScene3D as an effective frame-work for unified 3D scene understanding.

Abstract:
Defocus blur degrades fine image structures and limits vi-sual perception, which can adversely affect downstream vision tasks. Al-though recent deep learning deblurring methods have achieved strongperformance, their effectiveness depends on training data and often de-grades across cameras and lenses due to limited optical diversity and re-alism in existing datasets. In this paper, we propose a pipeline for synthe-sizing realistic defocus deblurring datasets for diverse compound lenses.It integrates efficient wave-optics PSF computation via Debye CZT prop-agation, depth-aware defocus rendering with occlusion handling, and blursynthesis in the radiometrically linear space with camera ISP simulation.This unified pipeline enables the scalable generation of photorealisticdefocus datasets with diverse lens characteristics. Using our pipeline,we generate CLDefocus, a large-scale synthetic dataset containing lens-diverse defocus image pairs. We further analyze the limitations of real-captured defocus datasets and show that such imperfections can biasfull-reference evaluation. Extensive experiments demonstrate that mod-els trained on CLDefocus achieve improved cross-device generalizationcompared to models trained on existing real and synthetic datasets. Codeand dataset are available at: https://github.com/lykelee/CLDefocus.

Abstract:
Remote photoplethysmography (rPPG) enables non-contactphysiological monitoring by capturing subtle skin color variations inducedby cardiac cycles. Despite its promise, rPPG remains highly sensitive toenvironmental illumination. Existing illumination-aware methods sufferfrom two key limitations: (1) limited out-of-distribution (OOD) gen-eralization due to domain discrepancies between training and testingdata, and (2) reliance on the Lambertian assumption, which neglectsnon-Lambertian specular highlights that frequently corrupt facial skinsignals. In this paper, we propose SHINE-PPG (Specular-Highlight In-trinsic Network for rPPG Estimation), a novel framework that leveragesnon-Lambertian intrinsic decomposition to decouple facial videos intoillumination, reflectance, and specular components in a self-supervisedmanner. By isolating physiological information within the intrinsic re-flectance, our method effectively suppresses both ambient lighting vari-ations and surface highlights to recover high-fidelity rPPG signals. Tofurther enhance robustness, we introduce an adversarial illuminationenhancement strategy that dynamically synthesizes challenging unseenlighting conditions during training, significantly improving OOD general-ization. Extensive experiments on five benchmark datasets demonstratethat SHINE-PPG consistently outperforms previous methods, particularlyunder complex and dynamic illumination scenarios. The code is availableat https://github.com/Edmond-Yang/SHINE-PPG.

Abstract:
Image restoration aims to reconstruct high-quality images from degraded low-quality inputs. As the computational demands of image restoration models continue to rise, there is growing interest in lightweight architectures optimized for fast and efficient inference. Logic gate networks (LGNs), which operate using fundamental logic operations such as NAND and XOR, have recently emerged as a promising direction for achieving highly efficient computation. However, their potential remains largely untapped in the domain of image restoration. In this work, we introduce LogicIR, the first LGN specifically designed for image restoration tasks. LogicIR incorporates a UNet-inspired architecture composed entirely of logic gates. In addition, we propose a differentiable bit decoding layer and an index shuffling mechanism that improves information propagation across logic gates. Experimental results across multiple image restoration benchmarks demonstrate that LogicIR achieves strong performance with significantly reduced computational cost, establishing LogicIR as a viable and efficient alternative for image restoration. The source code is available at our project page https://github.com/jimmy9704/LogicIR.

Abstract:
Sparse-view Computed Tomography (CT) reconstructs im-ages from a limited number of X-ray projections to reduce radiation andscanning time, which is an ill-posed inverse problem. Existing methodsachieve high-fidelity reconstructions but overfit to a fixed acquisitionsetup, failing to generalize well across sampling rates. For example, con-volutional neural networks (CNNs) use the same kernels across resolu-tions, leading to artifacts when data resolution changes. This is a criticallimitation in clinical practice, where acquisition sampling settings varyacross organs and diagnostic protocols.We propose Computed Tomography neural Operator (CTO), the firstneural operator (NO) framework for CT reconstruction. CTO extendslearning from fixed discretized grids to continuous function space, en-abling a single model to generalize across measurement sampling rateswithout retraining. We also propose new NO architectural designs forCT: (i) a dual-domain NO architecture in both sinogram and imagespaces, capturing complementary spatial–frequency information, and (ii)rotation-equivariant DIScrete–COntinuous convolutions (DISCO) thatexploit the rotational structure inherent in tomographic acquisition. Em-pirically, CTO outperform CNNs (> 3.4dB PSNR gain) and other base-lines in multi-resolution settings across multiple CT datasets. Comparedto state-of-the-art di!usion methods, CTO has 500→ faster inferencewith an average 3dB gain. CTO further demonstrates strong out-of-distribution robustness, maintaining gains under cross-dataset transferand noisy sinogram conditions. Ablation studies also validate each de-sign choice. CTO establishes neural operators as a principled and practi-cal paradigm for flexible, discretization-agnostic CT reconstruction. Ourcode is available at https://github.com/neuraloperator/sparse_ct.

Abstract:
The success of large-scale deep learning models in neuro-science is fundamentally constrained by severe data heterogeneity. NativefMRI data aggregated from diverse sources exhibit substantial variationin both spatial and temporal resolutions. Consequently, most existingframeworks rely on lengthy, rigid preprocessing pipelines that enforceuniformity across datasets. This practice introduces two critical limita-tions: (1) potential degradation of subject-speci x001C c anatomical informa-tion; (2) signi x001C cant computational overhead, often requiring hours of pro-cessing per subject. Here, we propose FlexiBrain, a resolution-agnosticvoxel-level encoding framework for native fMRI based on Mamba-JEPA.FlexiBrain de x001C nes patch sizes in real-world physical units and employs adynamic patch resizing, thereby bypassing destructive spatial standard-ization while enabling direct ingestion of data in native space. We instan-tiate the framework using an e x001E cient Mamba-JEPA backbone to modelhigh-dimensional 4D fMRI signals. Across x001C ve diverse downstream neuro-science tasks, FlexiBrain consistently outperforms recent state-of-the-artmethods, achieving gains of up to 12 percentage points without exter-nal data augmentation. Importantly, FlexiBrain functions as a seamlessplug-in module, substantially reducing preprocessing costs and accelerat-ing the development of robust voxel-level fMRI foundation models. Codeis available at https://github.com/OneMore1/FlexiBrain.

Abstract:
Image demoiréing remains a challenging task due to the com-plex interplay between texture corruption and color distortions causedby moiré patterns. Existing methods, especially those relying on directimage-to-image restoration, often fail to disentangle these intertwinedartifacts effectively. While frequency-aware approaches offer a promisingdirection, their potential is hindered by the discrete transform (e.g., Haarwavelet or block-based DCT), which may suffer from spatial discontinu-ity, channel redundancy, and further cause error accumulation duringtheir fixed inverse processes. In this paper, we present Freqformer, aTransformer-based framework specifically designed for image demoiréingthrough targeted frequency separation. Our method performs an effectivefrequency decomposition that splits moiré patterns into high-frequencyspatially-localized textures and low-frequency scale-robust color distor-tions, which are then handled by a dual-branch architecture and anasymmetric training scheme tailored to their distinct characteristics. Wefurther propose a learnable Frequency Composition Transform (FCT)module to adaptively fuse the frequency-specific outputs, enabling con-sistent and high-fidelity reconstruction. To better aggregate the spatialdependencies and the inter-channel complementary information, we in-troduce a Spatial-Aware Channel Attention (SA-CA) module that refinesmoiré-sensitive regions without incurring high computational cost. Ex-tensive experiments on various demoiréing benchmarks demonstrate thatFreqformer achieves state-of-the-art performance with a compact modelsize. The code will be made publicly available at https://github.com/xyLiu339/Freqformer.

Abstract:
Hybrid event–frame sensors integrate an Event Vision Sensor(EVS) and an Active Pixel Sensor (APS) within a single chip, combin-ing the high dynamic range and low latency of the EVS with the richspatial intensity information from the APS. While this tight integrationoffers compact, temporally precise imaging, the complex circuit architec-ture introduces non-trivial noise patterns that remain poorly understoodand unmodeled. In this work, we present the first unified, statistics-basedimaging noise model that jointly describes the noise behavior of APS andEVS pixels. Our formulation explicitly incorporates photon shot noise,dark current noise, fixed-pattern noise, and quantization noise, and linksEVS noise to illumination level and dark current. Based on this formula-tion, we further develop a calibration pipeline to estimate noise param-eters from real data and offer a detailed analysis of both APS and EVSnoise behaviors. Finally, we propose H-ESIM, a statistically groundedsimulator that generates RAW frames and events under realistic, jointlycalibrated noise statistics. Experiments on two hybrid sensors validateour model across multiple imaging tasks (e.g., video frame interpolationand deblurring), demonstrating strong transfer from simulation to realdata. https://yunfanlu.github.io/HESIM

Abstract:
Decomposing outgoing surface radiance into material and illumination during inverse rendering is essential for applications such as relighting and augmented reality, yet it is severely ill-posed since multiple combinations can result in the same observed colour. Capturing an object under multiple lighting conditions usually helps resolve this ambiguity as it constrains the optimization towards correct solutions. In this work, we explore the potential of reconstructing rigidly moving objects—which provides observations of diverse light-surface interactions—to resolve the material-lighting ambiguity in inverse rendering. For this purpose, we introduce a relightable approach that marries object tracking and reconstruction with inverse rendering for general rigidly moving objects. Our experimental analysis on synthetic data demonstrates that motion can be an advantage for disentangling material and lighting: the reconstructed material is significantly more accurate when the object is observed under rigid motion than when it is static. Moreover, results on RGB videos of real hand-held objects show that our pipeline preserves this advantage even under noisy real-world conditions.

Abstract:
State-of-the-art post-hoc out-of-distribution detection meth-ods rely on intermediate layer activation editing. However, they exhibitinconsistent performance across datasets and models. We show that thisinstability is driven by differences in the activation distributions, andidentify a failure mode of scaling-based methods that arises when penul-timate layer activations are not rectified. Motivated by this analysis,we propose RAS, a hyperparameter-free post-hoc method that replacessorted activation magnitudes with a fixed in-distribution reference profile.Our simple plug-and-play method shows strong and consistent perfor-mance across datasets and architectures without assumptions on thepenultimate layer activation function, and without requiring any hyperpa-rameter tuning, while empirically preserving in-distribution classificationaccuracy. We further analyze what drives the improvement, showing thatboth inhibiting and exciting activation shifts independently contribute tobetter out-of-distribution discrimination1 .

Abstract:
In this work, we study Cooperative Spatial Intelligence, the ability of decentralized embodied agents to coordinate effectively under dynamic environmental constraints across city-scale outdoor domains. We introduce Sentinel Challenge, a benchmark where multiple decentralized embodied agents must communicate in natural language to agree on a mutually safe and convenient meeting point within large, city-scale outdoor environments. Each agent must then navigate safely while avoiding dynamic sentinels patrolling the area, with a tool providing coarse spatial information. To address this, we propose CoSaR (Cooperative Spatial Reasoning and Planning), a framework that bridges the high-level communication and planning abilities of foundation models with the precision of classical spatial navigation algorithms. CoSaR enables agents to exchange situational updates, reason over evolving spatial constraints, and collaboratively replan trajectories. Evaluated across 14 city-level scenes with 3–5 agents, CoSaR consistently leads to faster gathering, shorter path lengths, and improved safety. Our results demonstrate that integrating dynamic communication with spatial reasoning is essential for robust multi-agent cooperation. By formalizing this new setting and providing a scalable benchmark, we aim to build a foundation for advancing cooperative spatial intelligence in embodied multi-agent systems. Code and challenge are available at https://github.com/UMass-Embodied-AGI/Sentinel.

Abstract:
Although highly effective in vision and language domains, applying in-context learning to robotics remains challenging. Existing autoregressive in-context imitation methods discretize continuous actions and exacerbate the accumulation of early prediction errors through next-token prediction, limiting their generalization on unseen task configurations. Meanwhile, flow-matching policies have been explored for continuous robot control and can help mitigate compounding errors; however, in-context imitation learning within a flow-matching framework remains underexplored. To address these limitations, we introduce ContextFlow, a conditional flow-matching model that learns continuous action distributions for in-context imitation learning. ContextFlow conditions flow-based action prediction on demonstrations and observations, enabling robust generation from noisy action distributions. To better encode multimodal in-context demonstrations, we adapt perceiver-style multimodal context compressors that distill visual, proprioceptive, and action sequences into compact, task-relevant latent representations. On LIBERO, ContextFlow outperforms ICRT by 35 percentage points in average success rate on unseen task configurations, while matching the performance of the task-specific fine-tuned VLA model π with- 0 out any fine-tuning on unseen tasks. On real robots, it generalizes to unseen configurations of both single-arm and bimanual tasks, achieving 40% success on a new pen-uncapping configuration. Project Page: https://dingjiansw101.github.io/contextflow-page/.

Abstract:
Recent advancements have explored agentic zero-shot 3D un-derstanding by reformulating it as video keyframe understanding withMultimodal Large Language Models (MLLMs). However, existing meth-ods face an intrinsic bottleneck due to the finite observation perspectivesinherent in videos and the implicit perception of 3D scenes. In this pa-per, we propose a collaborative multi-agent framework that assigns aPlanning Agent to handle high-level viewpoint planning and supplementnovel perspectives, and a Perception Agent to explicitly summarize the3D scene into a structured holistic cognitive map. Specifically, PlanningAgent first analyzes this cognitive map to determine query-relevant view-points and supplements missing critical perspectives to ensure compre-hensive observation. Subsequently, Perception Agent documents object-level attributes from these views by assigning consistent instance identi-fiers across viewpoints, thereby integrating fragmented observations intothe holistic cognitive map. In parallel, it provides feedback to filter outmismatched candidate objects and guide subsequent viewpoint planning.Through this closed-loop iterative process, two agents collaboratively fig-ure out candidates until Perception Agent determines that sufficient in-formation has been captured to complete the task. Extensive experimentsdemonstrate that our method achieves state-of-the-art performance on6 benchmarks, with improvements of 11.1% Acc@0.5 on ScanRefer, 14.6BLEU-1 on 3D-assisted dialog, and 2.1 EM on SQA3D. Project Page:https://zhangbo135.github.io/agentic-collaborative-cognition/

Abstract:
An embodied kitchen assistant must do more than recog-nize food in isolated frames. It must track ingredient states over timeand integrate visual observations with recipe and nutritional knowledgeto support constraint-aware decision-making. We formalize this capa-bility as Embodied Nutrition Management: perceiving nutrition-relevantevents, maintaining a persistent food state, and using it for knowledge-grounded planning. Existing benchmarks evaluate static food under-standing or embodied cooking actions, but do not measure whether anagent can continuously update and use nutrition-relevant states in dy-namic kitchens. To fill this gap, we introduce NutriBench-Kitchen,a benchmark containing 1,500 manually verified question–answer pairsfrom 160 cooking videos. It covers five task families: Ingredient Entry,Memory Management, Recipe Query, Long-Term Planning, and Short-Term Planning, spanning food-state construction, maintenance, knowl-edge retrieval, and decision-making across different planning horizons.Evaluations of proprietary and open-source large vision-language modelsreveal a substantial gap from human performance, particularly in quan-titative ingredient estimation, long-term state tracking, and reasoningunder interacting constraints. We further introduce Nutri-Vgent, a di-agnostic long-video agent with separate episodic, food-state, and recipememories. Its consistent improvements demonstrate the value of explicitstate representations and structured memory for nutrition management.Together, NutriBench-Kitchen and Nutri-Vgent provide a testbed forstudying persistent state tracking and knowledge-grounded reasoning indynamic kitchens. Code is available at https://github.com/V1ol1n/NutriBench-Kitchen.

Abstract:
We present HSImul3R1 , a unified framework for simulation-ready 3D reconstruction of human-scene interactions (HSI) from casualcaptures, including sparse-view images and monocular videos. Existingmethods suffer from a perception-simulation gap: visually plausible re-constructions often violate physical constraints, leading to instability inphysics engines and failure in embodied AI applications. To bridge thisgap, we introduce a physically-grounded bi-directional optimiza-tion pipeline that treats the physics simulator as an active supervisorto jointly refine human dynamics and scene geometry. In the forwarddirection, we employ Scene-targeted Reinforcement Learning to optimizehuman motion under dual supervision of motion fidelity and contactstability. In the reverse direction, we propose Direct Simulation RewardOptimization, which leverages simulation feedback on gravitational sta-bility and interaction success to refine scene geometry. We further presentHSIBench, a new benchmark with diverse objects and interaction sce-narios. Extensive experiments demonstrate that HSImul3R produces thefirst stable, simulation-ready HSI reconstructions and can be directlydeployed to real-world humanoid robots.

Abstract:
Vision-Language-Action (VLA) models excel in static manipulation but struggle in dynamic environments with moving targets. This performance gap primarily stems from a scarcity of dynamic manipulation datasets and the reliance of mainstream VLAs on single-frame observations, restricting their spatiotemporal reasoning capabilities. To address this, we introduce DOMINO, a large-scale dataset and benchmark for generalizable dynamic manipulation, featuring 35 tasks with hierarchical complexities, over 110K expert trajectories, and a multidimensional evaluation suite. Through comprehensive experiments, we systematically evaluate existing VLAs on dynamic tasks, explore effective training strategies for dynamic awareness, and validate the generalizability of dynamic data. Furthermore, we propose PUMA, a dynamicsaware VLA architecture. By integrating scene-centric historical optical flow and specialized world queries to implicitly forecast object-centric future states, PUMA couples history-aware perception with short-horizon prediction. Results demonstrate that PUMA achieves state-of-the-art performance, yielding a 6.3% absolute improvement in success rate over baselines. Moreover, we show that training on dynamic data fosters robust spatiotemporal representations that transfer to static tasks.

Abstract:
Vision-language-action models have advanced robotic ma-nipulation but remain constrained by reliance on the large, teleoperation-collected datasets dominated by the static, tabletop scenes. We propose asimulation-first framework to verify VLA architectures before real-worlddeployment and introduce MobileManiBench, a large-scale benchmarkfor mobile-based robotic manipulation. Built on NVIDIA Isaac Sim andpowered by reinforcement learning, our pipeline autonomously gener-ates diverse manipulation trajectories with rich annotations (languageinstructions, multi-view RGB–depth–segmentation images, synchronizedobject/robot states and actions). MobileManiBench features 2 mobileplatforms (parallel-gripper and dexterous-hand robots), 2 synchronizedcameras (head and right wrist), 630 objects in 20 categories, 5 skills(open, close, pull, push, pick) with over 100 tasks performed in 100 re-alistic scenes, yielding 300K trajectories. This design enables controlled,scalable studies of robot embodiments, sensing modalities, and policy ar-chitectures, accelerating research on data efficiency and generalization.We benchmark representative VLA models and report insights into per-ception, reasoning, and control in complex simulated environments, withall code, datasets, and models publicly released at our project website:https://dexhand.github.io/MobileManiBench/.

Abstract:
While Vision-Language Models (VLMs) have empowered em-bodied agents to execute complex household tasks, they struggle toproactively handle dynamically emerging hazards during closed-loop in-teractions. Existing safety approaches often rely on runtime guardrailsto block unsafe actions or induce excessive caution, which severely stallstask progress instead of actively resolving the underlying risks. To breakthis safety–progress trade-off, we introduce the Self-Evolving Just-In-Time Memory framework, which reframes embodied safety from progress-stalling guardrails to proactive hazard mitigation. The framework con-sists of a Risk-Sufficient Topological Belief Graph (RSG) for persistentsafety-relevant state tracking under partial observability, an Agency-Grounded Factual Memory for precise hazard anticipation, and an Expe-rience Memory that injects procedural Meta-Skills to guide executable,progress-preserving mitigation. Furthermore, we propose an automatedTest-Verify-Write loop, allowing agents to continually refine their mit-igation Meta-Skills from execution traces at test time. Experiments onIS-Bench demonstrate that our framework substantially boosts the Safe-Success rate across multiple VLM backbones (e.g., +30.3% on Qwen3-VL-8B), enabling agents to proactively mitigate hazards without stallingtask progress. Code is available at https://github.com/DyMessi/JIT-Memory.

Abstract:
Ever since the advent of foundation models and the pre-training–finetuning paradigm, there have been numerous efforts to mergemultiple task-specific experts into a single multi-task model. Prior worklargely focuses on finding a single merged model, but it often under-performs individual experts due to parameter interference. To resolvethis, dynamic model merging employs routing to activate task-relevantparameters per input. However, existing routers typically require eitheradditional training with abundant labeled datasets or assume the accessto task IDs of each input at inference time. In this work, we aim toclose the gap to expert performance without additional training or task-ID-access assumption. To this end, we formulate routing as training-freetask classification for each test input. Using singular value decomposition(SVD)-based low-rank manifold approximations for each task, SiM scorestasks by the projection residual of the test input feature onto each taskmanifold and routes accordingly. The task manifolds are pre-computableoffline from a pretrained backbone using a small per-task support set(e.g., 32 examples per task) prior to merging process, requiring no routertraining and no data during the merging process. Moreover, SiM inte-grates seamlessly with subspace-/mask-based merging that representstask-expert via lightweight compressed task vectors, avoiding the needto store full expert parameters. Experiments across computer vision andnatural language processing benchmarks under task-unknown inferencedemonstrate that SiM substantially improves merged-model performanceand consistently narrows the gap to individual task experts. Our code isavailable at https://github.com/BAIKLAB/SiM

Abstract:
Vision-language object detectors (VLODs) such as YOLO-World and Grounding DINO exhibit strong zero-shot generalization, buttheir performance degrades under distribution shift. Test-time adapta-tion (TTA) offers a practical way to adapt models online using onlyunlabeled target data. However, despite substantial progress in TTAfor vision-language classification, TTA for VLODs remains largely unex-plored. The only prior method relies on a mean-teacher framework thatintroduces significant latency and memory overhead. To this end, weintroduce VLOD-TTA, a TTA method that leverages dense proposaloverlap and image-conditioned prompts to adapt VLODs with low addi-tional overhead. VLOD-TTA combines (i) an IoU-weighted entropy ob-jective that emphasizes spatially coherent proposal clusters and mitigatesconfirmation bias from isolated boxes, and (ii) image-conditioned promptselection that ranks prompts by image-level compatibility and aggregatesthe most informative prompt scores for detection. Our experiments acrossdiverse distribution shifts, including artistic domains, adverse drivingconditions, low-light imagery, and common corruptions, indicate thatVLOD-TTA consistently outperforms standard TTA baselines and theprior state-of-the-art method using YOLO-World and Grounding DINO.Code: https://github.com/imatif17/VLOD-TTA

Abstract:
Rolling shutter (RS) cameras equip virtually all consumerdevices, yet RS-aware relative pose estimation has remained impracti-cal: the state-of-the-art solver requires a minimum of 20 point corre-spondences, making RANSAC-based robust estimation prohibitively ex-pensive due to the exponential dependence of the iteration count onthe sample size. We make RS relative pose estimation practical by in-troducing affine correspondences (ACs) into the RS two-view geome-try. We derive novel RS-corrected affine constraints that account forthe coupling between point perturbations and the row-dependent es-sential matrix, providing two equations per correspondence beyond thestandard epipolar constraint. Building on these constraints, we developa linearized algebraic solver that estimates pose and RS motion fromonly 7 ACs. The solver exploits the physical smallness of RS parametersto linearize the constraints, eliminates the 12 RS unknowns via null-space projection, and solves the remaining degree-20 system via actionmatrices in 1.2 ms. On the TUM RS benchmark, our method achievesthe best pose and RS parameter accuracy among all tested methodsand, uniquely among RS solvers, provides accurate translational veloc-ity estimates – which are poorly conditioned from point correspondencesalone due to a v-t coupling. On the global-shutter EuRoC MAV dataset,the solver achieves comparable accuracy to the standard 5-point algo-rithm, demonstrating that it generalizes well to the GS setting. Code isat https://github.com/danini/rolling shutter made_practical.

Abstract:
Metric scale monocular geometry estimation has seen signif-icant progress through large-scale data aggregation, yet current founda-tion models suffer from a persistent “scale-collapse” phenomenon: dis-tant landmarks and vast landscapes are metrically underestimated. Thisperformance gap stems from a training data bottleneck, where exist-ing metric-scale datasets are hardware-constrained to unvaried street-level LiDAR or short-range indoor scans, or consist of synthetic datathat lacks the semantic complexity of the physical world. To bridge thisgap, we curate a new metrically-grounded, in-the-wild dataset that wecall MetricScenes, gathered from a variety of sources including Inter-net photo collections and stereo imagery. We estimate camera poses andinitial depth maps for each scene using off-the-shelf methods, and re-cover absolute scale from geo-tagged metadata as well as known stereocamera baselines. We also improve the quality of depth maps derivedfrom MetricScenes via a new two-stage Poisson completion method. Fine-tuning MoGe-2 on our dataset significantly mitigates scale-collapse andachieves superior metric accuracy in unconstrained, open-domain sceneswhile maintaining state-of-the-art performance on standard benchmarks.Project page: https://metricscenes.github.io/.

Abstract:
Accurately recovering the camera’s calibration and metricposes for any unconstrained video would unlock large-scale training datafor navigation and scene understanding. The dominant approaches tothis problem are severely limited: Simultaneous Localization and Map-ping (SLAM) is sensitive to initialization and transient failures due to itscausal, incremental nature; it is often over-optimized for real-time oper-ation and generally requires known camera calibration; while Structure-from-Motion (SfM) typically forgoes any image ordering, enabling opti-mal initialization and global optimization, but lacks robustness to visualsymmetries and extreme motions. To bridge this gap, we introduce asystem that combines the strong sequential constraints of SLAM withthe flexibility and global optimization of offline SfM, enabling the met-ric reconstruction of arbitrary, long, uncalibrated videos. This systemleverages recent advances in wide-baseline dense image matching, treatstemporal ordering as a first-class citizen for reliable loop closure, andaugments global optimization with metric monocular depth priors. Asa result, thorough evaluations on diverse, challenging datasets that ex-hibit extreme motion and visual symmetries reveal that our approach issignificantly more robust and accurate than both state-of-the-art SLAMand SfM, classical or learned, with given or unknown camera calibration.

Abstract:
Human trajectory forecasting is crucial for safe navigationin crowded environments, requiring models that balance accuracy withcomputational efficiency. Efficiently modeling social interactions is key toperformance in dense crowds. Yet, most recent methods rely on attentionmechanisms, which are effective at capturing complex dependencies, butincur quadratic computational costs that scale poorly with the grow-ing number of neighbors. Recently, Selective State-Space Models haveprovided a linear-time alternative; however, their inherently sequentialdesign is misaligned with the unstructured and dynamic nature of socialinteractions. To address this challenge, we propose Social-Mamba, a fore-casting architecture that reformulates social interactions as structuredsequential processes. At its core is the Cycle Mamba block, a novel mod-ule that enables continuous bidirectional information flow. Social-Mambaorganizes agents on an egocentric grid and introduces social triplet fac-torization, which decomposes interactions into temporal, egocentric, andgoal-centric scans. These are dynamically integrated through a learnablesocial gate and global scan to generate accurate and efficient trajectorypredictions. Extensive experiments on five trajectory forecasting bench-marks show that Social-Mamba achieves state-of-the-art accuracy whileoffering superior parameter efficiency and computational scalability. Fur-thermore, embedding Social-Mamba into a flow-matching framework fur-ther enhances both accuracy and efficiency, establishing it as a flexibleand robust foundation for future trajectory forecasting research. Thecode is publicly available: https://github.com/vita-epfl/Social-Mamba.

Abstract:
We present RegHead, a framework for constructing semanticblendshape sets for animatable non-humanoid head avatars. With a fixedexpression vocabulary, semantic blendshapes provide a low-dimensionaland interpretable animation interface and support cross-identity retar-geting. Building such blendshape sets remains expensive because (i)expression-consistent supervision is scarce, (ii) generated 4D assets typ-ically lack correspondence, and (iii) facial motion is highly localized.We propose (1) a large-scale dataset of non-humanoid identities pairedwith a shared expression vocabulary, obtained by expanding a smallartist-rigged library via fine-tuned image editing; (2) a dense stochasticanchor motion representation tailored to localized facial deformations;and (3) a fast feed-forward registration model that converts unregis-tered expression meshes into a corresponded blendshape basis by pre-dicting anchor-based deformations from the neutral shape. Experimentsshow that our approach produces higher-fidelity expression meshes thanbaselines, while running orders of magnitude faster than optimization.We further demonstrate real-time retargeting from human face trackingsignals to non-humanoid characters, capturing both head pose and lo-calized facial motions. Our project page is available at https://snap-research.github.io/RegHead/.

Abstract:
Existing optical flow estimation methods broadly follow twoparadigms: iterative optimization and diffusion-based estimation. Iter-ative methods, exemplified by RAFT, achieve accurate flow estimationthrough recurrent refinement, but can still be challenged by large displace-ments and complex motion patterns. Diffusion-based methods introducegenerative modeling into optical flow and have shown promising resultsin such ambiguous regions. However, existing diffusion-based flow modelsusually denoise the entire dense flow field from Gaussian noise, includingsimple regions where reliable motion structure can already be estimatedby a lightweight network. This increases the denoising burden and maylead to slow convergence and unstable training. To address these issues,we introduce FlowPainter, a diffusion-based optical flow framework thatreformulates dense-flow generation as confidence-guided soft inpainting.FlowPainter first employs a lightweight confidence-aware network to pre-dict a rough flow and a pixel-wise confidence mask, which serves as areliability gate for distinguishing reliable simple regions from uncertainhard regions. The resulting simple-flow prior is used for confidence-basedinitialization and is further injected into the iterative denoising processthrough confidence-gated residual guidance. With a dynamically decayingguidance strength, FlowPainter stabilizes early denoising while preservingthe flexibility of the diffusion model for late-stage detail refinement. Ex-tensive experiments on public benchmarks, including Sintel, KITTI, andSpring, demonstrate that FlowPainter achieves strong accuracy undercomparable training settings and improves convergence efficiency overexisting diffusion-based optical flow methods, with notable gains on chal-lenging benchmark splits. Our approach provides a practical direction forintegrating reliable discriminative priors with diffusion-based refinementfor optical flow estimation.

Abstract:
Continuous visual autoregressive (AR) models have demon-strated promising performance in image generation, but their inherentlysequential nature results in slow inference speed. Speculative decoding, asuccessful acceleration technique for large language models (LLMs), haseffectively accelerated discrete visual AR models. However, the absenceof an analogous theory for continuous distributions precludes its use inaccelerating continuous AR models. To fill this gap, this work presentscontinuous speculative decoding, and addresses challenges from: 1) lowacceptance rate, caused by inconsistent output distribution modeled bytarget and draft models, and 2) modified distribution without analyticexpression, caused by a complex integral. For challenge 1), we addresslow acceptance rates through an approximated criterion, a novel denois-ing trajectory alignment strategy based on reparameterization proximity,and token pre-filling. For challenge 2), we introduce acceptance-rejectionContinuous speculative decodingDiscrete speculative decoding Proposal distribution VerificationProposal distribution VerificationDraft model Target model Draft model Target modelY Inconsistency existsYModified distribution NModified distributionResample Modify & Modify & NNormalize NormalizeNo analytic expression Hard to integrateFig. 2: Comparison between discrete and continuous speculative decoding. Discretesituation offers the convenience of directly computing probabilities and simply samplingfrom modified distributions. In contrast, continuous situation faces challenges in theinconsistency of output distributions, leading to low acceptance criterion as well as lowacceptance rate, and the modified distributions without analytic expression, caused bycomplex integral.sampling algorithm with an appropriate upper bound, thereby avoid-ing explicitly calculating the integral. Furthermore, our denoising trajec-tory alignment is also reused in acceptance-rejection sampling, effectivelyavoiding repetitive diffusion model inference. Extensive experiments onvarious models at 256×256 and 512×512 resolutions demonstrate thatour approach achieves over 2× wall-time speedup while preserving theimage generation quality.

Abstract:
Recent advances in multimodal generative models have drivensubstantial improvements in image editing. However, current generativemodels still struggle with handling diverse and complex image editingtasks that require implicit reasoning, underscoring the need for a compre-hensive benchmark to systematically assess their performance across var-ious reasoning scenarios. Existing benchmarks primarily focus on single-object attribute transformation in realistic scenarios, which, while effec-tive, encounter two key challenges: (1) they largely overlook multi-objectinteractions as well as game-world scenarios that involve human-definedrules, which are common in real-life applications; (2) they only rely ontextual references to evaluate the generated images, potentially leadingto systematic misjudgments, especially in complex reasoning scenarios.To this end, this work proposes UniREditBench, a unified benchmarkfor reasoning-based image editing evaluation. It comprises 2,700 metic-ulously curated samples, covering both real- and game-world scenariosacross 8 primary dimensions and 18 sub-dimensions. To improve evalua-tion reliability, we introduce multimodal dual-reference evaluation, pro-viding both textual and ground-truth image references for each sampleassessment. Furthermore, we design an automated multi-scenario datasynthesis pipeline and construct UniREdit-Data-100K, a large-scalesynthetic dataset with high-quality chain-of-thought (CoT) reasoning an-notations. We fine-tune Bagel on this dataset and develop UniREdit-Bagel, demonstrating substantial improvements in both in-domain andout-of-distribution settings. Through thorough benchmarking of bothopen-source and closed-source image editing models, we reveal theirstrengths and weaknesses across various aspects.

Abstract:
Recently, remarkable progress has been made in Uni x001C ed Mul-timodal Models (UMMs), which integrate vision-language generation andunderstanding capabilities within a single framework. However, a model'sstrong visual understanding often fails to transfer to visual generation:it may correctly judge prompt-image alignment while failing to gener-ate a faithful image from the same prompt. This raises a compellingquestion: Can a model improve itself by using its understanding moduleto reward its generation module? We introduce SRUM, a self-rewardingpost-training framework directly applicable to existing UMMs of vari-ous designs. SRUM creates a feedback loop where the model's own un-derstanding module acts as an internal x0010 evaluator x0011 , providing correctivesignals to improve generation without additional human-labeled data orexternal reward models. To provide comprehensive feedback, SRUM usesa global-local dual reward system: a global reward ensures overall vi-sual semantics and layout, while a local reward re x001C nes x001C ne-grained,object-level x001C delity. SRUM shows strong generalization, boosting perfor-mance on T2I-CompBench from 82.18 to 88.37 and on T2I-ReasonBenchfrom 43.82 to 46.75. Overall, our work establishes a powerful paradigmfor enabling a UMM's understanding module to guide and enhance itsown generation via self-rewarding.

Abstract:
Large vision-language models (LVLMs) have recently achievedremarkable progress across multimodal tasks, yet object hallucinationremains a persistent challenge where models generate descriptions in-consistent with the visual input. Recent work mitigates hallucinationsthrough training-free representation editing, typically by constructinghallucination-related directions from teacher-forcing (TF) contrasts be-tween hallucinated and truthful responses. However, LVLMs operatethrough autoregressive (AR) decoding during generation, raising thequestion of whether TF-based analysis fully reflects the generation dy-namics that lead to hallucinated outputs. In this paper, we analyzethe relationship between TF-based editing and AR generation behaviorand find that TF-based editing alone may be insu!cient to captureboth decoding dynamics and multimodal interactions associated withhallucinations. To address this limitation, we propose DARE (Dual-path Auto-Regressive-aware Editing), a hybrid hallucination editingframework that integrates two complementary contrast pathways: textualcontrasts and image contrasts, together with autoregressive-aware rep-resentation signals. Specifically, DARE constructs hallucination editingdirections from (1) TF-based textual contrasts, (2) AR-aware represen-tation transitions during decoding, and (3) controlled visual di"erencesbetween paired images. Extensive experiments on multiple LVLM hal-lucination benchmarks demonstrate that DARE consistently reducesobject hallucinations while preserving multimodal perception capabil-ity and inference e!ciency. Our implementation code is available athttps://github.com/KU-VGI/DARE.

Abstract:
When evaluating identity-focused tasks such as personalizedgeneration and image editing, existing vision encoders entangle objectidentity with background context, leading to unreliable representationsand metrics. We introduce a principled framework for matched-contextidentity confusion using Near-identity (NearID) distractors, where se-mantically similar but distinct instances are placed on the exact samebackground as a reference image, eliminating contextual shortcuts andisolating identity as the sole discriminative signal. Based on this prin-ciple, we present the NearID dataset (19K identities, 316K matched-context distractors) together with a strict margin-based evaluation pro-tocol. Under this setting, pre-trained encoders perform poorly, achievingSample Success Rates (SSR), a strict margin-based identity discrimina-tion metric, as low as 30.7% and often ranking distractors above truecross-view matches. We address this by learning identity-aware repre-sentations on a frozen backbone using a two-tier contrastive objectiveenforcing the hierarchy: same identity > NearID distractor > randomnegative. This improves SSR to 99.2%, enhances part-level discrimina-tion by 28.0%, and yields stronger alignment with human judgments onDreamBench++, a human-aligned benchmark for personalization.

Abstract:
Class-conditional generative models have emerged as accu-rate and robust classifiers, with diffusion models demonstrating clear ad-vantages over other visual generative paradigms, including autoregressive(AR) models. In this work, we revisit visual AR-based generative clas-sifiers and identify an important limitation of prior approaches: theirreliance on a fixed token order, which imposes a restrictive inductivebias for image understanding. We observe that single-order predictionsrely more on partial discriminative cues, while averaging over multipletoken orders provides a more comprehensive signal. Based on this insight,we leverage recent any-order AR models to estimate order-marginalizedpredictions, unlocking the high classification potential of AR models.Our approach consistently outperforms diffusion-based classifiers acrossdiverse image classification benchmarks, while being up to 25× more ef-ficient. Compared to state-of-the-art self-supervised discriminative mod-els, our method delivers competitive classification performance – a no-table achievement for generative classifiers. The code and models areavailable at: https://github.com/yandex-research/ar-classifier.

Abstract:
Ensuring prompt faithfulness remains a central challenge for text-to-image diffusion models. Classifier-Free Guidance (CFG) improves prompt adherence but exhibits an inherent quality–alignment tension: increasing the guidance scale to strengthen conditioning on the prompt often degrades visual quality and introduces artifacts. To probe the limit of greedy alignment maximization, we first introduce SAP (Semantic Alignment Projection), a greedy update rule that projects each sampling update onto the guidance direction to maximize per-step alignment progress. We then show that SAP can fail due to the loss of orthogonal corrective freedom, discarding high-dimensional components that are crucial for rectifying accumulated trajectory drift. Based on this diagnosis, we propose SCALE (Semantic-CALibrated Guidance Enhancement), a drop-in, training-free guidance mechanism. SCALE selectively amplifies the semantic component along the guidance direction while preserving the orthogonal corrective component, improving semantic alignment without compromising structural fidelity. Across multiple text-to-image benchmarks, SCALE delivers substantial and consistent gains in prompt adherence and compositional alignment with negligible overhead over standard sampling. Project page: https://github.com/SudongCAI/SCALE.

Abstract:
In multi-object text-to-image (T2I) diffusion, ensuring semantic consistency between textual prompts and generated visual content is crucial for image synthesis. However, such consistency constraint is often underemphasized in the denoising process of diffusion models. Although token supervised diffusion models can mitigate this issue by learning object-wise consistency between the image content and object segmentation maps, it tends to suffer from the problems of segmentation map bias and semantic overlap conflict, especially when involving multiple objects. In this paper, we propose ELDiff, a new evidential learningsupervised T2I diffusion model, which leverages the advantages of uncertainty metric and conflict detection to enhance the fault tolerance of unreliable segmentation maps and suppress semantic conflicts, strengthening object-wise consistency learning. Specifically, a pixel evidence loss is proposed to restrain overconfidence in unreliable labels through evidential regularization, and a token conflict loss is designed to weaken the contradiction between semantics through optimizing a measured conflict factor. Extensive experiments show that our ELDiff outperforms existing training based and train-free based T2I diffusion models on SD v1.4, SD v2.1, SDXL, SD v3.5, and Qwen-Image, without requiring additional inference-time manipulations. Notably, ELDiff can be seamlessly extended to the existing training pipeline of T2I diffusion models. Code can be found at https://github.com/QingtaoPan/ELDiff.

Abstract:
Flow matching (FM) models generate data by learning a ve-locity field that transforms samples from a simple latent distribution,typically an isotropic Gaussian, to the data distribution. However, thegeometric mismatch between a unimodal, full-dimensional Gaussian anda multimodal, approximately low-dimensional data distribution leads toa complex velocity field that is costly to learn at training time andto integrate at inference time. In this paper, we propose ParsimoniousFlow Matching (PFM), which adopts a mixture of Gaussians (MoG)as the latent distribution whose geometry better aligns with the data.We identify key design choices that enable efficient FM, including anoptimal-transport data-latent coupling, MoG estimation via k-means,and eigenvalue regularization of the per-mode covariances. Theoretically,when both data and latent distributions are MoGs, we show that gra-dient descent for FM with affine velocity fields converges faster whenthe corresponding modes of the latent and data are more similar andthe per-mode covariances are well-conditioned. Further, for data thatfollows a separated MoG, we prove that replacing the isotropic Gaus-sian latent with a MoG in FM training accelerates gradient descentconvergence and lowers the initial loss. On CIFAR-10 and ImageNet(32 × 32), PFM achieves the same generation quality as the baselinein fewer training iterations, and produces higher-quality samples for thesame number of function evaluations at inference time. Code is available:https://github.com/tianjiaoding/pfm.

Abstract:
This paper addresses the fundamental challenge of accelerating the inherently autoregressive sampling process in Flow Matching (FM) models through a numerical systems perspective. We introduce ParaFlow, a training-free framework that recasts sampling as a system of Triangular Nonlinear Equations (TNEs) to enable step-level parallelism. Unlike existing parallel samplers for diffusion models that require 2–3× more function evaluations (NFEs), we propose a velocity approximation scheme that leverages the temporal smoothness of FM trajectories. This allows ParaFlow to perform parallel sampling on a single GPU while requiring less total NFE count to the standard sequential sampler. We theoretically guarantee that our method converges to the exact autoregressive trajectory and that our approximation maintains negligible error bounds. Extensive experiments on Stable Diffusion 3 and Flux demonstrate that ParaFlow achieves up to 4.3× wall-clock speedup with negligible impact on quality, making high-performance parallel sampling truly practical for resource-constrained settings. The source code is released in the supplementary materials. Code is available at https://github.com/Jianrong-Lu/ParaFlow.

Abstract:
Real-world image restoration (RWIR) is a highly challeng-ing task due to the absence of clean ground-truth images. Many recentmethods resort to pseudo-label (PL) supervision, often within a Mean-Teacher (MT) framework, where a teacher network generates targets fora student network. However, these methods face a critical paradox: un-conditionally trusting the often imperfect, low-quality PLs forces the stu-dent model to learn undesirable artifacts, while discarding them severelylimits data diversity and impairs model generalization. In this paper, wepropose QualiTeacher, a novel framework that transforms pseudo-labelquality from a noisy liability into a conditional supervisory signal. In-stead of filtering, QualiTeacher explicitly conditions the student modelon the quality of the PLs, estimated by an ensemble of complementarynon-reference image quality assessment (NR-IQA) models spanning low-level distortion and semantic-level assessment. This strategy teaches thestudent network to learn a quality-graded restoration manifold, enablingit to understand what constitutes different quality levels. Consequently,it can not only avoid mimicking artifacts from low-quality labels but alsoextrapolate to generate results of higher quality than the teacher itself.To ensure the robustness and accuracy of this quality-driven learning,we further enhance the process with a multi-augmentation scheme to di-versify the PL quality spectrum, a score-based preference optimizationstrategy inspired by Direct Preference Optimization (DPO) to enforcea monotonically ordered quality separation, and a cropped consistencyloss to prevent adversarial over-optimization (reward hacking) of theIQA models. Experiments on standard RWIR benchmarks demonstratethat QualiTeacher can serve as a plug-and-play strategy to improve thequality of the existing pseudo-labeling framework, establishing a newparadigm for learning from imperfect supervision. Code will be releasedat https://github.com/fengyang1399-pixel/QualiTeacher.git .6.87.52 F. Xiao et al.(a) Unconditional Trust (b) Aggressive Filtering (c) QualiTeacher5.2 6.8 7.5 5.2 6.8 7.5Aug AugTeacher Teacher NR-IQA Teacher NR-IQAInput PL Input Optimal PL Input Quality-Conditioned PLsArtifacts are replicated rather than removed Limited data diversity, over-smoothed and blurry Better generalization via quality-conditioned pseudo-labelsDegraded Unconditional Trust Aggressive Filtering QualiTeacherFig. 1: Comparison of pseudo-label (PL) utilization strategies in the mean-teacherframework. (a) Unconditional Trust: PLs are used without filtering, causing degra-dation artifacts to be replicated by the student. (b) Aggressive Filtering: Low-quality PLs are discarded via NR-IQA filtering, yet over-smoothed outputs that receivedeceptively high scores survive, introducing blurriness. (c) QualiTeacher (Ours):Nearly all PLs are retained (with only extreme outliers discarded), with their qualityscores injected as continuous conditioning signals, enabling the student to leverage fulldata diversity while remaining aware of each sample’s reliability.

Abstract:
The success of modern text-to-image generation is largely at-tributed to massive, high-quality datasets. Currently, these datasets arecurated through a filter-first paradigm that aggressively discards low-quality raw data based on the assumption that it is detrimental to modelperformance. Is the discarded bad data truly useless, or does it hold un-tapped potential? In this work, we critically re-examine this question. Wepropose LACON (Labeling-and-Conditioning), a novel training frame-work that exploits the underlying uncurated data distribution. Insteadof filtering, LACON re-purposes quality signals, such as aesthetic scoresand watermark probabilities as explicit, quantitative condition labels.The generative model is then trained to learn the full spectrum of dataquality, from bad to good. By learning the explicit boundary betweenhigh- and low-quality content, LACON achieves superior generation qual-ity compared to baselines trained only on filtered data using the samecompute budget, proving the significant value of uncurated data. Ourproject page is available at https://zhiyangliang.github.io/LACON.

Abstract:
Diffusion models have achieved remarkable success in image generation, yet their deployment remains constrained by the heavy computational cost and the need for numerous inference steps. Previous efforts on fewer-step distillation attempt to skip redundant steps by training compact student models, yet they often suffer from heavy retraining costs and degraded generalization. In this work, we take a different perspective: we accelerate smartly, not evenly, applying smaller speedups to early semantic stages and larger ones to later redundant phases. We instantiate this phase-aware strategy with two experts that specialize in slow and fast denoising phases. Surprisingly, instead of investing massive effort in retraining student models, we find that simply equipping the base model with lightweight LoRA adapters achieves both efficient acceleration and strong generalization. We refer to these two adapters as Slow-LoRA and Fast-LoRA. Through extensive experiments, our method achieves up to 5× acceleration over the base model while maintaining comparable visual quality across diverse benchmarks. Remarkably, the LoRA experts are trained with only 1 samples on a single V100 within one hour, yet the resulting models generalize strongly on unseen prompts. Code is available at https://zhuobaidong.github.io/Glance/.

Abstract:
Multimodal Large Language Models (MLLMs) have beengrowing the capability for scienti x001C c writing and collaboration. For exam-ple, OpenAI Prism is a free workspace for scienti x001C c writing and collab-oration. One important feature in Prism is turning scienti x001C c diagramsdirectly into LATEX TikZ code. In this paper, we build a benchmark,Diagram-MMU, a multi-modal benchmark designed to assess MLLMs'ability for scienti x001C c diagram parsing and understanding. Diagram-MMUfeatures 3.7k curated diagrams and 18.3k human-validated questionsacross six domains. It evaluates MLLMs on three tasks common in vibewriting workspaces: diagram-to-code parsing, diagram-to-code editing,and diagram question answering, alongside agentic settings per task.The evaluation of 12 MLLMs reveals that diagram-to-code tasks aremore challenging than diagram question answering: models can reasonwell over diagrams but struggle to parse and edit them, underscoringthe need for methods to enhance MLLMs' capability in diagram-to-codegeneration. Under agentic settings, most models improve parsing andediting performance but degrade on question answering, while Claude-4.6 Opus consistently improves across all three tasks. Our benchmark ispublicly available at https://huggingface.co/datasets/AIGrounding/Diagram-MMU.

Abstract:
Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing training paradigms independently optimize understanding via sparse text signals and generation through dense pixel objectives. Such a decoupled strategy yields misaligned representation spaces, isolating visual understanding from generation and hindering their mutual reinforcement. This work presents the first systematic investigation into generative post-training, where we formulate hierarchical visual tasks as generative proxies to bridge the isolation in UMMs. Our empirical investigation reveals that high-level semantic tasks, particularly image segmentation, serve as optimal proxies. Unlike low-level tasks that distract models with texture details, segmentation provides structural semantics that significantly enhance both vision-centric perception and generative layout fidelity. Building upon these insights, we introduce Semantic Generative Tuning (SGT), a novel paradigm that leverages segmentation as a generative proxy to align and synergize multimodal capabilities. Mechanistic analyses further demonstrate that SGT fundamentally improves feature linear separability and optimizes visualtextual attention allocation pattern. Extensive evaluations show that SGT consistently improves both multimodal comprehension and generative fidelity across mainstream benchmarks. Our code is available on the Project Page.

Abstract:
Multimodal large language models have demonstrated strongdocument reasoning capabilities by incorporating explicit thinking pro-cesses. While this capability significantly improves performance on chal-lenging tasks, current models apply such deep reasoning uniformly to allquestions, resulting in unnecessary computational overhead for simpletask. This not only degrades user experience but also negatively impactaccuracy on benchmark datasets. We identify the critical need for adap-tive thinking mechanisms that can intelligently determine when to en-gage reasoning based on question complexity. To address this, we proposeAdaThinking-E, a novel reinforcement learning framework that learnsadaptive thinking through one-token entropy regulation. Our key insightis that model confidence in the decision to engage thinking (or not)can be quantified through entropy analysis of the predicted probabilitydistribution at critical decision tokens. This observation motivates ourentropy-governed reward mechanism: the training process naturally tran-sitions from high-entropy exploration, where the model experiments withdifferent thinking strategies, to low-entropy convergence with confident,generalizable decision-making policies. Crucially, this approach enablesmodels to intrinsically discover when to think without requiring manualintervention or external difficulty labels. Extensive experiments demon-strate that our approach enables models to be both accurate on complexproblems and efficient on simple ones across diverse document tasks.Code is available at https://github.com/PriNing/AdaThinking-E.

Abstract:
Understanding instructional videos requires recognizing fine-grained actions and modeling their temporal relations, which remainschallenging for current Video Foundation Models (VFMs). This diffi-culty stems from noisy web supervision and a pervasive “static bias”,where models rely on objects rather than motion cues. To address this,we propose InstrAct, a pretraining framework for instructional videos’action-centric representations. We first introduce a data-driven strategy,Project page: https://zyyangzy.github.io/InstrAct/which filters noisy captions and generates action-centric hard negativesto disentangle actions from objects during contrastive learning. At thevisual feature level, an Action Perceiver extracts motion-relevant tokensfrom redundant video encodings. Beyond contrastive learning, we intro-duce two auxiliary objectives: Dynamic Time Warping alignment (DTW-Align) for modeling sequential temporal structure, and Masked ActionModeling (MAM) for strengthening cross-modal grounding. Finally, weintroduce the InstrAct Bench to evaluate action-centric understanding,where our method consistently outperforms state-of-the-art VFMs onsemantic reasoning, procedural logic, and fine-grained retrieval tasks.

Abstract:
Personalizing Multimodal Large Language Models (MLLMs)aims to recognize users’ unique concepts from visual data and providepersonalized responses. Although prior work has shown the benefit ofconcept descriptions and reasoning for this task, MLLM descriptions ofteninclude information, such as state and context, that does not help andmay in fact hinder the unique identification of the target concept amongother visually similar items. Effective descriptions of personal conceptsshould instead be accurate, discriminative, and free of distracting details.To achieve such descriptions, we introduce Reinforced Reference Game(RRG), a learning framework that promotes discriminative descriptionsthrough a novel reinforced multimodal reference game. The MLLM playsboth the roles of speaker and listener in a contrastive game setting, whosegoal is to effectively communicate discriminative information about atarget concept. Our approach formulates a verifiable contrastive rewardover hard positives (dissimilar views of the same concept) and hardnegatives (visually similar but different concepts). Empirically, RRGachieves state-of-the-art across multiple tasks on three personalizationbenchmarks. RRG generalizes to unseen domains and outperforms existingmethods based on concept descriptions and personalization-specific RLframeworks. We will release code and models in the project page.

Abstract:
Retrieval-Augmented Generation (RAG) mitigates halluci-nations in Multimodal Large Language Models (MLLMs), yet existingsystems struggle with complex cross-modal reasoning. Flat vector retrievaloften ignores structural dependencies, while current graph-based methodsrely on costly “translation-to-text” pipelines that discard fine-grainedvisual information. To address these limitations, we propose MG2 -RAG,a lightweight Multi-Granularity Graph RAG framework that jointlyimproves graph construction, modality fusion, and cross-modal retrieval.MG2 -RAG constructs a hierarchical multimodal knowledge graph bycombining lightweight textual parsing with entity-driven visual ground-ing, enabling textual entities and visual regions to be fused into unifiedmultimodal nodes that preserve atomic evidence. Building on this repre-sentation, we introduce a multi-granularity graph retrieval mechanismthat aggregates dense similarities and propagates relevance across thegraph to support structured multi-hop reasoning. Extensive experimentsacross four representative multimodal tasks (i.e., retrieval, knowledge-based VQA, reasoning, and classification) demonstrate that MG2 -RAGconsistently achieves state-of-the-art performance while reducing graphconstruction overhead with an average 43.3× speedup and 23.9× costreduction compared with advanced graph-based frameworks. The sourcecode is publicly available at https://github.com/Daboolu/MG2-RAG.

Abstract:
Tool-augmented vision–language models (VLMs) can solve multi-modal, multi-step tasks by calling external tools, yet they remain fragile in prac-tice. Existing works have two common gaps. Supervised fine-tuning (SFT) is builtmostly on successful trajectories and offers little signal for recovery after tool fail-ures, while sparse trajectory-level RL rewards provide limited guidance on whichstep failed and how to repair it. We introduce ReGRPO (Reflection-augmentedGroup Relative Policy Optimization), a framework that learns reflection-guidedcorrection in tool-using agents. ReGRPO starts with a structured reflective dataengine: we execute near-miss actions to collect grounded failure observations,then build Reflection-of-Thought triplets (ErrorType, Evidence, FixPlan) pairedwith corrected actions for warm-start SFT. We then optimize reflection tokens andcorrective actions jointly within local trajectories using group-relative advantages,and include a reflection-cost term to reduce unnecessary reflection. Experimentson GTA and GAIA show that, under the same backbone and tool suite, ReGRPOconsistently outperforms strong open-source baselines and achieves the best resultsamong the compared open-source controllers. Code and RoT data are available athttps://github.com/showlab/ReGRPO.

Abstract:
Sign language translation (SLT) converts continuous signvideos into spoken language text. Gloss-free approaches leverage pre-trained visual encoders and language models but rely on implicit cross-modal alignment from translation supervision alone. We present VTaMo,a framework that introduces explicit multi-granularity alignment at threelevels: (1) local alignment via entropy-regularized optimal transport witha learnable null token for fine-grained frame-to-token correspondences;(2) global alignment via a learnable orthogonal transformation that cali-brates embedding space geometry through Earth Mover’s Distance; and(3) position-aligned contrastive learning for discriminative token-levelrepresentations. Experiments on Phoenix-2014T, CSL-Daily, How2Sign,and OpenASL demonstrate consistent state-of-the-art performance, withablations confirming the complementary contributions of each compo-nent. Code is available at https://github.com/junyi2005/vtamo.

Abstract:
Understanding field imagery such as detecting plants anddistinguishing individual crop and weed instances, is a central challengein precision agriculture. Despite progress in vision–language tasks likecaptioning and visual question answering, Visual Grounding (VG), local-ising language-referred objects, remains unexplored in agriculture. A keyreason is the lack of suitable benchmark datasets for evaluating groundingmodels in field conditions, where many plants look highly similar, appearat multiple scales, and the referred target may be absent from the image.To address these limitations, we introduce gRef-CW, the first dataset de-signed for generalised visual grounding in agriculture, including negativeexpressions. Benchmarking current state-of-the-art grounding models ongRef-CW reveals a substantial domain gap, highlighting their inabilityto ground instances of crops and weeds. Motivated by these findings, weintroduce Weed-VG, a modular framework that incorporates multi-labelhierarchical relevance scoring and interpolation-driven regression. Weed-VG advances instance-level visual grounding and provides a clear baselinefor developing VG methods in precision agriculture. Code and data areavailable at https://github.com/MHaghighat98/WeedVG-gRefCW.

Abstract:
Open-vocabulary object detection (OVD) aims to recognizeand localize object categories beyond the training set. Recent approachesleverage vision-language models to generate pseudo-labels using image-text alignment, allowing detectors to generalize to unseen classes withoutexplicit supervision. However, these methods depend heavily on single-step image-text matching, neglecting the intermediate reasoning stepscrucial for interpreting semantically complex visual contexts, such ascrowding or occlusion. In this paper, we introduce MSPL, a frameworkthat incorporates multi-step visual reasoning into the pseudo-labelingprocess for OVD. It decomposes complex scene understanding into threeinterpretable steps—object localization, category recognition, and back-ground grounding—where these intermediate reasoning states serve asrich supervision sources. Extensive experiments on standard OVD evalu-ation protocols demonstrate that MSPL achieves state-of-the-art perfor-mance with superior pseudo-labeling efficiency, outperforming the strongHuman Pseudo PseudoVLMs MLLMAnnotation Annotation AnnotationImage-Text RPN🔥 A boy sits near a dog Visual SAMMatching while watching television. ReasoningImage-Captions Input ImageSingle-step Assignment Multi-step Assignment🔥 🔥 OV Noisy PLs 🔥 OV Accurate PLsBase Detector Novel Large-Scale Novel Require captions Open-Vocab. Novel No captionsDetector DetectorClasses Classes Base Classes Classes BG collapse Base Classes Classes BG grounding(a) Manual Pseudo-Labeling Designs (b) Single-Step OV Pseudo-Labeling Methods (c) Our Method: Multi-Step OV Pseudo-LabelingFig. 2: (a) Manual pseudo-labels for novel classes is costly and does not scale. (b) Re-cent approaches automate this process via single-step semantic assignment with vision-language models and image captions, struggling in complex scenes. (c) Our caption-freemethod leverages multi-step reasoning to interpret semantically complex scenes.baseline by 9.4 AP50 for novel classes on OV-COCO and improving boxand mask APr by 3.2 and 2.2, respectively, on OV-LVIS.

Abstract:
Precisely manipulating objects in a single photograph (trans-lation, rotation, scaling) while obeying 3D physical constraints remainsunsolved for diffusion-based editors. Current 2D methods lack spatialawareness and produce perspective violations. Forcing structural proxiesinto the latent space also disrupts variance homogeneity, and the result-ing self-attention leakage leads to ghosting and background blur. Thecore difficulty is asymmetric: the relocated object must follow a rigidgeometry, yet the uncovered background needs freedom to synthesizeplausible content. We present GeoEdit, a training-free Lift-Manipulate-Render-Denoise pipeline that satisfies both constraints. We decouplescene and object in 3D, align them through point correspondence, andrender a geometry-aligned proxy with a structural depth map. A Dual-Branch Denoising stage then refines this proxy: a video diffusion back-bone preserves object identity, while 3D constraints are injected into theforeground within a narrow denoising window at matching noise vari-ance (variance-homogeneous injection). The background denoises freely.Because the injected signal matches the native latent statistics, self-attention stays undisturbed. We also introduce GeoEditBench, a pose-aware benchmark covering object translation, object rotation, and cam-era movement with pose-aware evaluation metrics. Experiments confirmconsistent gains in geometric accuracy, identity fidelity, and backgroundquality, validated by automatic metrics and human studies. Code anddata are publicly available at https://github.com/Heey731/GeoEdit.

Abstract:
Real-world object detection operates under ambiguous su-pervision, where unlabeled regions may correspond to missing annota-tions of known objects or genuinely unknown categories. These chal-lenges have been addressed separately in Sparsely Annotated Object De-tection (SAOD) and Open-World Object Detection (OWOD). In prac-tice, their co-occurrence remains an open problem. To address this prob-lem, we introduce Sparsely Annotated Open-World Object Detection(SA-OWOD), a new task that jointly considers sparse supervision andthe presence of unseen categories. We propose Dual-Perspective ObjectDiscovery (DPOD), a unified framework that jointly models unlabeledknown and unknown instances via two complementary mechanisms. TheKnown Target Recovery Module (KTRM) recovers supervision for unla-beled known instances and explicitly regularizes the feature space to sep-arate known and unknown representations. Complementarily, the Dual-Disagreement Target Generator (DDTG) identifies reliable unknown can-didates through cross-view semantic inconsistency. By integrating thesemodules, DPOD resolves contradictory supervision signals caused byambiguous unlabeled regions. As a result, it prevents misclassificationbetween known and unknown objects and stabilizes the decision bound-aries. Experimental results on sparsely annotated open-world bench-marks demonstrate that the proposed method outperforms existing open-world detection methods, particularly in detecting unknown objects. Thecode is publicly available at: https://github.com/HelloHeeju/SA-OWOD

Abstract:
Generalized Category Discovery aims to recognize knowncategories while identifying novel ones within unlabeled data. Existingmethods, typically based on self-supervision and contrastive learning,often struggle to capture fine-grained distinctions, relying on superficialvisual cues rather than the intrinsic attributes humans use for categoriza-tion. We introduce Fourier Self-Supervision, that leverages the Fouriertransform of images to enhance the discrimination of subtle differencesand support the discovery of new categories. Our method employs adual frequency filtering strategy: a low-pass filter first extracts broad,abstract attributes that capture high-level category information, whilea high-pass filter emphasizes fine details such as edges and texturesthat are essential for fine-grained recognition. Each operates on a dedi-cated latent space, and their overlapping representations together yielda richer, more complete feature space. This dual-frequency approachnot only refines feature extraction to identify novel categories, but alsostrengthens the model’s discriminative power in fine-grained categorydiscovery. Experiments on multiple fine-grained datasets show that incor-porating Fourier Self-Supervision outperforms state-of-the-art methods,even when the number of classes is unknown, demonstrating its effec-tiveness for Generalized Category Discovery. Our code is available at:https://github.com/SarahRastegar/FourEx.

Abstract:
Self-supervised RGB-T tracking seeks to learn robust cross-modal representations without dense manual annotations. A commonsupervision strategy relies on forward-backward tracking loops, but inpractice, it is highly vulnerable to error accumulation and modality driftcaused by the inconsistent reliability of visible and thermal cues. To ad-dress this issue, we propose LGCTrack, a self-supervised RGB-T trackerunderpinned by a local-to-global coordination strategy within a closed-loop verification paradigm. At the local level, a hierarchical coordina-tion module performs layer-wise, reliability-aware token interaction toenable adaptive cross-modal fusion. At the global level, a turning-phasecoordination module refines the target representation during the criticalforward-to-backward transition, anchoring target semantics and improv-ing backward tracking. By jointly coordinating cross-modal interactionat two granularities, LGCTrack intrinsically filters pseudo-label noise andmitigates drift. Experiments on standard RGB-T tracking benchmarksshow that LGCTrack consistently outperforms existing self-supervisedmethods and substantially narrows the gap to fully supervised trackers.Our code is available at https://github.com/zyyyya/LGCTrack.

Abstract:
Text-driven Referring Video Object Segmentation (RVOS)aims to locate and segment target objects in videos given natural lan-guage. However, existing models are typically trained on 2D image orvideo datasets with naive segmentation losses, which overlooks the geo-metric consistency across frames and leads to weak spatial understand-ing. In this paper, we propose Geometry-enhanced Language-guidedVideo segmentation (GeoLaV), a two-stage framework that distills 3Dgeometric knowledge from images to enhance text-driven video segmenta-tion. In the first stage, we perform monocular geometry pretraining withmonocular novel-view synthesis, enabling the model to acquire geometry-consistent visual representations via spatial alignment on large-scale single-image datasets. In the second stage, we introduce geometry-aware distil-lation and fine-tune the model on video segmentation datasets, transfer-ring 3D structural knowledge from a general 3D prior model. This processreinforces 3D awareness and improves both spatiotemporal coherence andlanguage grounding in segmentation. Extensive experiments show thatour method using only image segmentation data already provides notablezero-shot generalization in RVOS. When combined with geometry-awaredistillation for fine-tuning on videos, our method achieves state-of-the-artperformance across multiple RVOS benchmarks. The code is available athttps://github.com/Tony1882880/GeoLaV.

Abstract:
Domain Generalized Semantic Segmentation (DGSS) is achallenging task, as vision models often rely on low-level appearancecues that change across domains. In contrast, structural attributes ex-hibit cross-domain stability, motivating the use of structural priors forDGSS. Existing methods use prompt learning to transfer such priorsinto DGSS models, but typically encode each class as a single holisticprompt. Moreover, these methods apply prompts uniformly to all pixels,offering no mechanism to adapt when only a subset of object regions isvisible due to viewpoint changes, occlusion, and environmental variation.We address this with Spatial Hierarchical Prompts (SHP) that en-rich each class with region-level geometric anchors capturing structuralappearance from distinct viewing angles, ensuring complementary cov-erage under arbitrary viewpoints. Additionally, we propose the Hier-archical Prompt Injector (HPI), which enables spatially adaptiveprompt injection in foundation models. HPI spatially grounds promptsby modeling their semantic relevance and spatial influence with visualfeatures. Considering the difficulty of learning spatially and semanti-cally aware prompt injection, we further introduce auxiliary supervi-sion to align hierarchical prompts with their corresponding object re-gions. We achieve 70.62% and 72.74% mIoU on synthetic-to-real andreal-to-real benchmarks, respectively. Code and checkpoints are releasedat https://github.com/MosukFate/HPI.

Abstract:
Robust out-of-distribution (OOD) detection is crucial fordeploying deep learning systems in high-stakes settings such as medi-cal imaging and autonomous driving. Post-hoc feature-based detectorsare attractive because they operate on pre-trained models, yet they of-ten treat all feature dimensions as equally informative. We show thatOOD-discriminative information in deep feature representations is of-ten concentrated in subsets of features and is frequently axis-aligned. Toexploit this structure, we introduce a distribution-aware feature selec-tion strategy that ranks feature dimensions according to the discrepancybetween in-distribution (ID) and OOD feature distributions, using theWasserstein-1 distance as a principled metric.To avoid the need for curated OOD validation data, we construct proxy-OOD data based on cross-domain mixup and evaluate adversarial per-turbations as an alternative. Our method is a lightweight, broadly ap-plicable add-on to existing feature-based OOD detectors that requiresno retraining or changes to scoring functions. Evaluations across pub-lic benchmarks spanning medical and natural image domains show con-sistent performance improvements, while the reduced feature set lowerscomputational complexity and enables substantially faster inference. Ourcode is available at https://github.com/remic-othr/mfs-ood.

Abstract:
Globally consistent semantic digital twins require centimeter-accurate and geographically transferable 3D facade segmentation. How-ever, progress in facade parsing is limited by the lack of large-scale, stan-dardized benchmarks for evaluating cross-domain and cross-country gen-eralization. Existing datasets are geographically narrow, sensor-specific,semantically inconsistent, or insufficiently precise. We introduce Un-derOneFacade, the largest cross-continental 3D facade benchmark todate, comprising centimeter-accurate point clouds with hierarchical, har-monized, and architecturally grounded semantic labels totaling 2.7 bil-lion annotated points. Through a systematic evaluation of representativepoint-, graph- and transformer-based architectures, we show that cur-rent methods struggle to recognize fine-grained architectural elementsand degrade significantly across geographic regions, with the best mod-els achieving only up to 33 IoU on the fine-grained LoFG3 benchmark.By combining geometric precision with standardized semantics at un-precedented scale, UnderOneFacade establishes a rigorous benchmarkfor developing robust and transferable 3D segmentation models for andbeyond facade understanding. The dataset, evaluation scripts, and pre-trained models are available here: https://jiangyuanwangyi.github.io/UnderOneFacade_official/

Abstract:
Aerial geo-localization is increasingly essential for large-scale spatial retrieval. To overcome the limitations of single-retrieval systems caused by modality-specific uncertainty in open environments, this paper introduces the unified Aerial Multi-view Geo-localization (AMGL) task. However, when applying universal multimodal retrieval paradigms to this task, the semantic bias introduced solely by textual instructions fails to resolve and reconstruct the differentiated distribution structures necessary for cross-view alignment. Consequently, this paper proposes the Rectified Embedding Flow Learning (REFL) framework, formulating crossdomain alignment as a directed conditional distribution transport process. Initially, REFL employs velocity-prior flow learning to fit continuous ordinary differential equation trajectories, deriving transformation priors that map single-view distributions to a latent shared manifold. Subsequently, a trajectory-guided embedding rectification mechanism continuously transports query features to the target view distribution, explicitly compensating for distribution shifts. Extensive evaluations on the Aerial MVGL benchmark demonstrate that REFL achieves state-of-theart performance, yielding an average R@1 of 44.83% and R@10 of 65.85%. The code and benchmark are available at https://github.com/rhaohur/REFL.

Abstract:
Detecting anomalies in industrial objects is an importanttopic for increasing production efficiency. More complex objects oftenrequire the analysis of several view points, which has led to the fieldof multi-view anomaly detection. We present MATCH , the first multi-view anomaly detection method based on Flow Matching (FM). Withthe ODE formulation of Flow Matching, we can estimate likelihoods andthereby derive an anomaly score to detect anomalies in multi-view imagedata at object, image, and pixel-level. The architectural flexibility of FMmodels allows us to efficiently transform features of different spatial sizesto the normal distribution. We evaluate thoroughly on the already estab-lished Real-IAD data set and are also the first to provide a comprehen-sive evaluation of popular anomaly detection methods for the MANTA-Tiny data set. MATCH achieves state-of-the-art performance in bothanomaly detection and segmentation, all while running on consumer-levelhardware. By omitting the costly divergence term needed for likelihoodestimation, we ensure that MATCH is usable in real-time productionscenarios. Lastly, several ablation studies are conducted to validate themethodological choices. Code: https://github.com/m-kruse98/MATCH

Abstract:
Anomaly generation is often framed as few-shot fine-tuningwith anomalous samples, which contradicts the scarcity that motivatesgeneration and tends to overfit category priors. We tackle the settingwhere no real anomaly samples or training are available. We proposeDelta-Denoising (DeltaDeno), a training-free zero-shot anomaly gener-ation method that localizes and edits defects by contrasting two diffusionbranches driven by a minimal prompt pair under a shared schedule. Byaccumulating per-step denoising deltas into an image-specific localizationmap, we obtain a mask to guide the latent inpainting during later diffu-sion steps and preserve the surrounding context while generating realis-tic local defects. To improve stability and control, DeltaDeno performstoken-level prompt refinement that aligns shared content and strength-ens anomaly tokens, and applies a spatial attention bias restricted toanomaly tokens in the predicted region. Experiments on public datasetsshow that DeltaDeno achieves great generation, realism and consistentgains in downstream detection performance. Code will be made publiclyavailable at https://github.com/CROVO1026/DeltaDeno.

Abstract:
This paper concerns the problem of unsupervised temporalaction segmentation for long, untrimmed videos. Recent successful ap-proaches follow a joint representation learning and clustering paradigm,where optimal transport (OT) is adopted to produce pseudo labels forlearning frame representations. These approaches alternate between es-timating pseudo labels using OT and optimizing the parameters withgradient descent during training, where OT is used for obtaining the fi-nal temporal action segmentation. A major limitation of these works isthat they learn a deterministic embedding for frame representations. Theiterative procedure between learning deterministic embeddings based onpseudo labels and estimating pseudo labels from the learned embeddingcan thus get quickly stuck in a local optimum. As an alternative, we thuspropose to learn a probabilistic embedding for frame representations. Theembeddings are modeled by Gaussian distributions and we sample fromthe distributions before estimating the pseudo labels. We evaluate ourapproach on several challenging temporal action segmentation datasetsand achieve results comparable to, and in some cases, better than thestate of the art. Compared to baselines with deterministic embeddings,our approach improves MoF up to 20.7% and F1-score up to 19.0%. Ourcode is available at https://github.com/derkbreeze/PEOT.

Abstract:
Image forgery localization remains challenging due to di-verse manipulation techniques and distribution shifts. Existing recentforgery localization models achieve high accuracy on benchmarks butoften struggle with cross-domain generalization and robustness. In thispaper, we propose SARIF (Segment Anything for Robust Im-age Forensics), a framework that leverages Segment Anything Model(SAM), which has a promptable architecture and generalization abilityto overcome these limitations. SARIF introduces a feedback-guided maskdecoder and a dual-encoder design that extracts forgery-specific informa-tion to capture forensic traces while exploiting SAM’s architecture. Tolocalize manipulated regions, we design a block-wise prompting mecha-nism that derives forgery-specific cues from residual features between anadapted encoder and its frozen counterpart. These features are fused withthe previous mask prompt to drive a feedback-based mask refinementprocess, enabling automatic forgery segmentation without manual in-put. Extensive experiments on standard forgery-localization benchmarksshow that SARIF achieves strong average cross-dataset performance androbustness to common image corruptions. Our SARIF code is availablein GitHub Link.

Abstract:
Generalizable animal Re-Identification (ReID) aims to recognize individual animals across species with diverse morphologies and ecological contexts. Unlike person ReID, where different domains share similar body structures, animal species often exhibit drastically different anatomical structures and visual patterns, making it difficult to establish shared visual correspondences. As a result, representations learned across species tend to form fragmented embedding spaces, which severely limits cross-species generalization. To address this challenge, we propose Semantic Consistency Learning (SCL), a framework designed to learn representations that remain stable across appearance variations while preserving semantic structures shared across species. SCL consists of two complementary components. Foreground–Background Decoupled Spectral Normalization (FDSNorm) stabilizes feature statistics by suppressing environment-induced style variations in a region-aware manner, while Cross-species Neighborhood Modeling (CNM) captures transferable relational structures across species through dynamic feature neighborhoods. Extensive experiments on 11 public animal ReID datasets demonstrate that SCL consistently outperforms state-of-the-art methods under multiple cross-species evaluation protocols and generalizes effectively to previously unseen species and ecological domains. Code is available at https://github.com/Kemalau/ECCV-26-SCL.

Abstract:
Recent state-of-the-art visual trackers produce high qualityand long-term-stable segmentation masks. We propose to leverage thesestrengths for planar object tracking, in which the goal is to estimate aprecise 8-degrees-of-freedom homography pose, a geometric representationnot estimated by segmentation trackers. We present SAM-H – a planarobject tracker that estimates homographies from segmentation mask con-tours via a training-free pipeline. When SAM-H is applied to masks fromSAM 2 [21], it sets a new state-of-the-art performance on the challengingPlanarTrack [15] benchmark by a large margin, +18.4pp on the p@5metric. We further show that segmentation-based and correspondence-based homography estimation are complementary, and propose WOFT-SAM, which out-performs all prior methods on both PlanarTrack [15]and POT-210 [14]. We also provide precise re-annotations of Planar-Track initial poses, enabling more accurate benchmarking in the high-precision p@5 metric. The code and the re-annotations are available athttps://github.com/serycjon/WOFTSAM.

Abstract:
Multi-object tracking (MOT) via UAV fleets is crucial for low-altitudeapplications. However, existing multi-view multi-object tracking (MVMOT) meth-ods and datasets are constrained by fixed camera configurations and limited cov-erage of complex scenarios, thereby failing to address real-world applications. Tofill this gap, we first conduct a systematic study of MOT under arbitrary camerasetups and introduce MDMOT, a novel multi-UAV benchmark covering diversereal-world scenarios, exposing practical challenges like random object entry/exitand cross-view appearance inconsistencies. We further propose FusionTrack, anend-to-end MVMOT framework that abandons the traditional decoupled track-then-associate paradigm. It jointly optimizes tracking and association via bidirec-tional object fusion between a Tracklet Memory Pool and a Trajectory IdentityPool, leveraging spatio-temporal context to enhance representation discriminabil-ity. For inference, we design View-aware Hierarchical Clustering with neighborfiltering to ensure intra-view exclusivity and inter-view consistency in cross-viewassociation. Extensive experiments verify that FusionTrack achieves state-of-the-art performance in both single- and multi-view tracking tasks, outperforming ex-isting methods by at least 2.3% in relative improvement on our MDMOT bench-mark. Project page: https://github.com/aircas501/FusionTrack

Abstract:
Transformer-based object trackers are renowned for theirstrong performance, yet dense token processing often leads to prohibitivecomputational cost, limiting real-time deployment on edge devices. Whilerecent works explore token pruning to reduce computation, they oftenstop short of an end-to-end sparse pipeline, as early-layer token scores canbe noisy without a motion prior, and many trackers ultimately fall back todense reshaping to feed the dense prediction head that partially negatesthe savings. We introduce Motion-aware Sparse Tracker (MaST), a sparsetracking framework that makes sparsity effective from tokens to boxes.First, MaST injects a lightweight motion prior to refine cross-attention-based importance scores, enabling earlier and more stable token reductionin the search region. Second, we introduce a natively sparse predictionhead that operates directly on the retained unstructured tokens witha score-first, regress-once design, eliminating dense padding/reshapingand reducing redundant computation. Extensive experiments on multiplebenchmarks demonstrate that MaST establishes new state of the artamong lightweight trackers, where MaST-tiny attains 63.8 AUC on LaSOTand 80.1 SUC on TrackingNet, surpassing the prior best AsymTrack-Sby +1.0 AUC and +2.2 SUC while running at 152 FPS on Jetson Nano,nearly twice as fast as AsymTrack-S at 88 FPS. Code is available atgithub.com/TsingWei/MaST.

Abstract:
This paper presents CountEx, a discriminative visual count-ing framework designed to address a key limitation of existing prompt-based methods: the inability to explicitly exclude visually similar distrac-tors. While current approaches allow users to specify what to count viainclusion prompts, they often struggle in cluttered scenes with confus-able object categories, leading to ambiguity and overcounting. CountExenables users to express both inclusion and exclusion intent, specifyingwhat to count and what to ignore, through multimodal prompts includ-ing natural language descriptions and optional visual exemplars. At thecore of CountEx is a novel Discriminative Query Refinement module,which jointly reasons over inclusion and exclusion cues by first identi-fying shared visual features, then isolating exclusion-specific patterns,and finally applying selective suppression to refine the counting query.To support systematic evaluation of fine-grained counting methods, weintroduce CoCount, a benchmark comprising 1,780 videos and 10,086 an-notated frames across 97 category pairs. Experiments show that Coun-tEx achieves substantial improvements over state-of-the-art methods forcounting objects from both known and novel categories. The data, code,and model are available at https://github.com/bbvisual/CountEx.

Abstract:
Change detection aims to identify semantic changes betweenremote sensing images. However, features from models are easily dis-turbed by non-semantic variations, such as illumination, shadows, andatmospheric changes, leading to false alarms and limited generaliza-tion in real-world scenarios. In this paper, we propose SCDistill, aframework for learning semantic-robust change detection via semantic-invariant self-distillation. First, to strengthen semantic consistency, weintroduce a semantic-invariant self-distillation strategy that learns se-mantic robustness from perturbed yet semantically consistent data, em-powering the change detector to extract disturbance-resistant featuresand achieve more reliable and accurate semantic change identification.Second, to expand paired data with non-semantic variations, we designa diffusion-based perturbation simulation pipeline that synthesizes com-plex environmental changes, enabling the model to explicitly learn todistinguish semantic changes from appearance-level fluctuations and re-duce false alarms caused by non-semantic disturbances. These compo-nents promote robustness from data and representation perspectives,leading to synergistic performance gains. Extensive experiments demon-strate that SCDistill achieves state-of-the-art performance on multiplesemantic change detection benchmarks and exhibits strong generaliza-tion to binary change detection and change captioning tasks. Code isaccessible at https://github.com/elecreak/SCDistill.

Abstract:
We present LlamaSeg, a visual autoregressive framework that unifies multiple image segmentation tasks via natural language instructions. By reformulating segmentation as visual generation, LlamaSeg encodes masks as visual tokens and uses a LLaMA-style Transformer for direct next-token prediction, naturally fitting segmentation into autoregressive architectures. To support large-scale training, we introduce a data annotation pipeline and construct the SA-OVRS dataset, which contains 2M segmentation masks annotated with over 5,800 open vocabulary labels or diverse textual descriptions, spanning diverse realworld scenarios. This enables our model to localize objects in images based on text prompts and to generate fine-grained masks. We further introduce the composite metric average Hausdorff Distance (d ) AHD to evaluate mask contour fidelity for generative models better. Experiments show that LlamaSeg consistently outperforms existing generative approaches on multiple segmentation benchmarks and delivers finer, more accurate segmentation masks. Code and dataset are available at https://github.com/GML-FMGroup/llamaseg.

Abstract:
Exemplar-free class-incremental learning (EFCIL) requires stable decision boundaries within a shifting feature space. While maintaining class-conditional Gaussian statistics provides a principled classification strategy, these parametric summaries remain sensitive to anisotropic representation drift. Existing methods often transport these statistics across tasks using a decoupled, post-hoc paradigm: optimizing a backbone without explicit geometric constraints can distort the legacy manifold, limiting the precision of retroactive alignment. In this paper, we formulate feature transport as an endogenous training constraint rather than a separate post-task step, presenting the Geometry-Anchored Transport Framework. First, we derive an Analytic Geometric Anchor via Mahalanobis-aligned regression to mitigate macroscopic anisotropic drift. Second, we introduce a Topology-Aware Evolution objective that regularizes localized manifold degradation while calibrating a residual network against the analytic prior. By coupling manifold evolution with transport constraints during the primary training phase, our framework mitigates evaluation errors without requiring decoupled fine-tuning. Experiments across CIFAR-100, TinyImageNet, and ImageNet-100 demonstrate that the proposed framework consistently improves upon existing post-hoc alternatives under strict exemplar-free constraints. The code is available at https://github.com/HXuSz11/GATF_ECCV2026.

Abstract:
Converting static 3D meshes into interactable articulated as-sets is crucial for embodied AI and robotic simulation. However, exist-ing zero-shot pipelines struggle with complex assets due to a criticallack of physical grounding. Specifically, ungrounded Vision-LanguageModels (VLMs) frequently suffer from kinematic hallucinations, whileunconstrained joint estimation inevitably leads to catastrophic meshinter-penetration during physical simulation. To bridge this gap, we pro-pose MotionAnymesh, an automated zero-shot framework that seam-lessly transforms unstructured static meshes into simulation-ready dig-ital twins. Our method features a kinematic-aware part segmentationmodule that grounds VLM reasoning with explicit SP4D physical priors,effectively eradicating kinematic hallucinations. Furthermore, we intro-duce a geometry-physics joint estimation pipeline that combines robusttype-aware initialization with physics-constrained trajectory optimiza-tion to rigorously guarantee collision-free articulation. Extensive experi-ments demonstrate that MotionAnymesh significantly outperforms state-of-the-art baselines in both geometric precision and dynamic physicalexecutability, providing highly reliable assets for downstream applica-tions. Our project page is available at https://xwb0117.github.io/Motionanymesh/

Abstract:
Transferring articulated motion from monocular videos torigged 3D characters is challenging due to pose ambiguity in 2D obser-vations and morphological differences between source and target. Ex-isting approaches often follow a reconstruct-then-retarget paradigm, ty-ing transfer quality to intermediate 3D reconstruction and limiting ap-plicability to categories with parametric templates. We propose Mor-phGS, a framework that formulates motion retargeting as a target-drivenanalysis-by-synthesis problem, directly optimizing target morphology andpose through image-space supervision. A rig-coupled morphology pa-rameterization factorizes character identity from time-varying joint rota-tions, while dense 2D-3D correspondences and synthesized views providecomplementary structural and multi-view guidance. Experiments on syn-thetic benchmarks and real-world videos show consistent improvementsover baselines. Project page: https://xodus777.github.io/MorphGS/

Abstract:
Understanding animal motion is fundamental to modelinganimal behavior and biomechanics, yet progress in this area lags far be-hind human motion research due to the scarcity of high-quality motiondata. While human motion can be captured in controlled environments, itis impractical for most animal species, resulting in small, domain-limiteddatasets that restrict downstream applications such as animation. To ad-dress this challenge, we introduce Kirin, a framework that reconstructsmotion from video, learns motion priors at scale, and generates realisticmotion that can be directly applied to animated assets. Using large col-lections of in-the-wild animal videos, we reconstruct 3D motion sequencesand pair them with captions to create AiM3D, the first large-scale datasetoffering aligned video-text-motion tuples for quadruped animals. Build-ing on this dataset, we develop a visual-guided motion generation modelthat conditions on both text and image to guide the generation of re-alistic motion across diverse animal species. Finally, by leveraging anoff-the-shelf image-to-3D model, we automatically rig and animate 3Dmeshes using generated motion, producing ready-to-render animated an-imals. Together, our dataset and framework establish a new foundationfor large-scale, text and image conditioned animal motion generation andanimation. Project page: https://kirin-ani.github.io/.

Abstract:
Recent progress in text-to-video generation has achieved remarkable realism, yet fine-grained control over camera motion and orientation remains elusive, especially with extreme trajectories (e.g., a 180° turnaround, or looking directly up or down). Existing approaches typically encode camera trajectories using relative or ambiguous representations, limiting precise geometric control and offering limited support for large rotations. We introduce GimbalDiffusion, a framework that enables camera control grounded in physical-world coordinates, using gravity as a global reference. Instead of describing motion relative to previous frames, our method defines camera trajectories in an absolute coordinate system, allowing accurate, interpretable control over camera parameters. Using panoramic 360◦ videos for training, we cover the full sphere of possible viewpoints, including combinations of extreme pitch and roll that are out-of-distribution of conventional video data. To improve camera control, we introduce null-pitch conditioning, a strategy that prevents the model from overriding camera specifications in the presence of conflicting prompt content (e.g., generating grass while the camera points toward the sky). Finally, we propose new benchmarks to evaluate gravity-aware camera-controlled video generation, assessing models’ ability to generate extreme camera angles and quantify their input prompt entanglement.

Abstract:
Video-based world models have recently garnered increas-ing attention for their ability to synthesize diverse and dynamic vi-sual environments. In this paper, we focus on shared world modeling,where a model generates multiple videos from a set of input images,each representing the same underlying world in di!erent camera poses.We propose IC-World, a novel generation framework, enabling paral-lel generation for all shared world input images via activating the in-herent in-context generation capability of large video models. We fur-ther finetune IC-World via reinforcement learning, Group Relative Pol-icy Optimization, together with two proposed novel reward models toenforce scene-level geometry consistency and object-level motion consis-tency among the set of generated videos. Extensive experiments demon-strate that IC-World substantially outperforms state-of-the-art methodsin both geometry and motion consistency. To the best of our knowledge,this is the first work to systematically explore the shared world modelingproblem with video-based world models.

Abstract:
Precise 3D spatial orchestration in text-to-video generationremains a signi x001C cant challenge, particularly for multi-object scenes wheresemantic layout and temporal dynamics are often entangled. While exist-ing depth-conditioned models achieve good structural x001C delity, they neces-sitate dense, frame-accurate guidance that is labor-intensive to authorfor dynamic events involving deformable objects. We present LooseC-ontrolVideo (LCV), a framework that enables intuitive and expressivecontrol by using sparse, oriented 3D boxes as a x0010 blocking x0011 proxy. Thisallows users to author high-level layout and trajectory while leveraginga video generative model to generate realistic occlusions, dynamics andinteractions. We achieve this by x001C ne-tuning a Wan 2.2 backbone on avideo dataset annotated with DNOCS, a novel encoding for 3D size, ori-entation and depth-ordered occlusions. Furthermore, our method allowsfor localized re x001C nement x0016 such as adjusting a jump trajectory or addingan interaction x0016 with minimal disruption to the global scene context. Ex-tensive evaluations on the nuScenes, HO-3D, and BEHAVE benchmarksdemonstrate that LCV signi x001C cantly outperforms existing 2D-box and x001D ow-based baselines. Our x001C ndings indicate a 1.2-3x improvement in Tra-jectory Error; 2x improvement in Rigid Motion Consistency; and a 1.5-2x increase in Occlusion Accuracy over current state-of-the-art layout-conditioned models, demonstrating that oriented 3D primitives providegood geometric prior for complex, multi-agent video authoring.

Abstract:
While the shortage of explicit action data limits VisionLanguage-Action (VLA) models, human action videos offer a scalable yet unlabeled data source. A critical challenge in utilizing large-scale human video datasets lies in transforming visual signals into ontologyindependent representations, known as latent actions. However, the capacity of latent action representation to derive robust control from visual observations has yet to be rigorously evaluated. We introduce the Latent Action Representation Yielding (LARY) Benchmark, a unified framework for evaluating latent action representations on both highlevel semantic actions (what to do) and low-level robotic control (how to do). The comprehensively curated dataset encompasses over one million videos (1,000 hours) spanning 151 action categories, alongside 620K image pairs and 595K motion trajectories across diverse embodiments and environments. Our experiments reveal two crucial insights: (i) General visual foundation models, trained without any action supervision, consistently outperform specialized embodied latent action models. (ii) Latent-based visual space is fundamentally better aligned to physical action space than pixel-based space. These results suggest that general visual representations inherently encode action-relevant knowledge for physical control, and that semantic-level abstraction serves as a fundamentally more effective pathway from vision to action than pixel-level reconstruction.

Abstract:
We propose a step-by-step video-to-audio (V2A) generation method that provides finer control over the generation process and more realistic audio synthesis. Inspired by traditional Foley workflows, our approach enables incremental generation of complementary sounds, allowing users to author multiple sound events induced by a video. To avoid the need for costly multi-reference video-audio datasets, each generation step is formulated as a negatively guided V2A process that discourages duplication of sounds already present in previously generated tracks. The guidance model is trained by finetuning a pre-trained V2A model on audio pairs from non-overlapping segments of the same video, encouraging it to leverage acoustic context while remaining visually grounded, and enabling training with standard single-reference audiovisual datasets. Objective and subjective evaluations demonstrate that our method enhances the separability of generated sounds at each step and improves the overall quality of the final composite audio, outperforming existing baselines. Our project page is available at: https://ahykw.github.io/sbsv2a/.

Abstract:
Accurate future video prediction requires both high visual fidelity and consistent scene semantics, particularly in complex dynamic environments such as autonomous driving. We present Re2Pix, a hierarchical video prediction framework that decomposes forecasting into two stages: semantic representation prediction and representation-guided visual synthesis. Instead of directly predicting future RGB frames, our approach first forecasts future scene structure in the feature space of a frozen vision foundation model, and then conditions a latent diffusion model on these predicted representations to render photorealistic frames. This decomposition enables the model to focus first on scene dynamics and then on appearance generation. A key challenge arises from the train-test mismatch between ground-truth representations available during training and predicted ones used at inference. To address this, we introduce two conditioning strategies, nested dropout and mixed supervision, that improve robustness to imperfect autoregressive predictions. Experiments on challenging driving benchmarks demonstrate that the proposed semantics-first design significantly improves temporal semantic consistency, perceptual quality, and training efficiency compared to strong diffusion baselines. We provide the implementation code at https://github.com/Sta8is/Re2Pix.

Abstract:
To overcome the black-box nature of predictive AI and thehallucination risks of generative models, retrieval-based models o!er aninterpretable, evidence-based paradigm for pathology clinical workflow.However, real-world clinical queries are inherently interleaved (e.g., pathol-ogy images and text). Current dual-encoders su!er from an Archi-tectural Mismatch, lacking the mechanism to fuse such composedqueries. To address this, we formalize the task of Pathology ComposedRetrieval (PCR). While Multimodal Large Language Models (MLLMs)o!er deep-fusion capabilities, directly applying them exposes a TaskMismatch and a Domain Mismatch. To resolve these challenges,we propose HOMIE, a model-agnostic adaptation framework that trans-forms any generative MLLM into a specialized pathology retrieval expert.Evaluated on our newly introduced PCR Benchmark, a lightweight 2B-parameter HOMIE variant substantially outperforms existing paradigms,surpassing specialized 7B pathology MLLMs and dual-encoders by largemargins on composed retrieval, while maintaining strong performanceon traditional simple retrieval. The project page is available at https://qfchou.github.io/HOMIE_page/.

Abstract:
Text-guided 3D motion editing has seen success in single-person scenarios, but its extension to multi-person settings is less ex-plored due to limited paired data and the complexity of inter-personinteractions. We introduce the task of multi-person 3D motion editing,where a target motion is generated from a source and a text instruction.To support this, we propose InterEdit3D, a new dataset with man-ual two-person motion change annotations, and a Text-guided Multi-human Motion Editing (TMME) benchmark. We present InterEdit,a synchronized classifier-free conditional diffusion model for TMME. Itintroduces Semantic-Aware Plan Token Alignment with learnable to-kens to capture high-level interaction cues and an Interaction-AwareFrequency Token Alignment strategy using DCT and energy poolingto model periodic motion dynamics. Experiments show that InterEditimproves text-to-motion consistency and edit fidelity, achieving state-of-the-art TMME performance. The dataset and code will be released athttps://github.com/YNG916/InterEdit.

Abstract:
Scene understanding requires simultaneous prediction aboutgeometry, appearance, and semantics. However, existing task-specific an-notations are fragmented across incompatible, domain-specific datasets.Current unified systems circumvent this by restricting training to fullyco-annotated data, or by incurring the large computational cost of pseudo-labeling. To mitigate this, we introduce UniD, a unified video model thatjointly predicts eight dense scene properties—depth, surface normals, se-mantic segmentation, boundaries, human parts, albedo, shading, and ma-terials—all learned from disjoint, domain-specific datasets. We propose asimple yet effective distillation step in which per-task experts supervisea unified backbone through lightweight task projectors, eliminating theneed for annotation overlap or pseudo-labeling. Our key insight is thatthe strong visual priors of a pretrained diffusion model are sufficientto bridge the domain gaps introduced by disjoint training sources, en-abling robust generalization to scene-task combinations never seen dur-ing training. UniD achieves competitive performance against per-taskspecialists and multi-task baselines, with strong generalization to out-of-distribution scenarios and enhanced temporal and cross-task consistency.Code and video results are available at https://unid-video.github.io.

Abstract:
Existing video temporal grounding methods typically oper-ate under a closed-world assumption, relying on the implicit premise thatevery natural language query must have a corresponding event some-where in the video. This always-answer paradigm severely compressesthe decision boundary during training, as models are seldom exposed tothe alternative hypothesis that the queried event may be absent. Con-sequently, when users ask about specific events that cannot be matchedin the video within an open-world environment, these models exhibitsystematic temporal hallucinations. This may stem from incorrect at-tribution of textual, visual, or multimodal information, leading them toconfidently generate plausible time frames for events that do not actuallyexist. To address this limitation, we propose HRVTG, a test-time adapta-tion framework that dynamically calibrates the decision boundary of themodel during inference. Built upon a frozen grounding backbone, the pro-posed framework constructs three types of counterfactual hallucinationprobes in a self-supervised manner, targeting the aforementioned halluci-nations. We dynamically reshape the decision boundary through absten-tion signals from counterfactual probes and consistency rewards fromgenuine queries. This is executed by introducing the GRPO algorithm,effectively reformulating the task into an online policy optimization prob-lem in reinforcement learning. Furthermore, we introduce decoupled met-rics to independently evaluate hallucination resistance and real-eventgrounding. Experiments indicate that our method suppresses halluci-nated responses to fabricated queries. It achieves accurate selective pre-diction while maintaining strong grounding performance on real events.The code will be released at https://github.com/CVL-hub/HRVTG.

Abstract:
Multimodal Large Language Models (MLLMs) have demon-strated strong image-level visual understanding and reasoning, yet theirpixel-level perception across both images and videos remains limited.Foundation segmentation models such as the SAM series produce high-quality masks, but they rely on low-level visual prompts and cannotnatively interpret complex conversational instructions. Existing segmen-tation MLLMs narrow this gap, but are usually specialized for eitherimages or videos and rarely support both textual and visual promptsin one interface. We introduce X2SAM, a unified segmentation MLLMthat extends any-segmentation capabilities from images to videos. Givenconversational instructions and visual prompts, X2SAM couples an LLMwith a Mask Memory module that stores guided vision features for tem-porally consistent video mask generation. The same formulation sup-ports generic, open-vocabulary, referring, reasoning, grounded conversa-tion generation, interactive, and visual grounded segmentation across im-age and video inputs. We further introduce the Video Visual Grounded(V-VGD) segmentation benchmark, which evaluates whether a modelcan segment object tracks in videos from interactive visual prompts.With a unified joint training strategy over heterogeneous image and videodatasets, X2SAM delivers strong video segmentation performance, re-mains competitive on image segmentation benchmarks, and preservesgeneral image and video chat ability. Code is available at https://github.com/wanghao9610/X2SAM.

Abstract:
Estimating accurate 3D hand–object pose from in-the-wildegocentric RGB remains challenging due to severe occlusions and am-biguous contact. Existing learning-based methods often struggle to gen-eralise to in-the-wild scenes and are limited by the scarcity of super-vision. We address these issues with two contributions. First, we in-troduce EPIC-Contact, an in-the-wild egocentric dataset of 2.3K clips(62.3K frames) with dense, bijective 3D hand–object contact correspon-dences and posed meshes. Second, we propose HOPformer, an end-to-end transformer that jointly predicts bi-manual hand and object posein a single forward pass. A cross-attention decoder conditions objectfeatures on hand priors, producing robust pose estimation. We testHOPformer on the in-lab 3D dataset, ARCTIC, as well as our newlyintroduced EPIC-Contact dataset. HOPformer reaches 82.4% successrate on ARCTIC (+6.2 pts over current SOTA). On EPIC-Contact,it nearly doubles the success rate while reducing contact deviation by75%. EPIC-Contact, HOPformer code and checkpoints are released:https://sid2697.github.io/epic-contact.

Abstract:
Self-supervised video denoising methods typically extend image-based frameworks into the temporal dimension, yet they often struggleto integrate inter-frame temporal consistency with intra-frame spatialspecificity. Existing Video Blind-Spot Networks (BSNs) require noise in-dependence by masking the center pixel, this constraint prevents the useof spatial evidence for texture recovery, thereby severing spatiotempo-ral correlations and causing texture loss. To address this, we proposeFrames2Residual (F2R), a spatiotemporal decoupling framework thatexplicitly divides self-supervised training into two distinct stages: blindtemporal consistency modeling and non-blind spatial texture recovery.In Stage 1, a blind temporal estimator learns inter-frame consistencyusing a frame-wise blind strategy, producing a temporally consistent an-chor. In Stage 2, a non-blind spatial refiner leverages this anchor to safelyreintroduce the center frame and recover intra-frame high-frequency spa-tial residuals while preserving temporal stability. Extensive experimentsdemonstrate that our decoupling strategy allows F2R to outperform ex-isting self-supervised methods on both sRGB and raw video benchmarks.Code is available at https://github.com/m1NGGi/F2R.

Abstract:
Efficient strategies to jointly adapt and deploy large language models have seen a growing need under resource-limited conditions for downstream applications. However, when applied to visual foundation models, existing methods typically incur either high GPU memory consumption during adaptation or extra computation costs introduced by the adapters at deployment. In this paper, we propose Efficient Quantization-aware Adaptation (EQuA) that achieves high efficiency in both adaptation and deployment for visual foundation models. We observe that dominant memory consumption arises from intermediate activations cached for backpropagation in the deep backbone and activation quantizers. To address this issue, we split a lightweight sub-network from the backbone during adaptation as a side adapter branch, and tailor two adaptation strategies to eliminate these cached activations, thereby significantly reducing memory consumption. At deployment, the side adapter branch is merged back into the backbone, yielding a quantized model without any extra computation costs. Extensive experiments on representative visual foundation models and diverse downstream tasks exhibit that EQuA achieves an elegant trade-off between performance and efficiency. For example, EQuA yields over 70% GPU memory reduction compared to state-of-the-art baselines while maintaining competitive performance. Code: https://github.com/leenas233/EQuA.

Abstract:
The emergence of Multimodal Large Language Models hasenabled a new generative paradigm for multimodal emotion recognition(MER), where emotional interpretations are produced through multi-modal token-level reasoning. In view of multimodal inputs, existing stud-ies commonly observe that reallocating attention from generated tokensto modality tokens during inference consistently improves performance.However, in this work, we reveal a counterintuitive phenomenon: such astrategy only benefits two-modality settings, but degrades performancein three- or more-modality scenarios, where the opposite reallocation di-rection instead leads to improvements. To understand this phenomenon,through comprehensive analysis, we show that with more modalities, thecomplex interactions between modalities make the model hard to paymore attention to tokens with more information, especially in deeperlayers, leading to improper attention allocation and the observed contra-dictory behavior. Based on these insights, we propose a training-freeattention rectification method that leverages structured shallow-layerattention as a prior to regularize entangled final-layer attention dur-ing inference, without introducing additional parameters or modifyingthe backbone model. Extensive experiments on nine datasets in MER-UniBench demonstrate that our method achieves state-of-the-art per-formance, consistently outperforming both reallocation directions acrossdiverse multimodal scenarios. Our code is available at https://github.com/yzl77/ReAR.

Abstract:
Despite rapid progress, most existing vision-language models (VLMs) built from 2D visual inputs often struggle when handling various 3D tasks that require fine-grained spatial understanding and reasoning. To bridge this gap, we present VLM-IE3D, a unified framework that enhances the 3D spatial awareness of VLMs by equipping them with both implicit and explicit 3D geometries learned from RGB videos. Our VLMIE3D introduces Implicit Geometry Tokens (IGTs) that capture high-level geometric priors from input videos, as well as complementary Explicit Geometry Tokens (EGTs) that encode detailed geometric structures from reconstructed 3D attributes. On top of that, VLM-IE3D comes with a 3D-aware adapter that effectively fuses the two types of geometric representations with 2D visual cues. This RGB-only design injects strong 3D inductive biases for fine-grained spatial understanding and reasoning without requiring any additional 3D inputs. Extensive experiments show that VLM-IE3D achieves superior performance consistently across various 3D tasks including 3D video detection, 3D visual grounding, 3D dense captioning, and spatial reasoning. Code and models are available at https://github.com/Vegetebird/VLM-IE3D.

Abstract:
Recent adapters for vision-language models (VLMs) still suf-fer from dense cross-modal interactions and limited control over align-ment capacity. To address this issue, we propose a two-stage Shallow-Deep Squeezing Adapter (SDSA) that explicitly regulates cross-modalinteraction density and alignment capacity. In the shallow squeezingstage, SDSA leverages token-level masking to impose structured spar-sity within a sparse bottleneck space. Then, in the deep squeezing stage,SDSA applies a shared low-rank transformation and a cross-modal atten-tion module: the low-rank module consolidates alignment into a compactset of dominant shared directions, while cross-modal attention refinesrepresentations through selective interaction. Extensive experiments on11 datasets show that SDSA delivers superior base-to-novel generaliza-tion and cross-dataset evaluation, and significantly improves the gener-alizability of VLMs under few-shot conditions. The code can be found athttps://github.com/haowang-ac/SDSA.

Abstract:
Vision-language models (VLMs) have achieved strong per-formance across diverse multimodal tasks, yet they remain vulnerable tounreliable reasoning. Existing self-correction methods mitigate these is-sues but typically rely on post-training or carefully engineered feedback,incurring high computational cost. In this work, we revisit this challengethrough the lens of emotional cues, asking whether they can activate la-tent self-correction behaviors in VLMs without additional training. Wefind that emotional signals serve as an effective trigger for self-correction, encouraging more cautious and reflective reason-ing. Motivated by this finding, we propose ESC (Emotional Self-Correction), a training-free self-correction framework. ESC introducesan external verifier that detects potentially incorrect initial responsesand injects emotional feedback to encourage model to reflect, and pro-duce a better revised response without additional training. Extensiveexperiments across safety, hallucination, vision-centric perception, andmultimodal reasoning benchmarks show that ESC consistently improvesreliability while preserving overall model utility. These results suggestthat emotion can function not only as an ability to be recognized, butalso as a practical control signal for scalable self-correction in VLMs. Wetherefore believe that ESC provides a strong foundation for anew reliable human-like, emotion-integrated research direction.Our project is publicly available at https://genai4e.github.io/ESC/.

Abstract:
Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions. Common entropybased methods, such as Semantic Entropy (SE), rely on output diversity. Yet our analysis shows that overconfident visual embeddings suppress output diversity under stochastic decoding, causing SE to underestimate uncertainty in such cases. Recent methods instead probe output diversity through input perturbations, including textual paraphrasing or joint text-image perturbations, and show improved performance. We study these approaches and reveals that the resulting variability is often dominated by textual changes rather than visual evidence, causing uncertainty estimates to reflect prompt sensitivity rather than visual ambiguity. We therefore propose Visual Semantic Entropy (VSE), which perturbs only the image to probe nearby visual variations while keeping the text query fixed. VSE measures uncertainty by clustering generated answers into semantic prototypes and computing the massweighted dispersion among them. Extensive evaluation across five modern vision-language models and five diverse VQA benchmarks demonstrates that VSE effectively captures visual ambiguity, establishing a new state-of-the-art for VLM uncertainty estimation. Code is available: https://github.com/tadeephuy/visual-semantic-entropy

Abstract:
Video understanding benchmarks have long centered on sin-gle-camera settings, where modern multi-modal language models achievestrong performance across image and video tasks. Yet, the real world runson multi-camera networks: autonomous vehicles, security systems, androbots all gather data across many simultaneous views. We argue thatthis is not simply “more” of the single-camera problem; it is fundamen-tally different. Multi-camera reasoning requires handling context thatscales with the number of views, resolving occlusions visible from only asubset of cameras, judging which views matter, and integrating evidenceacross perspectives that may overlap or diverge. Current models strugglewith exactly these challenges, yet no benchmark systematically targetsthem. We introduce CrossView, a multi-camera video question-answeringbenchmark spanning autonomous driving, security surveillance, egocen-tric/exocentric video, and robotics. Evaluation of proprietary models,such as GPT-5.2, and open-source models, like Qwen3-VL, reveals con-sistently low accuracy, with open-source models trailing by a wide mar-gin. Performance scales strongly with a model’s ability to jointly pro-cess multiple viewpoints, positioning CrossView as a rigorous bench-mark for multi-camera video. We open-source our code and dataset athttps://utaustin-swarmlab.github.io/CrossView.

Abstract:
Identifying and grounding precise geometric entities, such as edges, planar regions, and curved surfaces within 3D objects, is foundational to computer-aided design (CAD), robotic manipulation, and scientific simulation. Although modern Vision Language Models (VLMs) have advanced referring segmentation (RIS) in the image domain, extending such language-driven localization to structured 3D geometry is substantially harder. The 3D object appearance is highly sensitive to viewpoints; a single perspective may render a target entity clearly observable, while another may suffer from severe occlusion or foreshortening. In this work, we attempt to solve these challenges with MV-GEL (Multi-View Geometric Entity Localization), a framework for localizing fine-grained geometric entities on polygon meshes from natural language queries. Our key insight is that reliable CAD entity (i.e., faces, edges or solids) localization depends on selecting views that make the queried entity maximally interpretable. We introduce GELviews, a prompt-conditioned ranking module that prioritizes viewpoints based on language prompted observability of geometric CAD entities. Selected views are processed by a VLM-based reasoning segmentation backbone, and predicted masks are lifted to the corresponding meshes via geometry-aware ray casting. Our framework is completely CAD agnostic and relies only on 3D meshes. Experiments show up to a 1.7× improvement in face-level IoU and over 4.5× gains in edge-level F1 compared to vanilla baselines, substantially outperforming CLIP-based and random view sampling, particularly for thin and view-sensitive structures. The dataset, code and trained checkpoints are available at https://github.com/kbali1297/MV-GEL.

Abstract:
Unified anomaly detection requires modeling highly heteroge-neous normal data without access to anomalous samples. While foundationmodels like DINOv2 provide rich token representations, leveraging thesespaces for explicit density estimation remains challenging. Energy-BasedModels (EBMs) offer a principled formulation, but their training inhigh-dimensional token spaces is unstable due to anisotropy and strongcross-dimensional correlations, which degrades finite-step Markov ChainMonte Carlo (MCMC) sampling. We identify this instability as fundamen-tally geometric and introduce ReFP-AD (Rectified Flow Preconditioningfor Anomaly Detection), which learns a geometric reparameterization thatmaps high-dimensional embeddings into a well-conditioned latent spacevia an optimal transport (OT)-coupled rectified flow. This precondition-ing enables stable persistent contrastive divergence with preconditionedStochastic Gradient Langevin Dynamics (SGLD) in full-dimensional tokenspaces. Anomaly scores are then derived from the learned energy landscapeusing gradient norms. Under a strict unified protocol on the MVTec-ADand VisA datasets, ReFP-AD achieves 98.6%/97.9% Image/Pixel AUROCon MVTec-AD and 97.3%/99.0% on VisA, outperforming prior unifiedEBM baselines by up to +10.8% in Image AUROC. Ablation experimentsdemonstrate that geometric reparameterization is critical for finite-stepMCMC and accurate anomaly localization in high-dimensional tokenspaces. Code is available at https://github.com/CLendering/ReFP-AD

Abstract:
Tactile graphics are a primary medium for blind and low-vision (BLV) individuals to access non-textual information. However,they are difficult to scale or personalize. While recent generative modelshave revolutionized visual content creation, they are optimized for screen-based visual realism and fail to satisfy the haptic perceptual and physicalfabrication constraints required for touch. We present the first integratedgenerative system that produces fabrication-ready 2.5D tactile graphicsdirectly from natural language prompts, jointly generating global basegeometry, fine-grained tactile surface textures, and standard-compliantbraille within a unified 3D-printable representation. Our approach intro-duces fabrication-aware techniques, including template-guided relief gen-eration, a fast diffusion-based text-to-texture module for high-resolutiontileable normal maps, and strict base flattening to ensure tactile readabil-ity and printability, while supporting both automatic generation and in-teractive texture control. Extensive evaluations, together with in-personuser studies with BLV participants and blindfolded sighted participantsusing physically 3D-printed outputs, show that participants consistentlyprefer our results over baselines. By extending generative graphics be-yond screens to touchable reliefs, our work broadens access to generativeAI for the BLV community and beyond. The code and project webpageare available at https://ruihangao.github.io/Text2TactileGraphics/.

Abstract:
Recently, 3D Gaussian Splatting (3DGS), which succeeded in perspective image rendering, has been extended to handle the panoramic image rendering task. However, the performance of the existing methods in literature is generally limited due to the severe distortions involved in panoramic images. To address this problem, we construct a distortion map under the panoramic imaging model, where the value of each pixel reflects its corresponding distortion degree, and accordingly, we propose a novel Panoramic Gaussian Splatting method by utilizing this Distortion map, called PaD-GS. The proposed PaD-GS, consisting of a distortion-aware Gaussian decoding module and a distortion-aware opacity modulation module, employs a whole-to-partial strategy to impose the distortion map on Gaussian representation learning. Specifically, the distortion-aware Gaussian decoding module is designed to decode the ensemble of learnable scene features and the distortion map into a set of Gaussian representations, so that the intrinsic distortion information of the distortion map could be injected into the whole Gaussian representations. The distortion-aware opacity modulation module is designed to further impose the distortion map by adaptively modulating the partial attribute (opacity) of each Gaussian representation. Thanks to the introduced distortion map, the proposed PaD-GS could alleviate the negative influence of severe distortion involved in panoramic images. Extensive experimental results on two panoramic datasets demonstrate that PaD-GS significantly outperforms several state-of-the-art methods for panoramic image rendering in most cases. The code is available at https://github.com/CosyXu/PaD-GS.

Abstract:
Recent advancements in 3D Gaussian Splatting (3DGS) have enabled language-guided scene understanding. However, existing Referring 3D Gaussian Splatting (R3DGS) methods are fundamentally restricted to single-target queries. To reflect the ambiguity of real-world instructions, we introduce the Generalized Referring 3D Gaussian Splatting Segmentation (GR3DGS) task, which requires dynamically segmenting an arbitrary number of targets (0, 1, or N). To facilitate comprehensive evaluation of this new task, we construct two new benchmarks: GR-LERF and GR-ScanNet. Crucially, existing R3DGS paradigms exhibit fundamental technical bottlenecks that severely limit their performance on the GR3DGS task: they lack intrinsic 3D point-level understanding by operating merely on 2D rendered pixels, and they incur prohibitive computational overhead by requiring per-scene optimization to embed heavy semantic features. To dismantle these bottlenecks, we propose ZeroSplat, a novel training-free and zero-feature framework. ZeroSplat lifts 2D Vision-Language Model (VLM) priors into 3D space through robust multi-view geometric constraints. This strategy enables intrinsic point-level understanding without incurring any additional feature storage. Extensive experiments demonstrate that ZeroSplat significantly outperforms state-of-the-art methods across generalized and single-target scenarios while maintaining exceptional efficiency. Project Page: https://inkmind-ai.github.io/ZeroSplat

Abstract:
3D Gaussian Splatting (3DGS) enables real-time, photorealistic novel view synthesis, making it a highly attractive representation for model-based video tracking. However, leveraging the differentiability of the 3DGS renderer “in the wild” remains notoriously fragile. A fundamental bottleneck lies in the compact, local support of the Gaussian primitives. Standard photometric objectives implicitly rely on spatial overlap; if severe camera misalignment places the rendered object outside the target’s local footprint, gradients strictly vanish, leaving the optimizer stranded. We introduce SpectralSplats, a robust tracking framework that resolves this "vanishing gradient" problem by shifting the optimization objective from the spatial to the frequency domain. By supervising the rendered image via a set of global complex sinusoidal features (Spectral Moments), we construct a global basin of attraction, ensuring that a valid, directional gradient toward the target exists across the entire image domain, even when pixel overlap is completely nonexistent. To harness this global basin without introducing periodic local minima associated with high frequencies, we derive a principled Frequency Annealing schedule from first principles, gracefully transitioning the optimizer from global convexity to precise spatial alignment. We demonstrate that SpectralSplats acts as a seamless, drop-in replacement for spatial losses across diverse deformation parameterizations (from MLPs to sparse control points), successfully recovering complex deformations even from severely misaligned initializations where standard appearancebased tracking catastrophically fails.

Abstract:
This work introduces holo-captioning, a novel task that strivesto seek the text equivalent of 3D scenes. As the initial step, we formu-late holo-captioning as generating a structured textual description thatcomprehensively depicts all entities within a 3D scene—including theirsemantic tags, spatial locations, attributes, and inter-entity relations. Totackle this challenging task, we first develop an effective captioning en-gine to produce detailed descriptions of individual entity instances andinstance pairs, and contribute a large-scale benchmark comprising over15K scenes for training and evaluation. Building upon this foundation,we propose HoloScribe, a novel model that features an instance-awaredecoupled pipeline for generating structured holo-captions, and furtherincorporates anchor-aware instance linking to identify relational instancepairs. Additionally, we propose a comprehensive evaluation metric namedHoloScore, and provide a human-curated test set to ensure reliable modelassessment. Experimental results demonstrate that HoloScribe signifi-cantly outperforms state-of-the-art 3D dense captioners and 3D LLMgeneralists, underscoring the effectiveness of our approach. Project page:https://visual-ai.github.io/holocap/

Abstract:
Advances in neural rendering have enabled high-fidelity multi-view reconstruction of 3D scenes. However, free-form non-rigid shapeediting remains a significant challenge. Point-based neural representationsare highly desirable for multi-view reconstruction because they lack fixedconnectivity, which does not constrain the learned surface topology tothat of the initialization. Yet this same property causes point-based rep-resentations to struggle with holes and surface discontinuities under largedeformations. To address this, we propose a novel self-supervised methodto enable point-based representations to adapt to large deformations with-out requiring ground truth multi-view images of deformed geometry. Thekey idea is to generate random deformations and to ensure consistencyin the predicted surface before and after deformation. In particular, thesurface prediction from the deformed point cloud should be the sameas the deformation applied to the surface prediction from the originalpoint cloud. We incorporate our approach into attention-based pointrepresentations, which differ from splatting-based point representationsin their use of a learned interpolation kernel between points as opposedto a Gaussian kernel around each point. This learned interpolation kernelcan learn to adapt to large deformations, without requiring addition orremoval of points. We show that our framework significantly enhancesits robustness to large deformations. Experiments on synthetic geometryediting benchmarks (Neural Editor, Objaverse) demonstrate that ourapproach outperforms existing point-based methods in zero-shot editingand significantly reduces artifacts. Furthermore, qualitative results on theDTU and Mip-NeRF 360 datasets demonstrate our method’s effectivenesson real-world scenes.

Abstract:
Photorealistic volumetric rendering of CT scans greatly ben-e x001C ts clinical work x001D ows, yet neural approaches such as Neural RadianceFields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitiveper-scan optimization (hours for NeRF, about 30 minutes for 3DGS),making them impractical in clinical settings. We propose Render-FM,a feedforward model that eliminates this bottleneck by directly regress-ing 6D Gaussian Splatting (6DGS) parameters from a CT volume ina single 2.8-second forward pass, a 500× speedup over per-scan opti-mization. To bridge the domain gap between natural scene reconstruc-tion and medical volumetric rendering, we introduce Anatomy-GuidedPriming (AGP), which incorporates segmentation masks and transferfunctions as structural and appearance priors, information that exist-ing Gaussian splatting methods overlook. Built on an nnU-Net-inspired3D U-Net trained on diverse CT scans, Render-FM predicts per-voxel6DGS parameters and supports immediate real-time rendering. Unlikeper-scan methods, it generalizes to unseen anatomies, novel transferfunctions, and enables compositional organ visualization with zero ad-ditional preparation time. Optional 89-second x001C ne-tuning further im-proves quality, surpassing per-scan optimized baselines. Project page:https://gaozhongpai.github.io/renderfm/.

Abstract:
Recent advances in Gaussian Splatting-based inverse rendering extend Gaussian primitives with shading parameters and physically grounded light transport, enabling high-quality material recovery from dense multi-view captures. However, the accuracy of these methods degrades under sparse-view settings, where limited observations lead to severe ambiguity between geometry, reflectance, and lighting. We introduce GAINS (Gaussian-based Inverse rendering from Sparse multi-view captures), a two-stage inverse rendering framework that leverages foundation models as priors to stabilize geometry and material estimation. The core technical contribution of this paper is an inverse rendering framework that unifies foundation model priors with physically-based representations in an optimization scheme. GAINS first refines geometry using monocular depth, normal, and diffusion priors, and then employs segmentation, intrinsic image decomposition (IID), and diffusion priors to regularize material recovery. Extensive experiments on synthetic and real-world datasets show that GAINS significantly improves material parameter accuracy, relighting quality, and novel-view synthesis compared to state-of-the-art Gaussian-based inverse rendering methods. While GAINS outperforms and remains competitive across a wide range of objects captured with 4 to 32 cameras, the improvement is particularly pronounced under sparse-view settings, where ambiguity is high and learning-based priors become especially beneficial. Project page: https://patrickbail.github.io/gains/

Abstract:
Recent advances in neural rendering have established 3DGaussian Splatting (3DGS) as a highly efficient representation for novelview synthesis, enabling fast training and real-time rendering with strongfidelity. However, when supervision is limited to sparse input views,3DGS tends to overfit to the observed images and generalize poorlyto unseen viewpoints. We address this challenge from the perspectiveof flat minima (FM) optimization, which seeks solutions that remainstable under small parameter perturbations. Viewing Gaussian parame-ters as trainable weights, we adapt FM principles to the geometric anddynamic nature of 3DGS with a lightweight training framework. Ourmethod regularizes optimization with controlled Gaussian perturbationsthat account for each Gaussian’s anisotropy and the training progress,preserving fine details while improving robustness to sparse-view overfit-ting. To further stabilize this flat minima optimization process, we intro-duce periodic reinitialization, which temporarily returns non-positionalparameters to their initial states for a short window. Together, thesetechniques integrate seamlessly into existing 3DGS pipelines without ar-chitectural changes. Experiments on LLFF and Mip-NeRF360 datasetsdemonstrate improved quantitative metrics and perceptual quality un-der sparse-view supervision, producing reconstructions that are sharper,more stable, and better generalized to novel viewpoints.

Abstract:
We introduce ReconSplat, a feed-forward model for 3D scenereconstruction that aims to address the longstanding trade-off betweenplausible view generation for unobserved regions and geometric consis-tency, providing both geometrically aligned novel views and sharp depthestimates. Our approach builds on 3D Gaussian splatting (3DGS) as anintermediate differentiable scene representation and integrates it with amulti-view latent diffusion model (MV-LDM) trained to act simultane-ously as a refiner and an inpainter for appearance and scene geometry.We enforce geometric consistency by guiding the diffusion process withvariational 3D latent features for appearance and geometry, encoded bythe feed-forward 3DGS representation and rasterized to 2D latent space.ReconSplat produces both photorealistic novel views and accurate depthmaps on real-world benchmarks, RealEstate10K and DL3DV-10K, out-performing existing methods in challenging extrapolation setups. No-tably, ReconSplat allows the extrapolation of unseen and challengingviewpoints jointly with coherent and precise scene geometry. Code andadditional visual results are available on our project page.

Abstract:
Di!erentiable rendering has advanced novel view synthesis(NVS), yet applying it to real-world driving remains di"cult due tosparse capture viewpoints, dynamic objects, and limited multi-trajectorydata. We introduce the Multi-View Multi-Vehicle (MV2 ) datasetand benchmark for evaluating NVS models under large viewpoint changesin dynamic urban scenes. MV2 features synchronized captures from a car,scooter, and drone, each following distinct yet synchronized trajectories.Training NVS methods on one vehicle’s camera stream and testing on an-other enables evaluation under substantially larger viewpoint variationsthan existing single-trajectory datasets. All sequences are registered viaStructure-from-Motion and camera poses verified using manual pixel-level correspondence annotations, yielding 50 high-quality scenes with12000 images. Benchmarking recent NVS and camera pose estimationmethods shows that NVS performance degrades with increasing view-point disparity, and that feed-forward pose estimators notably lag behindoptimization-based approaches, highlighting MV2 as a rigorous testbedfor NVS in driving. The dataset, benchmark protocol, and project re-sources are available at https://mv2-dataset.github.io/.

Abstract:
3D Gaussian Splatting (3DGS) has achieved remarkable suc-cess in real-time novel view synthesis, yet it suffers from severe overfit-ting under sparse-view settings due to insufficient geometric constraints.While recent methods introduce monocular depth priors to mitigatethis, they inherently struggle with scale ambiguity and cross-view in-consistency, leading to defective geometry. In this paper, we proposeStereoGS, a novel sparse-view 3DGS framework that integrates stereopriors to establish reliable binocular consistency. Unlike scale-agnosticmonocular constraints, StereoGS introduces a Stereo Depth Regulariza-tion by constructing virtual stereo pairs during optimization and lever-aging a foundation stereo model to enforce absolute scale and binocular-consistent structures. To further suppress overfitting and eliminate re-dundant primitives, we design a Gradient-Aware Opacity Decay strat-egy that dynamically penalizes Gaussians based on their relative opacitygradient magnitudes. Combined with a Consistency-Aware Dense Ini-tialization using zero-shot multi-view depth estimation, StereoGS effec-tively anchors primitives to accurate scene surfaces. Extensive experi-ments on LLFF, DTU, Mip-NeRF360, and Blender datasets demonstratethat StereoGS achieves state-of-the-art performance in sparse-view set-tings without incurring any additional inference overhead. Project Page:https://stringerywh00.github.io/StereoGS project page/

Abstract:
Despite the success of recent novel view synthesis methods,they tend to struggle in sparse-view settings. This poor generalizationto unseen viewpoints is an inherent challenge when training with lim-ited data. To address this, we investigate the relationship between losssharpness and generalization in novel view synthesis—an underexploreddirection. Interestingly, while pursuing flatter minima is widely knownto improve generalization in deep learning, reducing loss sharpness is notalways beneficial in novel view synthesis. We demonstrate that this dif-ference arises because high-detail regions inherently require a sharp losslandscape for accurate reconstruction, whereas low-detail regions benefitfrom a flat loss landscape for improving generalization. Based on this in-sight, we introduce structure-aware sharpness, defined within structure-adaptive neighborhoods, and propose to adaptively adjust the sharpnessregularization weight according to the local image structure. This strat-egy encourages flatter minima for generalization while preserving the losssharpness necessary to reconstruct fine details. Across various datasetsand configurations, our strategy consistently improves a wide range ofbaselines. Code is available at https://bbangsik13.github.io/FASR.

Abstract:
3D Gaussian Splatting (3DGS) achieves real-time novel-viewsynthesis by optimizing millions of anisotropic Gaussians, yet its trainingremains expensive, with the backward pass dominating runtime in thepost-densification refinement phase. We observe substantial update re-dundancy in this phase: many sampled views have near-plateaued lossesand provide diminishing gradient benefits, but standard training stillruns full backpropagation. We propose SkipGS with a novel view-adaptivebackward gating mechanism for efficient post-densification training. SkipGSalways performs the forward pass to update per-view loss statistics, andselectively skips backward passes when the sampled view’s loss is consis-tent with its recent per-view baseline, while enforcing a minimum back-ward budget for stable optimization. On Mip-NeRF 360, compared to3DGS, SkipGS reduces end-to-end training time by 23.1%, driven by a42.0% reduction in post-densification time, with comparable reconstruc-tion quality. Because it only changes when to backpropagate withoutmodifying the renderer, representation, or loss, SkipGS is plug-and-playand compatible with other complementary efficiency strategies, enablingadditive speedups. Code is available at https://github.com/ASU-ESIC-FAN-Lab/SkipGS.

Abstract:
The integration of novel view synthesis (NVS) and open-vocabulary segmentation (OVS) has recently yielded powerful feed-forward3D foundation models. However, their inherent reliance on static-sceneassumptions leads to severe misalignment of spatial features in uncon-strained dynamic environments. To bridge this critical gap, we proposeSPAR, a novel joint semantic-geometric encoding architecture that ex-plicitly isolates transient dynamic noise prior to latent space aggregation.Furthermore, we introduce a dynamic-region-aware end-to-end trainingparadigm that structurally couples motion estimation with multi-viewvisual and semantic learning. This unified approach enables the networkto inherently resolve motion conflicts and distill multi-view consistent,temporally stable scene representations from dynamic inputs. Extensiveexperiments on the challenging D-RE10K benchmark demonstrate thatSPAR achieves state-of-the-art performance. Our end-to-end approachachieves exceptional novel view synthesis quality, yielding a PSNR of22.15 dB and 23.33 dB given only 3 and 4 input views respectively. De-spite being trained in a self-supervised manner, our model achieves anmIoU of 88.5% for motion mask prediction. Furthermore, our analysisreveals a strong inter-task synergy between photometric scene recon-struction and semantic understanding, where semantic synthesis learningconsistently enhances photometric fidelity in novel view rendering. Codewill be available at https://github.com/dmucby/SPAR.

Abstract:
SceneHI is a framework that lifts high-resolution, illumina-tion-aware priors from 2D diffusion models to perform 3D texture syn-thesis. It is the first to demonstrate that high-resolution textures, previ-ously limited to 2D synthesis, can be generated directly on 3D objectswithout model fine-tuning or optimization. Designed for complex, multi-object environments, SceneHI uniquely combines 3D-consistency, high-resolution fidelity, and physically plausible baked shadows within a singlegenerative pipeline. To enforce strict geometric coherence, we introducean exact analytical pixel-to-texel mapping that aligns diffusion trajecto-ries across multiple viewpoints. We utilize High-Resolution Latent Tex-tures (HRLTs) as a persistent canvas for gradually denoised textures,while camera views perform the denoising steps in latent pixel space.This ensures a shared base texture that can be subsequently refined tohigh resolution without compromising multi-view consistency. Finally, alight-aware generative pass embeds realistic geometry-consistent shad-ows directly into the atlases, bridging the gap to production workflows.SceneHI achieves high visual fidelity while reducing generation time by80% compared to existing scene-level methods. The code for our work isavailable at https://github.com/Thanos-DB/SceneHI.

Abstract:
Accurate object geometry estimation is essential for manydownstream tasks, including robotic manipulation and physical interac-tion. Although vision is the dominant modality for shape perception, itbecomes unreliable under occlusions or challenging lighting conditions.In such scenarios, tactile sensing provides direct geometric informationthrough physical contact. However, reconstructing global 3D geometryfrom sparse local touches alone is fundamentally underconstrained. Wepresent TouchAnything, a framework that leverages a pretrained large-scale 2D vision diffusion model as a semantic and geometric prior for3D reconstruction from sparse tactile measurements. Unlike prior workthat trains category-specific reconstruction networks or learns diffusionmodels directly from tactile data, we transfer the geometric knowledgeencoded in pretrained visual diffusion models to the tactile domain.Given sparse contact constraints and a coarse class-level description ofthe object, we formulate reconstruction as an optimization problem thatenforces tactile consistency while guiding solutions toward shapes con-sistent with the diffusion prior. Our method reconstructs accurate ge-ometries from only a few touches, outperforms existing baselines, andenables 3D reconstruction of previously unseen object instances. Ourproject page is https://grange007.github.io/touchanything/.

Abstract:
Reconstructing 3D objects from brain signals offers a promis-ing avenue for understanding human visual cognition. While prior workhas shown initial success using EEG signals for 3D reconstruction, ex-isting methods typically employ a uniform diffusion decoder, overlook-ing the evolving semantic granularity of both EEG representations andthe diffusion denoising process. In this paper, we propose Mind2Cloud, anovel EEG-to-point-cloud generation framework based on two-granularitydiffusion decoding. The core of Mind2Cloud is a time-aware decoder thatintegrates a global Transformer branch and a local Point-Voxel CNN(PVCNN) branch across diffusion timesteps through a learnable fusionmask. Specifically, Transformer layers are incorporated into the earlyupsampling stages to capture global object structure under high uncer-tainty, while PVCNN modules are used in later stages to refine local ge-ometric details. Inspired by the hierarchical nature of EEG-based visualrepresentations, this design dynamically adapts its spatial granularity inaccordance with the coarse-to-fine trajectory of diffusion denoising. Wefurther introduce an adversarial refinement module to enhance geometricrealism and semantic consistency. Extensive experiments on the EEG-3Ddataset across all 12 subjects demonstrate that Mind2Cloud outperformsprior work in both geometric accuracy and semantic alignment, settinga new benchmark for EEG-to-point-cloud generation. Our source code isavailable at https://github.com/duasoi/Mind2Cloud.

Abstract:
In autonomous driving scenarios, the collected LiDAR pointclouds can be affected by occlusion and long-range sparsity, limitingthe perception of autonomous driving systems. Scene completion meth-ods can infer the missing parts of incomplete single LiDAR scans. Re-cent methods adopt point-level denoising diffusion probabilistic modelsthat rely on approximations to handle scene-scale data, leading to amismatch between training and inference initial distributions. We pro-pose LiFlow, the first point-level flow matching method for 3D LiDARscene completion. LiFlow improves upon existing diffusion-based meth-ods by directly aligning single LiDAR scans to complete scenes, en-suring consistent initial distributions between training and inference.LiFlow introduces nearest neighbor flow matching and Chamfer distancematching to enhance both local structure and global coverage in thealignment of point clouds. Compared to existing diffusion-based meth-ods, LiFlow reduces the number of inference steps, enabling more ef-ficient scene completion while maintaining stability and high genera-tion quality. Extensive experiments demonstrate that LiFlow achievesstate-of-the-art performance across multiple metrics. Code is availableat https://github.com/matteandre/LiFlow.

Abstract:
The rapid deployment of visual localization (VL) in aug-mented/virtual reality (AR/VR) and autonomous systems makes privacy-preserving localization a critical societal necessity. Existing VL systemsrely on cloud-based 3D scene representation storage and transmit clientside features to a remote server, exposing users to potential reconstruc-tion attacks from intercepted privacy preserving representations. The aimof this paper is therefore to investigate what sensitive information canan adversary actually recover, by comparing multiple privacy-preservingsolutions within the literature. We define privacy as the inability to re-cover personally identifiable information from these representations, ac-knowledging that general scene details do not inherently represent a pri-vacy breach. To model adversarial behavior and to thoroughly measure amethod’s degree of privacy preservation, we introduce a new privacy at-tack that trains a conditional diffusion model to reconstruct images fromprivacy-preserving representations. This reconstruction quality serves asa direct proxy for the amount of sensitive information each represen-tation contains. Leveraging this unified attack protocol, we present thefirst comprehensive comparison across major families of privacy preserv-ing VL methods. Our analysis reveals substantial differences in privacyleakage between representations and highlights limitations in current de-sign assumptions. Code and trained checkpoints may be found here:https://github.com/MaximePi/benchmark-privacy-inversion.

Abstract:
3D reconstruction of articulated objects from a single im-age is challenging because large training datasets with paired image and3D supervision are difficult to obtain. Recent point map–based methodsachieve strong performance but rely on synthetic datasets rendered frommanually created articulated 3D assets with carefully curated pose dis-tributions. While camera viewpoints can be easily sampled, generatingrealistic object articulations remains costly and labor-intensive. We pro-pose a training framework that reduces this requirement by leveragingunannotated 2D images collections with only a single rigged canonicalmesh per category. Starting from a weak 3D shape predictor trained oncanonical-pose renders, we iteratively estimate object articulation andcamera pose by fitting the mesh to predicted point maps. The recov-ered articulations and viewpoints are then used to render updated syn-thetic training data, progressively improving the predictor. Despite us-ing substantially weaker 3D supervision, our models achieve performancecomparable with DualPM, which requires manually curated articulatedtraining datasets.

Abstract:
Humans perceive and understand real-world spaces through a stream of visual observations. Therefore, the ability to streamingly maintain and update spatial evidence from potentially unbounded video streams is essential for spatial intelligence. The core challenge is not simply longer context windows but how spatial information is selected, organized, and retained over time. In this paper, we propose Spatial-TTT towards streaming visual-based spatial intelligence with test-time training (TTT), which adapts a subset of parameters (fast weights) to capture and organize spatial evidence over long-horizon scene videos. Specifically, we design a hybrid architecture and adopt large-chunk updates parallel with slidingwindow attention for efficient spatial video processing. To further promote spatial awareness, we introduce a spatial-predictive mechanism applied to TTT layers with 3D spatiotemporal convolution, which encourages the model to capture geometric correspondence and temporal continuity across frames. Beyond architecture design, we construct a dataset with dense 3D spatial descriptions, which guides the model to update its fast weights to memorize and organize global 3D spatial signals in a structured manner. Extensive experiments demonstrate that SpatialTTT improves long-horizon spatial understanding and achieves state-ofthe-art performance on video spatial benchmarks. Project page: https: //liuff19.github.io/Spatial-TTT.

Abstract:
Flow-matching methods for 3D shape assembly learn pointwise velocity fields that transport parts toward assembled configurations, yet they receive no explicit guidance about which cross-part interactions should drive the motion. We introduce TORA, a topology-first representation alignment framework that distills relational structure from a frozen pretrained 3D encoder into the flow-matching backbone during training. We first realize this via simple instantiation, token-wise cosine matching, which injects the learned geometric descriptors from the teacher representation. We then extend to employ a Centered Kernel Alignment (CKA) loss to match the similarity structure between student and teacher representations for enhanced topological alignment. Through systematic probing of diverse 3D encoders, we show that geometryand contact-centric teacher properties, not semantic classification ability, govern alignment effectiveness, and that alignment is most beneficial at later transformer layers where spatial structure naturally emerges. TORA introduces zero inference overhead while yielding two consistent benefits: faster convergence (up to 6.9×) and improved accuracy in-distribution, along with greater robustness under domain shift. Experiments on five benchmarks spanning geometric, semantic, and inter-object assembly demonstrate state-of-the-art performance, with particularly pronounced gains in zero-shot transfer to unseen real-world and synthetic datasets. Project page: https://nahyuklee.github.io/tora

Abstract:
We present Seen2Scene, the first flow matching-based approach that trains directly on incomplete, real-world 3D scans for scene completion and generation. Unlike prior methods that rely on complete and hence synthetic 3D data, our approach introduces visibility-guided flow matching, which explicitly masks out unknown regions in real scans, enabling effective learning from real-world, partial observations. We represent 3D scenes using truncated signed distance field (TSDF) volumes encoded in sparse grids and employ a sparse transformer to efficiently model complex scene structures while masking unknown regions. We employ 3D layout boxes as an input conditioning signal, and our approach is flexibly adapted to various other inputs such as text or partial scans. By learning directly from real-world, incomplete 3D scans, Seen2Scene enables realistic 3D scene completion for complex, cluttered real environments. Experiments demonstrate that our model produces coherent, complete, and realistic 3D scenes, outperforming baselines in completion accuracy and generation quality.

Abstract:
Multi-teacher distillation has emerged as a way to combine complementary teacher models into a single student model that exhibits the strengths of all its teachers. The student is trained to mimic the output of the teachers on a set of images, typically the union of the individual teacher’s training sets, assuming this data is available. In this paper, we question that assumption and explore alternative options. We first study how far one can go when distilling from teachers fed with different types of noise. Then, we show that information contained in the teachers can be leveraged to tailor the noise for multi-teacher distillation: we propose a method that, thanks to decorrelation losses at both patch and image levels, generates teacher-specific, improved samples optimized for data-free distillation. Experiments show that our most effective samples, IDeaL, lead to strong students that successfully capture complementary information from the teachers, yielding surprisingly competitive results that substantially narrow the gap with students distilled from real images. Moreover, given a limited budget of 1K images for distillation, students distilled using our IDeaL samples match or surpass the performance of those distilled using a 1K-image subset of ImageNet.

Abstract:
Magnetic Resonance Imaging (MRI) is often acquired with anisotropic resolution to reduce scan time, producing stair-step artifacts along the through-plane direction. In through-plane MRI superresolution, an efficiency–fidelity trade-off arises: feed-forward regressors are fast but oversmooth at large slice-thicknesses, while sampling-based methods improve fidelity at high inference cost. We propose DRIFT, a two-stage thickness-conditioned rectified flow framework for throughplane MRI super-resolution with continuous input slice-thickness. Stage 1 employs an Anatomical Projection Network (APN) to map low-resolution patches to a coarse high-resolution manifold, providing a deterministic anatomical initialization that shortens the residual transport of Stage 2 and stabilizes slice-wise refinement. Stage 2 refines details via rectified flow and introduces a Physics-Aware Difficulty (PAD) metric derived from slice-thickness induced through-plane bandwidth deficit to guide an Adaptive Integration Scheduler (AIS), allocating ODE steps by thickness. A Consistent Endpoint Trajectory Alignment (CETA) loss enforces thickness-consistent reconstructions. Experiments show that DRIFT outperforms super-resolution baselines while reducing inference cost. Code, models, and interactive demos are available at https://yoonseokchoiai.github.io/drift-eccv2026/.

Abstract:
Multimodal medical image registration is crucial for diagno-sis and treatment planning, yet remains challenging due to significantappearance differences between modalities such as MR and CT. Themodality gap requires that the encoder in learning-based registrationmethods has cross-modal capabilities. Meanwhile, recent vision founda-tion models (VFMs) have demonstrated strong capabilities in extractingrich cross-modal representations that help bridge such modality gaps.Inspired by this, we propose a foundation-guided multimodal represen-tation alignment framework, which transfers the rich cross-modal knowl-edge from a pretrained VFM to the registration encoder. We introduce anovel Data ReAssembly strategy to transform volumetric medical imagesinto VFM input while preserving spatial information. Additionally, weimplement a Hierarchical Representation Fusion module to dynamicallyintegrate multi-level features from the VFM, yielding semantically andstructurally rich representations. Extensive experiments on two publicMR–CT datasets for brain and abdominal imaging demonstrate the ef-fectiveness of the proposed method compared with existing approaches.The code is available at https://github.com/MeggieGuo/FGRA-Reg.

Abstract:
Neighboring-slice self-supervised denoising is attractive forvolumetric medical imaging, yet inter-slice misalignment breaks anatom-ical correspondence and often yields ghosting and blurred margins whenadjacent slices are used naïvely as targets. We propose Neighbor-GuidedPatch Sampling (NGPS), a lightweight framework that constructs neigh-boring supervision under local inter-slice misalignment. To avoid learn-ing from misleading targets, prior methods commonly mask discrepantregions, but this stabilizes training at the cost of leaving a non-trivialportion of neighboring evidence unexploited, particularly around high-frequency anatomical boundaries. NGPS addresses this by decouplingstructure matching from signal retrieval: for each masked location, itsearches a local neighborhood for structurally similar candidate patchesusing a simple guide image (e.g., fast bilateral filtering), while retriev-ing the supervision signal directly from the raw noisy neighbor at thematched coordinates. By matching on a noise-attenuated guide whileretrieving raw values from neighboring slices, NGPS constructs localpseudo targets without dense deformation-field estimation or spatial re-sampling. Across the evaluated CT and synthetic-Rician MRI settings,NGPS improves fidelity and structure-sensitive metrics. Code is availableat https://github.com/cv-cho/NGPS.

Abstract:
Cryo-electron microscopy (cryo-EM) enables single-particle analysis of biological macromolecules under strict low-dose imaging conditions, but the resulting micrographs often exhibit extremely low signalto-noise ratios and weak particle visibility. Image denoising is therefore an important preprocessing step for downstream cryo-EM analysis, including particle picking, 2D classification, and 3D reconstruction. Existing cryo-EM denoising methods are commonly trained with pixel-wise or Noise2Noise-style objectives, which can improve visual quality but do not explicitly account for structural consistency required by downstream analysis. In this work, we propose a score-based denoising framework for cryo-EM that learns the clean-data score to recover particle signals while better preserving structural information. Building on this formulation, we further introduce a target-guided variant that incorporates referencedensity guidance to stabilize score learning under weak and ambiguous signal conditions. Rather than simply amplifying particle-like responses, our framework better suppresses structured low-frequency background, which improves particle–background separability for downstream analysis. Experiments on multiple cryo-EM datasets show that our score-based methods consistently improve downstream particle picking and produce more structure-consistent 3D reconstructions. Experiments on multiple cryo-EM datasets show that our methods improve downstream particle picking and produce more structure-consistent reconstructions. Our code is available at https://github.com/xulabs/aitom.

Abstract:
In precipitation forecasting, not only accuracy but also tem-poral resolution is critical. However, increasing temporal resolution isconstrained by observational limitations and the computational cost ofdense discrete modeling. To overcome this limitation, we reformulate pre-cipitation forecasting as a continuous-time dynamical system and pro-pose RainODE, a framework that models precipitation evolution in la-tent space using a Neural ODE. This formulation enables derivative-consistent temporal dynamics and captures the dominant large-scaleadvective motion of precipitation systems. Nevertheless, a purely de-terministic ODE struggles to represent non-advective intensity changessuch as localized growth, decay, and sub-grid variability, often lead-ing to over-smoothed predictions. To address this issue, we introducea stochastic source modeling module based on a Brownian Bridge for-mulation, which refines residual intensity variations and restores fine-grained structures while preserving advective consistency. By combiningdeterministic continuous dynamics with stochastic refinement, RainODEenables arbitrary-time inference while maintaining sharp predictions. Ex-periments on SEVIR and the newly introduced Radar-based Precipita-tion Integrated Dataset (RAPID) demonstrate consistent improvementsacross multiple temporal intervals and precipitation regimes. The codeis available at https://github.com/SeongYE/RainODE.

Abstract:
The rapid expansion of low Earth orbit satellites such asStarlink is increasingly contaminating astronomical surveys. In practice,contaminated images are often identified through inspection. However,modern surveys generate terabytes of data each night, making manualscreening infeasible and necessitating reliable automated methods forsatellite trail removal. Unfortunately, existing general-domain line de-tection methods fail to generalize to astronomical images due to do-main mismatch, which are mostly grayscale with sparse bright stars andhave a low signal-to-noise ratio. Moreover, training new models fromscratch is impractical due to the lack of large-scale annotated astro-nomical datasets. To address these challenges, we introduce STARLINC,the first ML-based framework for satellite trail removal without requir-ing tedious pixel-level annotation of astronomical images. STARLINCcombines synthetic satellite trail generation for training, inter-frame dif-ferential maps from temporally adjacent exposures to highlight transienttrails, and heatmaps to provide additional localization cues for pixel-level segmentation. Extensive experiments on real-world data demon-strate substantial improvements over baselines, establishing STARLINCas a scalable solution for next-generation astronomical surveys. Code isavailable at https://github.com/starioKim/STARLINC.

Abstract:
Despite notable progress in text-guided medical image seg-mentation nowadays, these methods are limited to single-round dia-logues and fail to support multi-round reasoning, which is importantfor medical education scenarios. In this work, we introduce Multi-Round Entity-Level Medical Reasoning Segmentation (MEMR-Seg), anew task that requires generating segmentation masks through multi-round queries with entity-level reasoning, helping learners progressivelydevelop their understanding of medical knowledge. To support this task,we construct MR-MedSeg, a large-scale dataset of 177K multi-roundmedical segmentation dialogues, featuring entity-based reasoning acrossrounds. Furthermore, we propose MediRound, an effective baselinemodel designed for multi-round medical reasoning segmentation. To mit-igate the inherent error propagation within the chain-like pipeline ofmulti-round segmentation, we introduce a lightweight yet effective Judg-ment & Correction Mechanism during model inference. Experi-mental results demonstrate that our method effectively addresses theMEMR-Seg task and outperforms conventional medical referring seg-mentation methods. The project is available at https: // github. com/Edisonhimself/ MediRound .

Abstract:
DiT video generation is latency-intensive due to iterativefull-frame denoising, while prior cloud-edge methods largely rely on staticinter-step decoupling and cannot leverage inter-frame similarity or adaptto system dynamics. We propose EcoVideo, an entropy-orchestratedframework for dynamic inter-frame decoupling: early-stage self-attentionentropy provides a training-free estimate of frame-wise information den-sity for frame selection; a cloud large model denoises sparse high-entropykeyframes; and an edge lightweight model reconstructs the remainingframes via motion-aware interpolation with refinement for temporal sta-bility. EcoVideo further adapts the keyframe budget and edge refine-ment depth to real-time bandwidth and compute availability, optimizingend-to-end latency under constraints. Experiments on representative DiTvideo generators show improved quality–efficiency trade-offs and up to2.9× end-to-end speedup in low-bandwidth, compute-limited edge set-tings. Code is available at https://github.com/IF-LAB-PKU/EcoVideo.

Abstract:
Whole-slide images (WSIs) are widely used for computa-tional cancer prognosis. However, current research primarily focuses onin-domain performance and fails to generalize across clinical centers. Thislimitation stems from their reliance on pixel-derived representations thatare highly susceptible to domain-specific artifacts caused by stainingprotocols and scanner hardware. We hypothesize that high-level pathol-ogy semantics, such as tumor grade and micro-environmental architec-ture, provide a domain-invariant semantic representation that mirrorsthe robust diagnostic logic of human pathologists. Therefore, we proposea Semantic-Anchored Evidential Fusion Survival (SAEFS) framework,where SAEFS derives semantic anchors from WSIs via Visual QuestionAnswering (VQA), employs a dual-stream WSI evidence extraction archi-tecture, uses Dirichlet-based Subjective Logic to model uncertainty, andfuses semantic and visual evidence through a cautious conjunction rule toavoid overconfident fusion from correlated sources. Trained exclusively onone source domain and evaluated zero-shot across four unseen domains,SAEFS consistently outperforms state-of-the-art models both in predic-tion accuracy and reliability, improving the average C-index by 10.2%.Quantitative analyses further show that VQA-derived semantic featuresexhibit significantly lower cross-center divergence than pixel-derived fea-tures, highlighting their robustness for cross-center clinical applications.The code is available at https://github.com/YuchengXing99/SAEFS.

Abstract:
Monocular depth foundation models achieve remarkable gen-eralization by learning large-scale semantic priors, but this creates a criti-cal vulnerability: they hallucinate illusory 3D structures from planar/low-curvature but perceptually ambiguous inputs. We term this failure the3D Mirage. This paper introduces a novel end-to-end framework toprobe, score, and tame this under-quantified safety risk in monoculardepth under context variation. To probe, we present 3D-Mirage, thefirst benchmark to combine context variation and precise annotation forreal-world illusions with real object exclusions, multi-surface support;purpose-built to stress-test monocular depth on real-world illusions. Toscore, we propose a second-order magnitude-based evaluation with twometrics: the Deviation Composite Score (DCS) for high second-order 3D structure and the Confusion Composite Score (CCS) forcontextual instability. To tame this failure, we introduce GroundedSelf-Distillation, a parameter-efficient strategy on Depth-Anything-V2baseline that surgically targets and resolves hallucination on illusion ROIswhile preserving background knowledge, avoiding catastrophic forgetting.Our work provides an innovative pipeline for diagnosing and addressingthis phenomenon, urging a necessary shift in the evaluation of MDE frompixel-wise accuracy to structural and contextual robustness.Code: https://github.com/hdnndh/The- 3D- Mirage- Probing- and-Taming-3D-HallucinationsDataset: https://huggingface.co/datasets/3dmirage/3D-Mirage

Abstract:
Most advances in keypoint descriptions address monomodalsettings, where image variations arise from viewpoint, illumination, orcontrast changes. Multimodal scenarios involve images produced by fun-damentally different sensing processes, such as multispectral imaging,RGB-depth, satellite imagery, or medical imaging, causing the samestructures to appear differently. A common solution to cross-modal de-scription is to train descriptors for each modality pair, which requires re-training whenever the modalities change, or to train large models, whichincur a significant increase in runtime. Instead, we propose CrossFeat,a framework that enables an existing monomodal descriptor to operateacross modalities. Our method learns a crossing function in descriptorspace that maps features from one modality to a representation com-patible with another. To preserve the structural information capturedby the original descriptor, CrossFeat introduces a geometry–appearancedisentanglement such that only appearance is altered while the geomet-ric properties are preserved. Experiments across multiple domains anddatasets demonstrate improved performance in multimodal matching.Code available here: https://github.com/paulschneider01/CrossFeat

Abstract:
Recovering metric 3D geometry from monocular images is a fundamental computer vision task, yet current methods remain heavily fragmented by fixed camera model assumptions and inflexible input schemes. We present OmniPoint, a unified framework designed to generalize metric reconstruction across diverse imaging sensors, including pinhole, fisheye, and equirectangular projections, while accommodating varying geometric priors. To overcome projection rigidity, OmniPoint abandons conventional planar depth regression. It instead adopts a decoupled ray and distance representation alongside a decoupled training objective, explicitly separating the camera projection model from the scene structure. To address the severe scarcity of training data for alternative cameras, we introduce a bidirectional augmentation strategy that explicitly bridges labeled perspective data and unlabeled omnidirectional domains in 3D space. Furthermore, to seamlessly integrate optional inputs like camera intrinsics or sparse depth without destabilizing the network through feature distribution shifts, we propose a robust information injection mechanism. This mechanism utilizes learnable input state embeddings to resolve architectural ambiguity and applies vectorized Gaussian smoothing to densify irregular measurements. Extensive experiments demonstrate that OmniPoint achieves state-of-the-art zeroshot performance across multiple benchmarks, establishing a robust new standard for unified monocular 3D reconstruction.

Abstract:
Diffusion transformer (DiT) architectures show great poten-tial for real-world image super-resolution (Real-ISR). However, theircomputationally expensive iterative sampling necessitates one-step dis-tillation. Existing one-step distillation methods struggle with Real-ISRon DiT. They suffer from fundamental trajectory mismatch and gen-erate severe grid-like periodic artifacts. To tackle these challenges, wepropose StrSR, a novel one-step adversarial distillation framework fea-turing spectral and trajectory regularization. Specifically, we proposean asymmetric discriminative distillation architecture to bridge the tra-jectory gap. Additionally, we design a frequency distribution matchingstrategy to effectively suppress DiT-specific periodic artifacts caused byhigh-frequency spectral leakage. Extensive experiments demonstrate thatStrSR achieves state-of-the-art performance in Real-ISR, across bothquantitative metrics and visual perception. The code and models will bereleased at https://github.com/jkwang28/StrSR.

Abstract:
In fluorescence microscopy, spectral unmixing aims to re-cover individual fluorophore concentrations from spectral images thatcapture mixed fluorophore emissions. Since classical methods operatepixel-wise and rely on least-squares fitting, their performance degradeswith increasingly overlapping emission spectra and higher levels of noise,suggesting that a data-driven approach that can learn and utilize a struc-tural prior might lead to improved results. Learning-based approachesfor spectral imaging do exist, but they are either not optimized for mi-croscopy data or are developed for very specific cases that are not ap-plicable to fluorescence microscopy settings. To address this, we pro-pose λSplit, a physics-informed deep generative model that learns aconditional distribution over concentration maps using a hierarchicalVariational Autoencoder. A fully differentiable Spectral Mixer enforcesconsistency with the image formation process, while the learned struc-tural priors enable state-of-the-art unmixing and implicit noise removal.We demonstrate λSplit on 3 real-world datasets that we syntheticallycast into a total of 58 challenging spectral unmixing benchmarks. Wecompare our results against a total of 10 baseline methods, includingclassical methods and a range of learning-based methods. Our resultsconsistently show competitive performance and improved robustness inhigh noise regimes, when spectra overlap considerably, or when the spec-tral dimensionality is lowered, making λSplit a new state-of-the-art forspectral unmixing of fluorescence microscopy data. Importantly, λSplitis compatible with spectral data produced by standard confocal micro-scopes, enabling immediate adoption without specialized hardware mod-ifications. The code and all datasets used in the experiments are availableat https://github.com/juglab/lambdaSplit.

Abstract:
Traffic elements such as traffic lights and road signs play a fundamental role in human driving decisions and should naturally influence end-to-end driving performance. However, existing end-to-end driving research predominantly focuses on dynamic road participants (e.g., vehicles and pedestrians), while the role of traffic elements remains largely unexplored. To date, the community lacks a systematic study quantifying how traffic elements affect end-to-end driving models. This gap stems from two main challenges: first, existing public datasets rarely provide structured annotations for traffic elements; second, modern end-to-end driving systems vary widely in architectures and training paradigms, making conclusions drawn from a single method difficult to generalize. In this work, we present the first systematic investigation of traffic element awareness for end-to-end autonomous driving. We begin by constructing a unified research infrastructure by augmenting multiple public driving datasets with comprehensive traffic element annotations. To ensure broad applicability across diverse model families, we intentionally adopt a minimal and universal integration design, allowing traffic element signals to be incorporated into existing pipelines in a plug-and-play manner with negligible architectural modification. We evaluate this design across a wide spectrum of modern paradigms, including perception–prediction-planning pipelines, vision-language-action models (VLA), regression-based planners, diffusion-based policies, and trajectory scoring frameworks. Experiments are conducted on multiple widely used benchmarks, including nuScenes, NAVSIM-v1, NAVSIM-v2, and Bench2Drive. Across all paradigms and datasets, our simple integration consistently improves driving performance, demonstrating that traffic element awareness provides a robust and generalizable signal for end-to-end driving systems. Notably, on the challenging NAVSIM-v2 benchmark, our approach significantly boosts the performance of stateof-the-art architectures and data pipelines, establishing a new state of the art.

Abstract:
Next-Best-View (NBV) planning is widely adopted for robotic grasping in cluttered and occluded environments. However, existing methods typically assume that a single observation at an arbitrary camera–object distance is sufficient for accurate grasp prediction. This overlooks a critical factor: grasp perception quality is highly sensitive to viewing distance, and the optimal distance varies significantly across object categories and scales. To address this limitation, we propose DiNBVGrasp, a real-time, distance-aware two-stage NBV framework. In Stage I, we distill a large vision-language model (VLM) into a lightweight planner for global semantic NBV exploration, resolving occlusions and generating an initial grasp hypothesis with low-latency inference. In Stage II, we introduce a local distance-aware refinement mechanism that explicitly models the relationship between viewing distance and grasp success probability. During manipulator approach, a lightweight scoring network evaluates intermediate observations and selects the most informative one for final grasp prediction. Extensive simulation and real-world experiments demonstrate that DiNBV-Grasp consistently improves grasp success rates across diverse objects, particularly for small and occluded targets, while preserving real-time performance, highlighting the importance of distance-aware perception in NBV-based grasping. Our code is available at https://github.com/XieZilongAI/DiNBV-Grasp.

Abstract:
Although recent trajectory prediction and end-to-end au-tonomous driving methods improve robustness in urban environments,they still lack meaningful controllability. Existing benchmarks either pro-vide no persona-conditioned annotations or support only a single urgencyspectrum (i.e., emergency, normal, relaxed), which cannot distinguishpersonas that share the same urgency level but require different drivingdynamics. To address this, we propose (i) the Persona-Conditioned Tra-jectory (PCT) dataset, which decomposes driving personas along twoaxes—Temporal Urgency and Ride Comfort—and combines three levelsof each to form a grid of nine personas, each paired with natural-languagedescriptions and trajectories, and (ii) PersonaDrive, a framework thatcan learn driving personas from language and can generate persona-specific trajectories. PersonaDrive incorporates Persona-Conditioned An-chor Transform (PCAT), which hierarchically reshapes anchors alongboth axes, and Persona-Conditioned Multi-Modal Fusion (PCMF) forBEV-level persona fusion. Training is supervised by a Hierarchical GuideLoss enforcing axis-aligned physical orderings and an Axis-DecomposedDiversity Loss preventing diagonal mode collapse. Experimental resultsshow that PersonaDrive consistently improves over the compared base-lines across multi-dimensional scenarios. The code and PCT dataset areavailable at https://github.com/VisualAIKHU/PersonaDrive.

Abstract:
Recent progress in multimodal reasoning has enabled agentsthat interpret imagery, connect it with language, and execute struc-tured analytical tasks. Extending these capabilities to remote sensingremains challenging, as models must reason over spatial scale, geographicstructures, and multispectral indices while maintaining coherent multi-step logic. To address this gap, we introduce OpenEarthAgent, a unifiedframework for tool-augmented geospatial reasoning trained on satelliteimagery, natural-language queries, and structured reasoning traces. Be-yond serving as a benchmark, OpenEarthAgent establishes a cohesiveagentic architecture built around a unified executable tool registry andtrajectory-based policy learning. The framework standardizes heteroge-neous visual, spectral, GIS, and georeferenced raster operations undera consistent callable schema, enabling modular orchestration and deter-ministic execution. Training is performed via supervised fine-tuning onstructured reasoning trajectories with deterministic replay validation toensure executability and spatial correctness. The accompanying corpuscomprises 14,538 training and 1,169 evaluation instances with over 107Kreasoning steps, spanning urban, environmental, disaster, and infrastruc-ture domains and incorporating GIS operations alongside index analysessuch as NDVI, NBR, and NDBI. Grounded in explicit reasoning traces,the learned agent demonstrates structured reasoning, stable spatial un-derstanding, and interpretable tool-driven behavior across diverse EOscenarios. We report consistent improvements over a strong baseline andcompetitive performance against recent open and closed-source models.https://github.com/mbzuai-oryx/OpenEarthAgent.

Abstract:
4D occupancy forecasting models the spatio-temporal evo-lution of 3D scenes and is crucial for autonomous driving, especially forcorner-case simulation. Existing methods often rely on discrete tokeniza-tion followed by autoregressive prediction, yet struggle with geometricdistortion in static structures and inconsistent temporal coherence overthe forecasting horizon. In this work, we propose a Geometry-AwareSpatio-Temporal context modeling method (GAST) for 4D occupancyforecasting, built upon progressive explicit-implicit generation and dual-path spatio-temporal modeling. Specifically, the generation module pro-duces per-frame occupancy with high geometric fidelity and semanticplausibility through pose-driven warping, motion-aware feature modula-tion, and attention-based feature refinement. Subsequently, the spatio-temporal module enhances spatial consistency through global contextaggregation while capturing scene evolution through temporal dynam-ics extraction. This unified design enables joint optimization of histori-cal reconstruction and future forecasting in an end-to-end manner. Ex-tensive experiments on Occ3D-nuScenes demonstrate the superiority ofour method, outperforming the state-of-the-art by 7.67% in mIoU and6.44% in IoU with a 2.84× speedup, while maintaining strong perfor-mance in long-term forecasting. Our source code is publicly available athttps://github.com/chenst27/GAST.

Abstract:
Intermediate representations are key to bridging the modal-ity gap between generalizable manipulation policies and large-scale pre-trained vision-language models (VLMs). Among these, trajectory-basedrepresentations compactly represent motion-relevant cues, yet most exist-ing approaches predict trajectories in 2D image space, resulting in intrin-sic 3D ambiguity. Moreover, using 2D trajectories with depth still leavesthe free-space waypoints ambiguous, limiting reliable 3D reasoning. Toaddress this, we propose predicting 3D consistent waypoints (3DWay)from multi-view images. By reformulating 3D waypoints prediction asgenerating multi-view consistent 2D waypoints followed by geometric tri-angulation, we enable explicit 3D motion specification while preservingthe strong priors of pretrained VLMs. The predicted waypoints can guideexisting VLA models for better generalization or be directly executed onsimple tasks. Extensive experiments show that 3DWay substantially im-proves 3D spatial grounding and vision-language reasoning, demonstrat-ing strong potential for generalizable robot manipulation. Codes will bereleased at https://github.com/ziqin-h/3DWay.

Abstract:
Pretraining Vision-Language-Action (VLA) policies on internet-scale video is appealing, yet current latent-action objectives often learnthe wrong thing: they remain anchored to pixel variation rather thanaction-relevant state transitions, making them vulnerable to appearancebias, nuisance motion, and information leakage. We introduce VLA-JEPA,a JEPA-style pretraining framework that sidesteps these pitfalls by design.The key idea is leakage-free state prediction: a target encoder produceslatent representations from future frames, while the student pathwaysees only the current observation—future information is used solely assupervision targets, never as input. By predicting in latent space ratherthan pixel space, VLA-JEPA learns dynamics abstractions that are ro-bust to camera motion and irrelevant background changes. This yieldsa simple two-stage recipe—JEPA pretraining followed by action-headfine-tuning—without the multi-stage complexity of prior latent-actionpipelines. Experiments on LIBERO, LIBERO-Plus, SimplerEnv and real-world manipulation tasks show that VLA-JEPA achieves consistent gainsin generalization and robustness over existing methods. Code is availableat https://github.com/ginwind/VLA-JEPA.

Abstract:
Embodied Visual Tracking (EVT) requires an agent to con-tinuously follow a language-specified target from egocentric observations.Most existing methods adopt single-agent imitation learning, which de-pends on expert demonstrations and can struggle to generalize to sce-narios beyond the training distribution. Inspired by competition-drivencapability evolution in nature, we propose CoMaTrack, a competitivemulti-agent reinforcement learning (RL) framework that trains agentsin a dynamic adversarial setting with competitive subtasks, yieldingstronger adaptive planning and interference-resilient strategies. We fur-ther propose CoMaTrack-Bench, a Habitat-based benchmark for language-conditioned competitive EVT, featuring dynamic dueling scenarios be-tween a tracker and adaptive opponents across diverse environments andinstructions, enabling standardized robustness evaluation under activeadversarial interactions. Experiments show that CoMaTrack achievesstate-of-the-art results on both standard benchmarks and CoMaTrack-Bench. Notably, a 3B VLM trained with our framework surpasses pre-vious single-agent imitation learning methods based on 7B models onthe challenging EVT-Bench, achieving 92.1% in STT, 74.2% in DT, and57.5% in AT.

Abstract:
Data augmentation is fundamental to training modern deepvision and multimodal models. While individual methods, such as Ran-dAug, CutMix, Mixup, RandErase, and DropPath, offer strong regular-ization effects, their combined use has saturated in performance due tooverlapping functionalities, and aggressive pixel-level manipulations maydisrupt delicate cross-modal alignment. This saturation motivates thesearch for a new augmentation axis within the embedding space ratherthan the input space. We introduce Aether, a simple plug-in method thatapplies diffusion-style random perturbations in the embedding space viacontrolled alpha-mixing, specifically designed to provide isotropic regu-larization that remains semantically consistent. Inspired by feature-spaceperturbations in language models and image degradation in generativepretraining, Aether induces mild yet effective perturbations that smooththe representations without compromising the fine-grained structuralinformation required for strong vision-language encoders. Across diversearchitectures and across multiple recognition tasks, Aether delivers consis-tent gains over the advanced recipe combining CutMix, Mixup, DropPath,and RandAug—a level of improvement rarely observed with modern aug-mentation alternatives. Notably, Aether demonstrates superior effective-ness in multi-modal alignment, succeeding where traditional pixel-spaceaugmentations fail by providing a stable, isotropic regularization signalthat respects the integrity of the high-dimensional feature space. Code isavailable at https://github.com/naver-ai/aether.

Abstract:
Unified fashion generation integrates tasks like virtual try-onand garment reconstruction into a single model to reduce task-specificadaptation costs. However, naive parameter sharing across semanticallydistinct tasks induces negative transfer through severe inter-task gradientconflict. We propose OrthoTryOn, a unified framework mitigating thisinterference within a shared Low-Rank Adaptation (LoRA) module. ItsOrthogonal Subspace Projection (OSP) applies task-specific orthogonalrotations to bottleneck features, mapping them into decorrelated coordi-nate frames. To address residual semantic coupling at inference time, wefurther propose Fisher-guided Negative Guidance (FNG), a parameter-free strategy that utilizes diagonal Fisher information to quantify inter-task sensitivity overlap and explicitly repels generation trajectories fromthe most confusable task via Classifier-Free Guidance. Extensive exper-iments demonstrate that OrthoTryOn avoids the severe performancedegradation typical of naive unified training and even surpasses inde-pendently trained task-specific models, achieving state-of-the-art resultsacross multiple benchmarks while generalizing robustly across diversediffusion backbones. Code is available at https://github.com/NJU-PCALab/OrthoTryOn.

Abstract:
The existence of adversarial attacks is often attributed tothe presence of non-robust features in neural networks. While prior de-fenses reduce their impact via pruning, masking, or feature recalibra-tion, we instead propose to jointly learn to amplify and attenuate thesesignals through a simple activation scaling mechanism. To this end,we introduce Activation Amplification and Attenuation (A3), alightweight plug-in module that enhances adversarial robustness withminimal modifications of the activations. A3 dynamically rescales theactivations using a learnable mask and a scaling factor derived from theoriginal activation magnitudes. The influence of adversarial perturba-tions can be amplified or attenuated using the same learnable parametersby simply flipping the sign of the scaling operation. The amplified signalsserve as negative references to construct novel contrastive and rankingloss functions. Experimental analysis shows that learning to degrade thepredictions in amplification mode simultaneously improves adversarialrobustness in attenuation mode. Moreover, A3 relies on only a smallnumber of learnable parameters, with most of its behavior being deter-mined by the scaling mechanism rather than additional network capac-ity. Extensive experiments demonstrate that integrating A3 into differentbackbones, datasets, and training methods consistently improves adver-sarial robustness while introducing negligible computational and mem-ory overhead compared to existing plug-in modules. Code is available at:https://github.com/tgoncalv/A3.

Abstract:
Low-resolution face recognition (LR-FR) remains a challeng-ing task due to poor feature extraction and aggregation, as probe imagesoften contain limited identity information resulting from extreme degrada-tions such as blur, occlusion, and low contrast. Additionally, the domaingap between high-resolution (HR) gallery images and low-resolution (LR)probe images poses a significant challenge. A single feature encoder strug-gles to generalize effectively across both domains when fine-tuned on anLR dataset, and this issue is further magnified by catastrophic forget-ting. To address these challenges, we propose FaceMoE, an effectiveadaptation of Mixture of Experts (MoE) transfomer architecture for low-resolution face-recognition . Specifically, we introduce multiple specializedfeed-forward network (FFN) experts and incorporate a top-k router, whichdynamically assigns tokens to appropriate experts. This design emergentlypromotes specialization across experts for different semantic regions ofthe face, which enables FaceMoE to perform resolution-aware featureextraction. Moreover, the top-k router facilitates sparse expert activation,enabling the model to preserve pretrained knowledge when finetuned on aLR dataset, while increasing model capacity without proportional compu-tational overhead. FaceMoE is trained with a combined face recognitionloss, router z-loss, and load balancing loss to ensure expert specializa-tion and stable training. To the best of our knowledge, this is the firstwork leveraging MoE for LR-FR. Extensive experiments across elevendatasets, spanning HR, mixed-quality, and LR benchmarks, demonstratethat FaceMoE significantly outperforms state-of-the-art methods.

Abstract:
Designing efficient vision backbones is increasingly challeng-ing. An intriguing avenue is mergeable re-parameterization, which canreshape training dynamics by introducing auxiliary parameterizationswithout inference overhead. In particular, RepVGG-style structures can beviewed as adding a fixed-support correction on top of a dense base kernelafter merging. In this paper, we propose Critical Parameter Rectification(CaRe), which generalizes fixed corrections to learned selective rectifi-cation. CaRe factorizes each layer as W = Wbase + Waux ⊙ ϕ(Wgate ),where a static parameter-space gate induces sparse activation in thecorrection term, concentrating updates on a small subset of critical pa-rameters during training. Since the gate depends only on parameters,CaRe is losslessly mergeable into a standard weight tensor, incurring zeroinference overhead. Built on CaRe, the CaReNet family establishes astrong accuracy-throughput Pareto frontier on ImageNet-1K, with consis-tent gains across model scales and strong transfer to downstream denseprediction tasks. Code is available at https://github.com/lime-j/care.

Abstract:
Despite the practical relevance of sparse decision-based black-box threats, they have received limited attention in semantic segmenta-tion. To bridge this gap, we adapt the most representative decision-basedblack-box sparse attacks from the classification domain to serve as base-lines, establishing a rigorous benchmark for this underexplored setting.In this context, we demonstrate that one of the existing methods suf-fers from severe query inefficiency due to its image-centric pixel accu-mulation, which rapidly exhausts query budgets across the vast imagespace. To overcome this, we propose SegPAR, a novel decision-basedframework that shifts to a class-centric exploration paradigm. Further-more, to eliminate the misleading feedback generated by standard de-cision rewards during pixel accumulation, we introduce a novel discrep-ancy reward. Extensive experiments show that SegPAR significantly out-performs black-box baselines in sparsity efficiency and MIoU reduction,while remaining competitive with white-box sparse attacks. Code is avail-able at https://github.com/KAU-QuantumAILab/SegPAR.

Abstract:
Test-Time Adaptation (TTA) methods commonly updatethe affine parameters of normalization layers to adapt deployed modelsunder distribution shifts. However, per-channel affine parameters per-form axis-aligned scaling and shifting, making them geometrically inca-pable of correcting cross-channel structural changes induced by distribu-tion shift. To address this limitation, we propose MixTTA, a lightweightplug-in module that equips normalization layers with a low-rank cross-channel transformation, enabling inter-channel mixing at each layer. Toensure that the low-rank branch captures only cross-channel interac-tions, we also propose Decoupling Projection that enforces strict separa-tion from the diagonal affine path, along with Spectral Projection thatprevents rank-1 collapse under non-stationary test streams. MixTTAcan be seamlessly integrated into any existing normalization-based TTAmethod. Experiments in both standard and wild TTA settings show con-sistent improvements over strong baselines while mitigating adaptationfailure under challenging conditions. The source code is publicly availableat https://github.com/delta6189/MixTTA.

Abstract:
Recent advances in vision–language models (VLMs) such asCLIP have demonstrated strong generalization across natural-image do-mains. However, adapting these models to biomedical imaging is non-trivial: full-model fine-tuning is computationally expensive, while medi-cal data are often scarce and exhibit subtle, fine-grained inter-class differ-ences, making parameter-efficient adaptation particularly critical. VisualReprogramming (VR) offers a parameter-efficient alternative by inject-ing learnable perturbations into the input space, but existing VR ap-proaches for VLMs mainly focus on positive class prompts and overlookconfusing negatives, leading to miscalibrated predictions in fine-grainedmedical scenarios. We present BioMedVR, the first VR-based frame-work for biomedical imaging, enabling few-shot adaptation of pretrainedVLMs through compact learnable VR modules. To mitigate class confu-sion, we introduce a Confusion Minimization Mechanism that leveragesLLM-generated confusion-aware attributes together with a Confusion-Suppression Loss to explicitly reduce false-positive alignment. More-over, the designed Mixture-of-Prompt Experts combines a positive ex-pert for main-class discrimination and a negative expert for confusionsuppression, balanced via adaptive gating. Extensive experiments on 18datasets—including 11 biomedical datasets and 7 natural image bench-marks—demonstrate that BioMedVR achieves superior accuracy andgeneralization, effectively bridging VR and VLMs in biomedical domains.Project page: https://jxliu-ai.github.io/biomedvr-page/.

Abstract:
Continual learning in visual question answering (VQACL) re-quires a single vision–language model to acquire new multimodal reason-ing skills from a task stream while retaining prior capabilities. However,naïve sequential finetuning suffers from catastrophic forgetting. Existingcontinual VQA methods primarily rely on replay or parameter regulariza-tion but largely overlook how task-specific updates accumulate and inter-act in parameter space, particularly whether successive updates are syn-ergistic or conflicting across layers. To address this, we introduce SCoT(Similarity-guided Conflict-aware Task Consolidation), a continual learn-ing framework that represents each task as a parameter update relative to apretrained anchor model and integrates tasks through layer-wise parameter-space reasoning. For each layer, SCoT measures alignment between incom-ing and accumulated task vectors, removes only destructive components viaconditional projection when conflicts arise, and adaptively modulates con-solidation strength using similarity-guided weighting. This preserves ben-eficial transfer while suppressing harmful interference, enabling stable yetadaptive continual learning. Experiments on VQAv2 and NExT-QA demon-strate strong continual VQA performance, reducing forgetting to near-zero(0.07 and -1.90) while achieving rare positive backward transfer (+5.64 and+6.97), outperforming strong continual-learning and task-vector baselines.Project page: https://anand-patel05.github.io/SCoT

Abstract:
Recent advances in Continual Learning (CL) have adoptedfoundation models with Low-Rank Adaptation (LoRA) or prompt tuning,utilizing pre-trained representations to achieve more efficient and adapt-able learning. However, these methods suffer from two key challenges.First, they learn task-specific adapters, resulting in unbounded parameterexpansion as task sequences grow. Second, they maintain replay buffersor store feature representations from previous tasks, which introducessubstantial memory overhead and potential privacy risks. To address theselimitations, we propose Continual Orthogonal Low-Rank Adaptation(COLA), a novel rehearsal-free, parameter-efficient framework for CL.COLA integrates LoRA’s low-rank adaptation with an Oja-inspired learn-ing rule that incrementally approximates the dominant eigenstructure ofthe feature covariance across tasks. This mechanism continuously tracksthe principal directions of prior task knowledge and progressively reducesrepresentational interference between new and previously learned tasksubspaces. By dynamically adapting a shared low-rank subspace usingcovariance updates, COLA achieves stable feature projection and con-tinual knowledge integration without additional parameter expansionor memory replay. Extensive experiments on class-incremental bench-marks such as ImageNet-R, ImageNet-A, and CUB200 demonstrate thatCOLA effectively mitigates catastrophic forgetting while maintainingminimal memory overhead and strong generalization, outperforming ex-isting LoRA-based and prompt-based methods. The code is available at:https://github.com/autovisionproject/COLA.

Abstract:
Recent advancements in scaling dataset distillation rely heav-ily on decoupled information extraction pipelines, comprising Squeeze,Recover, and Relabel stages. Despite their scalability to large-scaledatasets, these methods suffer from prohibitive computational overheadand poor cross-architecture generalization. In this paper, we reveal theroot cause of these bottlenecks: the implicit dual-compression process,from data to model and back to images, inherently induces severe infor-mation loss. Crucially, we empirically and theoretically demonstrate thatthis loss creates a distribution shift that fundamentally compromises thewidely adopted Relabel strategy, transforming the pre-trained modelinto an unreliable labeler that yields sub-optimal labels. To overcomethese critical flaws, we propose CIM, a novel, metric-driven frameworkthat abandons the flawed dual-compression paradigm. Instead, CIM ex-plicitly quantifies and minimizes the information gap between the originaland synthetic datasets. By directly aligning the data distributions, ourapproach ensures high-fidelity information condensation and inherentlysatisfies the prerequisites for effective relabeling. Extensive experimentsdemonstrate that CIM establishes a new state-of-the-art. Notably, itdistills ImageNet-1K at an IPC =10 in merely 80 minutes on a singleRTX-4090 GPU, achieving an unprecedented 48.7% Top-1 accuracy onResNet-18 and significantly outperforming previous SOTA approaches,such as NRR-DD and DELT, by 2.6% and 2.9%, respectively. Our codeis available at https://github.com/LINs-lab/CIM.

Abstract:
Prompt learning has become a dominant paradigm for adapt-ing vision-language models (VLMs) such as CLIP to downstream taskswithout modifying pretrained weights. While extending prompts to bothvision and text encoders across multiple transformer layers significantlyboosts performance, it dramatically increases the number of trainableparameters, with state-of-the-art methods requiring millions of parame-ters and abandoning the parameter efficiency that makes prompt tuningattractive. In this work, we propose MMLoP (Multi-Modal Low-RankPrompting), a framework that achieves deep multi-modal promptingwith only 11.5K trainable parameters, comparable to early text-onlymethods like CoOp. MMLoP parameterizes vision and text prompts ateach transformer layer through a low-rank factorization that constrainsprompts to a compact subspace, providing parameter efficiency whilemotivating the need for our complementary regularization components.To further close the accuracy gap with state-of-the-art methods, we intro-duce three complementary components: a self-regulating consistency lossthat anchors prompted representations to frozen zero-shot CLIP featuresat both the feature and logit levels, a uniform drift correction that re-moves the global embedding shift induced by prompt tuning to preserveclass-discriminative structure, and a shared up-projection that couplesvision and text prompts through a common low-rank factor to enforcecross-modal alignment. Extensive experiments across three benchmarksand 11 diverse datasets demonstrate that MMLoP achieves a highly favor-able accuracy-efficiency tradeoff, outperforming the majority of existingmethods including those with orders of magnitude more parameters, whileachieving a harmonic mean of 79.70% on base-to-novel generalization.Code is available at https://github.com/sajjad-ucsb/MMLoP.

Abstract:
Out-of-Distribution (OOD) detection remains a fundamental challenge for neural networks, whose predictions can be overconfident on inputs that deviate from the training distribution. Most post-hoc OOD detection methods derive scores from a single representation level (e.g., logits or penultimate features) or combine multiple layers via depth selection or OOD-calibrated weighting. However, because OOD shifts are diverse, the most informative representation level can vary strongly across OOD types and domains, making fixed-layer choices and OOD-calibrated aggregation brittle. In this paper, we propose PRISM (Projected Representation with Intermediate-layer Subspace Modeling), a model-agnostic post-hoc OOD detection method that models a unified multi-layer feature representation rather than aggregating independently scored layers. PRISM fuses intermediate and deep features into a single hierarchical embedding, estimates an in-distribution (ID) principal subspace, and then combines two complementary signals: (i) a class-conditional Mahalanobis distance in the projected subspace and (ii) the residual energy orthogonal to the learned manifold. This simple design avoids OOD-tuned layer weighting while capturing both in-subspace semantic deviations and offsubspace anomalies. Across diverse benchmarks spanning natural images, medical imaging, and industrial visual inspection, PRISM achieves consistent state-of-the-art cross-domain OOD detection performance with a single default configuration across all evaluated domains and architectures. We further show that PRISM incurs minimal inference overhead, making it practical for real-world deployment.

Abstract:
Federated learning (FL) protects sensitive (Vision-LanguageNavigation) VLN data without centralizing trajectories or instructions,but severe non-IID environments make personalized FL (pFL) necessary.Moreover, VLN poses several coupled challenges for personalized feder-ated learning, including environment heterogeneity, multimodal language-vision fusion, and long-horizon navigation with time-varying decisioncontexts. To address these challenges, we propose FedNASP, a step-wisepersonalized federated learning framework for VLN. The key idea is todynamically calibrate personalization strength along a navigation trajec-tory. Specifically, we introduce a lightweight Step-wise Personalized Mod-ulator (SPM) that predicts personalization strength at each navigationstep. We further design a structure-aware adapter-based personalizedprefix injection mechanism that enables client-specific grounding whilekeeping the backbone shared across clients. Experiments on three rep-resentative datasets show that FedNASP consistently outperforms state-of-the-art federated VLN methods under substantial cross-client hetero-geneity. Compared with the non-centralized baselines, FedNASP improvesRemote Grounding Success on REVERIE by 13.0% and Success Rateon CVDN by 22.6%. Extensive ablation studies and visualizations fur-ther validate the effectiveness of adaptive step-wise personalization forfederated VLN. Code is available at: https://github.com/IntelliSys-Lab/FedNASP.git

Abstract:
Test-time adaptation (TTA) has been widely explored in single-label recognition, effectively mitigating distribution shifts, especially when combined with vision-language models. However, real-world images often contain multiple objects, while the more practical multilabel test-time adaptation (MLTTA) has received little attention so far. Recent cache-based TTA methods have shown promising efficiency and effectiveness, yet directly extending them to multi-label scenarios suffers from a one-to-many mapping problem: a shared global representation entangling co-occurring objects is stored as class-wise cache prototypes, inducing dominant-label bias and compromised cache calibration. While introducing region-level cues helps isolate class-specific evidence, such regional evidence can also be unreliable under distribution shifts, making its identification and utilization non-trivial. To address these issues, we introduce PuRF, a novel PuRiFication-driven cache-based method for multi-label test-time adaptation of vision-language models. Specifically, PuRF first performs region purification to identify reliable regions, providing comprehensive regional cues for multi-label recognition and enabling fine-grained alignment. Based on these purified regions, PuRF conducts cache purification to enhance cache representation and adaptability, where episodic purification builds a discriminative regionbased cache, and temporal refreshing further promotes long-term cache adaptability. Experiments demonstrate that PuRF consistently outperforms state-of-the-art methods, achieving a notable 4.05% mAP improvement on ViT-B/32 across five datasets. The code is available at: https://github.com/Evelyn1ywliang/PuRF-MLTTA.

Abstract:
Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pretraining, yet their rapidly increasing computational and memory requirements pose significant challenges for deployment in constrained environments. Existing pruning strategies often depend on task-specific criteria or LLM-oriented importance measures, making them unsuitable for task-agnostic pruning, where no task-specific samples are available at pruning time and the pruned model remains broadly applicable. We introduce a retraining-free VLM pruning framework called PORTA that derives a taskand modality-agnostic importance formulation based on activation variation, estimated from generic calibration data, which reliably captures feature-level representation utility across modalities. PORTA further incorporates an adaptive sparsity allocation mechanism that assigns layer-wise pruning ratios based on output feature variability, avoiding the limitations of uniform sparsity and reducing performance degradation at high compression levels. Extensive experiments across VLM architectures, such as CLIP, BLIP, and Qwen2-VL, demonstrate that PORTA achieves competitive downstream performance under high sparsity without requiring any retraining, supporting efficient VLM compression. Code is available at https://github.com/cau-hailab/PORTA.git.

Abstract:
We propose an image generation pipeline that preserves geometric consistency for 6D object pose estimation. Most pose estimation methods rely on training with large-scale annotated real datasets, but collecting such data is often difficult and expensive in practice. To address this limitation, we introduce GenerationPose. First, we create a largescale synthetic dataset by rendering objects from multiple viewpoints using their 3D meshes. We then convert these synthetic images into pseudoreal images using a geometry-conditioned appearance generation model trained on large-scale synthetic–real image pairs. Our experiments show that the model generalizes well to unseen objects and can generate realistic images from synthetic views only. Furthermore, we show that pose estimation models trained on the generated data achieve significantly better performance compared to baseline methods. The code is available at https://github.com/JiafengZhang-1117/GenerationPose.

Abstract:
Real-world deployment of vision models to broadly benefit society is arguably a main research objective. In optical flow, however, the difficulty to obtain the ground truth has focused research mainly on synthetic data and domain-specific benchmarks. Here, we investigate the severity of this mismatch. We study how well modern optical flow estimation models generalise to real-world video and question if accuracy on synthetic benchmark proxies actually predicts accuracy on real-world optical flow. To address this, we build a real-world evaluation benchmark and evaluate the real-world generalisability of a broad set of recent optical flow models using standard checkpoints. Our benchmark contains 8,204 frame pairs across TAP-Flow, Slow Flow, and our own dataset FlowFactor. FlowFactor is a manually annotated real-world benchmark of 1,000 HD frame pairs organised into four confounding factors: large displacements, repetitive textures, occlusions, and lighting variation. Each setting mainly varies only one factor, enabling diagnostic, confounder-specific analysis. Using FlowFactor, we reveal that performance on varying lighting and large displacements correlates most strongly with real-world accuracy, and that improvements on large-motion regimes can trade off against robustness in small-motion, stationary scenes. Our experiments show that progress on Sintel, KITTI and Spring only weakly predicts accuracy on real-world data, highlighting the need for a broad real-world optical flow benchmark. Interestingly, scaling up the amount of training data does not necessarily resolve the gap, calling for new innovative research instead of simply scaling data and compute. Code and datasets are available at https://github.com/Petter6/realworld-optical-flow.

Abstract:
Parametric 3D human models such as SMPL have driven significant advances in human pose and shape estimation, yet their simplified kinematics limit biomechanical realism. The recently proposed SKEL model addresses this limitation by re-rigging SMPL with an anatomically accurate skeleton. However, estimating SKEL parameters directly remains challenging due to limited training data, perspective ambiguities, and the inherent complexity of human articulation. In this work, we propose SKEL-CF, a new framework for estimating SKEL parameters. SKEL-CF adopts a standard transformer-based encoder–decoder architecture. The encoder first produces coarse predictions of the camera extrinsics and SKEL parameters. The decoder then iteratively refines these predictions across multiple layers, with explicit and auxiliary supervision applied at each layer. To provide anatomically consistent training data, we convert the existing SMPL-based dataset into a SKEL-aligned version, called HMR-SKEL. This new dataset offers high-quality supervision for SKEL estimation. In addition, to reduce depth and scale ambiguity, we explicitly incorporate camera intrinsic estimation into the SKEL-CF pipeline and show that it is important for accurate reconstruction. Extensive experiments validate the effectiveness of the proposed design. On the challenging MOYO dataset, SKEL-CF achieves 85.0 MPJPE / 51.4 PA-MPJPE, significantly outperforming the previous SKEL-based state-of-the-art HSMR (104.5 / 79.6). These results establish SKEL-CF as a promising framework for human motion analysis, facilitating the use of computer vision techniques in biomechanicsrelated analysis. Our implementation is available on the project page: https://pokerman8.github.io/SKEL-CF/.

Abstract:
LiDAR scene flow is the task of estimating per-point 3D mo-tion between consecutive point clouds. Recent methods achieve centimeter-level accuracy on popular autonomous vehicle (AV) datasets, but aretypically only trained and evaluated on a single sensor. In this paper, weaim to learn general motion priors that transfer to diverse and unseenLiDAR sensors. However, prior work in LiDAR semantic segmentationand 3D object detection demonstrate that naively training on multipledatasets yields worse performance than single dataset models. Interest-ingly, we find that this conventional wisdom does not hold for motionestimation, and that state-of-the-art scene flow methods greatly benefitfrom cross-dataset training without architectural modification. We positthat low-level tasks such as motion estimation may be less sensitive tosensor configuration; indeed, our analysis shows that models trained onfast-moving objects (e.g., from highway datasets) perform well on fast-moving objects, even across different datasets. Informed by our analy-sis, we propose UniFlow, a feedforward model that unifies and trainson multiple large-scale LiDAR scene flow datasets with diverse sensorplacements and point cloud densities. Our frustratingly simple solutionestablishes a new state-of-the-art on Waymo and nuScenes, improvingover prior work by 5.1% and 35.2% respectively. Moreover, UniFlowachieves state-of-the-art accuracy on unseen datasets like TruckScenesand AEVAScenes, outperforming prior dataset-specific models by 30.1%and 22.5% respectively. See our project page for additional visuals.

Abstract:
We introduce Edge-based Pose Optimization (EPO),a trackless geometric optimization framework specifically designed toboost the Structure-from-Motion reconstructions generated by 3D Foun-dation Models. These models achieve rapid inference by bypassing thetime-consuming feature extraction and matching stages of traditionalpipelines, where explicit correspondences between each 3D point andmultiple images, referred to as tracks, are established. However, their ge-ometric accuracy currently falls short of traditional pipelines. While thiscan be addressed in a post-processing step via Bundle Adjustment-likerefinement, doing so requires extracting feature tracks, thus defeatingthe original speed advantage. Instead, our fully differentiable frameworkuses edge map alignment as a proxy for geometric optimization, avoid-ing feature extraction and track construction entirely. Through exten-sive evaluation across multiple datasets and tasks, we demonstrate thatEPO matches or outperforms Bundle Adjustment-like methods while re-quiring significantly lower runtime and memory. Notably, its reducedmemory footprint makes EPO suitable for consumer-grade hardware,where competing refinement methods cannot run. Code is available athttps://github.com/mattiadurso/EPO.

Abstract:
Inferring physical properties from a single image is funda-mentally under-constrained. Attributes such as density, elasticity, andyield stress govern how objects move, yet they are largely invisible ina static frame. Existing physics-aware methods attempt to resolve thisambiguity through task-specific fine-tuning or supervised property es-timation, but both strategies struggle to generalize across diverse ma-terials and scenes. We observe that different motions expose comple-mentary physical cues. Building on this observation, we propose Phy-MAGIC, a training-free framework that actively probes physical prop-erties by synthesizing targeted motions from a single image. Specifically,PhyMAGIC uses a pretrained image-to-video model to construct mo-tion probes that generate diverse dynamic sequences from the inputimage. A vision-language model then analyzes these sequences to esti-mate physical parameters, each accompanied by a confidence score. Pa-rameters with low confidence trigger targeted prompt refinement, whichgenerates additional probe motions to gather complementary evidence.Once all parameters reach sufficient confidence, PhyMAGIC compilesthem into a complete physical specification and executes it in a differ-entiable Material Point Method simulator initialized from 3D Gaussianreconstructions. Experiments on diverse real-world scenes demonstratethat PhyMAGIC achieves stronger text-motion alignment and higherhuman-rated physical plausibility than state-of-the-art open-source videogenerators and physics-aware baselines. Code is available at: https://mengsiwei.github.io/MAGIC/.

Abstract:
Evaluation of autonomous vehicle (AV) planners in safety-critical closed-loop simulation is essential for real-world deployment. How-ever, generating controllable safety-critical scenarios remains challeng-ing. Existing approaches use soft guidance that provides only probabilis-tic preferences and cannot guarantee the satisfaction of geometric andseverity constraints associated with specific collision types. We introduceCollision-Constrained Flow Matching (CCFM), a novel framework thatguarantees precise collision control through hard physical constraints.CCFM consists of three key components: (i) a heuristic collision selectorthat optimally identifies an adversarial agent and collision type via com-posite scoring; (ii) structured hard constraints that explicitly define fourcollision types (rear-end, side, cut-in, head-on) through contact point,heading, and severity requirements; and (iii) a collision-constrained flowmatching sampler that enforces the constraints via Gauss-Newton man-ifold projection. CCFM achieves collision rate up to 46.4% on nuScenesand 83.1% on nuPlan, significantly outperforming baselines while pre-serving realistic driving behavior. By enabling controllable collision char-acteristics in safety-critical scenario generation, CCFM provides a reli-able foundation for AV safety evaluation and sim-to-real crash data gen-eration. The code and implementation details are available at https://github.com/KELISBU/CCFM.

Abstract:
Facial privacy has become a critical concern as unauthorized large-scale image scraping enables malicious actors to collect personal face images without consent and exploit them for training unauthorized recognition systems. Protecting facial privacy requires modifying a face image such that automated recognition systems fail to identify the individual, while the visual appearance remains unchanged to human observers, ensuring the image retains its social utility. However, existing methods fail to satisfy both requirements; they either irreversibly degrade visual quality, rendering images impractical, or alter facial identity to such an extent that the protected image bears no resemblance to the original subject. To address this fundamental privacy-utility tradeoff, we propose MIRAGE (Multi-scale Identity Removal via Attention-Guided Encoding), a frequency-selective facial privacy framework grounded in the observation that identity-discriminative features reside predominantly in low-frequency image components, while visual appearance is encoded in high-frequency components. We further introduce FAC (FrequencyAware Consistency) Loss, which jointly enforces identity separation and visual fidelity during training. A comprehensive evaluation across three benchmark datasets and nine state-of-the-art (SOTA) deep face recognition (DFR) models demonstrates that MIRAGE achieves robust facial privacy protection while preserving visual appearance, successfully bridging the privacy-utility gap left unresolved by existing methods. The code is available at: https://github.com/atulkr05/MIRAGE.git.

Abstract:
Visual Place Recognition (VPR) often fails under extremeenvironmental changes and perceptual aliasing. Beyond these limita-tions, standard systems cannot perform ’blind’ localization from verbaldescriptions alone, a capability critical for applications such as emer-gency response. To address these challenges, we introduce LaVPR, alarge-scale benchmark that extends existing VPR datasets with over650,000 rich natural-language descriptions. Using LaVPR, we investigatetwo paradigms: Multi-Modal Fusion for enhanced robustness and Cross-Modal Retrieval for language-based localization. Our results show thatlanguage descriptions yield consistent gains in visually degraded condi-tions, with the most significant impact on smaller backbones. Notably,adding language allows compact models to rival the performance of muchlarger vision-only architectures. For cross-modal retrieval, we establish abaseline using Low-Rank Adaptation (LoRA) and Multi-Similarity loss,which substantially outperforms standard contrastive methods acrossvision-language models. Ultimately, LaVPR enables a new class of lo-calization systems that are both resilient to real-world stochasticity andpractical for resource-constrained deployment. Our dataset and code areavailable at https://github.com/oferidan1/LaVPR.

Abstract:
RL (reinforcement learning) methods (e.g., GRPO) for mul-timodal LLM perception ability has attracted wide research interest ow-ing to its remarkable generalization ability. Nevertheless, existing rein-forcement learning methods still face the problem of low data quality,where data samples cannot elicit diverse responses from MLLMs, thus re-stricting the exploration scope for MLLM reinforcement learning. Somemethods attempt to mitigate this problem by imposing constraints onentropy, but none address it at its root. Therefore, to tackle this problem,this work proposes Syn-GRPO (Synthesis-GRPO), which employs anonline data generator to synthesize high-quality training data with di-verse responses in GRPO training. Specifically, Syn-GRPO consists oftwo components: (1) data server; (2) GRPO workflow. The data serversynthesizes new samples from existing ones using an image generationmodel, featuring a decoupled and asynchronous scheme to achieve highgeneration efficiency. The GRPO workflow provides the data server withthe new image descriptions, and it leverages a diversity reward to su-pervise the MLLM to predict image descriptions for synthesizing sam-ples with diverse responses. Experiment results across three visual per-ception tasks demonstrate that Syn-GRPO improves the data qualityby a large margin, achieving significant superior performance to exist-ing MLLM perception methods, and Syn-GRPO presents promising po-tential for scaling long-term self-evolving RL. Our code is available athttps: // github. com/ hqhQAQ/ Syn-GRPO .

Abstract:
Concept erasure methods aim to remove specific unsafe tar-get concepts in diffusion models while preserving image generation utility.To address the vulnerability that erased concepts can be easily recoveredunder adversarial attacks, adversarial concept erasure methods integrateadversarial optimization into the concept erasure process. However, exist-ing adversarial concept erasure methods face a trade-off between robust-ness and computational cost. We attribute this to adversarial optimiza-tion techniques that use random samples to approximate the adversarialobjective function. Adversarial optimization that uses a small numberof samples fails to produce adversarial embeddings that accurately cap-ture the target concept space. To mitigate this limitation, we proposeSemantic-Guided Adversarial Optimization, which uses a single sampleto produce adversarial embeddings that better capture the target con-cept space. We also propose Semantic-Guided Concept Erasure, whichautomatically maps the target concept to a semantically similar surro-gate. Extensive experiments on not-safe-for-work content, artistic styles,and object-related concepts demonstrate that our method, S-GRACE(Semantic-Guided Robust Adversarial Concept Erasure) achieves state-of-the-art erasure robustness and superior image generation utility, withsignificantly lower computational cost than existing methods. Our codeis available at https://github.com/Qhong-522/S-GRACE.

Abstract:
Reinforcement learning for diffusion models primarily relieson on-policy learning to optimize the model by using samples from itsown current policy. Despite offering a direct optimization signal, theapproach is fundamentally self-limiting and prone to reward overfittingand mode collapse due to its myopic guidance. In this paper, we pro-pose a new framework, namely DiffusionCompass, that breaks such lim-itation by strategically integrating off-policy guidance. DiffusionCom-pass establishes a dual learning objective: the model continues to ex-plore reward maximization on-policy, while simultaneously being an-chored by off-policy examples that define a stable quality benchmark.That is achieved through a novel integration of several key techniques:an off-policy objective with distribution-aware filtering to adaptively em-phasize relevant anchors, a perceptual anchored reward mechanism tomitigate reward hacking, and a reward renormalization strategy usingoff-policy samples for a stable quality baseline. The dual regimes com-plement each other: off-policy guidance prevents reward overfitting andcollapse, and on-policy exploration ensures the model does not merelyimitate a static dataset. Experiments across text-to-image/video gener-ation demonstrate that DiffusionCompass achieves superior reward per-formance while maintaining high sample diversity, shaping a robust andeffective path for fine-tuning generative models. Code is available athttps://github.com/HiDream-ai/DiffusionCompass.

Abstract:
Diffusion-based image editing models have achieved signifi-cant progress in real world applications. However, conventional modelstypically rely on natural language prompts, which often lack the pre-cision required to localize target objects. Consequently, these modelsstruggle to maintain background consistency due to their global im-age regeneration paradigm. Recognizing that visual cues provide an in-tuitive means for users to highlight specific areas of interest, we uti-lize bounding boxes as guidance to explicitly define the editing tar-get. This approach ensures that the diffusion model can accurately lo-calize the target while preserving background consistency. To achievethis, we propose FineEdit, a multi-level bounding box injection methodthat enables the model to utilize spatial conditions more effectively.To support this high precision guidance, we present FineEdit-1.2M,a large scale, fine-grained dataset comprising 1.2 million image edit-ing pairs with precise bounding box annotations. Furthermore, we con-struct a comprehensive benchmark, termed FineEdit-Bench, which in-cludes 1,000 images across 10 subjects to effectively evaluate region basedediting capabilities. Evaluations on FineEdit-Bench demonstrate thatour model significantly outperforms state-of-the-art open-source models(e.g., Qwen-Image-Edit and LongCat-Image-Edit) in instruction compli-ance and background preservation. Further assessments on open bench-marks (GEdit and ImgEdit Bench) confirm its superior generalizationand robustness. The dataset and code will be publicly available throughthe project page at https://laulampaul.github.io/FineEdit.

Abstract:
Text-to-image diffusion models deliver high-quality images,yet aligning them with human preferences remains challenging. We re-visit diffusion-based Direct Preference Optimization (DPO) for thesemodels and identify a critical pathology: enlarging the preference mar-gin does not necessarily improve generation quality. In particular, thestandard Diffusion-DPO objective can increase the reconstruction er-ror of both winner and loser branches. Consequently, degradation ofthe less-preferred outputs can become sufficiently severe that the pre-ferred branch is also adversely affected even as the margin grows. Toaddress this, we introduce Diffusion-SDPO, a safeguarded update rulethat preserves the winner by adaptively scaling the loser gradient ac-cording to its alignment with the winner gradient. A first-order analysisyields a closed-form scaling coefficient that guarantees the error of thepreferred output is non-increasing at each optimization step. Our methodis simple, model-agnostic, broadly compatible with existing DPO-stylealignment frameworks and adds only marginal computational overhead.Across standard text-to-image benchmarks, including validations on thelarge-scale FLUX.1-dev model, Diffusion-SDPO delivers consistent gainsover preference-learning baselines on automated preference, aesthetic,and prompt alignment metrics. Our code is available at https://github.com/AIDC-AI/Diffusion-SDPO.

Abstract:
We introduce GenAgent, an agentic framework that unifiesvisual understanding and generation. GenAgent overcomes the limita-tions of tightly coupled unified models (which suffer from understand-ing–generation trade-offs and high training data costs) and the struc-tural bottlenecks of existing complex multi-model pipelines through aflexible, decoupled architecture: Understanding is handled by a singlemultimodal model, while generation is achieved by treating image gen-eration models as invokable tools. Given an image prompt, GenAgentadaptively engages in reasoning, tool invocation, visual judgment, andreflection to iteratively refine outputs until the criteria are met. We em-ploy a two-stage training strategy: first, we cold-start the agent with su-pervised fine-tuning on high-quality tool-invocation and reflection datato bootstrap multi-turn agent behaviors; second, we perform end-to-endagentic reinforcement learning, combining pointwise rewards (final im-age quality) and pairwise rewards (reflection accuracy), and utilizinga round-aware trajectory resampling strategy to balance improvementsacross distinct capabilities. Without modifying the underlying genera-tors, GenAgent significantly boosts the base model (FLUX.1-dev) by23.6% on GenEval++ and 14.0% on WISE. Beyond substantial per-formance gains, our framework demonstrates three key emergent prop-erties: (1) cross-tool generalization to generators with varying capabili-ties, (2) test-time scaling with consistent improvements across interactionrounds, and (3) task-adaptive reasoning. Project page: https://deep-kaixun.github.io/genagent-page/

Abstract:
While instruction-guided image editing has seen significantstrides, most existing models fail to preserve physical laws and causal-ity. This limitation stems from the sparsity of physical supervision sig-nals, coupled with the static, non-causal architecture of prevailing frame-works, which together hinder a deep comprehension of the physical world.To bridge this gap, we present PhysEdit, a novel framework that en-forces physical consistency in image editing through causal generationand physics-aware reinforcement learning. Specifically, by harnessing theintrinsic physical priors of video generative models, we curate a high-quality dataset, dubbed PhysEdit-50K, wherein every sample encapsu-lates the complex physical causalities and dynamics involved in the tran-sition between input and edited images. Building upon this, a specializedCausal Image Editor is devised to factorize the image editing processalong the temporal evolution of visual transformations, thereby internal-izing the underlying physical laws and boosting causal coherence. Simul-taneously, an additional regularization term is incorporated to maintainvisual consistency in unedited regions beyond the simple flow matchingobjective, mitigating the impact of undesired viewpoint shifts or cam-era motions in the dataset synthesized by video models. Furthermore,we integrate physics-aware reinforcement learning with a tailored fine-grained reward to steer the editing process toward better adherence tophysical laws. Extensive experiments on PICABench and ImgEdit-Benchdemonstrate that our PhysEdit significantly outperforms state-of-the-artbaselines, yielding physically consistent and plausible edits. Code is pub-licly available at: https://github.com/HiDream-ai/PhysEdit/.

Abstract:
Reinforcement-learning alignment for autoregressive imagegeneration is bottlenecked by expensive rollouts: in GRPO-style groupoptimization, sampling G rollouts per prompt improves exploration andwithin-group credit estimation but multiplies sampling cost. This chal-lenge is amplified by vector-quantized (VQ) tokenization, where nearbycodebook IDs in embedding space can be locally substitutable; however,GRPO-style updates provide explicit positive credit only to sampled IDs,leading to insufficient coverage of success-supported alternatives undersmall-G training. We propose GR-GRPO (Graph-Regularized GRPO),which densifies token-level supervision without additional rollouts by dif-fusing positive evidence from positive rollouts over a precomputed code-book K-NN graph to construct graph-diffused soft targets, and regulariz-ing the policy toward these targets via an auxiliary cross-entropy term. Aconfidence-adaptive gate modulates diffusion strength based on the pol-icy’s logit margin, and a difficulty-aware budget allocation scheme fur-ther improves rollout utilization across prompts. Extensive experimentson compositional image generation (GenEval) and preference-style align-ment (DrawBench) validate the effectiveness of GR-GRPO. In particular,GR-GRPO achieves a substantially better compute–performance trade-off, surpassing large-group training (G=64) with ∼ 4.6× less rollout timeon Janus-Pro-1B. Our code and model weights are available athttps://github.com/vivoCameraResearch/GR-GRPO.

Abstract:
Text-to-image diffusion models excel at generating images from nat-ural language prompts, yet fail to interpret numerical colors such as hex codes(#FF5733) and RGB values (rgb(255,87,51)). This limitation stems fromsubword tokenization, which fragments color codes into semantically meaning-less tokens that text encoders cannot map to coherent color representations. Wepresent NumColor, that enables precise numerical color control across multi-ple diffusion architectures. NumColor comprises two components: a Color To-ken Aggregator that detects color specifications regardless of tokenization, anda ColorBook containing 6,707 learnable embeddings that map colors to embed-ding space of text encoder in perceptually uniform CIE Lab space. We introducetwo auxiliary losses, directional alignment and interpolation consistency, to en-force geometric correspondence between Lab and embedding spaces, enablingsmooth color interpolation. To train the ColorBook, we construct NumColor-Data, a synthetic dataset of 500K rendered images with unambiguous color-to-pixel correspondence, eliminating the annotation ambiguity inherent in photo-graphic datasets. Although trained solely on FLUX, NumColor transfers zero-shot to SD3, SD3.5, PixArt-α, and PixArt-Σ without model-specific adaptation.NumColor improves numerical color accuracy by 4–9× across five models, whilesimultaneously improving color harmony scores by 10–30× on GenColorBenchbenchmark. The code is available at https://github.com/moatifbutt/numcolor/.

Abstract:
Real-world object removal is challenging due to two key dif-ficulties: the target object’s non-local effects (e.g., shadows and reflec-tions) that are hard to model, and the fact that user-provided masksare often inaccurate or incomplete. With billions of parameters and tensof denoising steps, diffusion-based models achieve this goal at the ex-pense of massive computational cost, limiting their use in interactiveapplications and edge devices. To solve this problem, we present OSOR(One-Step Object Removal), which achieves efficient, effect-aware, andmask-robust object removal at the same time. Concretely, OSOR in-troduces (1) an occupancy-guided discriminator for precise boundarysupervision, enabling stable single-step diffusion training, (2) an alphahead, which leverages the knowledge in diffusion models to predict cor-rect removal regions with minimal overhead, handling imperfect masks,and (3) a semantic-anchored verification pipeline (SAVP) that filtersnoisy instruction-based triplets to produce effect-aware supervision atscale. Using SAVP, we curate CORNE with 280K verified removal pairsand further annotate AnimeEraseBench and TextEraseBench to evalu-ate model performance in more complex tasks. Experiments show thatOSOR surpasses strong multi-step diffusion baselines in perceptual qual-ity while achieving 4× to 30× faster inference. Code and resources areavailable at https://github.com/Zhouqm-Git/osor.

Abstract:
Generating diverse, coherent, and plausible content from par-tially given inputs remains a fundamental challenge for diffusion models.Existing approaches face clear limitations: training-based approachesoffer strong task-specific results but require costly computation, andthey generalize poorly across tasks. Training-free approaches offer betterefficiency, but they do not explicitly optimize over unobserved variables,leading to globally inconsistent results. To address these limitations, weintroduce Accelerated Likelihood Maximization (ALM), a novel training-free sampling strategy integrated into the reverse diffusion process thatsignificantly extends the applicability of diffusion models beyond simplegeneration tasks. Unlike previous methods that implicitly influence miss-ing regions through pre-generated region constraints, we directly optimizethe unobserved region during the sampling process, enabling globallycoherent and plausible generation. Furthermore, we incorporate an ac-celeration strategy that significantly improves computational efficiencywithout sacrificing performance. Experimental results demonstrate thatALM consistently outperforms state-of-the-art methods in various datadomains and tasks, establishing a powerful paradigm for versatile contentgeneration. Project website: http://hleephilip.github.io/ALM

Abstract:
Generating realistic cast shadows for inserted foreground ob-jects is a crucial yet challenging problem in image composition, wheremaintaining geometric consistency between objects and their shadows incomplex scenes remains difficult due to the ill-posed nature of shadowformation. To address this challenge, we propose VSDiffusion, a visibility-constrained two-stage framework that narrows the solution space by in-corporating visibility priors. In Stage I, a coarse shadow mask is pre-dicted to localize plausible shadow regions. In Stage II, conditional dif-fusion guided by lighting and depth cues estimated from the compositeimage is used to generate accurate shadows. Within VSDiffusion, visibil-ity priors are injected through two complementary pathways: (1) a vis-ibility control branch with shadow-gated cross-attention that providesmulti-scale structural guidance, and (2) a learned soft prior map thatreweights the training loss in error-prone regions to encourage geometriccorrection. In addition, we introduce a high-frequency guided enhance-ment module to sharpen shadow boundaries and improve texture inter-action with the background. Extensive experiments on the widely usedDESOBAv2 benchmark demonstrate that VSDiffusion produces geomet-rically consistent shadows in complex scenes and establishes new state-of-the-art results across most evaluation metrics. The code is availableat https://github.com/Jadelingli/VSDiffusion.

Abstract:
Unified Multimodal Models (UMMs) consolidate visual un-derstanding, image generation, editing, and interleaved image-text in-teraction, yet their evaluation increasingly depends on VLM-as-a-judgescoring that shifts as judges, prompts, or model versions evolve — aproblem we term evaluation drift. We present SpecV, a specification-verification framework for robust unified multimodal evaluation. SpecVreplaces holistic scoring with the Specification Verification Proto-col, which decomposes each prompt into atomic, binary specificationsand verifies them against model outputs, improving cross-judge agree-ment and stability while preserving alignment with human judgments.To produce reliable specifications, we propose Specification Ensem-ble and Refinement, a multi-model pipeline that aggregates candidatespecifications, deduplicates them semantically, and filters for verifiabil-ity and relevance. We also introduce SpecV-Bench, a 1,200-instancebenchmark covering six core UMM tasks with sub-tracks of increasingconstraint complexity, enabling fine-grained analysis of capability trade-offs and failure modes. Across extensive experiments with multiple judgemodels, SVP consistently reduces ranking flips and improves evalua-tion reproducibility. Benchmark is available at https://github.com/yuyouxixi/SpecV.

Abstract:
Video Super-Resolution (VSR) aims to restore high-quality video frames from low-resolution (LR) estimates, yet most existing VSR approaches behave like black boxes at inference time: users cannot reliably correct unexpected artifacts, but instead can only accept whatever the model produces. In this paper, we propose a novel interactive VSR framework dubbed SparkVSR that makes sparse keyframes a simple and expressive control signal. Specifically, users can first super-resolve or optionally a small set of keyframes using any off-the-shelf image superresolution (ISR) model, then SparkVSR propagates the keyframe priors to the entire video sequence while remaining grounded by the original LR video motion. Concretely, we introduce a keyframe-conditioned latent-pixel two-stage training pipeline that fuses LR video latents with sparsely encoded HR keyframe latents to learn robust cross-space propagation and refine perceptual details. At inference time, SparkVSR supports flexible keyframe selection and a reference-free guidance mechanism that continuously balances keyframe adherence and blind restoration, ensuring robust performance even when reference keyframes are absent or imperfect. Experiments on multiple VSR benchmarks demonstrate improved temporal consistency and strong restoration quality, surpassing baselines by up to 24.6%, 21.8%, and 5.6% on CLIP-IQA, DOVER, and MUSIQ, respectively, enabling controllable, keyframe-driven video super-resolution. Moreover, we demonstrate that SparkVSR is a generic interactive, keyframe-conditioned video processing framework as it can be applied out of the box to unseen tasks such as old-film restoration and video style transfer. Code is available at: https://github.com/tacogroup/SparkVSR.

Abstract:
Di(cid:27)usion Transformers (DiTs) achieve high-quality generation but are costly due to iterative sampling. Dynamic-resolution sampling reduces early-stage cost by denoising at low resolution; however, uniformly upsampling all latent tokens at resolution transitions incurs redundant computation and may degrade (cid:28)ne-detail consistency. Existing partial upsampling strategies typically rely on local latent structure cues or single-step statistics, making it di(cid:30)cult to jointly capture token(cid:21)text semantic relevance and token-wise representation dynamics across diffusion steps. We propose AViTS, an adaptive spatiotemporal token selection framework for dynamic-resolution DiTs. AViTS models spatial importance via latent(cid:21)text attention and temporal importance via token-level feature variation across di(cid:27)usion timesteps, and fuses them to enable spatiotemporal importance-aware selective upsampling: it prioritizes resolution re(cid:28)nement for critical tokens while deferring less important ones, thereby reducing redundant high-resolution computation and improving the quality(cid:21)e(cid:30)ciency trade-o(cid:27). AViTS achieves up to 6.34× on FLUX and nearly 9× FLOPs reduction on Qwen-ImageEdit and FLUX.1-Kontext-dev, orthogonal to distillation, quantization, and feature caching, and reaching 14.76× with distilled models. Code: https://github.com/QHR69/AViTS.

Abstract:
Benefiting from the strong ability to capture data distributions, diffusion models have become powerful tools for solving image inverse problems. The key is to controllably steer the sampling trajectory toward the measurements while respecting the diffusion prior. In this work, we introduce Stochastic Optimal Control Sampling (SOCS), which models the denoising process as a dynamical system and injects control signals via SOC. Previous SOC-based approach addresses inverse problems by optimizing over the entire trajectory, which is computationally expensive. In contrast, we derive a closed-form control update and apply it at each sampling step, pulling the measurement-consistent clean prediction back onto the denoising flow. In SOCS, we can readily modulate the control strength to align with the diffusion model’s native capabilities and thereby enhance perceptual quality. Our method is compatible with a variety of linear stochastic differential equation backbones. Extensive experiments across a broad spectrum of image inverse tasks demonstrate that SOCS achieves accurate measurement-aligned reconstructions with improved visual fidelity and stronger quantitative performance. Code is available at https://github.com/zjqwq01/SOCS-DIP.

Abstract:
Visible-to-infrared translation provides a scalable pathway tosynthesize infrared imagery from abundant visible data, offering a prac-tical complement to costly cross-modal paired data acquisition. However,most existing generative approaches formulate the task as a unified pro-cess that implicitly entangles macroscopic thermal semantics with mi-croscopic spatial detail reconstruction. This entanglement often forces asub-optimal trade-off, leading to blurred target boundaries or physicallyunnatural thermal artifacts. In this paper, we reformulate the transla-tion process into two explicitly decoupled but tightly coordinated con-tinuous flows, proposing a novel framework named D3F-IR. Leverag-ing the smooth and deterministic trajectories of flow matching, D3F-IRintroduces a semantic-domain flow to model macroscopic thermal dis-tributions within a pretrained latent space, alongside an independentpixel-domain flow to generate fine-grained spatial structures directly inthe image space. To seamlessly coordinate the two domains, we design aPixel-space Velocity Predictor equipped with SEmantic-Aligned Modula-tion (SEAM) Layers, which establish explicit correspondence between se-mantic tokens and spatial patches through patch-wise alignment. Exper-iments on three benchmarks demonstrate that D3F-IR achieves strongperformance in both thermal semantic plausibility and pixel-level spa-tial fidelity. These results highlight that explicit dual-domain decouplingoffers a highly effective paradigm for high-quality visible-to-infrared gen-eration. Code is available at https://github.com/WanrenZeng/D3F-IR.

Abstract:
Current diffusion models face a fundamental tension betweenthe computational efficiency of latent-space generation and the fine-detailfidelity of pixel-space generation. Latent Diffusion Models (LDMs) relyon VAE compression, which can discard high-frequency visual cues andlimit pixel-level quality, while pixel-space diffusion often suffers from diffi-cult optimization when global structure and high-frequency details mustbe learned jointly. In this paper, we propose the Hybrid Latent-PixelDiffusion Transformer (Hi-DiT), a unified Diffusion Transformer thatbridges these two regimes within a single architecture. Motivated by thetemporal heterogeneity of denoising—early timesteps primarily establishcoarse global structure, whereas late timesteps increasingly emphasizehigh-frequency detail—we introduce a dual-stream design with temporalspecialization. Hi-DiT performs semantic planning in a compact latentpathway at early stages, and activates a pixel pathway at later stagesto synthesize high-frequency details using time-consistent noisy pixelembeddings. A Time-Gated Injection mechanism schedules the partic-ipation of pixel tokens only in the low-noise regime, and a lightweightsub-pixel prediction head enables efficient dense detail generation. Ex-tensive experiments demonstrate that Hi-DiT achieves state-of-the-artperformance, obtaining an FID of 1.06 on ImageNet 256 × 256, 1.26 onImageNet 512 × 512, and 5.15 on MS-COCO text-to-image generation.Code is available at: https://github.com/HiDream-ai/Hi-DiT.

Abstract:
Material transfer seeks to re-render an object’s surface usinga reference exemplar while preserving its original geometry and identity.Current training-free methods often rely on attention-based injection,which entangles attributes like color, texture, and structural patternsinto a single representation. This entanglement leads to structural in-stability and imprecise results. Additionally, inversion-based editing inflow models is prone to reconstruction-induced trajectory drift, whereaccumulated errors degrade object details. We propose MaterialFlow, atraining-free framework for precise material transfer using pre-trainedflow models. Our approach introduces a trajectory-aware velocity modu-lation mechanism that rebalances the generative flow in an inversion-free manner. This ensures stable and semantically consistent editingdynamics without the need for explicit latent reconstruction. We fur-ther introduce an attribute-aware disentanglement paradigm that sepa-rates reference materials into color, texture, and meso-scale patterns forfine-grained control. Evaluations on the Material Transfer Benchmark(MTB) show that MaterialFlow surpasses state-of-the-art methods intransfer quality, inference efficiency, and controllability. Project page:https://github.com/Sung-Lin/MaterialFlow.

Abstract:
Flow-based models learn a target distribution by modelinga marginal velocity field, defined as the average of sample-wise velocitiesconnecting each sample from a simple prior to the target data. However,when sample-wise velocities conflict at the same intermediate state, thisaveraged velocity can misguide samples toward low-density regions, de-grading generation quality. To address this issue, we propose the FlowDivergence Sampler (FDS), a training-free framework that refines in-termediate states before each solver step. Our key finding reveals thatthe severity of this misguidance is quantified by the divergence of themarginal velocity field that is readily computable during inference witha well-optimized model. FDS exploits this signal to steer states towardless ambiguous regions. As a plug-and-play framework compatible withstandard solvers and off-the-shelf flow backbones, FDS consistently im-proves fidelity across various generation tasks including text-to-imagesynthesis, and inverse problems.

Abstract:
Transformer-based video diffusion models rely on 3D attention over spatial and temporal tokens, which incurs quadratic time and memory complexity and makes end-to-end training for ultra-highresolution videos prohibitively expensive. To overcome this bottleneck, we propose a pure image adaptation framework that upgrades a video Diffusion Transformer pre-trained at its native scale to synthesize higherresolution videos. Unfortunately, naively fine-tuning with high-resolution images alone often introduces noticeable noise due to the image–video modality gap. To address this, we decouple the learning objective to separately handle modality alignment and spatial extrapolation. At the core of our approach is Relay LoRA, a two-stage adaptation strategy. In the first stage, the video diffusion model is adapted to the image domain using low-resolution images to bridge the modality gap. In the second stage, the model is further adapted with high-resolution images to acquire spatial extrapolation capability. During inference, only the high-resolution adaptation is retained to preserve the video generation modality while enabling high-resolution video synthesis. To enhance fine-grained detail synthesis, we further propose a High Frequency Awareness Training Objective, which explicitly encourages the model to recover high-frequency components from degraded latent representations via a dedicated reconstruction loss. Extensive experiments demonstrate that our method produces ultra-high-resolution videos with rich visual details without requiring any video training data, even outperforming previous state-ofthe-art models trained on high-resolution videos by 0.8 on the VBench benchmark. Code is available here.

Abstract:
Single image reflection removal (SIRR) is challenging in realscenes, where reflection strength varies spatially and reflection patternsare tightly entangled with transmission structures. This paper presentsa diffusion model with prior modulation framework (FUMO) that in-troduces explicit priors for spatially adaptive conditioning and struc-turally faithful restoration. Two priors are extracted directly from themixed image, an intensity prior that estimates spatial reflection sever-ity and a high-frequency prior that captures detail-sensitive responsesvia multi-scale residual aggregation. We propose a coarse-to-fine train-ing paradigm. In the first stage, these cues are combined to gate theconditional residual injections, focusing the conditioning on regions thatare both reflection-dominant and structure-sensitive. In the second stage,a fine-grained refinement network corrects local misalignment and sharp-ens fine details in the image space. Experiments conducted on both stan-dard benchmarks and challenging images in the wild demonstrate com-petitive quantitative results and consistently improved perceptual qual-ity. The code is released at https://github.com/Lucious-Desmon/FUMO.

Abstract:
Despite their remarkable performance in real-world imagesuper-resolution, diffusion models remain challenged by the perception–distortion trade-off between structural fidelity and perceptual realism.Existing methods typically rely on a globally predefined restoration tra-jectory, applying spatially uniform noise and a fixed temporal schedule.Such rigid trajectories often lead to a dilemma: reliable structures may bedistorted, while severely degraded textures may become over-smoothed.To address this, we propose AdaBridge-SR, built upon a novel spatio-temporal adaptive bridge matching (ST-ABM) formulation that castsrestoration as a controlled bridge between degraded and clean imagedistributions. ST-ABM decouples the bridge stochasticity into spatialand temporal controls, adaptively determining where to inject stochas-ticity and when to allocate generative capacity conditioned on the inputdegradation. We instantiate these controls with a lightweight AdaptiveBridge Controller that predicts a spatial stochasticity map and a timereparameterization. Consequently, AdaBridge-SR unifies deterministicrestoration and stochastic exploration within a single model. Extensiveexperiments demonstrate that it achieves a superior perception–distortionbalance, supporting efficient deterministic one-step inference and high-quality stochastic few-step bridge refinement. Our code and models areavailable at https://github.com/W-JG/AdaBridge-SR.

Abstract:
The quality of the latent space in visual tokenizers (e.g.,VAEs) is crucial for modern generative models. However, the standardreconstruction-based training paradigm produces a latent space that isbiased towards low-level information, leading to a foundational flaw: bet-ter pixel-level reconstruction accuracy does not lead to higher-qualitygeneration. This implies that pouring extensive compute into visual tok-enizer pre-training translates poorly to improved performance in gener-ation. We identify this as the “pre-training scaling problem” and suggesta necessary shift: to be effective for generation, a latent space must con-cisely represent high-level semantics. We present VTP, a unified visualtokenizer pre-training framework, pioneering the joint optimization ofimage-text contrastive, self-supervised, and reconstruction losses. Ourstudy reveals that perception-oriented tokenizer pre-training un-locks a new scaling law for generation, where generative perfor-mance scales effectively with compute, parameters, and data allocatedto the pre-training of the visual tokenizer. Our large-scale pre-trainingexperiments demonstrate the following results: (1) Without modifyingDiT training specs and FLOPs, solely scaling VTP pre-training con-sistently achieves gains in both ImageNet class-conditional and LAIONtext-to-image generation, while conventional autoencoders stagnate veryearly at 1/10 of the FLOPs. (2) VTP achieves 0.36 rFID while simultane-ously delivering 78.2% zero-shot accuracy and 85.7% linear probing accu-racy, surpassing prior unified tokenizers such as VILA-U and UniTok. (3)Furthermore, the VTP-based diffusion model exhibits exceptionally fastconvergence—reaching 2.03 gFID in only 80 epochs without guidancetricks, outperforming previous methods like VA-VAE and RAE—and ul-timately scales to achieve a remarkable 1.11 gFID on ImageNet 256×256generation. Our code and models are publicly available at VTP.

Abstract:
Probe-based Confocal Laser Endomicroscopy (pCLE) enables real-time in vivo optical biopsies. However, acquiring large-scale, high-quality pCLE images is prohibitively expensive and technically complex, causing severe training data scarcity. Furthermore, when applied to pCLE image generation, existing cross-modal translation models consistently introduce artifacts and cellular structure distortions. To address this, we propose Y-diff, a novel generative framework translating widely accessible H&E-stained pathology images into the scarce pCLE modality with high fidelity, providing robust data support for computational pathology. Y-diff innovatively introduces a knowledge distillation mechanism into this task paradigm, efficiently decoupling the learning of pCLE-domain textures from that of H&E pathology staining domain spatial structures. Specifically, a Teacher model captures distinct pCLE optical textures via color encoding, while a Student model extracts and distills this knowledge under strict H&E spatial constraints. Extensive experiments demonstrate that Y-diff consistently outperforms baselines, while significantly alleviating conventional translation artifacts and structural distortions. What’s more, the synthesized data further expands the scale of this rare modality, reducing mean absolute percentage error to 13.84% in downstream cell counting tasks. Source code is available at: https://github.com/Hdw2agon/Y_diff.

Abstract:
Counting individual trees is a fundamental task for environ-mental monitoring, yet remains largely unexplored with satellite imagery.At these resolutions, isolated trees may still be identifiable, but crownboundaries become ambiguous in dense forests, making the notion of anindividual tree inherently ill-defined. Moreover, large-scale manual an-notations of individual trees are prohibitively expensive. While scalablesupervision can be derived from airborne LiDAR, the resulting anno-tations are noisy and difficult to exploit effectively. We address thesechallenges by formulating tree counting as a spatial density matchingproblem supervised through Unbalanced Optimal Transport. This formu-lation naturally accommodates both precise localization of isolate treesand robust density estimation in dense forests. We further introduce aself-correction mechanism that leverages transport residuals to progres-sively refine noisy supervision during training. We evaluate our approachon TinyTrees, a new benchmark spanning three continents and threesatellite sensors, comprising over 216 million tree annotations (including639k manually verified instances) across 25 890 km2 . Our method con-sistently outperforms detection-based, regression-based, and transport-based distribution-matching baselines, demonstrating the effectivenessof unbalanced transport and reliability-aware supervision for large-scaletree counting from satellite imagery. Code, data and models are availableat github.com/dgominski/treematch.

Abstract:
Table parsing aims to convert table images into structured,machine-readable representations, a task requiring the joint perceptionof complex spatial layouts and textual content. While recent vision-language models (VLMs) enable end-to-end parsing, they typically relyon direct supervision of the final output, thereby bypassing the explicitintermediate reasoning that is crucial for understanding complex tablestructures. Furthermore, attempts to optimize these models using re-inforcement learning (RL) are often hindered by unstable or ambigu-ous reward designs, limiting potential performance gains. To addressthese limitations, we propose StrucTab, a table parsing model learnedthrough intermediate structural supervision and reward decomposition.At the modeling level, by decomposing the parsing process into human-inspired subtasks, such as row-column counting and merged-cell analy-sis, StrucTab progressively unifies them through a sequential reasoningstrategy. At the optimization level, we introduce Uni-TabRL, a unifiedRL framework that leverages decomposed rewards (validity, structure,and content) to provide stable and informative optimization signals. Fi-nally, at the evaluation level, we present TableVerse-5K, a large-scale,challenging benchmark encompassing diverse, real-world table scenarios.Extensive experiments demonstrate the state-of-the-art performance ofStrucTab across all evaluated public benchmarks and significant improve-ments on TableVerse-5K, validating the effectiveness of explicit structuralmodeling and decomposed reward optimization. Code and benchmark arepublicly available at https://github.com/VirtualLUOUCAS/StrucTab.

Abstract:
Large vision-language models incur substantial inference costsbecause high-resolution inputs introduce thousands of visual tokens, manyof which are redundant for a given query. Existing pruning methods of-ten combine query relevance and token diversity, yet these objectivescan conflict under aggressive compression: relevance-driven selection mayoverconcentrate the budget on correlated local evidence, while diversity-driven selection may suppress indispensable tokens or retain distinctbut uninformative regions. We introduce AnchorPrune, a training-freeframework that first constructs a protected relevance anchor and thenexpands it with complementary visual context. AnchorPrune adaptivelydetermines the anchor size from the novelty profile of relevance-rankedtokens, preserving a compact set of query-critical evidence, and allocatesthe remaining budget through importance-weighted novelty to recover in-formative, non-redundant context relative to the anchor. This ordered de-sign prevents contextual expansion from displacing indispensable querycues while improving overall visual coverage. AnchorPrune is lightweight,architecture-aware, and requires neither retraining nor model modifica-tion. Across image and video vision-language models and benchmarks, itconsistently improves the accuracy–efficiency trade-off over training-freebaselines, particularly under severe compression. On LLaVA-NeXT-7B,AnchorPrune preserves 97.6% of full-token performance using only 160of 2,880 visual tokens. These results establish relevance-anchored contex-tual expansion as an effective principle for efficient multimodal inference.Code is available at https://github.com/MULTI-cau/AnchorPrune.

Abstract:
Recent advancements in LVLMs necessitate robust bench-marks for complex, visually grounded reasoning. A critical limitation isidentified in many document understanding benchmarks: visual contentis often reducible to text, enabling high performance without genuine vi-sual grounding. To address this limitation, OmniMapBench is introducedto foster visual-centric reasoning for map documents. The benchmarkcomprises 2,096 manually annotated question-answer pairs across 1,603map documents from nine categories. It is designed to probe a hierar-chy of skills, ranging from perception to multi-step visual reasoning. Toquantify benchmark properties, a simple yet effective benchmark-levelmetric is proposed: the Visual Dependency Index (VDI), defined as theaccuracy drop when images are replaced with question-agnostic descrip-tions. OmniMapBench exhibits higher VDI than established benchmarks,which quantitatively validates its focus on irreducible visual reasoning.Comprehensive evaluations of 25 leading LVLMs are conducted on Om-niMapBench. A significant performance gap is observed, with the top-performing model achieving only 75.03% accuracy. This result under-scores the challenges posed by OmniMapBench to current LVLMs. Thiswork aims to catalyze progress in visual-centric reasoning for documentunderstanding of LVLMs. The dataset and code are publicly available athttps://github.com/SIGMME/OmniMapBench.

Abstract:
Multi-view spatial reasoning remains difficult for currentvision-language models. Even when multiple viewpoints are available,models often underutilize cross-view relations and instead rely on single-image shortcuts, leading to fragile performance on viewpoint transforma-tion and occlusion-sensitive cases. We present ViewFusion, a two-stageframework that explicitly separates cross-view spatial pre-alignment fromquestion answering. In the first stage, the model performs deliberatespatial pre-thinking to infer viewpoint relations and spatial transfor-mations across views, forming an intermediate workspace that goes be-yond a simple re-description. In the second stage, the model conductsquestion-driven reasoning conditioned on this workspace to producethe final prediction. We train ViewFusion with synthetic reasoningsupervision followed by reinforcement learning using GRPO, which im-proves answer correctness while stabilizing the intended two-stage gen-eration behavior. On MMSI-Bench, ViewFusion improves accuracyby 5.3% over Qwen3-VL-4B-Instruct, with the largest gains on exam-ples that require genuine cross-view alignment. Our code is available athttps://github.com/taoxj2001/ViewFusion.

Abstract:
Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such as spatial reasoning. However, this paradigm suffers from two critical limitations: (i) MLLMs text encoder exhibit insufficient reasoning depth. Single-step encoding fails to activate the Chain-of-Thought process, which is essential for MLLMs to provide accurate guidance for complex tasks. (ii) The guidance remains invariant during the decoding process. Invariant guidance during decoding prevents DiT from progressively decomposing complex instructions into actionable denoising steps, even with correct MLLM encodings. To this end, we propose Endogenous Chain-of-Thought (EndoCoT), a novel framework that facilitates structured visual reasoning in MLLMs by iteratively refining latent thought states through an iterative thought guidance module, and then bridges these states to the DiT’s denoising process. Second, a terminal thought grounding module is applied to ensure the reasoning trajectory remains grounded in textual supervision by aligning the final state with ground-truth answers. With these two components, the MLLM text encoder delivers meticulously reasoned guidance, enabling the DiT to execute it progressively and ultimately solve complex tasks in a step-by-step manner. Extensive evaluations across diverse benchmarks (e.g., Maze, TSP, VSP, and Sudoku) achieve an average accuracy of 92.1%, outperforming the strongest baseline by 8.3 percentage points.

Abstract:
Vision-Language Models (VLMs) have advanced Visual Document Retrieval (VDR) by enabling OCR-free, multi-vector representations. However, these models generate excessive tokens to preserve dense visual details, which severely limits retrieval scalability. Existing compression methods mitigate this overhead but rely on isolated heuristics that fail to balance token importance, semantic redundancy, and coverage capacity. We propose a visual token compression framework formulated as an Unbalanced Optimal Transport (UOT) problem. Our approach unifies these three objectives into a single alternating optimization process. Furthermore, we theoretically show that minimizing the transport cost upper-bounds the MaxSim retrieval error, ensuring ranking stability. Extensive experiments on the ViDoRe V1 and V3 benchmarks demonstrate that our method preserves fine-grained document semantics and improves the efficiency-effectiveness trade-off over state-of-the-art baselines. Code is available at https://github.com/shhhhhyy/UnbalancedOptimal-Transport-for-EVDR.

Abstract:
Spatio-Temporal Video Grounding (STVG) aims to retrievethe visual trajectory of a specific object from a video stream as describedby a natural language expression. However, most advanced methodsstruggle to balance global context modeling with precise boundary lo-calization. Due to the prohibitive computational costs of processing longvideos, these approaches typically resort to low-rate temporal downsam-pling and implicit motion modeling. This inevitably suppresses high-frequency boundary cues and neglects the explicit inter-frame depen-dencies required for precise boundary delineation. To address these lim-itations, we present ScanFocus, a novel coarse-to-fine framework thatdecouples the STVG task into a global spatio-temporal scan and a localboundary focus. Specifically, we utilize a unified vision-language fusionencoder combined with a lightweight Deformable Semantic-Motion Fu-sion module to efficiently align multimodal features and generate coarseproposals. To recover the suppressed fine-grained details, we introducethe Semantic-Guided Temporal Aggregator (SGTA) in the refinementstage. By densely sampling around coarse boundaries, SGTA explicitlymodels short-term temporal interactions under semantic guidance, cap-turing rapid motion changes for precise timestamp regression. Extensiveexperiments on three widely used benchmarks demonstrate the perfor-mance superiority of our proposed method over previous approaches.Code will be released at https://github.com/TenMinutes209/ScanFocus.

Abstract:
Multimodal learning robust to missing modalities is essen-tial for real-world applications. Existing methods mainly focus on inter-modality missing, where entire modalities are absent, while overlookingintra-modality degradation, where modalities are present but severelycorrupted. In practice, these two types of missing often coexist, mak-ing existing approaches ineffective. To address this limitation, we pro-pose General Incomplete Multimodal Learning (GIML), a unified frame-work that simultaneously handles both inter-modality missing and intra-modality degradation through dynamic quality perception. Specifically,GIML models heterogeneous missing patterns as continuous modality in-formation degradation, enabling degradation-aware adaptive fusion. Toachieve reliable quality perception, we introduce a Noise-aware QualityEstimator that learns the mapping from corrupted features to noise in-tensity through controlled noise injection. Furthermore, we propose aNoise–Semantic Decoupled module that separates semantic informationfrom noise interference. This improves robustness and generalization tounseen corruption patterns. Extensive experiments across datasets withdiverse modality types demonstrate the effectiveness and generality ofGIML. Code is available at: https://github.com/Yu-Five/GIML.

Abstract:
Open-vocabulary semantic segmentation (OVSS) leveragestextual semantics to segment objects beyond predefined categories. Whilethe self-supervised model DINOv3 provides strong structured visual rep-resentations, its lack of native textual alignment hinders direct applica-tion to OVSS. To bridge this gap, we propose DINOde, an ODE-basedframework that continuously aligns CLIP text embeddings to the DINOvisual manifold. Our approach employs two complementary components:(i) Semantic Text Flow (STF), which evolves text embeddings toward theDINO manifold through a continuous ODE trajectory, and (ii) GlobalContext Flow (GCF), which progressively refines the holistic image rep-resentation carried by DINO’s CLS token. To preserve the hypersphericalgeometry of the feature space during this evolution, we further intro-duce Velocity Tangent Projection, which constrains the learned veloc-ity field to the tangent space through projection. By modeling align-ment as a continuous trajectory, DINOde avoids the manifold entan-glement inherent in discrete MLP projections and yields more robustcross-modal alignment. Extensive experiments demonstrate that DIN-Ode consistently outperforms existing methods and achieves state-of-the-art performance across multiple OVSS benchmarks. The code is availableat https://github.com/yoon307/DINOde.

Abstract:
Robust 3D object detection under adverse weather remains acritical hurdle for autonomous driving. Despite progress with LiDAR–4Dradar fusion, most methods are constrained by a closed-world assump-tion, implicitly requiring training and test weather to align in both typeand severity. This premise fails in practice: the open-ended nature ofweather, and even variations within a single type like rain, cause dramat-ically different LiDAR degradation patterns, leading to significant per-formance drops in unseen conditions. To address this, we present Dual-Critic Guided Diffusion Alignment (DCDA), a weather-agnostic frame-work that learns to recover degraded LiDAR features toward a cleanmanifold. Rather than modeling specific weather types, DCDA employsa 4D radar-conditioned diffusion process to progressively refine features,guided by two complementary critics. (i) A detection-guided critic, an-chored by a pre-trained clean-weather model, ensures that the refinedfeatures retain object-level discriminability and localization accuracy. (ii)A weather adversarial critic enforces holistic distributional consistencywith clean-weather representations. By aligning features through seman-tic and distributional constraints rather than explicit weather model-ing, DCDA generalizes effectively to unseen weather types and severitieswithout requiring paired data or weather labels. We further introduce astructured open-weather benchmark with held-out type–severity combi-nations and extensive experiments verify DCDA’s advantages. Code isavailable at https://github.com/Mangonn/DCDA.

Abstract:
Vision-Language-Action (VLA) models often fail to performthe same learned tasks under environmental shifts, such as changes incamera pose and shifts to a different but similar robot (e.g., from Pandato UR5e). Adapting these models to the shifted environment (i.e., targetdomain) often requires training on multiple demonstrations for each task,which are costly to collect. To reduce the burden of data curation andtraining, we propose an analogy-based method that adapts VLA modelsunder environmental shifts through weight vector arithmetic with domain-specific information addition, named Domain ARiThmetic (DART). Un-like prior approaches, DART requires collecting only a single demonstra-tion, enabling efficient adaptation. To accurately isolate domain-specificinformation for addition, DART performs subspace alignment between sin-gular components in weight vectors to filter out noisy components. In bothsimulated and real-world experiments, DART outperforms existing VLAadaptation methods in one-shot scenarios across diverse visual and embod-iment shifts. Code is available at https://github.com/snumprlab/dart.

Abstract:
Few-shot Chinese handwriting generation aims to render char-acters in a target style using limited references. Existing image-levelmethods often suffer from limited stylistic fidelity and poor general-ization. We propose Hierarchical Style Aggregation for Chinese hand-writing generation (HiSAC), a versatile framework that decouples stylemodeling from task-specific generation. Our core idea is to leverage thecompositional nature of Chinese characters: by decomposing charactersinto radicals, components, and strokes, we establish fine-grained spatialcorrespondences between target content and reference glyphs at eachlevel, enabling precise style transfer. To further capture nuanced stylecharacteristics, we design a multi-band frequency encoder that extractsstylistic representations across different spectral ranges. The aggregatedmulti-granularity features can be seamlessly integrated with task-specificdecoders (e.g., Transformer for online trajectory generation, Diffusion foroffline image synthesis) without architectural redesign. By jointly model-ing style in both spatial and frequency domains, our approach enhancesrealism in global structure and stroke dynamics. Extensive experimentsdemonstrate that HiSAC outperforms existing methods both quantita-tively and qualitatively, and generalizes well to out-of-vocabulary andcross-language scripts. Code has been released at https://github.com/IIP-Lab-XDU/HiSAC.

Abstract:
Few-shot object detection aims to detect novel object cate-gories from only a few labeled examples, avoiding costly large-scale an-notation. Recent prototype-based similarity learning approaches enabletraining-free adaptation by matching query features with class proto-types. However, they suffer from two fundamental limitations: (i) classconfusion arising from inter-class similarity margin collapse, and (ii)insufficient visual cues for precise localization, as similarity scorescapture only class-level semantic affinity while providing limited spa-tial information. To address these issues, we introduce two complemen-tary components. Text-Anchored Semantic Mask (TSMa) lever-ages class-level text features as semantic anchors to identify semanti-cally aligned channels through channel-wise interaction between visualand text features. By suppressing style-induced spurious responses andemphasizing class-intrinsic signals, TSMa enlarges inter-class similar-ity margins and mitigates class confusion. We further propose Stage-Aligned Hierarchical Autoregressive Regression (SHARe), whichreformulates localization as a hierarchical autoregressive process thatprogressively refines bounding boxes across multiple stages. SHARe lever-ages the layer-wise characteristics of ViT representations by aligning fea-ture abstraction levels with regression stages: deeper layers guide earlycoarse localization, while shallower layers rich in edge and texture cuesrefine spatial details in later stages. Experiments on COCO demonstratea new state of the art, outperforming the previous best by +10.1 nAP,with extensive analysis validating each component. The code is availableat https://github.com/VisualScienceLab-KHU/ReSet.

Abstract:
Dataset distillation (DD) compresses a large training set intoa small synthetic set, reducing storage and training cost, and has shownstrong results on general benchmarks. Decoupled DD further improvesefficiency by splitting the pipeline into pretraining, sample distillation,and soft-label generation. However, existing decoupled methods largelyrely on coarse class-label supervision and optimize samples within eachclass in a nearly identical manner. On fine-grained datasets, this oftenyields distilled samples that (i) retain large intra-class variation withsubtle inter-class differences and (ii) become overly similar within thesame class, limiting localized discriminative cues and hurting recognition.To solve the above-mentioned problems, we propose FD2 , a dedicatedframework for Fine-grained Dataset Distillation. FD2 localizes discrimi-native regions and constructs fine-grained representations for distillation.During pretraining, counterfactual attention learning aggregates discrim-inative representations to update class prototypes. During distillation,a fine-grained characteristic constraint aligns each sample with its classprototype while repelling others, and a similarity constraint diversifies at-tention across same-class samples. Experiments on multiple fine-grainedand general datasets show that FD2 integrates seamlessly with decou-pled DD and improves performance in most settings, indicating strongtransferability. Code is available at https://github.com/Guang000/FD2.

Abstract:
Generating high-fidelity remote sensing images is severelychallenged by their specific overhead perspectives, complex structures,and rigorous physical attributes. Although recent controllable RS gen-eration methods have made progress, they are still limited by two keybottlenecks. The first is semantic conflict, where dominant global conceptsoften overshadow fine-grained details. The second is physical inconsis-tency, which easily leads to structural collapse and radiometric distortionduring the generation process. To address these issues, we propose SGP2(Synergizing Geometric and Physical Priors), a novel coarse-to-fine frame-work for controllable multimodal RS image generation. Specifically, toresolve semantic conflicts, we introduce the Grassmann Miner, whichconstructs dynamic geodesic trajectories on the Grassmann Manifold,enabling the model to gradually shift its focus from global backgrounds tofine-grained local details during the denoising process. To ensure physicalconsistency, we propose Physical Prior Expert, a module that dynamicallyaligns multi-level features from Visual Foundation Models and employsa frequency-aware Spectral Loss to enforce both amplitude and phaseconsistency. Furthermore, to bridge RS data gaps, we present MMEarth-1.5M, a 1.5 million text-image dataset featuring strictly co-registeredoptical and SAR pairs for robust cross-modal simulation. Our approach,without bells and whistles, achieves favorable performance in controllablemulti-modal remote sensing image generation task. The dataset will beavailable in https://github.com/cpy0029/MMEarth-1.5M.

Abstract:
The proliferation of multimedia content on social platformshas fueled multimodal misinformation, where images are used to rein-force false claims. Consequently, Multimodal Fact-Checking (MFC) hasemerged as an increasingly important research area. However, currentprogress is hindered by a reliance on synthetic training data and cu-rated benchmarks that fail to capture the complexity of in-the-wild data.Furthermore, existing detection models rely on restricted intra-modalityconsistency or unconstrained all-to-all fusion, failing to capture nuancedrelations between posts and external evidence. To address these limi-tations, we introduce X-POSE, a benchmark of real-world, community-annotated multimodal posts from X (formerly Twitter), augmented withfull-length news articles retrieved via VLM-optimized search. Addition-ally, we propose TRENT, a novel MFC model that performs evidence tri-angulation using three parallel cross-attention streams alongside a rela-tional fusion mechanism that explicitly models entailment and contradic-tion. Extensive evaluations demonstrate that TRENT consistently out-performs state-of-the-art specialized models and commercial VLMs. Thecode, prompt templates, and dataset are available at https://github.com/stevejpapad/evidence-triangulation.

Abstract:
Cross-modal super-resolution (SR) on real-world misaligneddata is challenging, as only unlabeled low-resolution (LR) source andhigh-resolution (HR) guide images with complex spatial misalignmentare available. Previous methods either rely on simulated training dataor adopt suboptimal alignment strategies that overlook cross-modal de-pendencies, limiting their practical performance. To address these is-sues, we propose RobSelf, a self-supervised model that jointly optimizesa misalignment-aware feature translator and a content-aware referencefilter online. The translator resolves unsupervised cross-modal and cross-resolution alignment via weakly-supervised, misalignment-aware trans-lation, yielding an aligned guide feature. Guided by this feature, thefilter performs reference-based discriminative self-enhancement on thesource, enabling SR prediction with high resolution and high fidelity.Experiments on synthesized data and collected real-world data demon-strate that RobSelf achieves state-of-the-art performance, outperformingexisting self-supervised and supervised methods. Moreover, it achievessuperior efficiency, being up to 15.3× faster than prior self-supervisedmethods. https://github.com/palmdong/RobSelf

Abstract:
The deployment of Industrial Anomaly Detection (IAD) inreal-world manufacturing frequently encounters a challenging cold-startbottleneck, in which limited normal samples fail to represent the fullnormal distribution and only a few anomalies are available. Under sucha regime, existing methods struggle to form compact normal boundariesand fail to effectively exploit supervised signals from rare defects. To ad-dress this challenge, we propose Anomaly-Rectified Cold-start AD (Ar-cAD), a plug-and-play calibration framework for reconstruction-basedIAD baselines. ArcAD follows a push–pull learning paradigm to constructa compact and discriminative normal boundary under data scarcity. Onthe one hand, ArcAD projects limited normal samples onto a hypersphereand pulls them into multiple compact clusters to maximize coverage ofthe normal manifold. On the other hand, it synthesizes pseudo-anomalieson the hypersphere and leverages real anomalies to push the boundaryinward and sharpen anomaly discrimination. Extensive experiments onMVTec-AD, VisA, Real-IAD, and MANTA demonstrate that ArcADsignificantly outperforms state-of-the-art supervised and unsupervisedmethods in both single-class and multi-class settings under cold-startconditions. Code is available at: https://github.com/LGC-AD/ArcAD.

Abstract:
Semi-supervised few-shot learning (SSFSL) resembles real-world applications such as “auto-annotation”, as it aims to learn a modelfrom a few labeled and abundant unlabeled task-specific examples to an-notate the unlabeled ones. Despite the availability of powerful open-sourceVision-Language Models (VLMs) and open-world data, existing SSFSLliterature largely neglects these resources. In contrast, the related areafew-shot learning (FSL) has already exploited them to boost performance.Arguably, to solve real-world auto-annotation, SSFSL should leverage suchopen resources. To bridge this gap, we explore established SSL methodsto finetune a VLM. Unexpectedly, they significantly underperform FSLbaselines that do not use unlabeled data. Our in-depth analysis revealsthe root cause of failure: VLMs produce “flat” distributions of softmaxprobabilities, resulting in zero utilization of unlabeled data and weak su-pervision signals. To address this challenge, we propose an embarrassinglysimple solution that uses temperatures to sharpen the softmax output,which not only increases the confidence scores of pseudo-labels to improvethe utilization of unlabeled data, but also strengthens training supervisionfor effective finetuning. Furthermore, we exploit task-relevant open data,e.g., those retrieved from VLMs’ publicly available pretraining set. Tomitigate the imbalance and domain gaps in retrieved data, we employa stage-wise training strategy. Building on the successful finetuning ofVLMs and the exploitation of open data, we present a simple yet effectiveSSFSL method, Stage-Wise Finetuning with Temperatures (SWIFT).Across five benchmarks, SWIFT outperforms recent FSL and SSL meth-ods by ∼5 accuracy points. SWIFT even rivals supervised learning, whichfinetunes a VLM assuming unlabeled data having ground-truth labels!

Abstract:
Cervical cancer remains a leading cause of female morbidityand mortality, making early lesion detection critical. Although RCNN-and YOLO-based detectors have improved performance, they still relyon complex post-processing and are limited in global reasoning. DETRoffers a new paradigm, but its direct application is challenged due to(1) large intra-class scale variation, (2) subtle inter-class differences, and(3) high inter-annotator variability. To address these challenges, we pro-pose CerDETR, a DETR-based framework empowered by cell priors. Itintroduces a training-only Prior Corrector branch to capture intra-classscale and inter-class feature variations. It employs an Auto MultiscalePrior Generator (Auto MPG) to produce precise multiscale cell priorscovering nuclei, cytoplasm, and clusters, an IoU and Contain-Guided(ICG) matching strategy for robust prior assignment under annotationnoise, and a Prior Query Enhance module to leverage lesion character-istics and optimize prior embeddings for discriminative feature learning.Experiments on four public datasets show that CerDETR consistentlyoutperforms RCNN-, YOLO-, and DETR-based methods, demonstrat-ing its effectiveness, generalization, and clinical potential. Source code isavailable at https://github.com/imAzhou/CerDETR.

Abstract:
Aerial-Ground Person Re-IDentification (AG-ReID) aims toretrieve the same person across heterogeneous aerial and ground cameraplatforms. Although great progress, existing methods remain subopti-mal due to the direct feature alignment across views, overlooking view-specific cues. To address this issue, we propose a novel Hierarchical Hy-perbolic Representation (HiHR) framework for AG-ReID. More specif-ically, we first extract multi-granularity features based on pre-trainedvisual-text encoders. Then, we propose a Text-guided Multi-granularityFusion (TMF) to fuse multi-granularity features and enhance the repre-sentation ability of identity features. Furthermore, we introduce the Hi-erarchical Hyperbolic Learning (HHL) to construct a hierarchical featurestructure in a hyperbolic space. This hierarchy includes a coarse level thatensures identity separability and cross-view consistency, and a fine levelthat preserves view-specific discriminative cues. A a result, our proposedframework can effectively aggregate view-invariant and view-specific dis-criminative features for AG-ReID. Extensive experiments on four AG-ReID benchmarks demonstrate the effectiveness of our framework. Thesource code is available at https://github.com/YangQiWei3/HiHR.

Abstract:
Humanoid motion tracking is central to teleoperation andwhole-body imitation, yet evaluation often disagrees with what peo-ple perceive in videos. Kinematic errors average per-frame pose differ-ences but miss the physical artifacts that matter most, such as unstablesupport and incorrect contacts (e.g., foot skating and mistimed touch-downs). Meanwhile, widely used test suites are small and lack the diver-sity needed to stress contact-rich, long-horizon behaviors. We introduce# Corresponding author.HumanTracker to make humanoid tracking evaluation both perceptu-ally aligned and scalable. HumanTracker contributes 150 hours of newlycaptured optical motion from 24 professional performers, organized intofour motion families with text labels for fine-grained diagnosis. We fur-ther propose HumanScore, a preference-aligned metric trained from 12Khuman-labeled motion pairs on synchronized tracking videos via a trajec-tory reward model. Across representative state-of-the-art trackers, Hu-manScore better predicts held-out human preferences and reveals contactand stability failures that kinematic metrics often miss. The project pageis available at https://dairuliu.github.io/humantracker.

Abstract:
In this paper, we explore dense voxel streaming for accurateand eﬃcient 3D occupancy prediction. While dense voxel representa-tions oﬀer ﬁne-grained spatial details and streaming paradigm enableseﬃcient temporal processing, naively combining the two introduces keychallenges: (i) warping-induced distortions caused by interpolation usedfor temporal alignment, and (ii) degraded dynamic object representa-tions due to motion misalignment and detail loss in image-to-voxel pro-jection. To address these, we propose StreamOcc, a novel frameworkthat utilizes two aggregation strategies. Speciﬁcally, it ﬁrst reﬁnes prop-agated voxel features to reduce warping artifacts before temporal accu-mulation, and then selectively injects instance-level query features en-coding dynamic-object semantics into the corresponding occupied voxelregions, preserving temporally consistent modeling while strengtheningdynamic object representations. Unlocking eﬀective dense voxel stream-ing, StreamOcc achieves state-of-the-art performance on SurroundOcc-benchmark and Occ3D-nuScenes under real-time constraints, outper-forming the prior best methods by +1.3/2.5 and +1.5/2.0 in (over-all/dynamic object) mIoU, respectively, while running at 83.3 msper frame with only 2.8 GB of memory. The project page is available athttps://moonseokha.github.io/StreamOcc/.

Abstract:
Industrial anomaly inspection is severely hindered by thescarcity of real anomalous data. Zero-shot industrial anomaly genera-tion addresses this by generating anomalies on specific products withoutrequiring any of their real anomalous images. However, existing meth-ods suffer from two critical limitations, i.e., inaccurate anomaly informa-tion acquisition and uncontrolled anomaly-product fusion. To overcomethese challenges, we propose DeCo, which decouples the anomaly struc-ture from its source product, and explicitly recouples it with the nor-mal textures of the target product. During anomaly information acquisi-tion, Dual-Routing Flow (DR-Flow) binds the texture-invariant anomalystructure to an abnormal token, while a parallel constraint, Product-Invariant Flow (PI-Flow), prevents the abnormal token from binding thesource product. During anomaly-product fusion, we propose a hybrid in-jection to recouple the acquired anomaly structure with the target prod-uct, and Product Compatibility Correction (PCC) to compensate for theincompatibility between the acquired anomaly structure and the product.Extensive experiments demonstrate that DeCo establishes a new state-of-the-art. Training downstream detection models on our generated datayields massive pixel AP improvements of 5.1% on MVTec AD and 8.2%on VisA. Code is available at https://github.com/HUST-SLOW/DeCo.

Abstract:
Referring multi-object tracking (RMOT) aims to track every instance in a video that matches a given language expression. Despite the recent integration of multimodal large language models (MLLMs) to enhance generalization, existing methods predominantly relegate them to the role of caption generators, necessitating external modules for final decision-making. This paradigm not only introduces extra latency but also severely underutilizes the inherent vision–language alignment capabilities of MLLMs. To address these limitations, we propose YesTrack, a novel two-stage RMOT method that reformulates referring as a discriminative task, directly leveraging MLLMs for Yes/No verification without explicit text generation. To further enhance the reliability and efficiency of this MLLM-based verification, we introduce two lightweight temporal consistency constraints: Temporal Confidence Prior (TCP) and Temporal Reference Propagation (TRP). We further validate the generality of this discriminative paradigm by proposing YesTrack-MOT, a straightforward yet highly effective instantiation for generic multiobject tracking (MOT). Experiments on Refer-KITTI and Refer-KITTIV2 show that YesTrack significantly outperforms existing state-of-theart methods while maintaining high efficiency, even when implemented with the smallest variant of Qwen3-VL. Code is released at https: //github.com/ggbondrighthere24/YesTrack.

Abstract:
Video temporal grounding (VTG) aims to localize events in untrimmed videos given natural language prompts. Current VTG methods directly inherit the paradigm from 2D object detection, applying rigid IoU-based one-to-one matching between queries and ground truth spans. However, this paradigm fundamentally conflicts with the temporal fluidity of video events: unlike spatial objects, a sub-event (moment) can be semantically aligned with the prompt despite only covering part of the full event span. This matching scheme incorrectly suppresses such moment predictions as negatives, preventing models from learning rich temporal representations. To resolve this, we propose Moment-Event DETR (ME-DETR), a novel framework that embraces the natural part-whole structure of events through dynamic query specialization. During training, after standard Hungarian matching assigns primary event queries to ground truths, we identify other high-confidence predictions within each matched span and designate them as auxiliary moment queries. Through our synergistic supervision strategy, these moment queries are liberated from unreasonable suppression to explore semantically rich sub-events (moments). Extensive experiments demonstrate that ME-DETR establishes new state-of-the-art results, achieving +2.43% mAP improvement on QVHighlights test set without requiring post-processing NMS. Code: https://github.com/zouyuda220/ME-DETR.

Abstract:
Recent open-weight text-to-image (T2I) diffusion models still struggle with multi-instance prompts, often omitting or merging instances and mixing semantics among similar objects. We trace these failures to early denoising steps, before instance boundaries are reliably stabilized. Existing training-free guidance is largely driven by cross-attention or other token-conditioned semantic signals. Such guidance can separate concepts at the token level, but largely assumes that distinct instance regions have already emerged. In early denoising steps, it cannot reliably carve out these regions, so count failures and semantic mixing persist. By contrast, self-attention exposes class-agnostic instance layouts during early denoising. To exploit this asymmetry, we propose ISAC (Instance-to-Semantic Attention Control), a training-free, model-agnostic objective that first stabilizes self-attention layouts and then binds cross-attention semantics within them, without fine-tuning or external vision models. Across T2I-CompBench, HRS-Bench, and our newly curated IntraCompBench, ISAC consistently outperforms prior training-free methods. Furthermore, ISAC enhances layout-to-image controllers by refining coarse, overlapping bounding boxes into dense instance masks. Code and IntraCompBench are available at https://shjo-april.github.io/ISAC.

Abstract:
Universal person re-identification (ReID) aims to retrieve pedestrian identities across diverse real-world scenarios, including severe occlusions, clothing changes, and cross-modality shifts, within a unified model. However, existing 2D representations fundamentally struggle with spatial ambiguities due to a lack of depth and topological awareness, while naively introducing monocular 3D priors often causes severe negative transfer due to geometric estimation noise under extreme visual degradation. To safely harness the clothing-invariant and canonical structural properties of 3D geometry, we propose UniGeo, a Universal Monocular 3D-Enhanced ReID framework driven by a ConsistencyAware Reliability Gate and Dual-Stream Residual Fusion. Specifically, the processing of 3D information is strategically decoupled into geometric extraction and dynamic utilization. To provide pure structural compensation, we project monocular 3D parameters into kinematic joint representations, explicitly capturing instance-level geometric topology to resolve appearance-based ambiguities. To robustly incorporate these cues without perturbing the reliable 2D feature space, we isolate the 3D prior as a late-stage structural residual; modulated by the consistency-aware gate, this mechanism adaptively filters geometric noise and enables controlled fallback to the pure 2D baseline. Extensive experiments show that our method improves challenging, structure-sensitive scenarios while preserving competitive performance on clean domains. Code is available at https://github.com/BohanSu/UniGeo.

Abstract:
Vision Transformers (ViTs) achieve state-of-the-art performance but carry massive computational overhead that restricts edge deployment. Although structural pruning has emerged as a key strategy to reduce these costs, existing methods often suffer from severe accuracy degradation or require expensive retraining. Recently, Variance-Based Pruning (VBP) introduced a promising paradigm by selecting neurons based on activation variance; however, it remains limited by statistical noise in finite-sample activation covariance and reliance on bias-only updates that cannot fully account for structural reconstruction error. To address these limitations, we introduce Denoised Variance-Based Pruning with Optimal Brain Bias Compensation (DVBP + OB2C). We leverage random matrix theory to filter noise from the activation covariance spectrum for robust neuron selection and mathematically prove that integrating mean-shift compensation into the Optimal Brain Compression objective reduces the layer-wise Hessian exactly to the activation covariance matrix. This enables an optimal, closed-form update of the remaining weights using the same statistics gathered for selection. Extensive experiments on DeiT, Swin, and ConvNeXt architectures demonstrate that DVBP + OB2C achieves state-of-the-art training-free performance; at 50% MLP pruning, it retains over 90% of the original Top- 1 accuracy on Small and Base variants, outperforming VBP by up to 29.46% (ConvNeXt-T) and 7.33% (Swin-S). The code is available at: https://github.com/geontackee/DVBP_OB2C.

Abstract:
Existing multispectral detectors are limited by discrete spec-tral processing, a scale-dependent shift in the relative reliability of spec-tral and spatial cues across pyramid levels, and the lack of explicitrotation-equivariant geometric priors for arbitrarily oriented objects. Totackle these limitations, we propose FressDet, a fully rotation-equivariantspectral x0015 spatial learning framework for multispectral object detection,capable of capturing the continuous, ordered nature of spectral struc-ture and enabling reliable spectral x0015 spatial fusion across pyramid levelsunder arbitrary in-plane rotations. FressDet integrates three complemen-tary components. Spectral Implicit Warp (SpeIW) enables query-basedspectral resampling via a coordinate-conditioned implicit x001C eld, yieldinga monotone, order-preserving warp. Rotation-Equivariant ConsistencyWeighting (ReCoW) adaptively fuses spectral and spatial branches basedon branch reliability, reinforcing informative cues while suppressing noiseacross pyramid levels. The oriented-aware head exploits group-indexedfeatures to stably predict oriented objects without parameter replication.Taken together, FressDet learns more discriminative and robust spectral x0015 spatial representations even under rotational perturbations. By achievingstate-of-the-art performance with 93% fewer parameters on x001C ve publicbenchmarks, FressDet demonstrates its e x001B ectiveness and generalizability.Code is available at https://github.com/Riiluo/FressDet.

Abstract:
Open-set semi-supervised learning aims to leverage unlabeleddata that may contain out-of-distribution outliers while maintainingperformance on in-distribution classes. Existing methods mainly followtwo paradigms: filtering suspicious samples or incorporating unlabeledobjectives with soft weighting. We argue that both face a common trade-off: aggressive filtering can discard informative but hard ID samples,whereas utilization can introduce auxiliary gradients that conflict withsupervised learning when pseudo labels are wrong. We therefore shiftthe focus from sample selection to gradient-level control. We proposeGeometric Gradient Rectification (GGR), a plug-in framework that usesthe supervised gradient as an anchor and projects conflicting auxiliarygradients onto an admissible region in gradient space. This makes theapplied auxiliary update first-order non-opposing within the rectifiedcoordinate block while preserving orthogonal components that may stillcarry useful representation signals. We further extend GGR with subspace-aware rectification to stabilize the anchor under noisy mini-batch gradients.Experiments on CIFAR and ImageNet benchmarks show that GGRimproves representative OSSL baselines in most settings and yields gainsin both closed-set generalization and open-set robustness. Code will beavailable at https://github.com/JiaheChen2002/GGR.

Abstract:
Machine unlearning enables deep neural networks to selec-tively remove the influence of specific data in response to privacy andregulatory requirements. While prior work largely studies single-shot un-learning, real-world systems must accommodate continual unlearning,where multiple unlearning requests occur sequentially over time. In thiswork, we identify a fundamental limitation of this setting: plasticity col-lapse, a progressive breakdown in a model’s ability to eﬀectively forget.Through theoretical analysis of continual unlearning dynamics, we showthat continual unlearning operations accumulate geometric constraintsin parameter space, leading to saturated subspaces that restrict futureupdates. This structural eﬀect induces two distinct failure modes: (1)Forward failure – diminishing forgetting quality for subsequent tasks,and (2) Backward failure – spontaneous re-memorization of previouslyforgotten information. Extensive experiments across multiple architec-tures, datasets, and methods in image classification confirm that plastic-ity collapse is not an artifact of specific implementations, but a pervasivephenomenon inherent to continual unlearning. Our findings reveal a criti-cal barrier to the long-term reliability of machine unlearning systems andmotivate the development of plasticity-preserving unlearning algorithms.Our code is available at https://github.com/TIML-Group/Continual-Machine-Unlearning-Plasticity-Collapse.

Abstract:
Interpretable machine learning aims to develop models whoseinternal reasoning is transparent and aligned with human perception.While prototype-based networks provide intuitive part-based interpreta-tions, most existing methods rely on flat or independently learned proto-type sets, failing to capture the hierarchical and relational organization ofvisual concepts. Inspired by theories of structured part-whole perception,we introduce ProtoMappingNet, a prototype-based architecture thatlearns hierarchically structured visual concepts through learnable cross-layer prototype mappings. These mappings align representations acrosstexture, part, and object levels, enabling coherent parent-child relationsbetween prototypes. To quantitatively evaluate the learned hierarchy,we propose a relational evaluation framework consisting of HierarchicalRelational Consistency (HRC) and Hierarchical Spatial Stability (HSS),which measure cross-level relational consistency and spatial containmentwithout requiring part annotations. Experiments show that ProtoMap-pingNet produces coherent multi-level explanations and substantially im-proves structural consistency through bidirectional relational modelingwhile maintaining competitive classification accuracy. Our code is avail-able at https://github.com/pk9403/ProtoMappingNet

Abstract:
Models for image representation learning are typically de-signed for either recognition or generation. Various forms of contrastivelearning help models learn to convert images to embeddings that areuseful for classification, detection, and segmentation. On the other hand,models can be trained to reconstruct images with pixel-wise, perceptual,and adversarial losses in order to learn a latent space that is compati-ble with image generation. We seek to unify these two directions with afirst-of-its-kind model that learns representations which are simultane-ously well-suited for recognition and generation. We train our model asa hyper-network for implicit neural representation, which learns to mapimages to model weights for fast, accurate reconstruction. We furtherintegrate our INR hyper-network with knowledge distillation to improveits generalization and performance. Beyond the novel training design, themodel also learns an unprecedented compressed embedding space withoutstanding performance for various visual tasks. The complete modelcompetes with state-of-the-art results for image representation learning,enables downstream generative capabilities, and produces high-qualitytiny embeddings.

Abstract:
Multi-expert models have become the dominant paradigmfor long-tailed learning, largely attributed to their presumed ability tobenefit from expert diversity. However, we revisit this central assump-tion and reveal that diversity induced by logit adjustment or explicitregularizers does not guarantee better ensemble accuracy. Our work sug-gests that multi-expert models benefit more from variance reduction thandiversity maximization. We introduce VICAL, a VIcinal ConsistencyALignment framework that improves long-tailed recognition not by en-forcing expert diversity, but by reducing prediction variance. Specifically,our approach comprises two key components: Self-Consistency Learn-ing and Deep Ensemble Distillation. Self-Consistency Learning discour-ages reliance on unstable high-frequency information, smoothing the lo-cal loss landscape and mitigating overfitting, especially for tail classes.Deep Ensemble Distillation promotes cross-expert low-frequency seman-tic agreement using a low-resolution view, thereby sidestepping opti-mization conflicts with established knowledge. Extensive experiments onCIFAR-LT, ImageNet-LT, and iNaturalist 2018 show that VICAL consis-tently outperforms state-of-the-art methods, validating the effectivenessof our consistency-driven design.

Abstract:
Real-world video super-resolution (VSR) faces in-the-wilddegradations whose artifacts accumulate over time, yet edge deploymentrequires static execution, and quantization parameters must be fixed af-ter compilation for low-overhead inference. We propose Temporal-AwareQuantization (TAQ), a novel static post-training quantization frame-work that uses video structure only in offline calibration. TAQ calibratessequence-specific activation bounds, refines them with a temporal con-sistency objective we propose that aligns inter-frame changes betweenfloating-point and quantized outputs without weight retraining, and en-sembles the refined bounds into one deployable set of static param-eters. Across REDS, SPMCS, UDM10, and VideoLQ, TAQ improvesperceptual and temporal quality, reducing LPIPS by up to 0.0334 andTLPIPS by up to 4.7344 over Static PTQ baselines under identical cal-ibration. On NVIDIA Jetson Orin Nano, TAQ enables TensorRT INT8with up to 3.56× speedup (1.15 fps), while a dynamic INT8 baseline isslower than FP32 (0.79×, 0.266 fps vs. 0.325 fps). Code is available athttps://github.com/imaboybut/TAQ.

Abstract:
Generating interactive and dynamic 4D scenes from a single static image remains a core challenge. Most existing generate-then-reconstruct and reconstruct-then-generate methods decouple geometry from motion, causing spatiotemporal inconsistencies and poor generalization. To address these, we present MoGe4D (Motion and GeometryAware image-to-4D Synthesis), a geometry-conditioned framework for single-image 4D synthesis that models a scene as dense 4D point trajectories. Instead of treating geometry and dynamics as two disconnected stages, our method starts from an initial geometric prior inferred from the input image and predicts future time-varying trajectories in a diffusion process, improving spatiotemporal coherence while preserving structural stability. To support this task, we first introduce TrajScene-60K, a large-scale dataset of 60,000 video samples with dense 4D point trajectories, addressing the scarcity of high-quality training data for scene-level 4D generation. Built on this, our diffusion-based 4D Scene Trajectory Generator (4D-STraG) predicts geometry-consistent and motionplausible trajectory fields conditioned on the input image, with a depthguided motion normalization strategy to reduce scale ambiguity and a Motion Perception Module (MPM) to inject motion-aware priors. We further propose a 4D View Synthesis Module (4D-ViSM) to render the generated 4D representation into videos under arbitrary camera trajectories. Experiments show that MoGe4D produces high-quality 4D scenes with strong temporal coherence, favorable geometry-aware consistency, and compelling novel-view synthesis from a single image. Code: https://github.com/Zhangyr2022/MoGe4D.

Abstract:
Physics-based Human-Scene Interaction (HSI) imitation learn-ing is crucial for embodied intelligence as it bridges the gap betweenkinematic 3D motions and real-world dynamics. However, most exist-ing methods focus on simplified scene settings, leaving complex envi-ronments largely unexplored, which limits their applicability in real-world scenarios. In this paper, we focus on HSI mimicry in complexenvironments. Under this complex setting, we observe an inherent trade-off between successfully performing interaction and maintaining natu-ral, physically plausible motions. To address this challenge, we proposeComplexMimic, a framework that reconstructs diverse HSI by inter-preting imperfect MoCap data. First, we introduce a Dual Flow Strat-egy, which learns two complementary experts: an imitation expert foraccurate motion tracking and an interaction expert for collision-awareadaptation in complex scenes. Second, naive multi-expert distillation,which treats all experts equally, often under-samples challenging be-haviors, limiting effective learning. To mitigate this issue, we proposea difficulty-aware distillation strategy that adaptively weights super-vision and prioritizes hard-yet-learnable trajectories guided by failurestatistics and learning progress signals. Extensive experiments on threebenchmark datasets demonstrate that our approach outperforms cur-rent state-of-the-art methods. Our implementation is available at https://github.com/LuPan23/ComplexMimic.

Abstract:
We present ReinDriveGen, a framework that enables full con-trollability over dynamic driving scenes, allowing users to freely edit actortrajectories to simulate safety-critical corner cases such as front-vehiclecollisions, drifting cars, vehicles spinning out of control, pedestrians jay-walking, and cyclists cutting across lanes. Our approach constructs adynamic 3D point cloud scene from multi-frame LiDAR data, introducesa vehicle completion module to reconstruct full 360° geometry from par-tial observations, and renders the edited scene into 2D condition imagesthat guide a video diffusion model to synthesize realistic driving videos.Since such edited scenarios inevitably fall outside the training distri-bution, we further propose an RL-based post-training strategy with apairwise preference model and a pairwise reward mechanism, enablingrobust quality improvement under out-of-distribution conditions with-out ground-truth supervision. Extensive experiments demonstrate thatReinDriveGen outperforms existing approaches on edited driving scenar-ios and achieves state-of-the-art results on novel ego viewpoint synthesis.

Abstract:
We address the problem of recovering the full-body meshfrom only the head pose. This task has become essential for variousapplications based on head-mounted devices or smart glasses. The chal-lenge of this task lies in estimating the pose information of unobservedbody parts based solely on a single joint (i.e., head) trajectory. Severalstudies have begun to adopt head-conditioned generative models, how-ever, such previous methods are costly and time-consuming due to thediffusion-based iterative process. As an alternative, we propose a simpleyet novel method that leverages the latent space of the guidance network,which is designed as a variational autoencoder taking full-body poses asinputs. By enforcing latent distributions of this guidance network andour head-to-motion network to be similar, latent features sampled fromthe ‘guided’ distribution, i.e., distribution learned in our head-to-motionnetwork, can be reliably decoded for natural representations of full-bodyposes even only with the head pose. One important advantage of theproposed method is that one-step sampling scheme achieves remark-ably fast inference (more than 50 times faster) compared to diffusion-based approaches. Experimental results on benchmark datasets showthat the proposed method efficiently improves the performance of ego-body mesh reconstruction. The code and model are publicly available at:https://github.com/DCVL-3D/EgoGVAE_release.

Abstract:
WordArt (artistic text) features highly customized fonts,textures, and layouts, making WordArt-oriented scene TExt Recogni-tion (WATER) substantially more challenging than general Scene TextRecognition (STR). Existing STR datasets and methods, typically builtaround regular scene text and fixed-template inputs, struggle to scale toWATER. Thus, we aim to advance this task from both data and modelperspectives. On the data side, we construct a 2M synthetic dataset,WATER-S, with the scale improved by hundreds of times comparedto existing artistic text data. WATER-S consists of two complementarysubsets. One rendered by an upgraded rendering pipeline (SynthWor-dArt), which provides highly accurate and controllable synthetic Wor-dArt data. The other is generated by combining Qwen3-VL for promptmining and Z-Image for image synthesis, which improves the coverage ofrealistic and diverse data. On the model side, we propose WATERec.It adopts a visual encoder supporting arbitrary-shaped inputs and anautoregressive decoder to model complex layouts, structurally breakingthe bottleneck of fixed-template STR on WordArt. Experiments showthat this architecture outperforms prior STR methods, achieving state-of-the-art performance on irregular texts such as WordArt. Togetherwith WATER-R, carefully reorganized from existing real STR data, ourstrong baseline with the new synthetic data and model design reaches90.40% accuracy on WordArt-Bench, surpassing both general-purposeand OCR-specialized vision-language models by a large margin. Codeand data are available at https://github.com/YesianRohn/WATER.

Abstract:
The key challenge in articulated 3D object generation from a single image is accurately predicting the underlying kinematic structure. Existing methods either infer kinematic parameters directly from a static image that lacks dynamic part-level kinematic relationships, or estimate parameters from visual dynamics generated from a single image, which is prone to accumulated errors of two steps. Moreover, the limited scale and diversity of existing annotated datasets further hinder generalization to complex, real-world objects. To overcome these limitations, we propose to learn the joint distribution of visual dynamics and kinematic parameters. Recognizing that articulated objects can be formulated as dynamic systems, we propose a unified Part World Model called PWM-ArtGen. To leverage unannotated data, this model couples action diffusion and image diffusion with independent diffusion timesteps, which enables visual branch co-training. We further curate a photorealistic dataset of 19.7k part-level image pairs without kinematic annotations, to support co-training. Experiments demonstrate that PWM-ArtGen substantially outperforms existing baselines in the resting state and exhibits strong zero-shot generalization to out-of-distribution objects. We will release our code at https://github.com/Wentap123/PWM-ArtGen.

Abstract:
In this work, we focus on photorealistic sign avatar modeling,which is crucial for effective communication with the Deaf community andis characterized by complex hand gestures and nuanced facial expressions.To this end, we introduce MVSign, the first multi-view Chinese signlanguage dataset co-designed with Deaf experts, featuring diverse gesturesand rich annotations. For precise SMPL-X annotation, we develop a hybridfitting pipeline that produces accurate body, hand, and facial parametersand can also be applied to the monocular setting. Building on MVSign,we propose a decoupled sign avatar representation that isolates body,head, and hand components to capture complex articulations, togetherwith a motion-aware sampling strategy to handle motion blur and balancegesture diversity. Extensive experiments demonstrate that our methodachieves high-fidelity visual results on MVSign, particularly in detailedhand and facial regions, and generalizes well to in-the-wild monocularsign language videos. Project page: https://naaapi.github.io/PHOSA.

Abstract:
We present TimeWalker, a novel framework that models realistic, full-scale 3D head avatars of a person on lifelong scale. Unlike current human head avatar pipelines that capture a person’s identity only at the momentary level (i.e., instant photography, or short videos), TimeWalker constructs a person’s comprehensive identity from unstructured data collection over his/her various life stages, offering a paradigm to achieve full reconstruction and animation of that person at different moments of life. At the heart of TimeWalker’s success is a novel neural parametric model that learns personalized representation with the disentanglement of shape, expression, and appearance across ages. Central to our methodology are the concepts of two aspects: 1) We track back to the principle of modeling a person’s identity in an additive combination of his/her average head representation in the canonical space, and moment-specific head attribute representations driven from a set of neural head basis. To learn the set of head basis that could represent the comprehensive head variations of the target person in a compact manner, we propose a Dynamic Neural Basis-Blending Module (Dynamo). It dynamically adjusts the number and blend weights of neural head bases, according to both shared and specific traits of the target person over ages. 2) We introduce Dynamic 2D Gaussian Splatting (DNA-2DGS), an extension of Gaussian splatting representation, to model head motion deformations like facial expressions without losing the realism of rendering and reconstruction of the full head. DNA-2DGS includes a set of controllable 2D oriented planar Gaussian disks that utilize the priors from a parametric morphable face model, and move/rotate with the change of expression. Through extensive experimental evaluations, we show TimeWalker’s ability to reconstruct and animate avatars across decoupled dimensions with realistic rendering effects, demonstrating a way to achieve personalized “time traveling” in a breeze. Project page: https://TimeWalker2026.github.io/.

Abstract:
Movie dubbing aims to synthesize speech that preserves thevocal identity of a reference audio while synchronizing with the lip move-ments in a target video. Existing methods fail to achieve precise lip-syncand lack naturalness due to explicit alignment at the duration level.While implicit alignment solutions have emerged, they remain suscep-tible to interference from the reference audio, triggering pronunciationand lip-sync degradation in in-the-wild scenarios. In this paper, we pro-pose a novel flow matching-based movie dubbing framework driven bythe Cognitive Synchronous Diffusion Transformer (CoSyncDiT), in-spired by the cognitive process of professional actors. This architectureprogressively guides the noise-to-speech generative trajectory by execut-ing acoustic style adapting, fine-grained visual calibrating, and time-aware context aligning. Furthermore, we design the Joint Semantic andAlignment Regularization (JSAR) mechanism to simultaneously con-strain frame-level temporal consistency on the contextual outputs andsemantic consistency on the flow hidden states, ensuring robust align-ment. Extensive experiments on both standard benchmarks and chal-lenging in-the-wild dubbing benchmarks demonstrate that our methodachieves the state-of-the-art performance across multiple metrics. Thecode is available at https://github.com/GalaxyCong/CoSyncDiT.

Abstract:
Diffusion Transformers (DiTs) power high-fidelity video worldmodels but remain computationally expensive due to sequential denois-ing and costly spatio-temporal attention. Training-free feature cachingaccelerates inference by reusing intermediate activations across denois-ing steps; however, existing methods largely rely on a Zero-Order Holdassumption i.e., reusing cached features as static snapshots when globaldrift is small. This often leads to ghosting artifacts, blur, and motion in-consistencies in dynamic scenes. We propose WorldCache, a Perception-Constrained Dynamical Caching framework that improves both whenand how to reuse features. WorldCache introduces motion-adaptive thresh-olds, saliency-weighted drift estimation, optimal approximation via blend-ing and warping, and phase-aware threshold scheduling across diffusionsteps. Our cohesive approach enables adaptive, motion-consistent featurereuse without retraining. On Cosmos-Predict2.5-2B evaluated on PAI-Bench, WorldCache achieves 2.3× inference speedup while preserving99.4% of baseline quality, substantially outperforming prior training-free caching approaches.

Abstract:
Recent progress in video diffusion models has spurred grow-ing interest in camera-controlled novel-view video generation for dy-namic scenes, aiming to provide creators with cinematic camera con-trol capabilities in post-production. A key challenge in camera-controlledvideo generation is ensuring fidelity to the specified camera pose, whilemaintaining view consistency and reasoning about occluded geometryfrom limited observations. To address this, existing methods either traintrajectory-conditioned video generation model on trajectory–video pairdataset, or estimate depth from the input video to reproject it alonga target trajectory and generate the unprojected regions. Nevertheless,existing methods struggle to generate camera-pose–faithful, high-qualityvideos for two main reasons: (1) reprojection-based approaches are highlysusceptible to errors caused by inaccurate depth estimation; and (2)the limited diversity of camera trajectories in existing datasets restrictslearned models. To address these limitations, we present InfCam, a depth-free, camera-controlled video-to-video generation framework with highpose fidelity. The framework integrates two key components: (1) infi-nite homography warping, which encodes 3D camera rotations directlywithin the 2D latent space of a video diffusion model. Conditioningon this noise-free rotational information, the residual parallax term ispredicted through end-to-end training to achieve high camera-pose fi-delity; and (2) a data augmentation pipeline that transforms existing syn-thetic multiview datasets into sequences with diverse trajectories and fo-cal lengths. Experimental results demonstrate that InfCam outperformsbaseline methods in camera-pose accuracy and visual fidelity, generaliz-ing well from synthetic to real-world data.

Abstract:
Synthesizing multi-pass videos is important for autonomousdriving. While current repair-based methods often struggle with out-of-distribution artifacts, camera-controlled methods often produce 3D-inconsistent results due to sparse LiDAR cues. We propose ReCamDriv-ing, a purely vision-based framework that achieves camera-controlledgeneration by leveraging dense, structurally complete 3DGS renderingsas geometric guidance. Specifically, to prevent the model from overfit-ting to a trivial repair solution when conditioning on 3DGS render-ings, we adopt a two-stage progressive training paradigm: the first stageuses camera poses for coarse control, while the second stage incorpo-rates 3DGS renderings for fine-grained viewpoint and geometric guid-ance. Furthermore, to align training and inference camera transforma-tion patterns, we propose a 3DGS-based cross-trajectory data curationstrategy, enabling consistent lateral-trajectory supervision from single-pass videos. Based on this strategy, we construct the ParaDrive dataset,containing approximately 110K parallel-trajectory video pairs. Exten-sive experiments demonstrate that ReCamDriving achieves state-of-the-art camera controllability and structural consistency. Project website:https://recamdriving.github.io/.

Abstract:
Modern generative video models excel at producing convinc-ing, high-quality outputs, but struggle to maintain multi-view and spa-tiotemporal consistency in highly dynamic real-world environments. Inthis work, we introduce AnyView, a diffusion-based video generationframework for dynamic view synthesis with minimal inductive biasesor geometric assumptions. We leverage multiple data sources with var-ious levels of supervision, including monocular (2D), multi-view static(3D) and multi-view dynamic (4D) datasets, to train a generalist spa-tiotemporal implicit representation capable of producing zero-shot novelvideos from arbitrary camera locations and trajectories. We evaluateAnyView on standard benchmarks, showing competitive results with thecurrent state of the art, and propose AnyViewBench, a challengingnew benchmark tailored towards extreme dynamic view synthesis in di-verse real-world scenarios. In this more dramatic setting, we find thatmost baselines drastically degrade in performance, as they require signif-icant overlap between viewpoints, while AnyView maintains the abilityto produce realistic, plausible, and spatiotemporally consistent videoswhen prompted from any viewpoint.

Abstract:
Cross-identity character animation aims to drive a targetidentity (from a reference image) to follow the motion of a source char-acter (from a driving video). The core challenge lies in the inherent en-tanglement of two essential capabilities: cross-identity spatial mapping-aligning position, scale, and skeletal proportions between the referenceand the driving pose-and subsequent motion control-refining joint articu-lation, volumetric consistency, and view coherence during generation. Inthis paper, we introduce Reference-Aware Structural Alignment(RASA), a novel framework that systematically disentangles spatialmapping from motion control by injecting structured priors into a Diffu-sion Transformer (DiT). Our approach operates in two complementarystages. First, a Spatial Prior Calibrator (SPC) fuses the referenceidentity with the driving pose to generate an initial noise latent thatis spatially grounded—it ensures the target character is correctly po-sitioned, scaled, and proportionally aligned with the driving skeleton.This resolves cross-identity spatial mismatches at the very start of gen-eration. Second, to achieve identity-agnostic motion control, we proposean Inherent Motional Guider (IMG). Moving beyond appearance-biased 2D keypoints, IMG encodes shape-agnostic SMPL articulationparameters into a semantic motion vector. Injected into the interme-diate layers of the DiT, this vector serves as complementary guidancethat works in tandem with the base pose condition, providing anatomi-cally consistent joint articulation and view-aware volumetric refinement.To rigorously evaluate this challenging task, we curate CIM-Bench, ahigh-quality benchmark with rigorous manual curation. Extensive exper-iments demonstrate that RASA significantly outperforms state-of-the-artmethods in both motion fidelity and visual quality. Our work establishesa new paradigm for cross-identity animation, showing that disentan-gled spatial and motional priors are key to achieving robust and con-sistent character animation. Our project page is at https://hidream-ai.github.io/RASA/.

Abstract:
Video generation models aspire to simulate dynamic environ-ments, and several benchmarks now evaluate memory consistency acrossframes. However, most assess consistency only while the target remainsin view, and the few that force objects out of view evaluate static sceneswhere nothing changes during occlusion. To bridge this gap, we introduceMemoBench, a diagnostic benchmark built around the disappear-and-reappear paradigm in dynamically changing environments: a target ob-ject undergoes a physical process, disappears from view, and must be cor-rectly recovered in its updated state upon reappearance. We curate 360ground-truth clips spanning synthetic and real-world scenes, and designan evaluation suite combining automated metrics with VQA-based as-sessment across four diagnostic pillars. Evaluation of ten state-of-the-artmodels reveals key insights and open challenges regarding memory con-sistency under the disappear-and-reappear paradigm. Our dataset, code,and leaderboard are available at https://github.com/MemoBench-Team.

Abstract:
3D head stylization enables expressive reimagining of hu-man faces for creative visual experiences in digital media. Existing 3D-aware methods often require computationally intensive optimization orper-style fine-tuning, limiting flexibility and user control. To overcomethese challenges, we introduce StyleFusion360, a diffusion-based frame-work for multi-view consistent, identity-preserving 3D head stylizationfrom a single style reference image, without per-style training. Our ap-proach enhances the Style Fusion Attention mechanism with a style-conditioned key modulation mechanism that aligns content and stylerepresentations for fine-grained and controllable stylization. We furtherprovide a user-controllable slider for adjusting stylization intensity. Inaddition, StyleFusion360 supports local multi-edit stylization, enablingtargeted edits such as modifying hair or eyes independently. Extensiveexperiments on FFHQ and RenderMe360 demonstrate that StyleFu-sion360 produces high-quality, controllable, and visually compelling styl-izations, outperforming state-of-the-art GAN- and diffusion-based meth-ods across diverse style domains. Code is available at: https://github.com/furkanguzelant/StyleFusion360

Abstract:
Video diffusion models have recently been applied with success to problems in content generation, novel view synthesis, and, more broadly, world simulation. Many applications in generation and transfer rely on conditioning these models, typically through perceptual, geometric, or simple semantic signals, fundamentally using them as generative renderers. At the same time, high-dimensional features obtained from large-scale self-supervised learning on images or point clouds are increasingly used as a general-purpose interface for vision models. The connection between the two has been explored for subject specific editing, aligning and training video diffusion models, but not in the role of a dense conditioning signal for pretrained video diffusion models. Features obtained through self-supervised learning like DINOv3, contain a lot of entangled information about style, lighting and semantics of the scene. This makes them great at reconstruction tasks but limits their generative capabilities. In this paper, we show how we can use the features for tasks such as video domain transfer and video-from-3D generation. We introduce a lightweight control architecture and training strategy that decouples appearance from other features that we wish to preserve, enabling robust control for appearance changes such as stylization and relighting. Furthermore, we show that low spatial resolution can be compensated by higher feature dimensionality, improving controllability in generative rendering from explicit spatial representations.

Abstract:
Lineart colorization is a critical stage in professional content creation, yet achieving precise and flexible results under diverse user constraints remains a significant challenge. To address this, we propose OmniColor, a unified framework for multi-modal lineart colorization that supports arbitrary combinations of control signals. Specifically, we systematically categorize guidance signals into two types: spatially-aligned conditions and semantic-reference conditions. For spatially-aligned inputs, we employ a dual-path encoding strategy paired with a Dense Feature Alignment loss to ensure rigorous boundary preservation and precise color restoration. For semantic-reference inputs, we utilize a VLM-only encoding scheme integrated with a Temporal Redundancy Elimination mechanism to filter repetitive information and enhance inference efficiency. To resolve potential input conflicts, we introduce an Adaptive Spatial-Semantic Gating module that dynamically balances multi-modal constraints. Experimental results demonstrate that OmniColor achieves superior controllability, visual quality, and temporal stability, providing a robust and practical solution for lineart colorization. The source code is available at https://github.com/zhangxulu1996/OmniColor.

Abstract:
Creating photorealistic, animatable 3D human avatars from monocular images still largely depends on Linear Blend Skinning (LBS) and parametric body models which constrain expressivity and often introduce artifacts due to imperfect fitting. We propose LUNA, an LBSfree universal neural animation model that directly maps multiple 2D controls like images, keypoints, sketch and unseen characters into 3D Gaussian deformations, bypassing explicit body fitting. At its core, a transformer-based motion regressor disentangles global rigid motion from fine-grained local dynamics to capture both coherent movement and subtle non-rigid effects. To resolve the inherent ambiguity of 2D-to-3D lifting while scaling beyond fitted datasets, we introduce hybrid supervision that distills soft structural priors from an LBS teacher and a loss that supports training on both limited fitted data and large in-the-wild unlabeled videos. Extensive experiments show LUNA achieves competitive visual fidelity compared to LBS-based approaches, while delivering realistic human motion and zero-shot cross-identity generalization across diverse driving modalities. To the best of our knowledge, LUNA is the first end-to-end 3D animatable model that supports implicit 2D driving.

Abstract:
Exo-to-Ego video generation aims to synthesize a first-personvideo from a synchronized third-person view and corresponding cam-era poses. While paired supervision is available, synchronized exo-egodata inherently introduces substantial spatio-temporal and geometricdiscontinuities, violating the smooth-motion assumptions of standardvideo generation benchmarks. We identify this synchronization-inducedjump as the central challenge and propose Syn2Seq-Forcing, a sequen-tial formulation that interpolates between the source and target videosto form a single continuous signal. By reframing Exo2Ego as sequentialsignal modeling rather than a conventional condition–output task, ourapproach enables diffusion-based sequence models, e.g. Diffusion Forc-ing Transformers (DFoT), to capture coherent transitions across framesmore effectively. Empirically, we show that interpolating only the videos,without performing pose interpolation already produces significant im-provements, emphasizing that the dominant difficulty arises from spatio-temporal discontinuities. Beyond immediate performance gains, this for-mulation establishes a general and flexible framework capable of uni-fying both Exo2Ego and Ego2Exo generation within a single contin-uous sequence model, providing a principled foundation for future re-search in cross-view video synthesis. The code will be released at https://github.com/insait-institute/Syn2Seq.

Abstract:
Subject-driven video generation (SDV-Gen) aims to produce videos of a specific subject by adapting a pretrained video model, enabling personalized and application-driven content creation. To achieve this goal, per-subject tuning methods require approximately 200 A100 GPU hours to generate a customized video, whereas zero-shot methods avoid per-subject tuning but typically rely on millions of subjectvideo pairs for the supervision, incurring massive network fine-tuning costs (10K–200K A100 GPU hours). We propose a dataand computeefficient zero-shot SDV-Gen framework that avoids test-time per-subject tuning and the use of large-scale subject-video pairs. Our key idea decomposes SDV-Gen into (i) identity injection learned from subject-image pairs and (ii) motion-awareness preservation maintained by a small set of arbitrary videos. We optimize the two tasks with stochastic switching, using random reference-frame sampling and image-token dropout to prevent trivial first-frame copying. Our gradient analysis shows that the two objectives rapidly evolve toward nearly orthogonal update subspaces, explaining the stable optimization. Using CogVideoX-5B, we adapt a single model with 200K subject-image pairs and 4,000 arbitrary videos in 288 A100 GPU hours. This yields about 1% of compute compared to prior zero-shot baselines (i.e., 0.4% of VACE and 2.8% of Phantom) while using no subject-video pairs, yet remaining competitive in subject fidelity and motion quality. We show that the same recipe transfers to Wan 2.1-1.3B and Wan 2.2-5B.

Abstract:
Vision–Language–Action (VLA) models have become a com-pelling route toward robotic foundation models by mapping multimodalperception and language instructions directly into executable actions. Be-yond the prevalent flow-matching VLAs, discrete diffusion VLA (dVLA)models further unify perception and control in a single discrete space,offering inherent advantages in unified multimodal alignment and under-standing, while better preserving the prior knowledge of vision-languagemodels. Yet this strength comes with a practical obstacle that inferenceis far too slow for real-time control, as the parallel decoding prevents theutilization of Key-Value (KV) cache. In this paper, we aim to tackle thischallenge by leveraging an intriguing observation that the dVLA withbidirectional attention still adheres to a block-wise left-to-right order,which motivates the application of block diffusion. However, directlyapplying block diffusion precludes inter-block parallelism, a key accel-eration factor. To address the limitation, we introduce Fast-dVLA, anblock-wise diffusion acceleration strategy. Fast-dVLA takes the full actiontoken sequence at each timestep as an action block and denoises themtogether. It then decodes different blocks in an autoregressive mannerto allow KV cache reusing, while allowing inter-block parallel decodingin a diffusion-forcing manner. For efficient training, we directly conductan asymmetric distillation on a finetuned dVLA. Extensive evaluationson Discrete Diffusion VLA, Dream-VLA, and UD-VLA across diversesimulated benchmarks validate that our Fast-dVLA achieves 2.8×-4.1×speedup and maintains state-of-the-art performance. Moreover, the resultson real-world high-dynamics tasks demonstrate the potential of real-timedeployment and application of our Fast-dVLA.

Abstract:
Sports video analysis is a challenging domain for multimodalunderstanding because it involves complex and dynamic human activi-ties. Despite rapid progress in Multimodal Large Language Models (MLLMs),long-horizon reasoning in sports videos remains difficult, as answeringquestions requires both locating and integrating temporally sparse evi-dence into reasoning. We attribute this limitation to two closely relatedfactors: insufficient supervision over temporally dispersed evidence andthe lack of methods for explicit temporal evidence localization and justi-fication. To address these gaps, we introduce SportsTime, a large-scalebenchmark for long-form sports video understanding, comprising 14K+open-ended QA pairs and 50K+ step-wise temporal evidence annota-tions. Building on SportsTime, we propose Chain-of-Time Reasoning(CoTR), which treats reasoning as a process of temporally groundedevidence composition. Specifically, during training, CoTR introduces atemporal-reward GRPO to encourage temporally grounded reasoning.During inference, it employs an anchor-observe-infer evidence-seekingloop to iteratively localize, verify, and compose temporal evidence beforeproducing the final answer. Experiments show that SportsTime exposessubstantial gaps in current MLLMs, while CoTR yields consistent gainsover strong baselines, improving both temporal compositional reasoningperformance and step-wise grounding quality. The dataset and code areavailable at https://github.com/ustiniansy/SportsTime.

Abstract:
Recent advances in Diffusion Transformers (DiTs) have en-abled high-quality joint audio-video generation, producing videos withsynchronized audio within a single model. However, existing control-lable generation frameworks are typically restricted to video-only con-trol. This restricts comprehensive controllability and often leads to sub-optimal cross-modal alignment. To bridge this gap, we present MM-Control, which enables users to perform Multi-Modal Control in jointaudio-video generation. MMControl introduces a dual-stream conditionalinjection mechanism. It incorporates both visual and acoustic controlsignals—including reference images, reference audio, depth maps, andpose sequences—into a joint generation process. These conditions are in-jected through bypass branches into a joint audio-video Diffusion Trans-former, enabling the model to simultaneously generate identity-consistentvideo and timbre-consistent audio under structural constraints. Further-more, we introduce modality-specific guidance scaling, which allows usersto independently and dynamically adjust the influence strength of eachvisual and acoustic condition at inference time. Extensive experimentsdemonstrate that MMControl achieves fine-grained, composable controlover character identity, voice timbre, body pose, and depth-guided scenestructure in joint audio-video generation.

Abstract:
Autoregressive video diffusion models hold promise for worldsimulation but are vulnerable to exposure bias arising from the train–testmismatch. While recent works address this via post-training, they typi-cally rely on a bidirectional teacher model or discriminator. To achievean end-to-end solution, we introduce Resampling Forcing, a teacher-free framework that enables training autoregressive video models fromscratch and at scale. Central to our approach is a self-resampling schemethat simulates inference-time model errors on history frames during train-ing. Conditioned on these degraded histories, a sparse causal mask en-forces temporal causality while enabling parallel training with frame-leveldiffusion loss. To facilitate efficient long-horizon generation, we furtherintroduce history routing, a parameter-free mechanism that dynamicallyretrieves the top-k most relevant history frames for each query. Experi-ments demonstrate that our approach achieves performance comparableto distillation-based baselines while exhibiting superior temporal consis-tency on longer videos owing to native-length training. See our ProjectPage for more details.

Abstract:
Audio-Visual Question Answering (AVQA) extends classical VQA by requiring joint reasoning over video and synchronized audio. However, many AVQA systems rely on deeply stacked layers of selfand cross attention across text, video, and audio. Such sequential stacking may incur loss of information such as subtle inter-modal cues over the layers, causing errors to accumulate across sequential attention layers during the fusion. We introduce Q-TriM which performs multi-modal fusion in a shallow and parallel manner instead of a deep and sequential manner. For Q-TriM, we propose a novel framework for attention operation incorporating video and audio conditioned on text. As a result, we obtain not only standard cross attention outputs but also Tri-Modal Attention representations in which Query, Key, and Value come from distinct modalities. These attention representations are combined in parallel at a single stage, thus avoiding the multi-modal fusion with deep stacks in order to mitigate error accumulation and depth-induced issues. QTriM achieves state-of-the-art performance on three AVQA benchmarks, including substantial gains on MUSIC-AVQA-R, which demonstrates its robustness and out-of-distribution generalization. Code is available at https://github.com/Sunghun95/Q-TriM

Abstract:
Recent advancements in video generation has shifted from bidi-rectional models for short videos to autoregressive ones for ultra long videogeneration. Previous models, which usually use sliding window attention torestrict inference cost, lack effective memory compression and retrieval forlong-term generation beyond the window size, leading to issues of forgettingand spatiotemporal inconsistencies. To enhance the retention of historicalinformation with a fixed memory budget, we additionally incorporate tem-poral recurrent neural network (RNN) layers into the diffusion transformer(DiT) model. Specifically, we found that a LSTM layer after attention at eachDiT layer achieves comparable performance to other state-of-the-art RNNblocks, such as Test-Time Training (TTT) and Mamba2. Moreover, existingdiffusion-RNN approaches often suffer from performance degradation dueto training-inference gap or the lack of overlap across windows. To addressthese limitations, we propose a novel Recurrent Autoregressive Diffusion(RAD) framework, which leverages recurrent blocks for memory update andretrieval and preserves local details by full attention on overlapping slidingwindows, with no training and inference gap. Experiments on Memory Mazeand Minecraft datasets demonstrate the superiority for long video generationby our framework with global memory and local attention.Project page: https://yeyutaihan.github.io/recurrent-autoregressive-diffusion/

Abstract:
Recent Video Super-Resolution (VSR) methods rely heavilyon transformers and explicit optical flow, creating computational over-head and custom operations that hinder deployment on hardware acceler-ators like TensorRT. To address this, we introduce NanoVSR, a scalable,fully convolutional architecture designed for resource-constrained edgedevices. Using structural reparameterization, NanoVSR collapses intostandard convolutions during inference, ensuring seamless hardware com-patibility and negligible runtime overhead. Furthermore, despite lackingexplicit motion compensation, it maintains competitive restoration qual-ity by implicitly learning spatio-temporal alignments through progres-sive training. Evaluated on the REDS4 benchmark, NanoVSR demon-strates an exceptional balance between accuracy and computational ef-ficiency, significantly improving the trade-off for compact architectures.Our NanoVSR-644k baseline yields 28.64 dB PSNR on REDS4 whiledelivering 27.20 FPS on the NVIDIA Jetson Orin NX 16GB (25W),offering massive speed gains over heavier models. The scaled NanoVSR-1.7M variant reaches 29.15 dB with a throughput of 19.58 FPS, providingsuperior, edge-optimized upscaling.

Abstract:
Computer-use agents, which leverage multimodal large lan-guage models (MLLMs) to operate computers and complete tasks, haveattracted significant attention for their utility and versatility. A majorchallenge in developing these agents is collecting large-scale, high-qualitytrajectories. The standard approach generates synthetic data through aself-improving loop: an agent is placed in a verifiable environment anditeratively fine-tuned on its successful trajectories. Despite its effective-ness, this paradigm exploits only successful trajectories and discards thefailed ones, even though failures carry rich information about a model’sweaknesses. In this work, we explore a complementary failure-driven self-improvement loop, a data-centric paradigm that turns failed trajecto-ries into agent improvements. Specifically, we employ an LLM to diag-nose failure modes, propose inference-time solutions, and generate codepatches—lightly verified by humans—that upgrade the agent. We vali-date this approach with the state-of-the-art OpenCUA-72B model on theOSWorld benchmark, improving the success rate from 42.3% to 48.9%,a gain of 6.6 percentage points, without any additional training costand with only modest inference overhead. Our results demonstrate thatfailure-driven self-improvement is a viable complement to success-basedpipelines, enabling more efficient agent improvement. Code is availableat https://github.com/snow10072740/Learning from Failure.

Abstract:
Chain-of-Thought (CoT) reasoning significantly elevates thecomplex problem-solving capabilities of multimodal large language mod-els (MLLMs). However, adapting CoT to vision typically discretizes sig-nals to fit LLM inputs, causing early semantic collapse and discard-ing fine-grained details. While external tools can mitigate this, theyintroduce a rigid bottleneck, confining reasoning to predefined oper-ations. Although recent latent reasoning paradigms internalize visualstates to overcome these limitations, optimizing the resulting hybriddiscrete-continuous action space remains challenging. In this work, wepropose HyLaR (Hybrid Latent Reasoning), a framework that seamlesslyinterleaves discrete text generation with continuous visual latent rep-resentations. Specifically, following an initial cold-start supervised fine-tuning (SFT), we introduce DePO (Decoupled Policy Optimization) toenable effective reinforcement learning within this hybrid space. DePOdecomposes the policy gradient objective, applying independent trust-region constraints to the textual and latent components, alongside anexact closed-form von Mises-Fisher (vMF) KL regularizer. Extensive ex-periments demonstrate that HyLaR outperforms standard MLLMs andstate-of-the-art latent reasoning approaches across fine-grained percep-tion and general multimodal understanding benchmarks. Code is avail-able at https://github.com/EthenCheng/HyLaR.

Abstract:
Computer-use agents (CUAs) are becoming increasingly ca-pable; however, it remains difficult to scale evaluation of whether a tra-jectory truly fulfills a user instruction. In this work, we study rewardmodeling from execution video: a sequence of keyframes from an agenttrajectory that is independent of the agent’s internal reasoning or ac-tions. Although video-execution modeling is method-agnostic, it presentskey challenges, including highly redundant layouts and subtle, localizedcues that determine success. We introduce Execution Video Reward 53k(ExeVR-53k), a dataset of 53k high-quality video–task–reward triplets.We further propose adversarial instruction translation to synthesize neg-ative samples with step-level annotations. To enable learning from long,high-resolution execution videos, we design spatiotemporal token prun-ing, which removes homogeneous regions and persistent tokens while pre-serving decisive UI changes. Building on these components, we fine-tunean Execution Video Reward Model (ExeVRM) that takes only a user in-struction and a video-execution sequence to predict task success. Our Ex-eVRM 8B achieves 84.7% accuracy and 87.7% recall on video-executionassessment, outperforming strong proprietary models such as GPT-5.2and Gemini-3 Pro across Ubuntu, macOS, Windows, and Android, whileproviding more precise temporal attribution. These results show thatvideo-execution reward modeling can serve as a scalable, model-agnosticevaluator for CUAs.Code: https://github.com/limenlp/ExeVRMModel: https://huggingface.co/lime-nlp/ExeVRM-8BDataset: https://huggingface.co/datasets/lime-nlp/ExeVR-53K

Abstract:
Dense video captioning aims to generate temporally groundeddescriptions of video events, benefiting both event-level video understand-ing and generation. In this domain, autoregressive video large languagemodels have emerged as a prevalent paradigm due to their strong gen-erative and cross-modal modeling capacity. However, generating densecaptions under the token-by-token paradigm severely limits inference effi-ciency and hinders scalability as video length and event density increase.In this work, we propose a parallelized autoregressive framework thatnot only improves generation efficiency but also enhances temporallygrounded captioning performance. Our key insight is to exploit the weaklocal dependencies across temporally distinct events to restructure thecausal dependency graph, thereby enabling lossless parallel generation.Specifically, tokens with weak cross-event dependencies can be decoded inparallel, while tightly coupled tokens within each event retain sequentialdecoding to preserve local semantic coherence. To realize this insight, weintroduce two key components for lossless parallel decoding: (1) a latentglobal planning mechanism that automatically learns the event-level struc-ture and produces compact tokens encoding global inter-event causalitywhile adaptively aggregating event-level audio-visual semantics, guidingsubsequent dependency restructuring and parallel decoding; and (2) anevent-factorized parallel decoding mechanism that effectively balanceslocal focus with global inter-event awareness. Experiments on variousbenchmarks demonstrate the clear advantage of our approach in bothefficiency and performance in omni-modal event grounding and captioning.Project website: https://github.com/showlab/PadCaptioner.

Abstract:
The performance of vision-language models (VLMs) in videounderstanding declines with increasing video duration, as video momentsunrelated to the query confuse their language components. Multimodalretrieval has emerged as a critical component of video understanding,addressing this challenge by localizing key visual evidence. However, ex-isting multimodal retrieval methods suffer from biased relevance estima-tion, limited diversity, and temporal collapse. In this paper, we proposeQSVideo, a unified framework that systematically addresses relevance,diversity, and temporal modeling in video retrieval. We first introducea query-conditioned semantic ranker, QSRanker, which reformulates ar-bitrary questions into retrieval-friendly queries and estimates structuredrelevance along object, action, and location dimensions. Building uponthis, we design QSRetrieval to jointly optimize relevance and diversityfor more informative frame selection. Moreover, we propose temporalalignment strategies tailored for both long and streaming videos to im-prove evidence recall. Extensive experiments on long and streaming videobenchmarks demonstrate that QSVideo greatly enhances video VLM per-formance under strict frame limit constraints. The code is available athttps://github.com/human-analysis/QSVideo.

Abstract:
Despite the remarkable capabilities of Multimodal Large Lan-guage Models (MLLMs), they still suffer from visual fading in long-context scenarios. Specifically, the attention to visual tokens diminishesas the text sequence lengthens, leading to text generation detached fromvisual constraints. We attribute this degradation to the inherent induc-tive bias of Multimodal RoPE, which penalizes inter-modal attention asthe distance between visual and text tokens increases. To address this, wepropose inter-modal Distance Invariant Position Encoding (DIPE), asimple but effective mechanism that disentangles position encoding basedon modality interactions. DIPE retains the natural relative positioningfor intra-modal interactions to preserve local structure, while enforc-ing an anchored perceptual proximity for inter-modal interactions. Thisstrategy effectively mitigates the inter-modal distance-based penalty, en-suring that visual signals remain perceptually consistent regardless ofthe context length. Experimental results demonstrate that by integrat-ing DIPE with Multimodal RoPE, the model maintains stable visualgrounding in long-context scenarios, significantly alleviating visual fad-ing while preserving performance on standard short-context benchmarks.Code is available at https://github.com/lchen1019/DIPE.

Abstract:
Zero-shot video temporal grounding (VTG) localizes eventsin untrimmed videos from natural language queries without task-specifictraining. Existing methods rely on frame-query feature matching, whichsuffices for simple events but struggles with complex multi-stage queriesthat require understanding temporal ordering and causal structure—a disparity we call the reasoning gap. We propose DART (Difficulty-Adaptive Routing for Temporal Grounding), which bridges this gapby coupling difficulty-aware routing with structured reasoning in largevision-language models. A query-conditioned Determinantal Point Pro-cess (DPP) serves a dual role: selecting diverse, query-relevant keyframesas temporal evidence, and providing spectral entropy as a difficulty indi-cator. Simple queries are routed to a Fast path for direct prediction, whilecomplex queries follow a Slow path with Temporal Markup Prompting,which decomposes localization into global event analysis, per-frame tem-poral role annotation, and boundary extraction. On Charades-STA andActivityNet Captions, DART achieves state-of-the-art zero-shot perfor-mance across both identically distributed and multiple out-of-distributionsettings, improving mIoU by up to 3.5 points over the strongest baselinewhile using over 7× fewer frames. The project homepage is available athttps://dart-vtg.github.io/.

Abstract:
We present PAI-Studio, a new reference-conditioned videosynthesis task that addresses a long-standing challenge in cinematic back-ground replacement: generating dynamic backgrounds aligned with fore-ground motion while preserving foreground identity, matching referencescene appearance, and achieving globally consistent illumination with re-alistic foreground relighting. Existing open-source systems and commer-cial APIs cannot simultaneously ensure motion-consistent backgroundgeneration, high-fidelity foreground relighting and foreground identitypreservation, often resulting in static backgrounds, inconsistent bound-aries, and noticeable compositing artifacts. To bridge this gap, we buildupon a Diffusion Transformer video backbone and reformulate the prob-lem as an in-context conditional generation task. Through bidirectionalattention, our model jointly captures foreground dynamics and back-ground reference information within a unified architecture. We furtherconstruct a 30K-scale dataset sourced from high-quality films and on-line videos to support this task. Extensive evaluations demonstrate thatour method significantly outperforms existing open-source and commer-cial API solutions. Project page: https://github.com/showlab/PAI-Studio.

Abstract:
Video-to-video diffusion models achieve impressive singleturn editing performance, but practical editing workflows are inherently iterative. When edits are applied sequentially, existing models treat each turn independently, often causing previously generated regions to drift or be overwritten. We identify this failure mode as the problem of cross-turn consistency in multi-turn video editing. We introduce Memory-V2V, a memory-augmented framework that treats prior edits as structured constraints for subsequent generations. Memory-V2V maintains an external memory of previous outputs, retrieves task-relevant edits, and integrates them through relevance-aware tokenization and adaptive compression. These technical ingredients enable scalable conditioning without linear growth in computation. We demonstrate Memory-V2V on iterative video novel view synthesis and text-guided long video editing. Memory-V2V substantially enhances cross-turn consistency while maintaining visual quality, outperforming strong baselines with modest overhead.

Abstract:
Multi-shot video generation is crucial for long narrative storytelling, yet current bidirectional architectures suffer from limited interactivity and high latency. We propose ShotStream, a novel causal multi-shot architecture that enables interactive storytelling and efficient on-the-fly frame generation. By reformulating the task as next-shot generation conditioned on historical context, ShotStream allows users to dynamically instruct ongoing narratives via streaming prompts. We achieve this by first fine-tuning a text-to-video model into a bidirectional nextshot generator, which is then distilled into a causal student via Distribution Matching Distillation. To overcome the challenges of inter-shot consistency and error accumulation inherent in autoregressive generation, we introduce two key innovations. First, a dual-cache memory mechanism preserves visual coherence: a global context cache retains conditional frames for inter-shot consistency, while a local context cache holds generated frames within the current shot for intra-shot consistency. And a RoPE discontinuity indicator is employed to explicitly distinguish the two caches to eliminate ambiguity. Second, to mitigate error accumulation, we propose a two-stage distillation strategy. This begins with intra-shot self-forcing conditioned on ground-truth historical shots and progressively extends to inter-shot self-forcing using self-generated histories, effectively bridging the train-test gap. Extensive experiments demonstrate that ShotStream generates coherent multi-shot videos with sub-second latency, achieving 16 FPS on a single GPU. It matches or exceeds the quality of slower bidirectional models, paving the way for real-time interactive storytelling. Code and models will be released.

Abstract:
We present MGM-Omni, an Omni MLLM for omni-modalunderstanding and expressive, long-horizon speech generation. MGM-Omni adopts a “brain–mouth” design with a dual-track, token-based ar-chitecture that cleanly decouples multimodal reasoning from real-timespeech generation. This design enables efficient cross-modal interactionand low-latency, streaming speech generation. For multimodal under-standing, a unified training strategy coupled with a dual audio encoderdesign enables long-form omni modal perception across diverse acous-tic conditions. For speech generation, a chunk-based parallel decodingscheme narrows the text–speech token-rate gap, accelerating inferenceand supporting streaming zero-shot voice cloning with stable timbre overextended durations. Compared to concurrent work, MGM-Omni achievesthese capabilities with markedly data-efficient training. Extensive exper-iments demonstrate that MGM-Omni outperforms existing open sourcemodels in preserving timbre identity across extended sequences, produc-ing natural and context-aware speech, and achieving superior long-formaudio and omnimodal understanding. MGM-Omni establishes an effi-cient, end-to-end paradigm for omnimodal understanding and control-lable, personalized long-horizon speech generation. Code and models areavailable at https://github.com/dvlab-research/MGM-Omni.

Abstract:
Deep vision models exploit shortcuts, relying on cues thatcorrelate with supervision signals. Prior work has focused on visiblebiases, such as object-background or texture correlations. We identifya different source of shortcut learning: invisible metadata traces em-bedded at the pixel level, for metadata such as image processing andphoto acquisition. We hypothesize that large-scale semantic supervi-sion, whether through categorical labels (ImageNet) or billion-scale cap-tions (LAION), naturally induces metadata-semantics correlations dur-ing pretraining, leading models to convert low-level signals into predic-tive features. By introducing controlled metadata-semantics correlations,we show that stronger ones produce systematically higher sensitivity tometadata traces and larger performance degradation under metadatadistribution shifts. We further explore mitigation strategies applied dur-ing and after pretraining that reduce sensitivity not only to targetedmetadata but also to unseen ones, without sacrificing performance ondownstream tasks. Metadata sensitivity also has a positive side: it partlyexplains the strong generated-image detection ability of some encoders,while its mitigation can improve out-of-distribution generalization. Code:

Abstract:
Deepfake detection faces challenges in generalization, prompt-ing recent studies to fine-tune pretrained Vision Transformers for im-proved performance. Existing fine-tuning-based detection approaches of-ten assign the same rank to the tunable residual components of alllayers. However, the shallow layers exhibit a distinctly low-rank struc-ture, whereas the middle and deep layers present more complex struc-tures. Therefore, we argue that only slight fine-tuning is required forthe shallow layers, which are able to effectively capture low-level forgerycues, while the middle and deep layers require higher residual ranks tomodel long-range dependencies and extract high-level forgery artifacts.To achieve these goals adaptively, we propose the Stable-Rank-BasedResidual Adaptation (SRRA) strategy. It utilizes singular value decom-position to decompose the pretrained weight matrices and adaptively al-locates frozen principal components and learnable residual componentsbased on the Stable Rank. In addition, we design the Residual En-ergy Constraint (REC) and Residual Subspace Orthogonalization (RSO),which suppress excessive perturbations and promote subspace decou-pling, avoiding overfitting and improving the generalization performance.Extensive experiments demonstrate that SRRA achieves remarkable im-provements in cross-domain generalization across multiple deepfake de-tection benchmarks. Our code is available at https://github.com/LHK-CodeLab/SRRA.

Abstract:
Existing feature coding methods typically minimize feature-space reconstruction error, implicitly assuming that preserving featuresimilarity ensures downstream task performance. However, in VisionTransformers (ViTs), global correlation mixing makes downstream pre-dictions more sensitive to small feature perturbations, so similar fea-ture distortion can lead to significantly different task performance. Inparticular, our analysis reveals that under fixed-group product quanti-zation, task sensitivity is highly uneven across channel subspaces. Con-sequently, near-uniform per-group rates may waste bits on insensitivedirections while leaving critical ones underrepresented, causing rapidtask-performance degradation at low bitrate. To address this issue, wepropose an Orthogonal Reparameterized product quantization for inter-mediate Feature Coding (ORFC). It introduces a learnable orthogonaltransform that rotates the representation before grouped quantization,enabling fixed-size groups to better align with task-relevant directions.We further use the output deviation of the remaining layers as a proxydistortion, and optimize an entropy-constrained rate-distortion objectiveto jointly learn the orthogonal transform and grouped quantization in anend-to-end manner. Extensive experiments across multiple ViT architec-tures and downstream tasks demonstrate that ORFC achieves a superiorrate-task trade-off, especially in the low-bitrate regime. The code is avail-able at https://github.com/zhangletian2/ORFC.

Abstract:
CLIP and its variants are widely adopted visual backbonesin multimodal systems, but their pretraining remains dominated by de-scriptive image-text alignment. As downstream applications increasinglydemand visually grounded commonsense inference and compositionalreasoning, it remains unclear whether CLIP-style encoders can supportsuch reasoning without architectural changes. To address this, we presentReasonCLIP-58M, a continual pretraining framework that integrates large-scale reasoning supervision into CLIP-style models through our two-stage strategy, which progressively integrates reasoning signals while pre-serving descriptive alignment, followed by category-structured reason-ing supervision. To support this framework, we construct two comple-mentary datasets and a benchmark: ReasonLite-42M, with open-form,visually verifiable reasoning captions; ReasonPro-16M, with category-specific reasoning supervision; and RCLIP-Bench for diagnostic evalua-tion of visually grounded reasoning. We train a family of ReasonCLIPthat improves visually grounded commonsense and compositional reason-ing while also enhancing zero-shot retrieval performance. As a drop-invisual encoder for multimodal large language models such as LLaVA-NeXT, ReasonCLIP delivers consistent gains without additional infer-ence cost, demonstrating that structured reasoning supervision enhancesthe expressive capacity of CLIP-style visual representations. All datasets,models, and training code are available at https://github.com/RISys-Lab/ReasonCLIP.

Abstract:
As Vision-Language Models are increasingly deployed in safetycritical applications, the trustworthiness of their explanations becomes crucial. Explainable AI (XAI) methods for Vision-Language Models often suffer from explanation hallucination, where attribution maps highlight prominent image regions even when prompted with incorrect text descriptions (e.g., highlighting a dog when prompted “cat”). Although this problem is widespread, a formal mathematical analysis of XAI methods and CLIP embeddings is largely missing in the literature. We demonstrate that this phenomenon is not specific to a single architecture but is a fundamental consequence of Linear Semantic Leakage in high-dimensional embedding spaces. We propose a unified theoretical framework, Linear Semantic Attribution (LSA), which generalizes across discriminative methods. We introduce Orthogonal Semantic Projection (OSP), a geometric intervention that utilizes the residual property of OMP to disentangle unique semantic signals from shared concepts. We prove theoretically and demonstrate empirically that OSP minimizes hallucination by orthogonalizing the query vector against distractor concepts, rendering the attribution model blind to shared features while preserving fidelity for correct prompts. The project website, including the code and animations, is available at: https://emirhanbilgic.github.io/OrthogonalSemantic-Projection

Abstract:
Modern Vision-Language Models (VLMs) often suffer from granularity mismatch, failing to encode high-density visual details not explicitly described in abstract text prompts, which results in low-frequency bias and object hallucination. While additive noise naturally introduces high-frequency variations, suggesting corruption-to-reconstruction (C2R) as a potential remedy, existing noise-based extensions of masked image modeling (MIM) surprisingly yield little improvement on recognition tasks. We identify the cause as a structural limitation: recent trends toward decoder-style designs prevent noise-induced signals from being deeply integrated into the encoder representations. To address this issue, we propose Encoder-Centric Corruption (ECC), a framework guided by three principles: (i) Encoder-Centricity, performing restoration within the encoder to shape transferable features; (ii) FeatureLevel Noising, injecting noise at intermediate layers to capture finegrained textures; and (iii) Task Disentanglement, separating mask reconstruction and denoising via a disruption loss. ECC surpasses prior MIM and noise-based methods by up to 8.1%. Moreover, integrating ECC into large-scale frameworks such as CLIP and AIM-v2 consistently improves performance, demonstrating its effectiveness as a scalable pretraining strategy for next-generation foundation models. Code is available at https://github.com/doihye/ecc.

Abstract:
Concept customization typically binds rare tokens to a target concept. Unfortunately, these approaches often suffer from unstable performance as the pretraining data seldom contains these rare tokens. Meanwhile, these rare tokens fail to convey the inherent knowledge of the target concept. Consequently, we introduce Knowledgeaware Concept Customization, a novel task aiming at binding diverse textual knowledge to target visual concepts. This task requires the † Corresponding author. 2 C Zhu et al. model to identify the knowledge within the text prompt to perform highfidelity customized generation. Meanwhile, the model should efficiently bind all the textual knowledge to the target concept. Therefore, we propose MoKus, a novel framework for knowledge-aware concept customization. Our framework relies on a key observation: cross-modal knowledge transfer, where modifying knowledge within the text modality naturally transfers to the visual modality during generation. Inspired by this observation, MoKus contains two stages: (1) In visual concept learning, we first learn the anchor representation to store the visual information of the target concept. (2) In textual knowledge updating, we update the answer for the knowledge queries to the anchor representation, enabling high-fidelity customized generation. To further comprehensively evaluate our proposed MoKus on the new task, we introduce the first benchmark for knowledge-aware concept customization: KnowCusBench. Extensive evaluations have demonstrated that MoKus outperforms state-of-the-art methods. Moreover, the cross-model knowledge transfer allows MoKus to be easily extended to other knowledge-aware applications like virtual concept creation and concept erasure. We also demonstrate the capability of our method to achieve improvements on world knowledge benchmarks.

Abstract:
Image Quality Assessment (IQA) is a long-standing problem in computer vision. Previous methods typically focus on predicting numerical scores without explanation or provide low-level descriptions lacking precise scores. Recent reasoning-based vision language models (VLMs) have shown strong potential for IQA, enabling joint generation of quality descriptions and scores. However, we notice that existing VLM-based IQA methods tend to exhibit unreliable reasoning due to their limited capability of integrating visual and textual cues. In this work, we introduce Zoom-IQA, a VLM-based IQA model to explicitly emulate key cognitive behaviors: uncertainty awareness, region reasoning, and iterative refinement. Specifically, we present a two-stage training pipeline: 1) supervised fine-tuning (SFT) on our Grounded-RationaleIQA (GR-IQA) dataset to teach the model to ground its assessments in key regions; and 2) reinforcement learning (RL) for dynamic policy exploration, primarily stabilized by our KL-Coverage regularizer to prevent reasoning and scoring diversity collapse, and supported by a Progressive Re-sampling Strategy to mitigate annotation bias. Extensive experiments show that Zoom-IQA achieves improved robustness, explainability, and generalization. The application to downstream tasks, such as image restoration, further demonstrates the effectiveness of Zoom-IQA.

Abstract:
Multimodal large language models (MLLMs) have advancedrapidly, yet heterogeneity in architecture, alignment strategies, and effi-ciency means that no single model is uniformly superior across tasks. Inpractical deployments, workloads span lightweight OCR to complex mul-timodal reasoning; using one MLLM for all queries either over-provisionscompute on easy instances or sacrifices accuracy on hard ones. Query-level model selection (routing) addresses this tension, but extending rout-ing from text-only LLMs to MLLMs is nontrivial due to modality fusion,wide variation in computational cost across models, and the absence ofa standardized, budget-aware evaluation.We present MMR-Bench, a unified benchmark that isolates the mul-timodal routing problem and enables comparison under fixed candidatesets and cost models. MMR-Bench provides (i) a controlled environmentwith modality-aware inputs and variable compute budgets, (ii) a broadsuite of vision–language tasks covering OCR, general VQA, and mul-timodal reasoning, and (iii) strong single-model baselines, oracle upperbounds, and representative routing policies. Using MMR-Bench, we showthat incorporating multimodal signals improves routing quality. Empiri-cally, these cues improve the cost–accuracy frontier and enable the routedsystem to exceed the strongest single model’s accuracy at roughly 33%of its cost. Furthermore, policies trained on a subset of models and tasksgeneralize zero-shot to new datasets and text-only benchmarks withoutretuning, establishing MMR-Bench as a foundation for studying adaptivemultimodal model selection and efficient MLLM deployment.

Abstract:
Open-set object detection leverages language prompts toguide the perception of categories outside the training set. However,for categories that are difficult to describe or cannot be effectively ab-stracted semantically, models exhibit reduced receptiveness to guidanceprovided by textual descriptions. Recent approaches have explored vi-sual prompting, but they often lack inherent generalization capabilitiesand suffer from limitations in semantic, spatial, and contextual aware-ness during prompt construction. Additionally, isolated prompt path-ways hinder unified optimization. Inspired by the general process of hu-man object search, we designed Ceptor, a unified detector guided bydiverse prompts for open-set object detection. Specifically, we proposeInfusion Strategies to infuse our framework with the generalizationcapabilities of a vision-language model (VLM), leveraging its alignmentproperties and generalizable feature space to construct diverse promptsfor different object types and varying category distributions. Further-more, we introduce a novel prompt generation method, PRE, whicheffectively preserves generalization, contextual information, and spatialaccuracy. Harmonization strategies are also incorporated to ensurecoordinated optimization across various prompts. Ceptor achieves strongperformance with various prompts across multiple datasets, demonstrat-ing the effectiveness of our methodology. Models and code are releasedat https://github.com/jinyanglii/Ceptor.

Abstract:
Vision-language models (VLMs) have demonstrated remark-able capabilities in understanding and reasoning about visual content,but significant challenges persist in tasks requiring cross-viewpoint under-standing and spatial reasoning. We identify a critical limitation: currentVLMs excel primarily at egocentric spatial reasoning (from the cam-era’s perspective) but fail to generalize to allocentric viewpoints whenrequired to adopt another entity’s spatial frame of reference. We introduceViewSpatial-Bench, a dedicated multi-perspective spatial localizationbenchmark covering both camera-perspective and human perspective rea-soning, supported by an automated 3D annotation pipeline that generatesprecise directional labels. Comprehensive evaluation of diverse VLMs onViewSpatial-Bench reveals a significant performance disparity: modelsdemonstrate reasonable performance on camera-perspective tasks butexhibit reduced accuracy when reasoning from a human viewpoint. Byfine-tuning VLMs on our multi-perspective spatial dataset, we achievean overall performance improvement of 46.24% across tasks, confirmingthe strong latent spatial reasoning capacity of VLMs under targetedsupervision. Our work establishes a crucial benchmark for spatial intel-ligence in embodied AI systems and provides empirical evidence thatmodeling 3D spatial relationships enhances VLMs’ corresponding spatialcomprehension capabilities.Our benchmark is available at HuggingFace.

Abstract:
Microscopic imaging provides essential visual evidence forstudying plant biology and pathology at the cellular and subcellular lev-els. However, existing benchmarks for vision-language models primarilyfocus on macroscopic plant imagery, while the microscopic domain re-mains underexplored. To address this gap, we present PlantMicro, a com-prehensive benchmark for evaluating vision–language models (VLMs) inmicroscopic plant imagery. PlantMicro integrates more than 5,000 imagescollected from diverse hosts, biological domains, and imaging modali-ties. Building on this diversity, we design a set of complementary tasksthat capture different aspects of microscopic image understanding. Tosupport these tasks, we construct over 9,000 VQA pairs that system-atically evaluate the capabilities of VLMs. Experiments on PlantMicroshow that current VLMs struggle with fine-grained recognition and bio-logically grounded reasoning. For example, GPT-5 achieves 34.93% accu-racy on the pathogen classification task, which is only modestly above a24.95% random guessing baseline. The results highlight a significant gapin the ability of current VLMs to comprehend microscopic plant images.PlantMicro provides a standardized foundation for advancing VLMs to-ward reliable and comprehensive microscopy-level plant understanding.PlantMicro is available at https://github.com/tqwei05/PlantMicro.

Abstract:
Reasoning segmentation is a challenging vision–language taskthat performs pixel-level segmentation guided by language reasoning overimplicit textual descriptions. Existing methods typically employ largelanguage models (LLMs) to achieve such reasoning capability, yet theirsubstantial computational and memory demands limit practical deploy-ment. To overcome this, we present LReSeg, the first attempt to tacklereasoning segmentation with a small language model (SLM), achievingstrong performance with substantially reduced model size and computa-tional cost. To address the challenges of scaling reasoning segmentationto SLMs, we propose two key designs: First, unlike LLMs, SLMs havelimited capacity to provide sufficient spatial instruction cues for mask de-coding. We introduce register tokens that aggregate text-conditioned spa-tial features via the SLM’s self-attention and inject them into the visualtoken stream to enrich the mask decoder’s inputs. Second, we adopt aunified encoder architecture to eliminate the redundant visual backboneof conventional dual-encoder designs, naturally ensuring feature consis-tency between reasoning and mask decoding. LReSeg surpasses modelsof similar scale with superior efficiency, while being nearly 10× smaller(800M vs. ∼8B) and 3.3× faster than conventional 7B-scale LLM-basedmethods. Code is available at https://github.com/downdric/LReSeg.

Abstract:
Vision-Language-Action (VLA) models provide a unifiedparadigm for robotic manipulation, yet their real-world deploymentremains limited by execution efficiency. While existing efforts predomi-nantly focus on compute-centric efficiency to reduce per-step inferencelatency, the intrinsic policy efficiency of these models remains largelyunexplored. Policy efficiency is fundamentally affected by two factors,namely the effective executable length of predicted action chunks and thetotal physical steps required to complete a task. These two factors jointlydetermine the number of forward inference calls during execution. Weobserve that current VLA policies struggle with planning unreliabilityand action redundancy, suffering from severe tail degradation in actionchunks and tending to generate redundant physical steps. To address this,we propose PolicyTrim, a reinforcement learning-based post-trainingframework that extends reliable action chunk length and reduces redun-dant physical steps. For reliable chunk extension, we employ a dynamicexploration strategy that rewards the successful completion of longer exe-cutable lengths, progressively pushing the trustworthy prediction horizonto its empirical limit. For step efficiency, we design a redundancy-awarereward that favors successful task completion with fewer steps whilepenalizing unreproducible shortcuts, effectively eliminating redundantphysical actions. Experiments on three benchmarks and three VLA mod-els show that PolicyTrim improves action chunk utilization by 3×, re-duces physical execution steps by 51.4%, and delivers up to a 5.83× end-to-end deployment speedup without compromising task success rates.Project Page: https://inceptionwang.github.io/PolicyTrim/

Abstract:
Vision–Language Models (VLMs) are increasingly deployedin safety-critical applications, yet remain vulnerable to backdoor attacks.Existing methods primarily manipulate final outputs, often producingreasoning traces that are inconsistent or easily detectable. In this paper,we propose ReShift, the novel aha-moment-driven reasoning-level back-door framework that explicitly redirects the internal chain-of-thought(CoT) trajectory while preserving surface-level coherence. ReShift intro-duces a Poisoned Reasoning-Aware Data Construction (PRDC) pipelineand a Supervised–Reinforcement Joint Optimization (SRJO) strategyto induce stable trigger-conditioned reasoning shifts. We further for-malize Entropy Rebound as a principled signal for characterizing rea-soning redirection and provide theoretical guarantees linking entropygaps to trajectory-level divergence. Extensive experiments demonstratethat ReShift achieves high attack success rates while maintaining clean-task performance and realistic reasoning traces, substantially improv-ing stealthiness against existing defenses. Code can be found at https://github.com/AlbertZhaoCA/ReShift.

Abstract:
Vision-Language-Action (VLA) models have emerged as apromising paradigm for building general-purpose robotic agents. How-ever, the VLA landscape remains highly fragmented and complex: asexisting approaches vary substantially in architectures, training data,embodiment configurations, and benchmark-specific engineering. In thiswork, we introduce StarVLA-α, a simple yet strong baseline designedto study VLA design choices under controlled conditions. StarVLA-αdeliberately minimizes architectural and pipeline complexity to reduceexperimental confounders and enable systematic analysis. Specifically,we re-evaluate several key design axes, including action modeling strate-gies, robot-specific pretraining, and interface engineering. Across uni-fied multi-benchmark training on LIBERO, SimplerEnv, RoboTwin, andRoboCasa, the same simple baseline remains highly competitive, indi-cating that a strong VLM backbone combined with minimal design isalready sufficient to achieve strong performance without relying on ad-ditional architectural complexity or engineering tricks. Notably, our sin-gle generalist model outperforms π0.5 by 20% on the public real-worldRoboChallenge benchmark. We expect StarVLA-α to serve as a solidstarting point for future research in the VLA regime. Code will be re-leased at https://github.com/starVLA/starVLA.

Abstract:
We introduce a novel learning problem: decoding gaze intonatural language descriptions of human goals across diverse visual tasks.Unlike prior work, which frames gaze decoding as a discriminative taskover predefined categories, we formulate it as a generative learning prob-lem: training a model to produce free-form descriptions that capture therich nuances and open-ended nature of human intentions beyond fixedlabels. To this end, we introduce Gazette, the first gaze-to-text decod-ing framework. Based on multimodal large language models (MLLMs),Gazette learns to decode gaze scanpaths into natural language for goalsthat may extend beyond categorical labels and require articulation innatural language. To help Gazette filter out individual differences in gazebehavior and learn the goal-specific spatiotemporal dynamics crucial forgenerating accurate natural language goal descriptions, we propose anovel strategy that leverages the encyclopedic knowledge and reason-ing abilities of a large language model to synthesize natural languageexplanations of goal-directed attentional behavior called think-aloud tran-scripts. Instruction tuning on these synthetic narratives allows Gazetteto achieve state-of-the-art performance in gaze decoding across mul-tiple tasks, demonstrating its generalizability and versatility, therebyenabling gaze to serve as a powerful, non-intrusive cue for inferringhuman goals and intentions in diverse scenarios. Code is available athttps://github.com/cvlab-stonybrook/Gazette.

Abstract:
Vision–language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis. However, their trustworthiness and clinical utility remain limited by three key challenges: heterogeneous datasets with inconsistent diagnostic labels and concept annotations, the lack of grounded diagnostic rationales for reliable reasoning supervision, and limited scalability when transferring knowledge from small, densely annotated datasets to large collections with sparse labels. To address these challenges, we propose Skin-R1, a dermatology-oriented VLM that integrates textbook-grounded clinical reasoning supervision with reinforcement learning (RL) to improve the accuracy and robustness of diagnostic prediction. First, we construct a textbook-based reasoning generator that synthesizes hierarchy-aware and differential-diagnosis (DDx) diagnostic trajectories derived from authoritative dermatology knowledge. Second, these trajectories are used for supervised fine-tuning (SFT), establishing a clinically grounded reasoning foundation for the model. Finally, we introduce an RL training framework that incorporates the hierarchical structure of dermatological diseases into the reward design, enabling the model to generalize grounded diagnostic reasoning to large-scale datasets with sparse annotations. Extensive experiments across multiple dermatology benchmarks demonstrate that Skin-R1 consistently improves diagnostic accuracy and robustness compared to state-of-the-art Med-VLM baselines. Ablation studies further highlight the critical role of grounded reasoning supervision introduced during the SFT stage. Code and model weight are available at https://github.com/l593191569/Skin-R1.

Abstract:
Normalizing Flows (NFs) learn invertible mappings betweenthe data and a Gaussian distribution. Prior works usually suffer fromtwo limitations. First, they add random noise to training samples orVAE latents as data augmentation, introducing complex pipelines in-cluding extra noising and denoising steps. Second, they use a pretrainedand frozen VAE encoder, resulting in suboptimal reconstruction and gen-eration quality. In this paper, we find that the two issues can be solvedin a very simple way: just fixing the variance (which would otherwise bepredicted by the VAE encoder) to a constant (e.g., 0.5). On the one hand,this method allows the encoder to output a broader distribution of tokensand the decoder to learn to reconstruct clean images from the augmentedtoken distribution, avoiding additional noise or denoising design. On theother hand, fixed variance simplifies the VAE evidence lower bound, mak-ing it stable to train an NF with a VAE jointly. On the ImageNet 256×256generation task, our model SimFlow obtains a gFID score of 2.15, outper-forming the state-of-the-art method STARFlow (gFID 2.40). Moreover,SimFlow can be seamlessly integrated with the end-to-end representa-tion alignment (REPA-E) method and achieves an improved gFID of1.91, setting a new state of the art among NFs.

Abstract:
Automated segmentation of cryo-electron tomograms rou-tinely produces masks that are voxel-accurate but topologically broken:membranes fragment, organelles merge into one another and enclosedcavities collapse. Existing topology-aware losses reduce these violationsbut cannot eliminate them because topology is encouraged through gradi-ent pressure rather than structurally enforced. We introduce TopoFuse,which reframes topology as a differentiable projection operator rather thana loss penalty. At each forward pass, the projection operator ProjT (a PH-guided sparse edit) identifies the critical voxels responsible for topologicalviolations via bottleneck matching and applies sparse edits to satisfy aspecified topology target (diagram feature counts and lifetime budgets)for dimensions d ∈ 0, 2. If the projection converges, the output satisfiesthose constraints on the downsampled grid (s = 2), when it does not, arepair certificate exposes this explicitly enabling downstream filtering.A topology prior head predicts the correction target directly from inputfeatures, removing any dependence on ground-truth topology at inference.Across three cryo-ET benchmarks, TopoFuse reduces Betti Number Errorby 54% over the strongest soft-loss baseline (p < 0.001), improves Diceby 4.6 pp and edits only 3.1% of voxels to achieve this. Code and SYNdataset are available at: https://github.com/rohitsalla/TopoFuse

Abstract:
We present MagnetGS-Mesh, an integrated framework thatdirectly recovers high-quality, object-wise surface meshes from 3D Gaus-sian Splatting (3DGS) while preserving photorealistic novel-view syn-thesis. Prior 3DGS-to-mesh methods generate meshes by considering allGaussians without distinguishing objects from the background, whichinevitably yields excessive and unnecessary vertices in irrelevant regions.To address this efficiency bottleneck, we adopt an object-wise reconstruc-tion approach. However, decomposing 3DGS representations into indi-vidual objects introduces distinct geometric challenges: numerous out-lier Gaussians appear far from true surfaces, and the remaining surfaceGaussians often exhibit highly non-uniform density. Since robust meshextraction fundamentally requires a uniform distribution of Gaussiansto prevent surface holes and artifacts, these irregularities severely de-grade geometric fidelity. To resolve these issues, we propose a geometry-aligned two-stage training procedure. In Stage 1, we employ EnhancedOccupancy Learning to produce crisp boundaries, ensuring accurate 3Dsegmentation. In Stage 2, to address the uneven distribution, we in-troduce the Adaptive Local Normal Consistency (ALNC) Loss.This mechanism acts as a “magnet,” strategically relocating outlier Gaus-sians to sparse surface regions to ensure uniform coverage. By adap-tively aligning these relocated Gaussians with local surface normals,ALNC effectively fills geometric gaps while maintaining structural co-herence. Experimentally, MagnetGS-Mesh achieves competitive or supe-rior mesh quality compared to state-of-the-art methods while simultane-ously reducing model size by substantial margins. The code is availableat https://github.com/MinsuPark0752/MagnetGS-Mesh.

Abstract:
Single-image 3D human reconstruction often suffers fromover-smoothed textures and geometric inconsistencies. While diffusionmodels improve generative quality, their reliance on multi-view synthe-sis prior to 3D reconstruction is computationally expensive and proneto view inconsistency. We propose DiGS-Avatar, which reformulates thistask as an efficient, diffusion-based UV-latent completion task, ensur-ing 3D consistency by design. To capture accurate spatial structure, weintroduce a teacher-student framework where a multi-view teacher pro-vides geometrically aligned pseudo-ground-truth latents to supervise asingle-view diffusion student. Treating this inferred latent as a robuststructural skeleton, our method injects high-level semantic features to ac-curately recover fine textural details without disrupting spatial integrity.The refined representation is then decoded into 3D Gaussian primitives.Extensive experiments demonstrate that DiGS-Avatar achieves state-of-the-art or highly competitive visual fidelity and zero-shot generalization,while reconstructing a fully animatable 3D avatar in just 0.71 seconds.Code is available at https://github.com/KLMAV-CUC/DiGS-Avatar.

Abstract:
Current dexterous grasp planners primarily optimize for phys-ical stability, focusing on whether an object can be grasped rather thanhow it should be grasped to support downstream functional tasks. How-ever, conditioning grasp synthesis on specific human grasp taxonomiestypically requires prohibitively expensive, object-annotated datasets. Toaddress these limitations, we propose CoToGrasp, a novel generativeframework that synthesizes diverse, stable grasps strictly conditioned onspecific contact topologies. To bypass the data collection bottleneck, Co-ToGrasp is trained entirely in an object-agnostic manner. We introducea feature-based canonical workspace that projects local object featuresinto a unified gripper-centric domain, effectively decoupling the semanticfunctional intent from the arbitrary object geometry. By learning the in-trinsic contact manifold of the gripper within this workspace, our modelachieves zero-shot generalization to unseen objects at inference. Exten-sive evaluations on the large-scale DexGraspNet dataset demonstratethat CoToGrasp achieves state-of-the-art performance, outperformingexisting taxonomy-guided planners. Finally, we demonstrate the physicalviability and kinematic feasibility of our synthesized contact topologieson a physical robot platform. Code is available on our project websitehttps://cea-list.github.io/cotograspweb/.

Abstract:
Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding the skeleton kinematics, severely limiting generalization, preventing cross-dataset training, and requiring complex data retargeting. We introduce EquiFusion, the first kinematics-agnostic model to solve this bottleneck, implementing a latent diffusion model with a permutation equivariant architecture. EquiFusion treats the kinematics’ connectivity as an explicit input parameter, ensuring its internal computations are inherently agnostic to joint ordering and graph structure. This novel design enables truly cross-dataset generalization to unseen kinematics and unlocks novel zero-shot directions, such as motion prediction from partial or occluded observations and targeted limb generation. EquiFusion achieves state-of-the-art results on major benchmarks, being up to 75% more compact than previous kinematics-specific methods, while achieving faster training and inference. EquiFusion thus establishes a new, flexible standard for robust human motion prediction.

Abstract:
Diffeomorphic image registration is central to medical im-age analysis, enabling anatomically consistent alignment across subjects.Most learning-based diffeomorphic methods model autonomous ODEs(ordinary differential equations) by parameterizing a stationary velocityfield and recovering deformations via scaling-and-squaring. While non-autonomous ODEs with time-dependent velocities increase expressive-ness, existing approaches rely on numerical integration to implicitly en-force flow structure that entangles model expressiveness with discretiza-tion accuracy. We propose a framework to directly learn the continuous-time solution of a non-autonomous ODE formulated as a two-parameterflow map. By enforcing cocycle consistency, a fundamental structuralproperty of time-varying flows, we learn the flow maps without timediscretization and velocity integration during training. The frameworkrecovers diffeomorphic mappings at inference using a small number ofcompositions. Our proposed framework seamlessly incorporates stan-dard registration backbones and improves alignment accuracy consis-tently across nine datasets while preserving diffeomorphic structure. No-tably, the proposed method achieves an average Dice improvement of2.1% on brain MRI benchmarks, a 12% TRE reduction on lung CT,and a 2.6% Dice gain on cardiac MRI and ultrasound datasets (https://mattkia.github.io/TPFMDIR/).

Abstract:
End-to-end pixel-space diffusion models bypass the lossy com-pression of Latent Diffusion Models (LDMs) but struggle to jointly modellow-frequency semantics and high-frequency signals in high-dimensionalspace. Existing works heavily rely on complex pixel decoders to alleviatethis issue. In this paper, we challenge this trend by revealing that thesedecoders primarily compensate for the optimization difficulties inherentto velocity prediction (v-prediction). Under the clean data paradigm (x-prediction), they are redundant. Motivated by this insight, we advocatefor simplicity over complexity and introduce PixelU, a minimalist, single-stage U-shaped Diffusion Transformer tailored for pixel space. PixelUabandons auxiliary decoders in favor of zero-cost skip connections, whichprovide an “information highway” that directly routes uncorrupted high-frequency spatial details from shallow to deep layers. To further enablethe backbone to focus exclusively on modeling low-frequency semantics,we introduce a constant-channel spatial down-sampling mechanism asa natural low-pass filter, which compresses deep features into a com-pact, low-frequency semantic manifold. Extensive experiments demon-strate that this decoupling of frequencies could outperform the strongbaseline (JiT-G) with only about 1/3 of its computation cost. On Im-ageNet 256 × 256 and 512 × 512, PixelU achieves FID of 1.63 and 1.92respectively, surpassing recent pixel-space methods and establishing asimple yet powerful new paradigm for end-to-end diffusion models. Codewill be available at https://github.com/gzp6688/PixelU.

Abstract:
Automating filament tracing in Cryo-Electron Microscopy(Cryo-EM) is essential for 3D helical reconstruction but challenged byintersecting topologies and extremely low Signal-to-Noise Ratios (SNR =σs2 /σn2 < 0.1 or -10 dB). Existing paradigms fail: pixel-wise segmenterssuffer from severe topological fracturing, box-based detectors face ghostcenter drift, sequential trackers derail due to error accumulation, and tra-ditional active contours collapse under artificial closed-curve constraints.To resolve these bottlenecks, we present FPicker, the first topology-guided framework reconciling these incompatibilities. It unifies percep-tion via a center-endpoint representation and an open-curve evolutionmodule to explicitly model non-cyclic connectivity. On simulated bench-marks, FPicker outperforms top baselines by over 40% relative gain inmean spatio-angular precision (mSAP) and reduces topological gap ratesby over 60% under extreme noise (−20 dB). By learning intrinsic phys-ical geometry rather than local texture, FPicker demonstrates strongpotential as a resilient geometric backbone. Its zero-shot performance onthe real-world EMPIAR dataset exhibits robust topological resistance,achieving a state-of-the-art 82.9% mSAP upon fine-tuning. Our resultsalso suggest modeling physical priors is a highly robust path towardbridging the sim-to-real gap in signal-starved scientific imaging. The codeis publicly available at: https://github.com/tomzhaosky/FPicker.

Abstract:
Recent advancements in 3D generation have significantly en-hanced the fidelity and geometric details of synthesized 3D assets. How-ever, due to the inherent ambiguity of single-view input and the lackof robust global structural priors–caused by limited 3D training data–the unseen regions generated by existing models remain stochastic anddifficult to control. This often results in geometries that are either phys-ically implausible or misaligned with user intent. In this paper, we pro-pose Know3D, a novel framework designed to incorporate rich knowl-edge from Multimodal Large Language Models (MLLMs) into 3D gen-eration processes. By leveraging latent hidden-state injection, Know3Dsupports language-controllable generation of the back-view for 3D as-sets. We utilize a VLM-diffusion-based architecture: the Vision LanguageModel (VLM) is used to provide high-level semantic understanding, whilethe diffusion model serves as a bridge, transferring semantic knowledgeinto the 3D generation model. Extensive experiments demonstrate thatKnow3D effectively bridges the gap between abstract textual instruc-tions and the geometric reconstruction of invisible regions. By trans-forming the traditionally stochastic back-view hallucination into a se-mantically controllable process, Know3D offers a promising direction forhighly plausible and user-friendly 3D generation in the future. Projectpage: https://xishuxishu.github.io/Know3D.github.io/.

Abstract:
Large 3D foundation models such as MASt3R achieve state-of-the-art stereo reconstruction but are computationally demanding fordeployment under strict hardware constraints—a critical limitation indomains such as planetary exploration, where onboard computing isseverely restricted. We study how far such models can be compressedthrough knowledge distillation, using lunar stereo reconstruction as achallenging and practically relevant case study. Starting from a 688M-parameter MASt3R teacher fine-tuned on lunar imagery, we distill itsdense geometric predictions into a family of lightweight students span-ning different encoder types (CNN vs. ViT), decoder widths and depths,and training strategies. To bridge the dimensional mismatch betweenteacher and student, we propose a structured SVD-based initializationthat projects the teacher’s decoder weights into the student’s smallerlatent space, yielding a warm start that significantly improves conver-gence and final performance. Based on our results on lunar data, wecan obtain a distilled student that retains most of teacher’s reconstruc-tion accuracy while reducing the model size up to 7×, and even out-performs a baseline trained directly with sparse ground-truth annota-tions. Beyond compression, our study highlights both principles andpractical insights for distilling geometric foundation models: a convo-lutional encoder underperforms transformer-based alternatives (thoughpretraining availability remains a confounding factor), preserving en-coder capacity is more critical than maintaining a large decoder, feature-level distillation consistently outperforms output-only supervision, andSVD-based initialization improves optimisation stability. These findingsprovide practical guidelines for deploying 3D reconstruction models inresource-constrained environments. Code is publicly available at https://clementinegrethen.github.io/publications/ECCV.html.

Abstract:
Most sign language translation (SLT) methods focus on isolated native sign–spoken pairs (e.g., American Sign Language–English). Extending language-specific SLT models to multilingual translation would improve accessibility by enabling communication across diverse sign and spoken language communities. However, existing multilingual SLT approaches still struggle to learn a unified model that minimizes crosslingual conflicts while capturing shared cross-lingual semantics and preserving language-specific variations across different sign languages. Therefore, we propose Q-BridgeNet, a unified framework for multilingual SLT that jointly mitigates cross-lingual conflicts across both the sign language and spoken language sides. On the sign language side, QBridgeNet learns discrete Q-units via adaptive segmentation and residual vector quantization: a shared base codebook provides language-agnostic semantic primitives, while language-specific residual codebooks refine heterogeneous signing semantics. On the spoken language side, a multilingual LLM is fine-tuned to operate in the Q-unit space, leveraging cross-lingual priors to enable a unified SLT model. Experiments on PHOENIX14T, How2Sign, and CSL-Daily show that Q-BridgeNet effectively mitigates cross-lingual conflicts, achieving state-of-the-art performance on native sign–spoken pairs while also demonstrating strong generalization to non-native pairs. Our source code is publicly available at: https://github.com/FengLiQ/Q-BridgeNet

Abstract:
Realistic handwritten text generation plays an important rolein numerous applications, such as font design, biometric authentication,and robotic calligraphy. Existing methods are typically divided into twoindependent paradigms: online approaches that estimate handwritingtrajectories and offline approaches that synthesize realistic handwritingimages. While online models capture structural and temporal dynamics,they often lack fine-grained textures, whereas offline models reproducerealistic appearance but discard stroke order. However, unifying onlineand offline models remains challenging due to (1) the lack of an explicitphysical model linking stroke kinematics to pixel-level appearance and(2) the absence of paired trajectory–image datasets. Moreover, enablingend-to-end learning requires a differentiable rendering process acrossmotion and appearance domains. To address these challenges, we proposea compact physical brush model that bridges stroke dynamics and visualappearance, together with a differentiable rendering module that convertsstroke trajectories into stylized images. By integrating these components,we propose a unified online–offline handwriting generation frameworkvia differentiable brush rendering. The proposed framework consistsof four core modules: 1) a text-to-stroke generator that predicts thetarget stroke conditioned on the given text and style image, 2) a brushparameter observer that extracts brush model parameters from stylereferences, 3) a differentiable brush renderer that maps a stroke sequenceand physical brush parameters into a handwritten image, and 4) a zero-shot image refiner that refines rendered images via diffusion models.Extensive experiments and real-world robotic calligraphy demonstrationsvalidate our approach, achieving both structural and visual fidelity.

Abstract:
Accurate LiDAR-camera calibration is essential for robust multi-modal perception. Targetless approaches avoid manual setup but remain limited by the scarcity of discriminative cross-modal features. Recent methods address this by reconstructing the scene within a differentiable model, enabling extrinsic optimization through dense photometric supervision. Among these, 3D Gaussian Splatting (3DGS) has been widely adopted as a geometric proxy that bridges LiDAR and camera within a single differentiable framework. However, since 3DGS was originally designed for novel view synthesis, existing methods tend to prioritize rendering quality, causing the proxy geometry to drift from the true LiDAR structure. We propose a framework that preserves the metric geometry of the Gaussian proxy by aggregating multi-view LiDAR observations for dense depth supervision and blocking photometric gradients from updating the Gaussian spatial parameters. We validate our method on public driving datasets, where it consistently outperforms existing targetless methods in calibration accuracy. Code and dataset are available at https://github.com/AIRLABkhu/GeoP-Calib

Abstract:
We present Wid3R, a feed-forward neural network for multi-view visual geometry reconstruction that supports wide field-of-viewcamera models. Unlike existing methods that assume rectified or pinholeinputs, Wid3R directly models wide-angle imagery without explicit cali-bration or undistortion. Our approach leverages a ray-based representa-tion with spherical harmonics and introduces a novel camera model tokento enable distortion-aware reconstruction. To the best of our knowledge,Wid3R is the first multi-frame feed-forward 3D reconstruction methodthat supports 360◦ imagery. Moreover, we show that conditioning on di-verse camera types improves generalization to 360◦ scenes and alleviatesdata sparsity issues. Wid3R achieves significant performance gains, im-proving AUC@30◦ by up to +33.67 on Zip-NeRF (fisheye) and +77.33 onStanford2D3D (360). Project Page: https://jdk9405.github.io/Wid3R/

Abstract:
We present StructSplat, a feed-forward and generalizable3D Gaussian reconstruction framework that operates directly on uncal-ibrated images without requiring camera parameters. Existing methodseither rely on per-scene optimization or assume known camera poses,and often entangle geometry and appearance within a unified backbone,limiting reconstruction fidelity and generalization. Our key idea is toadopt a structured representation that organizes geometry, seman-tic, and texture cues with explicit roles in the reconstruction process.Specifically, we introduce a pixel-aligned feature injection mechanismto enable accurate texture modeling from 2D observations, incorporatesemantic-aware priors to improve global consistency, and design a cameraalignment strategy to prevent information leakage and improve general-ization. Experiments show that our method significantly outperformsprior approaches on challenging benchmarks. On DL3DV, our methodachieves 28.045 PSNR, surpassing AnySplat (22.377) by +5.67 dB. Incross-dataset evaluation, our method achieves +1.94 dB over AnySplaton ACID and +1.72 dB on RealEstate10K.

Abstract:
Gaussian Splatting (GS) has emerged as a dominating tech-nique for image rendering and has quickly been adapted for the X-ray Computed Tomography (CT) reconstruction task. However, despiteits growing popularity, the benefits of GS are typically not substantialenough to motivate a transition from well-established reconstruction al-gorithms. This paper addresses the most significant remaining limita-tions of the GS-based approach by introducing FaCT-GS, a frameworkfor fast and flexible CT reconstruction. Enabled by an in-depth optimiza-tion of the voxelization and rasterization pipelines, our new method issignificantly faster than its predecessors and scales well with projectionand output volume size. Furthermore, the improved voxelization enablesrapid fitting of Gaussians to pre-existing volumes, which can serve as aprior for warm-starting the reconstruction, or simply as an alternative,compressed representation. FaCT-GS is over 4× faster than the State ofthe Art GS CT reconstruction on standard 5122 projections, and over13× faster on 2k projections. Implementation and data available through:https://papieta.github.io/fact-gs/.

Abstract:
Road surfaces serve as the only contact medium between ve-hicle wheels or robot feet and the physical world. Reconstructing the roadsurface is crucial for unmanned vehicles and mobile robots. Recent stud-ies on Neural Radiance Fields (NeRF) and Gaussian Splatting (GS) haveachieved remarkable results in scene reconstruction. However, they typi-cally rely on multi-view image inputs and require prolonged optimizationtimes. In this paper, we propose BEV-GS, a real-time single-frame roadsurface reconstruction method based on feed-forward Gaussian splatting.BEV-GS consists of a prediction module and a rendering module. Theprediction module introduces separate geometry and texture networksfollowing the Bird’s-Eye-View (BEV) paradigm. Geometric and textureparameters are directly estimated from a single frame, avoiding per-sceneoptimization. In the rendering module, we utilize grid Gaussian for roadsurface representation and novel view synthesis, which better aligns withroad surface characteristics. Our method achieves state-of-the-art per-formance on the real-world dataset RSRD. The road elevation error isreduced to 1.73 cm, and the PSNR of novel view synthesis reaches 28.36dB. The prediction and rendering FPS are 26 and 2061, respectively,enabling high-accuracy and real-time applications. Our code is availableat https://github.com/IRMVLab/BEV-GS.

Abstract:
Existing Vision Foundation Model (VFM)-based iterativestereo pipelines under-exploit three information pathways: multi-scalebackbone features are collapsed into single-level correlations, geometricpriors remain untapped at initialization, and context propagates only lo-cally. These gaps widen under degraded photometric cues, making under-water scenes a stringent generalization test. To address this, we proposeLinStereo, built upon Depth Anything V3, whose core is a Position-Aware Linear Attention (PALA) module that replaces local recurrencewith global aggregation at linear cost, propagating reliable estimatesfrom well-matched regions into degraded areas while preserving dispar-ity structure. PALA is made effective by two enabling components: Hi-erarchical Semantic Cost Volumes (HSCV), which supply scale-alignedcorrelations from the VFM feature hierarchy, and a Depth Prior Ini-tialization (DPI) that converts monocular depth into a metrically cali-brated warm start. LinStereo achieves state-of-the-art-level accuracy onstandard benchmarks and strong cross-domain generalization, particu-larly on underwater scene where severe photometric degradation makesstereo matching particularly challenging, attaining the best overall ac-curacy with consistent gains (28% lower AbsRel on TartanAir-UW,26% on SQUID, a real-world underwater dataset). Code is availableat https://u7079256.github.io/LinStereo/.

Abstract:
Despite rapid progress in learning-based stereo matching,high accuracy is often achieved at the cost of heavy backbones andcomputationally intensive 3D cost volume processing, resulting in sub-stantial memory and runtime overhead. More critically, these methodsfrequently struggle to generalize across domains, limiting their practi-cal deployment. We present LiteMatch, a lightweight stereo matchingframework that achieves strong zero-shot generalization through costvolume stabilization-without expensive 3D convolutions. LiteMatch em-ploys two complementary encoders: a Cross-View Correspondence En-coder (CVCE) to capture global cross-view interactions, and a High-Frequency Encoder (HFE) that enhances fine structural details via FFT-based frequency cues. To stabilize the cost volume, we introduce the CostVolume Consistency Loss (CVC-Loss), a voxel-wise binary cross-entropyobjective applied to softmax-normalized cost distributions. By encour-aging sharp and unimodal disparity probabilities, CVC-Loss promotesstable cost distributions and enables rapid convergence. A lightweightrefinement module further produces sharp full-resolution disparities withlow-iteration updates, avoiding heavy recurrent refinement. With a flexi-ble design ranging from 3.36M to 9.58M parameters, LiteMatch achievesexceptional zero-shot generalization, delivering competitive EPE and D1performance across Scene Flow, KITTI, Middlebury, ETH3D, and Driv-ingStereo. Our results establish that lightweight architectures can indeedgeneralize across domains without sacrificing accuracy. Code

Abstract:
Feed-forward 3D Gaussian Splatting (3DGS) has emergedas a highly effective solution for novel view synthesis. Existing meth-ods predominantly rely on a pixel-aligned Gaussian prediction paradigm,where each 2D pixel is mapped to a 3D Gaussian. We rethink this widelyadopted formulation and identify several inherent limitations: it rendersthe reconstructed 3D models heavily dependent on the number of inputviews, leads to view-biased density distributions, and introduces align-ment errors, particularly when source views contain occlusions or lowtexture. To address these challenges, we introduce VolSplat, a new multi-view feed-forward paradigm that replaces pixel alignment with voxel-aligned Gaussians. By directly predicting Gaussians from a predicted3D voxel grid, it overcomes pixel alignment’s reliance on error-prone 2Dfeature matching, ensuring robust multi-view consistency. Furthermore,it enables adaptive control over density based on 3D scene complex-ity, yielding more faithful Gaussians, improved geometric consistency,and enhanced novel-view rendering quality. Experiments on widely usedbenchmarks demonstrate that VolSplat achieves state-of-the-art perfor-mance, while producing more plausible and view-consistent results.

Abstract:
Feed-forward 3D Gaussian Splatting methods enable single-pass reconstruction and real-time rendering. However, they typicallyadopt rigid pixel-to-Gaussian or voxel-to-Gaussian pipelines that uni-formly allocate Gaussians, leading to redundant Gaussians across views.Moreover, they lack an effective mechanism to control the total numberof Gaussians while maintaining reconstruction fidelity. To address theselimitations, we present F4 Splat, which performs Feed-Forward predic-tive densification for Feed-Forward 3D Gaussian Splatting, introduc-ing a densification-score-guided allocation strategy that adaptively dis-tributes Gaussians according to spatial complexity and multi-view over-lap. Our model predicts per-region densification scores to estimate therequired Gaussian density and allows explicit control over the final Gaus-sian budget without retraining. This spatially adaptive allocation reducesredundancy in simple regions and minimizes duplicate Gaussians acrossoverlapping views, producing compact yet high-quality 3D representa-tions. Extensive experiments demonstrate that our model achieves su-perior novel-view synthesis performance compared to prior uncalibratedfeed-forward methods, while using significantly fewer Gaussians.

Abstract:
Recent 4D Gaussian Splatting (4DGS) methods achieve im-pressive dynamic scene reconstruction but often rely on piecewise linearvelocity approximations and short temporal windows. This disjointedmodeling leads to severe temporal fragmentation, forcing primitives tobe repeatedly eliminated and regenerated to track complex nonlinear dy-namics. This makeshift approximation eliminates the long-term temporalidentity of objects and causes an inevitable proliferation of Gaussians,hindering scalability to extended video sequences. To address this, wepropose TRiGS, a novel 4D representation that utilizes unified, contin-uous geometric transformations. By integrating SE(3) transformations,hierarchical Bézier residuals, and learnable local anchors, TRiGS mod-els geometrically consistent rigid motions for individual primitives. Thiscontinuous formulation preserves temporal identity and effectively mit-igates unbounded memory growth. Extensive experiments demonstratethat TRiGS achieves high fidelity rendering on standard benchmarkswhile uniquely scaling to extended video sequences (e.g., 600 to 1200frames) without severe memory bottlenecks, significantly outperformingprior works in temporal stability.

Abstract:
Recent advances in 3D Gaussian Splatting have demonstratedunprecedented success in novel view synthesis. However, the substantialinference and storage overhead driven by high-order Spherical Harmon-ics (SH) are primary bottlenecks for mobile platforms. In this paper,we present Flux-GS, a real-time Gaussian Splatting method designedto achieve high-fidelity rendering with significantly reduced overhead forresource-constrained mobile platforms. We first propose a Monte CarloSpecular Energy Aggregator, sampling third-order radiance residualsand aggregating specular energy into a compact latent space. In thisway, our method effectively preserves visually salient lighting features inlower-order bands without expensive distillation or pre-training. To miti-gate the high-frequency details lost during compression, we introduce anAttribute-Conditioned SH Enhancement module. This module predictsGaussian-aware offsets based on intrinsic Gaussian attributes, which en-hance the first-order SH representation prior to inference, without extrainference costs. Furthermore, the original single-view gradient-based den-sification is prone to producing excessive Gaussians and overfitting to aa. Rotation Opacity Opacity Opacity b.Scale 7% 2% 4% 6%5%PositionRotation SH5%Rotation 35%18%23% 3DGS Third-orderScaleSH SH Rest52%13%SH ScalePosition Position81% 18%13% 18%61%↓ 26%↓ First-orderThird-order SH First-order SH Flux-GS Ground Truth Flux-GS SH RestFig. 2: Gaussian parameter distribution and Spherical Harmonic fidelityanalysis. a. Per Gaussian memory footprint across 3DGS variants. Flux-GS achievessignificant compression (61% and 26% reductions) by optimizing Spherical Harmonics(SH) coefficients and decoupling SH into the base and view-independent components.b. Qualitative comparison demonstrates that Flux-GS with only first-order SH canrender high-fidelity high-frequency details comparable to 3DGS.certain view. We address these limitations by proposing a Multi-viewAlpha-based Densification and Pruning strategy. By leveraging multi-view guidance, we ensure multi-view structure consistency and the pre-cise removal of redundant primitives. Extensive experiments demonstratethat Flux-GS achieves substantial parameter reduction while maintain-ing competitive visual quality, offering a robust and scalable solution forreal-time mobile rendering. Code: https://xiaobiaodu.github.io/flux-gs-project/.

Abstract:
In this paper, we address the challenging problem of 4D re-construction from sparse-view videos. This setup usually relies on monoc-ular depth estimation to provide priors for the reconstruction model. Akey challenge arises from limited cross-view overlap and temporal vari-ation, making monocular depth predictions inconsistent across viewsand time. Existing methods align spatial and temporal dimensions inseparate stages, requiring foreground segmentation masks while failingto leverage temporal cues for cross-view alignment. Contrary to thesemethods, we propose a unified spatial-temporal depth alignment frame-work that jointly resolves cross-view and cross-time inconsistencies with-out distinguishing foreground/background. Our method represents depthmaps across views and time as a set of spatio-temporal neural fields.This representation not only yields fast convergence, but also capturesspatio-temporal correlation among depth maps implicitly, without de-pendence on external segmentation/tracking models. We also propose amulti-view depth-order loss while leveraging the classic scale-and-shift-invariant loss to further improve the final depth quality. The aligneddepths initialize and supervise Gaussian splatting models for 4D recon-struction. Experiments on Ego-Exo4D and EgoHuman demonstrate thatour improved depth alignment substantially benefits dynamic Gaussian-splatting-based reconstruction methods for novel-time/view synthesis andgeometry accuracy/consistency.

Abstract:
RGBT tracking on edge devices is often constrained by lim-ited computational power and data transmission bandwidth, necessitat-ing the use of low-resolution inputs in real-world deployments. However,such resolution reduction typically leads to severe semantic loss and per-formance degradation. In this paper, we propose GAP-Track, an efficientframework that bridges the resolution gap by enabling high-precisiontracking of low-resolution inputs. Specifically, we introduce a hierarchicalknowledge distillation strategy that guides the low-resolution student bymulti-level teacher supervision. To recover the missing semantic details,a generative reconstruction module trained by a random masking strat-egy is introduced to strengthen the student’s feature representation byforcing the backbone to recover fine-grained structural information fromlow resolution data. Furthermore, we adapt a polar-geometric sensitivityloss that transforms the bounding box regression into a polar coordinatesystem, effectively mitigating localization ambiguities in extremely low-resolution scenarios by providing higher sensitivity to center-point driftand shape deformations. Extensive experiments on the comprehensiveLasHeR and RGBT234 datasets demonstrate that our method achievesstate-of-the-art performance. Notably, GAP-Track delivers superior pre-cision at 1/2 resolution and continues to significantly outperform thebaseline even at 1/4 resolution, ensuring a robust balance between track-ing accuracy and inference efficiency. The source code will be availableat https://github.com/shijiahahaha/GAPTrack.

Abstract:
Uncalibrated volumetric video streaming for human reconstruction is essential for holographic communication and AR/VR, yet remains challenging due to the need for temporal consistency and computational efficiency from sparse-view inputs. Existing methods rely on perscene optimization or calibrated cameras, while recent feed-forward models are limited to low-resolution (0.5K) single-frame synthesis. We present HiReFF, a feed-forward method for 2K-resolution 360° human video reconstruction from uncalibrated sparse-view videos. Our framework decomposes the problem into two key tasks: foreground 3D Gaussian reconstruction from sparse-view videos (four views separated by 90°) and computationally efficient high-resolution synthesis. To enable the former, we propose Scale-synchronized Camera Calibration to resolve scale ambiguity for multi-view supervision, and Gaussian-wise Foreground Masking to reconstruct clean foregrounds by modulating Gaussian parameters. For efficient high-resolution synthesis, our High-resolution Side-tuning achieves 2K rendering by augmenting the Gaussian head with supplementary features while keeping the backbone at 0.5K, drastically reducing computational overhead. Experiments demonstrate that HiReFF significantly outperforms existing methods in high-resolution streaming volumetric video reconstruction. https://iridescentjiang.github.io/ HiReFF

Abstract:
Generative novel view synthesis faces a fundamental dilemma:geometric priors provide spatial alignment but become sparse and inaccu-rate under view changes, while appearance priors offer visual fidelity butlack geometric correspondence. Existing methods either propagate geo-metric errors throughout generation or suffer from signal conflicts whenfusing both statically. We introduce MoCam, which employs structureddenoising dynamics to orchestrate a coordinated progression from geom-etry to appearance within the diffusion process. MoCam first leveragesgeometric priors in early stages to anchor coarse structures and toleratetheir incompleteness, then switches to appearance priors in later stagesto actively correct geometric errors and refine details. This design natu-rally unifies static and dynamic view synthesis by temporally decouplinggeometric alignment and appearance refinement within the diffusion pro-cess. Experiments demonstrate that MoCam significantly outperformsprior methods, particularly when point clouds contain severe holes ordistortions, achieving robust geometry-appearance disentanglement.

Abstract:
Pre-trained Vision Foundation Models (VFMs) have becomecentral to modern computer vision due to their powerful semantic rep-resentations and strong generalization ability. However, their patchifiedor pooled outputs are inherently low-resolution, limiting their effective-ness in tasks requiring fine-grained, pixel-level reasoning. Existing fea-ture upsampling approaches either degrade semantic fidelity or rely onVFM-specific retraining and heavy architectures, hindering efficiency andscalability. To address these challenges, we propose RaysUp, an ultra-lightweight, task-agnostic, and VFM-agnostic feature upsampling frame-work that reconstructs high-resolution feature maps at arbitrary resolu-tions. Unlike conventional 2D interpolation or attention-based schemes,RaysUp lifts feature reconstruction into a geometry-aware ray domain.Specifically, we introduce a Spatially Decoupled Guidance Encoder fordirection-aware guidance encoding, an Any-Resolution Cross-Attentionmechanism for resolution-flexible reconstruction, and a novel Ray Posi-tional Encoding (RayPE) that injects implicit 3D geometric priors via6D Plücker ray coordinates. Finally, A Geometry-Aware NeighborhoodAttention module further ensures content-adaptive bilateral aggregationwhile preserving geometric consistency. Extensive experiments across di-verse dense prediction tasks demonstrate that RaysUp achieves state-of-the-art performance while using only 16% of the parameters of AnyUpand delivering approximately 7× faster inference. These results highlighta substantially improved accuracy–efficiency trade-off and establish Ray-sUp as a practical and scalable solution for universal feature upsampling.Code is available at https://github.com/MAP-RaysUp/RaysUp.

Abstract:
We propose HeadsUp, a scalable feed-forward method forreconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method employs an efficient encoder-decoder archi-tecture that compresses input views into a compact latent representation.This latent representation is then decoded into a set of UV-parameterized3D Gaussians anchored to a neutral head template. This UV represen-tation decouples the number of 3D Gaussians from the number andresolution of input images, enabling training with many high-resolutioninput views. We train and evaluate our model on an internal dataset withmore than 10 000 subjects, which is an order of magnitude larger than ex-isting multi-view human head datasets. HeadsUp achieves state-of-the-artreconstruction quality and generalizes to novel identities without test-timeoptimization. We extensively analyze the scaling behavior of our modelacross identities, views, and model capacity, revealing practical insightsfor quality-compute trade-offs. Finally, we highlight the strength of ourlatent space by showcasing two downstream applications: generating novel3D identities and animating the 3D heads with expression blendshapes.

Abstract:
In this paper, we propose RoomPlanner, the first fully au-tomatic 3D room generation framework for painlessly creating realisticindoor scenes with only short text as input. Without any manual layoutdesign or panoramic image guidance, our framework can generate explicitlayout criteria for rational spatial placement. We begin by introducing ahierarchical structure of language-driven agent planners that can auto-matically parse short and ambiguous prompts into detailed scene descrip-tions. These descriptions include raw spatial and semantic attributes foreach object and the background, which are then used to initialize 3Dpoint clouds. To position objects within bounded environments, we im-plement two arrangement constraints that iteratively optimize spatialarrangements, ensuring a collision-free and accessible layout solution.In the final rendering stage, we propose a novel ReachView Samplingstrategy for camera trajectory, along with the Interval Timestep FlowSampling (ITFS) strategy, to efficiently optimize the coarse 3D Gaussianscene representation. These approaches help reduce the total generationtime to under 30 minutes. Extensive experiments demonstrate that ourmethod can produce geometrically rational 3D indoor scenes, surpass-ing prior approaches in both rendering speed and visual quality whilepreserving editability. The code will be available at https://kaitlina-s.github.io/RoomPlanner/.

Abstract:
Recent works in novel view synthesis, e.g., Neural RadianceField (NeRF) and 3D Gaussian Splatting (3DGS), have significantly ad-vanced rendering quality and efficiency. However, existing Gaussian-basednovel view synthesis methods typically follow a single-view optimizationparadigm. We observed that this optimization paradigm suffers fromunstable gradients, leading to suboptimal rendering quality. To tackle thisissue, we present a novel multi-view regulated Gaussian Splatting (MVGS)that fully leverages a multi-view coherent (MVC) constraint throughoutthe optimization process. Specifically, our proposed MVC enhances 3DGaussian multi-view consistency and thus ensures smoother gradient up-dates. Furthermore, since single-scale training usually leads to suboptimalsolutions, we propose a cross-intrinsic guidance scheme in a coarse-to-fine manner to improve the convergence of multi-view optimization in3DGS. In particular, by incorporating more multi-view images at the lowresolution, we can optimize 3D Gaussians with more comprehensive per-spectives. Then, finer-scale Gaussians are initialized by coarsely estimatedones instead of optimizing full-scale 3D Gaussians from scratch. Moreover,we found that 3D Gaussians usually struggle to fit 2D training views withminimal overlap. Thus, we propose a novel multi-view cross-ray densifica-tion strategy, where 3D Gaussians are dynamically split to accommodatedrastic viewpoint variations in the multi-view optimization process. Inthis way, the multi-view consistency can be further improved. Notably,our proposed MVGS method is a plug-and-play optimizer. Extensiveexperiments across various tasks demonstrate that our proposed MVGSimproves existing Gaussian-based methods and achieves state-of-the-artperformance. Project Page: https://xiaobiaodu.github.io/mvgs-project/

Abstract:
Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success. However, scaling them to long image sequences remains challenging, as the quadratic complexity of cross-view global attention quickly becomes the dominant computational bottleneck. While recent efforts attempt to improve efficiency through compressed or sparse attention, they fail to fully exploit the inherent sparsity and dynamic behavior of global attention. In this work, we present a comprehensive analysis of global attention across multiple F3R transformers and reveal that attention patterns are highly heterogeneous, dynamic, and extremely sparse across layers and attention heads. Motivated by these findings, we propose SAF3R, a training-free dynamic sparse attention framework tailored to F3R transformers. SAF3R integrates tailored sparse attention mechanisms with offline head profiling and an efficient online adaptation strategy to match input-dependent attention behaviors. Extensive experiments demonstrate that SAF3R achieves high sparsity ratios while preserving camera pose estimation and 3D reconstruction quality, translating into substantial end-to-end speedup on F3R transformers compared to existing methods. Code is available at https://github.com/jndeng/SAF3R.

Abstract:
Estimating the 3D trajectory of every pixel from a monocularvideo is crucial and promising for a comprehensive understanding of the3D dynamics of videos. Recent monocular 3D tracking works demon-strate impressive performance, but are limited to either tracking sparsepoints on the first frame or a slow optimization-based framework fordense tracking. In this paper, we propose a feedforward model, calledTrack4World, enabling an efficient holistic 3D tracking of every pixel inthe world-centric coordinate system. Built on the global 3D scene repre-sentation encoded by a VGGT-style ViT, Track4World applies a novel3D correlation scheme to simultaneously estimate the pixel-wise 2D and3D dense flow between arbitrary frame pairs. The estimated scene flow,along with the reconstructed 3D geometry, enables subsequent efficient3D tracking of every pixel of this video. Extensive experiments on multiplebenchmarks demonstrate that our approach consistently outperforms ex-isting methods in 2D/3D flow estimation and 3D tracking, highlighting itsrobustness and scalability for real-world 4D reconstruction tasks. Projectpage: https://jiah-cloud.github.io/Track4World.github.io/.

Abstract:
Large Multimodal Models (LMMs) have achieved remark-able success on images and short videos, yet scaling them to long videosremains challenging due to frame-centric tokenization and limited con-text windows. 3D geometry provides a natural compression mechanismfor visual streams: depth and camera pose enable observations from mul-tiple views and time steps to be fused into a persistent, world-aligned rep-resentation. While recent 3D LMMs leverage geometry-aware representa-tions to improve spatial reasoning, they continue to lag behind specialist3D perception systems on grounding and segmentation tasks. We arguethat a key limitation is geometry-aware decoding: existing methods com-municate 3D predictions through language tokens, proposal selection, orlightweight grounding queries, creating a bottleneck between languagereasoning and dense geometric prediction. Building on these insights, weintroduce Qwen-3D, a geometry-aware LMM that compresses visual in-formation within the Qwen backbone using multi-view geometric cues,enabling efficient long-horizon visual reasoning over static scenes. Qwen-3D augments visual tokens with 3D Rotary Positional Embeddings, al-lowing attention to operate directly in 3D scene space rather than acrossindependent image frames and thereby facilitating scalable cross-viewand temporal reasoning. To bridge language and geometry, Qwen-3D in-corporates a query-based segmentation decoder that grounds languagedirectly in the underlying 3D scene representation, unifying referentialgrounding, instance segmentation, and visual question answering acrossboth images and videos. Across a diverse set of benchmarks, Qwen-3Dsurpasses existing 3D LMMs and outperforms several large proprietary2D models. Notably, Qwen-3D achieves these improvements while main-taining strong performance on standard 2D vision–language benchmarksby jointly training on 2D and 3D data. Our code and checkpoints can befound at the project website https://qwen-3d.github.io/.

Abstract:
Existing diffusion-based 3D scene generation methods pri-marily operate in 2D image/video latent spaces, which makes maintain-ing cross-view appearance and geometric consistency inherently chal-lenging. To bridge this gap, we present OneWorld, a framework thatperforms diffusion directly within a coherent 3D representation space.Central to our approach is the 3D Unified Representation Autoencoder(3D-URAE); it leverages pretrained 3D foundation models and augmentstheir geometry-centric nature by injecting appearance and distilling se-mantics into a unified 3D latent space. Furthermore, we introduce token-level Cross-View-Correspondence (CVC) consistency loss to explicitly en-force structural alignment across views, and propose Manifold-Drift Forc-ing (MDF) to mitigate train–inference exposure bias and shape a robust3D manifold by mixing drifted and original representations. Comprehen-sive experiments demonstrate that OneWorld generates high-quality 3Dscenes with superior cross-view consistency compared to state-of-the-art2D-based methods.

Abstract:
Recent advances in sparse voxel representations have significantly improved the quality of 3D content generation, enabling highresolution modeling with fine-grained geometry. However, existing frameworks suffer from severe computational inefficiencies due to the quadratic complexity of attention mechanisms in their two-stage diffusion pipelines. In this work, we propose Ultra3D, an efficient 3D generation framework that significantly accelerates sparse voxel modeling without compromising quality. Our method leverages the compact VecSet representation to efficiently generate a coarse object layout in the first stage, reducing token count and accelerating voxel coordinate prediction. To refine pervoxel latent features in the second stage, we introduce Part Attention, a geometry-aware localized attention mechanism that restricts attention computation within semantically consistent part regions. This design preserves structural continuity while avoiding unnecessary global attention, achieving up to a 6.7× speed-up in latent generation. To support this mechanism, we construct a scalable part annotation pipeline that converts raw meshes into part-labeled sparse voxels. Extensive experiments demonstrate that Ultra3D supports high-resolution 3D generation at 1024 resolution and achieves state-of-the-art performance in both visual fidelity and user preference.

Abstract:
High-quality world-scale 3D data is scarce, making completeand generalizable 3D world generation difficult. Existing approaches typi-cally rely on either generated images/videos or domain-specific 3D worlddatasets, resulting in worlds that are often incomplete or restricted tonarrow domains. In this paper, we present Map2World, a map-conditionedframework that converts semantic layouts into complete and generalizablelarge-scale 3D worlds. Given a segment map with per-region text prompts,Map2World uses the map as a controllable world-level interface and lever-ages TRELLIS, a powerful pretrained 3D asset generator, as a general3D prior. To scale asset-level priors to world-level synthesis, Map2Worldenables arbitrary spatial expansion via MultiDiffusion, controls globalscale through initial noise optimization, and enhances local geometry andappearance with a dedicated enhancer. Experiments demonstrate thatMap2World generates complete, layout-controllable, scale-consistent, anddetailed 3D worlds across diverse layouts and semantics.

Abstract:
LiDAR point clouds are fundamental to various applications, yet the extreme sparsity of high-precision geometric details hinders efficient context modeling, thereby limiting the compression speed and performance of existing methods. To address this challenge, we propose a compact representation for efficient predictive lossless coding. Our framework comprises two lightweight modules. First, the Geometry Re-Densification Module iteratively densifies encoded sparse geometry, extracts features at a dense scale, and then sparsifies the features for predictive coding. This module avoids costly computation on highly sparse details while maintaining a lightweight prediction head. Second, the Cross-scale Feature Propagation Module leverages occupancy cues from multiple resolution levels to guide hierarchical feature propagation, enabling information sharing across scales and reducing redundant feature extraction. Additionally, we introduce an integer-only inference pipeline to enable bit-exact cross-platform consistency, which avoids the entropy-coding collapse observed in existing neural compression methods and further accelerates coding. Experiments demonstrate competitive compression performance at real-time speed. Code is available at https://github.com/pengpeng-yu/FastPCC.

Abstract:
Forecasting longitudinal brain lesion evolution is critical for disease monitoring and treatment planning. Existing approaches typically learn a direct mapping from a baseline image to a future observation, without explicitly modeling the physical mechanisms underlying the lesion progression. Such an entangled modeling of structural deformation and image intensity variation limits physical plausibility, model generalization, and interpretability. To address this, we propose PDF, a Physics-grounded Disentangled Flow matching framework for longitudinal brain disease forecasting. We explicitly decompose the longitudinal modeling of lesion growth into two processes, each learned by a dedicated flow matching network: morphology evolution, which captures lesion growth and structural deformation; and intensity evolution, which models signal changes driven by variations in lesion concentration. To enforce physics-grounded constraints, we introduce a PDE-regularized loss based on lesion growth dynamics, that enforces a diffusion-reactionadvection formulation for morphological evolution. Experiments on three public longitudinal datasets spanning diverse brain diseases demonstrate state-of-the-art performance, validating the effectiveness of the disentangled modeling framework and physics-grounded learning design. Code is publicly available at https://github.com/jhuldr/PDF.

Abstract:
Medical image segmentation relies on the ability of encoder-decoderarchitectures to translate rich feature representations into accurate pixel-level pre-dictions under challenging conditions such as low contrast, structural ambigu-ity, and scale variability. While recent advances in large-scale pretraining andtransformer-based encoders have substantially improved feature extraction, seg-mentation accuracy remains constrained by decoder design, particularly in termsof cross-scale alignment, contextual integration, and boundary preservation. Inthis work, we revisit medical image segmentation from a decoder-centric per-spective and propose a context-aware gated decoder that systematically regulatesfeature fusion and contextual aggregation throughout the decoding process. Theproposed decoder integrates lightweight multi-scale channel recalibration, gatedskip fusion with spatial competition and a global context aggregation mechanismthat injects encoder-wide information into intermediate decoding stages. This de-sign enables effective translation of strong pretrained encoder representations intospatially consistent predictions. Extensive experiments across 11 medical imagesegmentation benchmarks validate the effectiveness and demonstrate that the pro-posed approach consistently outperforms strong baselines while remaining com-putationally practical. Code: https://github.com/saadwazir/MedCAGD

Abstract:
Event cameras offer superior sensitivity to high-speed motionand extreme lighting, making event-based monocular depth estimation apromising approach for robust 3D perception in challenging conditions.However, progress is severely hindered by the scarcity of dense depthannotations. While recent annotation-free approaches mitigate this bydistilling knowledge from Vision Foundation Models (VFMs), a criticallimitation persists: they process event streams as independent frames. Byneglecting the inherent temporal continuity of event data, these meth-ods fail to leverage the rich temporal priors encoded in VFMs, ultimatelyyielding temporally inconsistent and less accurate depth predictions. Toaddress this, we introduce EventVGGT, a novel framework that ex-plicitly models the event stream as a coherent video sequence. To thebest of our knowledge, we are the first to distill spatio-temporal andmulti-view geometric priors from the Visual Geometry Grounded Trans-former (VGGT) into the event domain. We achieve this via a comprehen-sive tri-level distillation strategy: (i) Cross-Modal Feature Mixture(CMFM) bridges the modality gap at the output level by fusing RGBand event features to generate auxiliary depth predictions; (ii) Spatio-Temporal Feature Distillation (STFD) distills VGGT’s powerfulspatio-temporal representations at the feature level; and (iii) Tempo-ral Consistency Distillation (TCD) enforces cross-frame coherenceat the temporal level by aligning inter-frame depth changes. Extensiveexperiments demonstrate that EventVGGT consistently outperforms ex-isting methods—reducing the absolute mean depth error at 30m by over53% on EventScape (from 2.30 to 1.06)—while exhibiting robust zero-shot generalization on the unseen DENSE and MVSEC datasets. Thecode is available at https://github.com/yinruiRen/EventVGGT.

Abstract:
Despite the rapid progress of Multimodal Large Language Models (MLLMs), their ability to perform reliable visual grounding in high-stakes clinical software environments remains underexplored. Existing GUI benchmarks largely focus on isolated, single-step grounding queries, overlooking the sequential, workflow-driven reasoning required in real-world medical interfaces, where tasks evolve across interdependent steps and dynamic interface states. We introduce MedSPOT, a workflow-aware sequential grounding benchmark for clinical GUI environments. Unlike prior benchmarks that treat grounding as a standalone prediction task, MedSPOT models procedural interaction as a sequence of structured spatial decisions. The benchmark comprises 216 task-driven videos with 597 annotated keyframes, in which each task comprises 2–3 interdependent grounding steps within realistic medical workflows. This design captures interface hierarchies, contextual dependencies, and fine-grained spatial precision under evolving conditions. To evaluate procedural robustness, we propose a strict sequential evaluation protocol that terminates task assessment upon the first incorrect grounding prediction, explicitly measuring error propagation in multistep workflows. We further introduce a comprehensive failure taxonomy, including edge bias, small-target errors, no prediction, near miss, far miss, and toolbar confusion, to enable systematic diagnosis of model behavior in clinical GUI settings. By shifting evaluation from isolated grounding to workflow-aware sequential reasoning, MedSPOT establishes a realistic and safety-critical benchmark for assessing multimodal models in medical software environments. Code and data are available at https://github.com/Tajamul21/MedSPOT

Abstract:
Forecasting the future anatomy of slow-evolving neurode-generative diseases could enable earlier, more targeted intervention andimprove clinical trial design, but it remains challenging because true pro-gression signals are subtle in longitudinal MRI. In this low-signal regime,transferring modern generative sequence models directly is unreliable:training is dominated by stable baseline anatomy and confounded bydense, sample-specific nuisance variation. We first provide a theoreticalanalysis that explains these failures through two modes. Identity col-lapse occurs when optimization is driven toward reproducing the currentanatomy, which prevents the model from learning faint temporal change.The continuous interpolation trap arises when standard smooth networkscannot separate localized biological drift from pervasive noise, whichleads to spurious changes that diffuse across the volume. To addressboth issues, we propose Latent Drift, a progressive generative frame-work that learns change in a compressed semantic representation ratherthan synthesizing full-resolution anatomy. This design removes pixel-levelidentity from the prediction target and concentrates model capacity onprogression-relevant dynamics. We further apply Finite Scalar Quan-tization to the learned change representation, which suppresses small,high-frequency nuisance fluctuations while preserving consistent struc-tural drift. Experiments on longitudinal 3D brain MRI show that LatentDrift improves patient-specific neuro-forecasting over diffusion and au-toregressive transformer baselines across generative fidelity and clinicallyrelevant evaluation metrics.

Abstract:
While large language models (LLMs) have advanced chestCT report generation, existing methods typically encode 3D volumesholistically, failing to distinguish informative cues from redundant anatom-ical background. Inspired by radiological cognitive subtraction, we pro-pose Differential Visual Prompting (DiffVP), which conditions reportgeneration on explicit, high-level semantic scan-to-reference differencesrather than solely on absolute visual features. DiffVP employs a hier-archical difference extractor to capture complementary global and localsemantic discrepancies into a shared latent space, along with a difference-to-prompt generator that transforms these signals into learnable visualprefix tokens for LLM conditioning. These difference prompts serve asstructured conditioning signals that implicitly suppress invariant anatomywhile amplifying diagnostically relevant visual evidence, thereby facil-itating accurate report generation without explicit lesion localization.On two large-scale benchmarks, DiffVP consistently outperforms priormethods, improving the average BLEU-1-4 by +10.98 and +4.36, respec-tively, and further boosts clinical efficacy on RadGenome-ChestCT (F1score 0.421). Code will be available at https://github.com/ArielTYH/DiffVP/.

Abstract:
Amodal segmentation aims to recover the full shape of objects that are partially occluded; however, most existing approaches are trained and assessed on in-domain benchmarks and exhibit limited generalization in zero-shot scenarios. Human-intuition-guided depth-aware (HIDA) is proposed as a plug-and-play framework that combines two frozen vision foundation models with a lightweight trainable segmentation network to estimate amodal masks using only a visible bounding box. HIDA initially derives a visible prior mask and a pseudo-depth map (PDM) to enable target-centered cropping, which reduces irrelevant context while highlighting occlusion boundaries and hidden regions. An occlusion-aware token-routing soft MoE module is also introduced within a lightweight ViT encoder to adaptively handle varied occlusion patterns and strengthen representations of invisible regions. Evaluations on COCOA-cls, KINS, and D2SA demonstrate that HIDA attains state-of-the-art performance in fully supervised settings and state-of-the-art zero-shot results on COCOA-cls and D2SA while training only a lightweight segmentation network. Code is available at: https://github.com/lipenghuai/HIDA.

Abstract:
Album highlight recommendation (AHR) focuses on identifying images from personal collections that are most suitable for personal enjoyment and social sharing. However, due to the inherent subjectivity and ambiguity involved in selecting highlights, AHR research has long been hindered by limited data availability, resulting in relatively slow progress in the field. To mitigate this issue, we first engage multiple annotators to re-annotate the public PEC and CUFED album datasets, producing high-quality labels for 910 albums comprising 87K images. These binary annotations are then aggregated into finegrained highlight scores to reduce individual annotation bias. To assess how well public consensus aligns with personal preferences, we further collect 10K images from 100 volunteers and invite the album owners themselves to provide personalized highlight annotations. This newly constructed dataset is named C-AHR-100. In addition to contributing these three benchmark datasets, we propose Highlight4U, an agentic multi-modal large language model (MLLM) framework specifically optimized for AHR. Highlight4U first identifies highlight-worthy reference images through localized scoring of individual images, and then refines its recommendations by incorporating contextual and semantic information across the reference set. Extensive experiments demonstrate that Highlight4U effectively captures public highlight perception and can further generalize to accommodate the personalized preferences of individual album owners. The source code and dataset are provided in the https://github.com/naninasm/Finding-Highlight-Images-InYour-Albums-From-Benchmark-To-MLLM.

Abstract:
High-resolution multi-contrast MRI is essential for capturingrich anatomical structures and supports more accurate disease diagnosisthrough the reference images from another contrast. It often suffers fromlimited spatial resolution due to acquisition constraints, and requestsexploring arbitrary-scale reconstruction beyond fixed scale factors. How-ever, related research exists two following issues: 1) Implicit neural rep-resentations (INR) as mainstream methods are prone to spectral bias,which limits their ability to recover high-frequency details; 2) Multi-contrast MRI is often used as the effective prior, but lacks the targetednetwork design to further merge INR positional information. To solvethese problems, we propose a Fourier-enhanced implicit framework forarbitrary-scale multi-contrast MRI super-resolution (MArFE). First, theFourier reparameterization module (FRM) is introduced to character-ize frequency features through fixed Fourier basis decomposition. Then,Fourier neural operators can alleviate frequency degradation by the non-local Galerkin-type linear attention (NLGLA) based on kernel integralmechanisms. Additionally, we design a hybrid loss that jointly supervisesboth spatial and frequency domains to improve high-frequency texturereconstruction. Extensive experiments on two public multi-contrast MRIdatasets demonstrate that MArFE outperforms existing methods undervarious scale factors with superior visual performances and quantitativemetrics. These results highlight the clinical potential of MArFE in en-hancing MRI quality across multi-contrast sequences. The project codecan be found at https://github.com/zhiwen-shi/MArFE.

Abstract:
Test-time adaptation (TTA) aims to mitigate distributionshifts by adapting models with unlabeled target data at inference time.While TTA with vision-language models (VLMs) has shown promisingresults in classification, extending it to medical image segmentation re-mains challenging. In this setting, the adaptation gains from optimizingon VLM-generated predictions are often outweighed by the degradationto the VLM’s strong pretrained features caused by noisy, update-drivenlearning, resulting in limited and unstable improvements. We thereforepropose Memory-Supported Synergistic Adaptation (MSSA), anovel training-free TTA framework for medical image segmentation.Without updating model parameters, MSSA dynamically selects reli-able image–text predictions to construct an online memory, uses themas text-guided semantic priors, and couples them with cross-image struc-tural alignment for robust adaptation. Specifically, MSSA consists of (i) anoise-aware memory construction module that filters and stabilizes cross-modal predictions, and (ii) a relevance-driven prototype alignment mod-ule that aligns the target sample with structurally consistent memorysamples and their reliable predictions to improve adaptation. Extensiveexperiments on multiple medical segmentation benchmarks demonstratethat MSSA consistently improves VLM-based segmentation models andoutperforms existing fine-tuning-based TTA methods by a clear mar-gin, with gains of up to 12.2% DSC and 11.7% mIoU. Project Page:https://lingrayy.github.io/MSSA/.

Abstract:
Whole slide image (WSI) analysis is central to computationalpathology, with multiple instance learning (MIL) emerging as the stan-dard pipeline for slide-level diagnosis. However, conventional approachesformulate WSI diagnosis as a flat classification task over discrete labels,contradicting the inherently hierarchical, coarse-to-fine nature of clinicalreasoning. Although recent hierarchical classifiers and vision–languagemodels (VLMs) have sought to address this structural gap, they eitherfail to capture semantic continuity between related diagnoses or sufferfrom unconstrained text generation that produces taxonomic hallucina-tions and parent–child label violations. To address these limitations, wepropose TaxoMIL, a taxonomy-constrained framework that reformulatesWSI diagnosis as a multi-granularity text generation task. TaxoMIL uti-lizes a dual-head Transformer decoder to generate coarse- and fine-leveldiagnostic text, and introduces taxonomy-guided objectives that explic-itly structure the label embedding space and strictly ground slide-level vi-sual representations within the clinical taxonomy. Extensive experimentsacross three diverse WSI datasets demonstrate that TaxoMIL consis-tently outperforms state-of-the-art MIL classifiers and VLM-based gen-erative methods, yielding accurate and hierarchy-aware diagnostic pre-dictions. The code is released at https://github.com/QuIIL/TaxoMIL..

Abstract:
Automated chest X-ray report generation has recently bene-fited from reinforcement learning (RL) and large language models. How-ever, RL training often suffers from instability or limited explorationdue to fixed Kullback-Leibler (KL) regularization and a static referencepolicy that accumulates KL pressure over time. We propose Response-Weighted and Validation-Anchored Policy Optimization (REVA-PO), aRL framework that stabilizes long-term training via Response-WeightedRegularization (RER) and Validation-Anchored Policy Reset (VAPR).RER dynamically adjusts per-response KL weights based on advantageand reference-policy entropy, relaxing constraints for high-quality re-sponses while tightening them for low-quality ones. Complementarily,VAPR periodically synchronizes the reference and current policies tothe best validation checkpoint, resetting accumulated regularization pres-sure to expand the viable exploration space. To ensure a robust startingpoint, we employ a three-stage pipeline consisting of warm-up training,classifier-guided supervised fine-tuning, and RL. Extensive evaluationson MIMIC-CXR and IU-Xray demonstrate that REVA-PO sets newstate-of-the-art benchmarks in both linguistic quality and clinical accu-racy. Notably, BLEU-4 improves by 5.1% on MIMIC-CXR and 3.6% onIU-Xray, while CheXpert F1 and RadGraph F1 scores increase by 4.5%and 12.8%, respectively, over prior leading methods. The code is publiclyavailable at https://github.com/LiGuo12/REVA_PO/.

Abstract:
Parsing images with high precision for matting and segmentation is sig-nificantly more challenging than conventional dense prediction, as it requires accurateestimation of fine-grained details. Existing methods either lack semantic awareness orproduce suboptimal predictions; even interactive matting approaches rely on manualverification and repetitive checking, making fully automatic matting still unattainable.In this work, we propose Kiroshi , an agentic perception system for high-accuracyimage parsing. We train an Action Model with iterative refinement and mine pairedtrajectories by sampling grid prompts from residual maps, where each step yields apositive and a negative transition under the same intermediate prediction based onquantitative quality gains. These within-context preference pairs form a reliable su-pervision signal for post-training to align the MLLM policy toward more effectivegrid decisions. We also contribute a new High-Fidelity Referring Matting and Seg-mentation (HiFiRefMS ) benchmark to evaluate the performance of different mod-els. Experimental results demonstrate that our method surpasses state-of-the-art ap-proaches both quantitatively and qualitatively, and extensive ablation studies furthervalidate the effectiveness and superiority of our agentic design. Project will be releasedat https://github.com/haipengzhou856/Kiroshi.

Abstract:
Active learning (AL) performance is known to be budget-dependent, yet regimes are typically defined by heuristic label countsthat fail to generalize across datasets or architectures. We character-ize AL dynamics by reframing budget regimes as shifts in the domi-nant generalization mechanism. By reinterpreting PAC-style risk com-ponents as dynamic interacting terms, we prove that dominance shiftsare structurally unavoidable, creating a moving bottleneck for gener-alization. We operationalize this using measurable proxies and a seg-mented regression procedure to identify a tripartite taxonomy: data-driven, transition, and model-driven phases. Our framework explains thelong-standing observation that representativeness, coverage, and uncer-tainty strategies excel at different stages. Experiments across natural andmedical imaging show that AL efficiency depends on the alignment be-tween the strategy’s inductive bias and the active bottleneck. Moreover,self-supervised representation shift transitions earlier along the labelingtrajectory, highlighting the role of representation quality in shaping ALdynamics. Overall, this work provides a unified framework for the nextgeneration of transition-aware AL algorithms. The code is available at:https://github.com/juliamachnio/PALM.

Abstract:
Bundle Adjustment (BA) is a cornerstone of 3D computervision and has benefited from decades of advances in sparse optimiza-tion and numerical methods. It was originally developed for jointly op-timizing camera intrinsics, poses and sparse 3D points. While exten-sions incorporate lines and other primitives, integrating richer geometricstructures such as parallelism, coplanarity, or wireframes often intro-duces significantly increased computational cost and reduced numericalstability. In this paper, we propose a unified framework that extendsbundle adjustment to jointly optimize geometric features and higher-order relations. We first introduce a taxonomy that distinguishes scal-able geometric features with direct 2D measurements (e.g., points andlines), from groups encoding higher-order relations (e.g., coplanarity, par-allelism, etc.), where we show that groups can be modeled as camera-likeentities within the bundle adjustment framework. Building on this formu-lation, we propose that both group constraints and cross-feature relations(i.e., point–line associations) can be expressed through 2D reprojectionmeasurements. By formulating group-induced and cross-feature repro-jection errors, we preserve the sparsity structure of classical point-basedBA under Schur elimination, while avoiding direct 3D regularization thatdegrades the conditioning and stability. Experiments on both real-worldand synthetic datasets demonstrate runtime performance comparableto classical point-only bundle adjustment, while producing significantlyricher 3D structures and improved geometric accuracy.

Abstract:
We propose a novel training-free method for inpainting with off-the-shelf text-to-image models. While guidance-based methods in theory allow generic models to be used for inverse problems such as inpainting — in practice their effectiveness is limited, leading to the necessity of specialized inpainting-specific models. In this work, we argue the missing ingredient for training-free generic model usage is proper optimization of the initial noise sample. We optimize the initial noise to approximately reproduce the unmasked image, in as few as tens of optimization steps, then use it with a conventional training-free inpainting method. Critically, we propose two core ideas that make this possible: (i) we perform linear approximation that avoids the costly and often impractical unrolling required to relate the initial noise sample to model output—which potentially is why this relationship was previously overlooked; and (ii) perform spectral preconditioning by optimizing the initial noise sample in the spectral domain with Adam, which stabilizes the optimization. We demonstrate our method on various inpainting tasks, outperforming the state of the art. Project website: https://ubc-vision.github.io/sonic/

Abstract:
Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species. Unoccupied Aerial Vehicles (UAVs) capturing high-resolution imagery from a single RGB camera offer a cost-effective and scalable approach for mapping and measuring individual trees. We introduce BIRCH-Trees, the first benchmark for individual tree height and species estimation from tree-centered UAV images, spanning three datasets: temperate forests, tropical forests, and boreal plantations. We also present DINOvTree, a unified approach using a Vision Foundation Model (VFM) backbone with task-specific heads for simultaneous height and species prediction. Through extensive evaluations on BIRCH-Trees, we compare DINOvTree against commonly used vision methods, including VFMs, as well as biological allometric equations. We find that DINOvTree achieves top overall results with accurate height predictions and competitive classification accuracy while using only 54% to 58% of the parameters of the second-best approach.

Abstract:
Bird’s-eye-view (BEV) perception is a core component of camera-based 3D understanding in autonomous driving, where view transformation (VT) maps multi-camera image features into a uni(cid:28)ed BEV representation. Sampling-based view transformation (Sampling-VT) is attractive because it supports dense and continuous BEV aggregation for high-resolution and long-range perception. Its deployment bottleneck, however, is systems-level: standard tensorized implementations of Sampling-VT(cid:22)which we refer to as Tensorized Sampling-VT(cid:22)explicitly materialize large height-dependent intermediate tensors, causing memory and latency costs that scale poorly with vertical resolution and the number of cameras. We revisit Tensorized Sampling-VT from an operator-execution perspective and show that it follows a gather(cid:21)reduction pattern: each BEV query independently accumulates contributions across cameras and height bins. Unlike splatting-based VT, which requires index sorting and prevents fully thread-local reduction from voxel construction to BEV output, the gather(cid:21)reduction structure of Sampling-VT enables thread-local accumulation with on-the-(cid:29)y recomputation, eliminating the need to materialize heightand camera-dependent intermediates. Based on this insight, we propose FlashBEV, a fully fused and IOaware execution strategy that is mathematically equivalent to Tensorized Sampling-VT (same operator output) while substantially reducing global memory tra(cid:30)c and kernel-launch overhead. Experiments show that FlashBEV achieves more than an order of magnitude lower peak GPU memory and signi(cid:28)cant inference-latency speedups, with memory usage effectively independent of the number of height bins, reducing the operator’s peak memory to O(BCXY ) (output only). This unlocks higher BEV range/resolution and vertical discretization within (cid:28)xed deployment budgets on memory-constrained devices, where tensorized execution would otherwise be infeasible. Our contribution is therefore an execution redesign(cid:22)same math, di(cid:27)erent execution(cid:22)that removes a key scalability barrier for deployment-ready Sampling-VT. Code is available at https://github.com/yokosyun/FlashBEV. • •

Abstract:
While contemporary monocular depth estimation (MDE)methods achieve remarkable overall acacy, they consistently produce er-roneous depth discontinuities at object contact regions, particularly be-tween objects and supporting surfaces. In this paper, we address this crit-ical limitation by presenting a boundary-aware monocular depth estima-tion framework that enforces depth continuity at contact areas throughthe principled exploitation of contact boundaries as explicit structuralpriors. Specifically, we propose a boundary detection and filtering modulethat explicitly identifies object contact regions, yielding a novel boundary-aware representation that enables depth-consistent learning at contactareas. Furthermore, we introduce a boundary-aware feature fusion strat-egy that seamlessly incorporates contact boundary priors into the depthdecoding process, effectively rectifying the persistent discontinuities thatelude existing approaches. Our framework further supports interactiverefinement, allowing users to manually specify missing contact bound-aries for controllable depth correction. Extensive experiments on threeunseen benchmarks with dense object interactions demonstrate the ef-fectiveness of our approach, consistently outperforming baselines withparticularly pronounced gains in contact regions. Code is available athttps://github.com/abai969/contact-depth-refinement.git

Abstract:
Vision foundation models are capable of generalizing across3-dimensional (3D) scenes with high-fidelity estimates; their empiricalsuccess can be attributed to training on large-scale datasets of perspec-tive images. However, when transferred to wide field-of-view (FoV) im-ages, such as those captured by fisheye cameras, they return erroneousoutputs due to a covariate shift stemming from the radial distortion onthe image pixels. We propose a method to generalize vision foundationmodels to fisheye cameras. The crux of our method lies in a set of learn-able parameters, termed Distortion Extenders (DEX), that model thefisheye distortion coefficients and the distributional shift between fish-eye and perspective images encoded in the latent space. By minimizing aself-supervised alignment loss, DEX transforms the latent embeddings offisheye images to resemble those of perspective images to recover high-fidelity estimates. DEX is architecture- and task-agnostic: We demon-strate DEX on monocular depth estimation and open-vocabulary seg-mentation for convolution- and Transformer-based architectures, wherewe consistently improve over baselines across indoor and outdoor fisheyedatasets. As a byproduct, the activations of DEX can also be decoded todistortion coefficients to support camera calibration. Code available at:https://github.com/Suchisrit/DEX.

Abstract:
Injection molding is the cornerstone of mass-producing plastic components. While current algorithms can automate mold design for basic geometries using standard two-piece molds, complex parts featuring undercuts, side holes, or re-entrant features present a significant challenge. These geometries often necessitate auxiliary components beyond the primary upper and lower molds. In practice, designing these intricate assemblies is a laborious process that relies heavily on expert knowledge. Furthermore, the scarcity of public datasets has hindered the development of effective learning-based solutions. To bridge these gaps, we introduce MoldCAD, a curated dataset that pairs complex single-body CAD parts with industry-standard mold assemblies. Each entry includes the upper and lower molds, parting surfaces, demolding orientations, and necessary auxiliary components. The dataset comprises 4,934 CAD models and over 3,850 mold assemblies, totaling more than 23k individual models. Building upon this dataset, we propose a comprehensive pipeline that predicts demolding orientations, identifies auxiliary components, and constructs parting surfaces to derive a complete, manufacturing-ready mold assembly for downstream CAD/CAM workflows. Our results demonstrate a promising path toward fully automated industrial mold design and contribute to the broader advancement of manufacturing-aware CAD generation. Code is available at https://github.com/tb2-sy/AIMold.

Abstract:
Dual-pixel (DP) imaging enables metric depth estimation from a single camera using sub-aperture disparity. However, the extremely small e(cid:27)ective baseline limits disparity observability, leading to structural degradation and depth failure in textureless, low-contrast, or downsampled regions. Existing DP-based methods rely primarily on local disparity cues and therefore become unreliable when disparity signals are weak or ambiguous. To address this limitation, we propose FoundDP, a uni(cid:28)ed framework that integrates metric DP depth with global structural priors from a monocular depth foundation model. Our method preserves metric scale through DP-derived depth and leverages Vision Transformer (ViT) features to restore structural consistency in weakdisparity regions. To ensure reliable metric guidance under DP imaging conditions, we identify and mitigate ViT representation degradation induced by DP defocus blur via ViT feature alignment, enabling stable metric-guided depth estimation. Extensive experiments on synthetic and real-world DP benchmarks show that FoundDP delivers superior performance, with consistent gains in structural (cid:28)delity and metric accuracy, especially under reduced disparity observability. Code will be available at: https://github.com/EchoLighting/FoundDP • •

Abstract:
Visual navigation requires agents to reach goals in complexenvironments through perception and planning. World models addressthis task by simulating action-conditioned state transitions to predictfuture observations. Current navigation world models typically learnstate evolution under actions within the compressed latent space of aVariational Autoencoder, where spatial compression often discards fine-grained structural information and makes precise action-conditioned pre-diction more difficult. To better understand the propagation character-istics of different representations, we conduct a linear dynamics probeand observe that dense DINOv2 features exhibit stronger linear pre-dictability for action-conditioned transitions. Motivated by this obser-vation, we propose the Representation Autoencoder-based NavigationWorld Model (RAE-NWM), which generatively models navigation dy-namics in a dense visual representation space. We employ a Condi-tional Diffusion Transformer with a Decoupled Diffusion Transformerhead (CDiT-DH) to model continuous transitions, and introduce a sep-arate time-driven gating module for dynamics conditioning to regulateaction injection strength during generation. Extensive evaluations showthat modeling sequential rollouts in this space improves structural stabil-ity and action accuracy, benefiting downstream planning and navigation.Code is available at https://github.com/20robo/raenwm.

Abstract:
Evaluating the physical consistency of embodied world mod-els (EWMs) is a critical open challenge. While closed-loop evaluationvia simulator rollouts offers a more faithful assessment of physical plau-sibility than open-loop alternatives, existing frameworks almost exclu-sively rely on Inverse Dynamics Models (IDMs) for action extraction.Due to the intricate mapping from 2D pixel space to 3D kinematicspace, the learned IDMs can be brittle to data outside their trainingdistribution, resulting in unreliable action extraction from the gener-ated videos with novel objects and scenarios. This creates an unavoid-able attribution ambiguity between world model inaccuracies and ex-tractor errors. To reduce this ambiguity, we present KineBench, anIDM-free closed-loop benchmark for EWMs, built upon an explicit kine-matic grounding pipeline. Given a generated video, KineBench employscascaded visual foundation models to directly extract 6D end-effectorposes from individual frames, which are then executed in a physics sim-ulator for closed-loop validation. This explicit grounding directly teststhe physical feasibility rather than visual plausibility, while remainingsensitive to general physical hallucinations such as gripper vanishing orspatial inconsistency. Beyond execution-based task success, KineBenchincorporates two classical 3D kinematic metrics—Spectral Arc Length(SPARC) and the Maruyama Manipulability Index—to characterize tra-jectory smoothness and kinematic feasibility from a robot-centric per-spective. Across the evaluated models and tasks, these metrics exhibittask- and model-dependent associations with physical success rates, sug-gesting that they provide complementary diagnostic signals for assessingembodied generation quality. Built on 20 diverse manipulation tasks inManiSkill3, KineBench evaluates EWMs across four progressive suites:basic execution, task transfer, visual out-of-distribution generalization,and complexity-conditioned scaling. Evaluation across frontier models re-veals task-complexity-bounded nonlinear scaling in embodied video gen-eration, providing empirical guidance for future data-scaling strategies.The code and datasets are available on GitHub at https://github.com/minecraft-zzz/KineBench and on Hugging Face at https://huggingface.co/datasets/Zorkzak/KineBenchDatasets.

Abstract:
Cooperative perception through Vehicle-to-Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating occlusions and expanding the field of view. However, past research has predominantly focused on improving accuracy metrics without addressing the crucial system-level considerations of efficiency, latency, and real-world deployability. Noticeably, most existing systems rely on full-precision models, which incur high computational and transmission costs, making them impractical for real-time operation in resource-constrained environments. In this paper, we introduce QuantV2X, the first fully quantized multi-agent system designed specifically for efficient and scalable deployment of multi-modal, multi-agent V2X cooperative perception. QuantV2X introduces a unified end-to-end quantization strategy across both neural network models and transmitted message representations that simultaneously reduces computational load and transmission bandwidth. Remarkably, despite operating under low-bit constraints, QuantV2X achieves accuracy comparable to full-precision systems. More importantly, when evaluated under deployment-oriented metrics, QuantV2X reduces system-level latency by 3.2× and achieves a +9.5 improvement in mAP30 over full-precision baselines. Furthermore, QuantV2X scales more effectively, enabling larger and more capable models to fit within strict memory budgets. These results highlight the viability of a fully quantized multi-agent intermediate fusion system for real-world deployment. The system will be publicly released to promote research in this field: https://github.com/ucla-mobility/QuantV2X.

Abstract:
Manipulating fast and dynamically moving targets in un-structured 3D environments remains challenging for embodied AI. Ex-isting visual-language-action models and world models struggle with ac-curate 3D geometry and physically meaningful forecasting. We proposePhysMani, a framework that couples a physics-principled 3D Gaussianworld model with a future-aware action policy model. The world modellearns a divergence-free Gaussian velocity field via online optimizationfor fast and physically grounded future dynamics prediction. The pol-icy model integrates the predicted 3D scene future dynamics through alearnable token based cross-attention module. We introduce PhysMani-Bench, a dynamic manipulation benchmark with 16 tasks, and demon-strate a superior success rate over strong baselines in both simulationand real-world robot experiments. Our code and data are available athttps://github.com/vLAR-group/PhysMani

Abstract:
Video world models can generate realistic futures from a sin-gle instruction, but they often fail to track the same physical pointsconsistently across time. As a result, the generated videos appear plausi-ble, yet lack the physical grounding required for reliable action execution,such as robot manipulation. We present GEM-4D, a geometry-groundedvideo world model that resolves this limitation by injecting dense 4Dcorrespondence supervision distilled from a pretrained geometry foun-dation model into the video generative backbone during training. Thissupervision enables the video world model to jointly capture appearanceand geometric structure while retaining a single-stream architecture withno additional inference cost. We further introduce an inverse dynam-ics module that converts correspondence-consistent video rollouts intoexecutable robot trajectories, enabling direct deployment in both real-world and simulated manipulation. GEM-4D achieves state-of-the-artperformance on both video prediction and geometric consistency acrossboth simulation and realistic scenarios and improves real-world manipu-lation success from 61% to 81%. Additional results are available at thehttps://gem-4d.github.io/.

Abstract:
Learning effective robot control policies on physical hard-ware is challenging due to costly data collection and the difficulty ofreward specification. Prior work has incorporated demonstrations intoreinforcement learning (RL), yet existing approaches either require largenumbers of demonstrations or depend on continuous human interventionduring training. To address these limitations, we present AutoSERL, aframework that leverages a single demonstration to fully automate the in-tervention process in real-world robot RL. The framework includes threecomplementary mechanisms to accomplish certain tasks: a sliding win-dow intervention that continuously guides exploration to prevent localoptima and unsafe deviations, a safety recovery mechanism that detectsand corrects failure states via predefined trajectory recovery points, andan intervention termination criterion that automatically disables guid-ance once the policy can independently complete the task, preserving itsexploration advantage. We evaluate AutoSERL on six contact-intensivemanipulation tasks across two robot platforms, spanning insertion, hang-ing, and hinge-based tasks. AutoSERL consistently outperforms SERLinitialized with 20 demonstrations, behavior cloning, and MILES — adedicated one-shot imitation learning baseline — across all tasks whilematching HIL-SERL, achieves 100% success rate on insertion tasks, anddemonstrates improved robustness to positional variations, all from a sin-gle demonstration. Code and videos are available on our project website:https://autoserl.github.io/.

Abstract:
Pretrained video generative models are promising backbonesfor visuomotor control, but their imagined futures often drift from taskintent and are not reliably action-conditional. As a result, these mod-els can be difficult to use for planning or policy extraction. To addressthese limitations, we propose RoboTALES, a single-stage framework thatlearns task-aligned simulated futures and uses them to train robot poli-cies. Our approach introduces two key innovations: (1) a hierarchicalLLM-based planner that breaks complex tasks into a sequence of sub-goals to guide the model’s imagination; and (2) a VLM-based critic thatevaluates these “imagined” futures and uses reward-based feedback tokeep the model’s internal representations focused on the goal. By an-choring the video generator in abstract reasoning, we produce temporallyconsistent rollouts and more coherent actions. We evaluate RoboTALESon diverse manipulation tasks from RoboCasa and LIBERO10, and showthat our method consistently outperforms existing methods, especiallyin long-horizon tasks. Our code and models are publicly available athttps://github.com/hananshafi/RoboTALES.

Abstract:
End-to-end autonomous driving systems commonly follow a cascaded two-stage pipeline where a perception stage compresses multimodal sensor inputs into a compact context and a downstream planner predicts trajectories conditioned on this context. We argue that this oneway perception-to-planning interface forces sensor inputs into a compact representation, losing the fine-grained details critical for planning. Moreover, by constraining the planner to this compressed context, it is difficult to leverage the rich representations offered by modern vision foundation models. To address these issues, we propose MOJITO, a unified sensorto-action framework for end-to-end autonomous driving built on modal joint learning. MOJITO removes the cascaded interface and instead performs block-wise Modal Joint Attention that simultaneously updates action, image, and LiDAR features, allowing the planner to directly access multi-modal features during action generation. MOJITO achieves 88.9 PDMS on the NAVSIM v1 dataset and 88.4 EPDMS on the more challenging NAVSIM v2 dataset, setting a new state-of-the-art. Extensive experiments further demonstrate strong scalability, instruction following, and diverse trajectory generation. Code and models are available at https://github.com/mumucc01/MOJITO.

Abstract:
Online vectorized map construction provides autonomous ve-hicles with essential, real-time semantic and geometric scene understand-ing. While leveraging prior maps presents an opportunity to improveaccuracy, they are often biased due to outdated information or tem-porary road changes. Existing methods, which typically treat priors asfully reliable or only consider limited noise patterns, suffer severe perfor-mance degradation when these priors are imperfect. To address this, wepropose PriorMaskMap, a robust framework for prior-guided vectorizedmap construction. Its first innovation is a Prior-Aware Confidence Esti-mator (PACE) that evaluates the input rasterized map against currentvisual observations. It assigns confidence scores to regions classified asreliable, erroneous, or blank, creating a foundational mask that guidestwo subsequent branches. In one branch, the Dual-Prior Query Genera-tor (DPQG) selectively filters and embeds both rasterized and vectorizedpriors to initialize informative queries from trustworthy map structures.In the other, the Confidence-Guided Prior Fusion (CGPF) module spa-tially propagates and refines the mask, using the refined version to gov-ern the fusion of BEV features with the prior map, thereby providingrich contextual representations for the map decoder. Evaluation on thenuScenes dataset shows that PriorMaskMap achieves superior mappingprecision over existing approaches and exhibits marked robustness whenprovided with priors containing apparent errors. Our code is released athttps://github.com/healenrens/PriorMaskMap.

Abstract:
World models that forecast scene evolution by generatingfuture video frames devote the bulk of their capacity to photometricdetails, yet the resulting predictions often remain geometrically inconsis-tent. We present VGGT-World, a geometry world model that side-stepsvideo generation entirely and instead forecasts the temporal evolution offrozen geometry-foundation-model (GFM) features. Concretely, we re-purpose the latent tokens of a frozen VGGT as the world state and traina lightweight temporal flow transformer to autoregressively predict theirfuture trajectory. Two technical challenges arise in this high-dimensional(d=1024) feature space: (i) standard velocity-prediction flow matchingcollapses, and (ii) autoregressive rollout suffers from compounding ex-posure bias. We address the first with a clean-target (z-prediction) pa-rameterization that yields a substantially higher signal-to-noise ratio,and the second with a two-stage latent flow-forcing curriculum that pro-gressively conditions the model on its own partially denoised rollouts.Experiments on KITTI, Cityscapes, and TartanAir demonstrate thatVGGT-World significantly outperforms the strongest baselines in depthforecasting while running 3.6-5× faster with only 0.43B trainable pa-rameters, establishing frozen GFM features as an effective and efficientpredictive state for 3D world modeling. The code will be open-source athttps://github.com/SimonSun0810/VGGT-World.

Abstract:
Robotic Vision-Language-Action (VLA) models generalizewell for open-ended manipulation, but their perception is fragile undersensing-stage degradations such as extreme low light, motion blur, andblack clipping. We present E-VLA, an event-augmented VLA frameworkthat improves manipulation robustness when conventional frame-basedvision becomes unreliable. Instead of reconstructing images from events,E-VLA directly leverages motion and structural cues in event streamsto preserve semantic perception and perception-action consistency un-der adverse conditions. We build an open-source teleoperation platformwith a DAVIS346 event camera and collect a real-world synchronizedRGB-event-action manipulation dataset across diverse tasks and illumi-nations. We also propose lightweight, pretrained-compatible event in-tegration strategies and study event windowing for stable deployment.Experiments show that even a simple parameter-free fusion, i.e., over-laying accumulated event maps onto RGB images, could substantiallyimprove robustness in dark and heavy-blur scenes: on Pick-Place at 20lux, success increases from 0% (image-only) to 60% with overlay fusionand to 90% with our event adapter; under severe motion blur (1000 ms-exposure proxy), Pick-Place improves from 0% to 20-25%, and Sortingfrom 5% to 32.5%. Overall, E-VLA provides systematic evidence thatevent-driven perception can be effectively integrated into VLA mod-els, pointing toward robust embodied intelligence beyond conventionalframe-based imaging. Code and dataset will be available at E-VLA.

Abstract:
Multi-arm collaboration is becoming a core capability in embodied manipulation. Recent vision-language-action (VLA) models integrate perception, language, and control, but most represent language as a single global instruction and do not provide an explicit mechanism for assigning and composing arm-specific behaviors. This design limits transfer to collaboration patterns that differ from those observed during training. We present MA-VLA, a unified framework for multi-arm collaboration via atomic action assignment. MA-VLA decomposes cooperative behavior into mid-level atomic prompts and allocates them to individual arms, enabling explicit subgoal specification and compositional reuse across tasks. To reduce reliance on fixed execution roles, we introduce Arm Shuffle, a training-time permutation of the observation, state, and assigned atomic prompts for each arm. This permutation enforces role-agnostic instruction following and supports recomposition into unseen coordination patterns, which we term multi-arm compositional generalization. We also construct a benchmark in which test-time collaboration patterns are absent in training set. Across simulation and real-world evaluations, prior state-of-the-art VLAs largely fail under these unseen collaborations, while MA-VLA consistently succeeds. These results indicate that structured, per-arm atomic action assignment offers a practical route to scalable generalization in multi-arm embodied systems. Code, models, and data are available at https://github.com/zhangzaibin/future-robots

Abstract:
Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses ondomain-specific data production, leaving a critical void: the absence of aprincipled, open-source engine capable of fully unleashing the potentialof high-quality spatial data. To bridge this gap, we elucidate the designprinciples of a robust data generation system and introduce OpenSpa-tial—an open-source data engine engineered for high quality, extensivescalability, broad task diversity, and optimized efficiency. OpenSpatialadopts 3D bounding boxes as the fundamental primitive to constructa comprehensive data hierarchy across five foundational tasks: SpatialMeasurement (SM), Spatial Relationship (SR), Camera Perception (CP),Multi-view Consistency (MC), and Scene-Aware Reasoning (SAR). Lever-aging this scalable infrastructure, we curate OpenSpatial-3M, a large-scale dataset comprising 3 million high-fidelity samples. Extensive eval-uations demonstrate that versatile models trained on our dataset achievestate-of-the-art performance across a wide spectrum of spatial reasoningbenchmarks. Notably, the best-performing model exhibits a substantialaverage improvement of 19%, relatively. Furthermore, we provide a sys-tematic analysis of how data attributes influence spatial perception. Byopen-sourcing both the engine and the 3M-scale dataset, we provide arobust foundation to accelerate future research in spatial intelligence.Code: github.com/VINHYU/OpenSpatialDataset: huggingface.co/datasets/JoyAI-Image-OpenSpatial

Abstract:
Autonomous agents require representations that are gen-eral, causal, and physically structured to operate in real-time stream-ing environments. However, current vision foundation models remainfragmented, specializing narrowly in image semantic perception, offlinetemporal modeling, or spatial geometry. This paper introduces Om-niStream, a unified streaming visual backbone that effectively per-ceives, reconstructs, and acts from diverse visual inputs. By incorporat-ing causal spatiotemporal attention and 3D rotary positional embeddings(3D-RoPE), our model supports efficient, frame-by-frame online process-ing of video streams via a persistent KV-cache. We pre-train OmniStreamusing a synergistic multi-task framework coupling static and temporalrepresentation learning, streaming geometric reconstruction, and vision-language alignment on 29 datasets. Extensive evaluations show that, evenwith a strictly frozen backbone, OmniStream achieves consistently com-petitive performance with specialized experts across image and videoprobing, streaming geometric reconstruction, complex video and spatialreasoning, and robotic manipulation. Rather than pursuing benchmark-specific dominance, our work demonstrates the viability of training a sin-gle, versatile vision backbone that generalizes across semantic, spatial,and temporal reasoning, i.e., a meaningful step towards general-purposevisual understanding for interactive and embodied agents.

Abstract:
Vision Language Models (VLMs) have shown promising plan-ning capabilities, yet their success remains confined to the text domain,leaving visual decision-making relatively underexplored. Addressing thisgap, we introduce Corrective Sequence Planning (CoSPlan) bench-mark, where VLMs must plan a sequence of visual actions from an initialscene to a target scene. CoSPlan evaluates models on their ability to imag-ine and execute a coherent set of visual steps required to reach the goal(Step Completion). To prevent any shortcuts that simply describethe final scene, we introduce an erroneous action in decision making,which must be detected (Error Detection) and corrected to reach thegoal, enabling a deeper understanding of the task. CoSPlan spans across4 tasks: maze navigation, block re-arrangement, image reconstruction,and object re-organization. Despite using advanced reasoning strategiessuch as Chain-of-Thought and Scene Graphs, VLMs struggle on CoSPlan,while still showing promising performance in the text domain. Addressingthis, we propose Scene Graph Incremental updates (SGI), a noveltraining-free method to transform images into ‘textual’ scene graphs, en-abling step-by-step reasoning through iterative scene graph refinement.SGI yields an average of ≃ 4.4% ↑ on CoSPlan w/ generalization onPlanBench and VQA. Link for solving puzzles on the project page.

Abstract:
Multi-task learning (MTL) has been widely adopted for itsability to simultaneously learn multiple tasks. While existing gradientmanipulation methods often yield more balanced solutions than simplescalarization-based approaches, they typically incur a significant compu-tational overhead of O(K) in both time and memory, where K is thenumber of tasks. In this paper, we propose LDC-MTL, a simple andscalable loss discrepancy control approach for MTL, formulated froma bilevel optimization perspective. Our method incorporates two keycomponents: (i) a bilevel formulation for fine-grained loss discrepancycontrol, and (ii) a scalable first-order bilevel algorithm that requiresonly O(1) time and memory. Theoretically, we prove that LDC-MTLguarantees convergence not only to a stationary point of the bilevelproblem with loss discrepancy control but also to an ϵ-accurate Paretostationary point for all K loss functions under mild conditions. Extensiveexperiments on diverse multi-task datasets demonstrate the superior per-formance of LDC-MTL in both accuracy and efficiency. Code is availableat https://github.com/OptMN-Lab/LDC-MTL.

Abstract:
Few-shot Test-Time Domain Adaptation (FSTT-DA) seeksto adapt models to novel domains using only a handful of unlabeledtarget samples. This setting is more realistic than typical domain adap-tation setups, which assume access to target data during source training.However, prior FSTT-DA approaches fail to effectively leverage sourcedomain-specific knowledge, relying on shallow batch normalization up-dates, prompt-based methods that treat the model as a black box, orensembling strategies that do not capture cross-domain relationships. Toaddress these limitations, we introduce a new FSTT-DA framework thatintegrates LoRA fine-tuning with model merging. In our approach, sep-arate LoRA modules are fine-tuned on CLIP’s vision encoder for eachsource domain. Since LoRA modifies only a small fraction of the model’sparameters, it retains the base model’s generalized knowledge while in-ternally learning domain-specific features. To adapt the learned knowl-edge to a specific target domain, we propose a hypernetwork trainedvia meta-learning that generates per-column merging factors to combineLoRA modules. Given a small batch of target images, the hypernetworkproduces merging weights that fuse source LoRA modules into a singleadapted representation. Our results demonstrate state-of-the-art perfor-mance across various domain adaptation datasets. Our code is publiclyavailable at https://github.com/nahbois4321/DA-MergeLoRA.

Abstract:
While knowledge distillation (KD) is widely adopted for train-ing lightweight models by leveraging supervision from larger teachermodels, relying solely on output token distributions has proven insuf-ficient for compressing Multimodal Large Language Models (MLLMs).Since output tokens are a byproduct of the model attending to visualinputs, prior works have explored explicitly distilling attention to pro-vide a direct supervisory signal. While promising, the precise utility ofwhich attention signals to distill remains under-explored. In this work, wechallenge the conventional reliance on prompt-to-vision attention by re-vealing that downstream performance correlates strongly with response-to-vision attention similarity to the teacher, but negligibly with that ofprompt-conditioned attention. Furthermore, we observe that attentiondistributions exhibit significant variance across individual tokens, indi-cating that a uniform distillation objective is suboptimal. To this end,we introduce Token-level Response-visual Attention Guidance(TRAG), a distillation objective that 1) shifts the focus to response-to-vision signals and 2) employs token-specific objectives by adaptivelyweighting the Kullback-Leibler divergence based on attention entropy, ef-fectively guiding the student to mirror the teacher’s precise visual focus.Extensive experimental results on multiple benchmarks demonstrate thatTRAG significantly outperforms prior distillation baselines. Our code isavailable at https://github.com/jhjangjh/TRAG.

Abstract:
Multi-teacher distillation has emerged as a powerful paradigmfor consolidating diverse visual knowledge from multiple Vision Founda-tion Models (VFMs). However, existing methods typically rely on si-multaneous feature alignment, which forces the student to match mul-tiple teacher distributions directly. This rigid approach often inducesrepresentational dissonance and semantic conflicts, where teachers withdisparate architectures and divergent optimization goals lead to sub-optimal performance and restricted architectural flexibility. In this pa-per, we propose a novel framework termed ACTok that recasts multi-teacher distillation as a token-mediated aggregation process. By treat-ing visual features from both CNNs and Transformers as sets of tokens,we introduce learnable tokens that serve as flexible neural proxies tobridge diverse representational spaces. To avoid specialized modifica-tions on student backbones, we further design an alternating interactionmechanism that enables these learnable tokens to dynamically recon-cile complementary knowledge across teachers. Our ACTok is inherentlyarchitecture-agnostic, facilitating effective knowledge transfer to bothCNN and ViT students. Furthermore, by resolving teacher conflicts atthe token level, our method achieves superior performance using onlythe ImageNet-1K dataset, rendering it orders of magnitude more data-efficient than existing billion-scale distillation baselines. Extensive exper-iments on classification, segmentation, and depth estimation demonstratethat our token-based aggregation consistently outperforms state-of-the-art multi-teacher distillation methods, providing a robust and scalablesolution for VFM knowledge consolidation. Code is publicly availableon https://github.com/VISION-SJTU/ACTok.

Abstract:
Image restoration under diverse degradations remains chal-lenging for unified all-in-one frameworks due to feature interference andinsufficient expert specialization. We propose SLER-IR, a sphericallayer-wise expert routing framework that dynamically activates special-ized experts across network layers. To ensure reliable routing, we in-troduce a Spherical Uniform Degradation Embedding with contrastivelearning, which maps degradation representations onto a hypersphereto eliminate geometry bias in linear embedding spaces. In addition, aGlobal–Local Granularity Fusion (GLGF) module integrates global se-mantics and local degradation cues to address spatially non-uniformdegradations and the train–test granularity gap. Experiments on three-task and five-task benchmarks demonstrate that SLER-IR achievesconsistent improvements over state-of-the-art methods in both PSNRand SSIM. Code and models will be available at https://github.com/PSR666/SLER-IR.

Abstract:
The training-free integration of expert models via modelmerging has exposed significant security risks, enabling free-riders tocombine specialized models without authorization. Recent works pro-pose parameter-level defenses that employ linear parameter transforma-tions to neutralize this threat. In this paper, we systematically analyzesuch defenses and reveal that their protected task vectors are inherentlysmall in magnitude. Consequently, the protected weights remain over-whelmingly dominated by the pretrained model. Based on this observa-tion, we designate the pretrained model as a static reference anchor andpropose the Anchor-Guided Attack (AGA) to circumvent existing safe-guards. Specifically, AGA aligns the protected model with this anchorto recover the transformation matrix analytically. Extensive evaluationsvalidate that AGA consistently bypasses both individual and compositedefenses under realistic defense-agnostic scenarios. Furthermore, we pro-vide Anchor-Repulsive Fine-tuning (ARF), a defense method to mitigatethe anchor dominance leveraged by AGA. Empirical results confirm thatARF effectively defeats the proposed attack. Our code is available athttps://github.com/krumpguo/secure-merge-attack.

Abstract:
Although recent robot perception research emphasizes train-ing on data from diverse environments to improve generalization, mostexisting methods still rely on centralized learning, which is inefficient anddifficult to scale across heterogeneous robot platforms. Federated learn-ing (FL) offers an alternative by enabling distributed training withoutraw data transfer, but it suffers from severe performance degradation un-der domain shifts caused by heterogeneity across clients. In real roboticdeployments, data distributions often overlap across platforms, environ-ments, and sensing conditions, making it difficult to partition clientsinto clearly separated domains. However, this characteristic breaks theassumption of clearly separable client domains commonly used in clus-tered FL. To address this gap in robot perception, particularly in depthestimation, we introduce two realistic and unexplored non-IID scenariosthat reflect heterogeneity in terms of platform, environment, and depthdistribution. We then propose FeDepth, a descriptor-based clusteredFL framework that models client relationships through soft clustering.Unlike hard clustering methods that assume clearly separated clusters,FeDepth allows clients to participate in multiple clusters, capturing con-tinuous and ambiguous domain transitions commonly observed in roboticenvironments. Extensive experiments demonstrate that FeDepth con-sistently improves robustness over standard FL and clustered FL base-lines across multiple depth estimation architectures, providing a practicaland effective solution for federated robot perception. Our project pageis available at https://vision3d-lab.github.io/fedepth/.

Abstract:
Customized image editing equips diffusion models with spe-cific visual transformations using limited paired data, typically via Low-Rank Adaptation (LoRA). However, combining acceleration and effectLoRAs during inference often introduces interference, causing conceptbleeding, semantic drift, and degraded stylistic fidelity. In this paper, wepropose a unified paradigm that distills numerous customized conceptsand fast-inference capabilities into a single LoRA, effectively resolvingcompositional conflicts while reducing deployment overhead. To addressthe challenges of multi-concept distillation, particularly gradient van-ishing and training instability, we introduce CollectionLoRA, a multi-teacher hybrid distillation framework. Our framework incorporates: (1)Probabilistic Dual-Stream Routing (PDSR) that leverages general-domaindata as regularization to enhance robustness. (2) Automated Asymmet-ric Conditioning (AAC) utilizing Vision-Language Models (VLMs) andorthogonal trigger words to ensure concept isolation, and (3) the De-coupled Hybrid Objective (DHO) that synergizes Trajectory-AnchoredFlow Matching (TA-FM) priors with target simulation and distribu-tion matching, ensuring robust optimization across heterogeneous multi-task distributions. Extensive experiments demonstrate that our unifiedarchitecture mitigates multi-LoRA interference and achieves enhancedconcept fidelity, effective feature isolation, and high-quality rapid syn-thesis. Code is available at https://github.com/Qwen-Applications/CollectionLoRA.

Abstract:
The reliance of mobile GUI agents on Multimodal LargeLanguage Models (MLLMs) introduces a severe privacy vulnerability:screenshots containing Personally Identifiable Information (PII) are of-ten sent to untrusted, third-party routers. These routers can exploit theirown MLLMs to mine this data, violating user privacy. Existing privacyperturbations fail the critical dual challenge of this scenario: protect-ing PII from the router’s MLLM while simultaneously preserving taskutility for the agent’s MLLM. To address this gap, we propose the Dual-Task Adversarial Protector (DualTAP), a novel framework that,for the first time, explicitly decouples these conflicting objectives. Dual-TAP trains a lightweight generator using two key innovations: (i) a con-trastive attention module that precisely identifies and targets only thePII-sensitive regions, and (ii) a dual-task adversarial objective that si-multaneously minimizes a task-preservation loss (to maintain agent util-ity) and a privacy-interference loss (to suppress PII leakage). To facilitatethis study, we introduce PrivScreen, a new dataset of annotated mobilescreenshots designed specifically for this dual-task evaluation. Compre-hensive experiments on six diverse MLLMs (e.g., GPT-5) demonstrateDualTAP’s state-of-the-art protection. It reduces the average privacyleakage rate to 31.7 percentage points (a 2.6× relative improvement)while, critically, maintaining an 80.8% task success rate—a negligibledrop from the 83.6% unprotected baseline. DualTAP presents the firstviable solution to the privacy-utility trade-off in mobile MLLM agents.Our code is available at https://github.com/fyzhang1/DualTAP.

Abstract:
Modern vision-language-model (VLM) based graphical user interface (GUI) agents are expected not only to execute actions accurately but also to respond to user instructions with low latency. While existing research on GUI-agent security mainly focuses on manipulating action correctness, the security risks related to response efficiency remain largely unexplored. In this paper, we introduce SlowBA, a novel backdoor attack that targets the responsiveness of VLM-based GUI agents. The key idea is to manipulate response latency by inducing excessively long reasoning chains under specific trigger patterns. To achieve this, we propose a two-stage reward-level backdoor injection (RBI) strategy that first aligns the long-response format and then learns trigger-aware activation through reinforcement learning. In addition, we design realistic pop-up windows as triggers that naturally appear in GUI environments, improving the stealthiness of the attack. Extensive experiments across multiple datasets and baselines demonstrate that SlowBA can significantly increase response length and latency while largely preserving task accuracy. The attack remains effective even with a small poisoning ratio and under several defense settings. These findings reveal a previously overlooked security vulnerability in GUI agents and highlight the need for defenses that consider both action correctness and response efficiency. Code can be found in https://github.com/tu-tuing/SlowBA.

Abstract:
Converting multi-view RGB observations into simulationready 3D environments remains challenging because current reconstruction pipelines produce monolithic scene representations without explicit physical structure. They are typically defined up to an arbitrary global rotation and entangle rigid foreground objects with background geometry, which hinders stable physical interaction. Existing solutions often recover interactivity by replacing reconstructed objects with retrieved CAD assets, but this introduces a slow retrieval-and-replacement stage and weakens scene-specific geometric fidelity. We propose GARDEN, an RGB-only framework that reformulates reconstruction as physicallygrounded scene factorization and outputs a structured hybrid scene representation. The key idea is to use gravity as a universal physical prior: we first align the reconstruction to a unified Gravity-View frame to resolve gauge ambiguity, then recover object-centric rigid meshes with accurate 6-DoF placement, and finally remove duplicate object geometry from the background through conditional 3D point classification. The resulting representation combines explicit rigid bodies with a decoupled background, enabling direct physics simulation while preserving visual realism. Experiments on both simulated and real multi-view scenes show that GARDEN improves object placement reliability, disentanglement quality, and rendering-simulation efficiency compared with retrieval-based baselines. Project page: https://sunjiahaovo.github.io/garden/

Abstract:
Lifting perspective images and videos to 360◦ panoramas enables immersive 3D world generation. Existing approaches often rely on explicit geometric alignment between the perspective and the equirectangular projection (ERP) space. Yet, this requires known camera metadata, obscuring the application to in-the-wild data where such calibration is typically absent or noisy. We propose 360Anything, a geometry-free framework built upon pre-trained diffusion transformers. By treating the perspective input and the panorama target simply as token sequences, 360Anything learns the perspective-to-equirectangular mapping in a purely data-driven way, eliminating the need for camera information. Our approach achieves state-of-the-art performance on both image and video perspective-to-360◦ generation, outperforming prior works that use ground-truth camera information. We also trace the root cause of the seam artifacts at ERP boundaries to zero-padding in the VAE encoder, and introduce Circular Latent Encoding to facilitate seamless generation. Finally, we show competitive results in zero-shot camera FoV and orientation estimation benchmarks, demonstrating 360Anything’s deep geometric understanding and broader utility in computer vision tasks. Additional results are available at https://360anything.github.io.

Abstract:
Diffusion Transformers (DiTs) have advanced video gener-ation with high-quality, temporally coherent results. However, extend-ing them to motion transfer, which requires following reference motionwhile aligning with a target prompt, remains challenging due to lim-ited understanding of motion and structure representations within DiTs.We analyze video DiTs at the attention-head level and identify distinctheads specialized for motion and spatial structure. Based on this insight,we propose a head-aware controllable motion transfer framework thatrequires no parameter updates. Our method refines motion cues frommotion-specialized heads via semantic correspondence guidance and pre-serves structure through selective feature injection. This head-level con-trol not only enables accurate motion transfer but also provides an in-terpretable foundation for controllable video generation with DiTs.

Abstract:
Optical flow methods typically rely on task-specific inductivebiases, such as correlation volumes, feature warping, and iterative refine-ment, among others, to reach high accuracy. While effective, such biasesconstrain the model to predefined heuristics, which can limit its expres-sivity and lead to more complex pipelines and additional computationalcost. We present FreeFlow, a hierarchical transformer built without anyflow-specific components, using instead a single feed-forward encoder–decoder. FreeFlow combines three attention variants: window attentionfor local processing, shifted-window attention for cross-window informa-tion exchange, and a global attention operating at a reduced resolu-tion. The resulting architecture scales naturally with model capacity,enabling a consistent accuracy gain from small to large variants. Despitethe absence of standard inductive biases, FreeFlow achieves state-of-the-art results on major benchmarks, including Sintel (0.68/1.48 EPE onClean/Final), KITTI-2015 (3.23 Fl-all), and Spring (3.192 1px), whileremaining memory efficient at 1080p inference.

Abstract:
We present FoundationGeo, a two-stage framework that ex-plicitly bridges relative and metric prediction via spatial calibration andprincipled data design. Stage 1 learns a high-fidelity, affine-invariant ge-ometry model by initializing with DINOv3 and training on a curated10.2M-sample multi-domain corpus with complementary local–detail su-pervision, yielding sharp boundaries and strong cross-domain general-ization. Stage 2 moves beyond global scaling by introducing lightweightpixel-wise calibration fields for metric estimation: a scale field for spa-tially varying metric alignment and a ray-direction correction field thatmitigates directional bias in point-map geometry, together producingmetrically consistent 3D point maps. Beyond model design, we identifycamera intrinsic coverage, especially focal length distribution mismatchbetween training and test data, as a key bottleneck for zero-shot met-ric generalization: performance drops sharply when test intrinsics falloutside the training distribution. To address this, we synthesize addi-tional training data across diverse focal lengths using a Blender-baseddata engine, repairing under-covered focal regimes and improving robust-ness under intrinsic shift. Extensive zero-shot evaluations across sevenbenchmarks show that FoundationGeo significantly strengthens cross-domain robustness, staying near the top across diverse domains whileavoiding the sharp cross-domain performance drops observed in othermethods. This consistency translates into the best overall performance,surpassing heavier baselines by over 5.2% on average. Project page:https://mx-liu6.github.io/FoundationGeo-web/

Abstract:
Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets and paired text descriptions. However, how to effectively and efficiently leverage such single-purpose motion LFMs, i.e., text-to-motion synthesis, in more diverse cross-modal and in-context motion generation downstream tasks remains largely unclear. Prior work typically adapts pretrained generative priors to individual downstream tasks in a task-specific manner. In contrast, our goal is to unlock such priors to support a broad spectrum of downstream motion generation tasks within a single unified framework. To bridge this gap, we present UMO, a simple yet general unified formulation that casts diverse downstream tasks into compositions of atomic per-frame operations, enabling in-context adaptation to unlock the generative priors of pretrained DiT-based motion LFMs. Specifically, UMO introduces three learnable frame-level meta-operation embeddings to specify per-frame intent and employs lightweight temporal fusion to inject in-context cues into the pretrained backbone, with negligible runtime overhead compared to the base model. With this design, UMO finetunes the pretrained model, originally limited to text-tomotion generation, to support diverse previously unsupported tasks, including temporal inpainting, text-guided motion editing, text-serialized geometric constraints, and multi-identity reaction generation. Experiments demonstrate that UMO consistently outperforms task-specific and training-free baselines across a wide range of benchmarks, despite using a single unified model. Code and models will be publicly available.

Abstract:
Scaling has driven major advances in vision foundation mod-els, yet extending it to metric depth estimation remains challenging dueto heterogeneous sensor noise, camera-dependent biases, and metric am-biguity in noisy cross-source 3D data. We introduce MetricAnything,a simple and scalable pretraining framework that learns metric depthfrom diverse, noisy 3D sources without manual prompts, camera-specificmodeling, or task-specific architectures. At its core is the Sparse Met-ric Prompt, formed by randomly masking depth maps, which acts asa universal interface to decouple spatial reasoning from sensor and cam-era biases. Pretrained on ∼20M image–depth pairs from reconstructed,captured, and rendered 3D data across 10,000+ camera models, Met-ricAnything demonstrates—for the first time—a clear scaling trend inmetric depth. The model excels at prompt-driven tasks such as depthcompletion, super-resolution, and Radar-camera fusion, while its distilledprompt-free student achieves state-of-the-art results in monocular depthestimation, camera intrinsics recovery, single/multi-view metric 3D re-construction, and VLA planning. Using its pretrained ViT as a visualencoder also significantly enhances Multimodal Large Language Modelsin spatial intelligence. These results show that metric depth estimationcan follow the same scaling laws as modern foundation models, paving ascalable and efficient path toward real-world metric perception. Projectpage: https://metric-anything.github.io/metric-anything-io/

Abstract:
Reconstructing and tracking dynamic 3D scenes is a funda-mental challenge in computer vision. Existing methods typically decou-ple geometry from motion: static multi-view reconstruction systems as-sume a rigid world, whereas dynamic tracking frameworks rely on explicitego-motion estimation or separate object motion models. In this work,we propose Flow4R, a unified framework that treats relative scene flowas the central representation linking 3D structure, camera ego-motion,and dynamic object motion. Given a two-view input, Flow4R employs ashared Vision Transformer to predict a compact, pixel-aligned propertyset comprising 3D point positions, scene flow, pose weights, and con-fidence maps. This flow-centric formulation allows local geometry andbidirectional motion to be jointly inferred in a single feedforward pass,eliminating the need for explicit pose regression heads or complex bundleadjustment. By training jointly on static and dynamic datasets, Flow4Rachieves state-of-the-art performance on 4D reconstruction and trackingbenchmarks, demonstrating the power of the flow-centric formulation forspatiotemporal scene understanding.

Abstract:
Quantifying plant growth dynamics from sparse longitudinal3D observations is fundamental for agriculture and plant sciences. Yet,plants pose unique challenges: they undergo intricate non-rigid deforma-tions, exhibit changing topology as new organs emerge, and often lackexplicit temporal correspondences between consecutive data acquisitionsdue to newly formed tissue. Methods designed for general scenes struggleto model topology changes and asynchronous organ growth characteris-tic of plants. To address these challenges, we introduce GrowFields, acompositional dynamic neural field representation for organ-aware 4Dplant growth modelling from point cloud time series. Our approach de-composes a plant into its constituent organs and aligns each organ intoits own canonical coordinate frame, isolating intrinsic growth patternsfrom global plant motion. We then learn a shared continuous neural de-formation field that models temporal dynamics across all organs, condi-tioned on learnable per-organ latent codes capturing organ identity andgrowth characteristics. The resulting modular yet unified representationnaturally accommodates the asynchronous development of plant organswhile remaining grounded in the practical setting of organ-level planttracking. We evaluate GrowFields on growth sequences from four plantspecies, assessing geometric fitting and organ tracking accuracy usingmanually annotated leaf-tip trajectories. Results demonstrate consistentimprovements in spatial precision, temporal coherence, and morphologi-cal fidelity over a range of existing representations.

Abstract:
Computer-aided design (CAD) plays a fundamental role inmodern manufacturing by providing the high precision required for in-dustrial production. Recent large language model based approaches for-mulate CAD generation as a sequence prediction problem and haveachieved promising results. However, existing methods and evaluationprotocols primarily emphasize visual similarity, while overlooking precisegeometric parameters and correct metric scale. Small numerical devia-tions that are negligible at the shape-level may still violate industrial tol-erance requirements, a problem further compounded by current autore-gressive paradigms that utilize command sequence representations, ag-gressively quantize numerical parameters to ease LLM prediction. In thiswork, we present Pointer-CAD v2. Compared with v1 [36], this versiondirectly predicts continuous values, bypassing the need for quantized nu-merical parameters and thereby eliminating quantization errors. Specif-ically, we propose a unified framework that decouples parameter rea-soning from geometric construction through a Plan-Then-Constructparadigm. Our method first produces a structured design plan withexplicit metric scale parameters. These parameters are organized intoa dictionary and directly referenced during sequence generation via apointer mechanism, eliminating discretization errors and ensuring dimen-sionally consistent execution. In addition, we construct a new large-scaledataset with plan-level annotation and introduce three hierarchical geom-etry accuracy metrics to evaluate parametric fidelity at the vertex, edge,and face levels. Extensive experiments demonstrate that Pointer-CADv2 consistently outperforms existing baselines and achieves substantialimprovements in geometric accuracy, enabling reliable CAD generationfor precision-critical engineering applications. Our code is available athttps://github.com/Snitro/Pointer-CAD-v2.

Abstract:
Aerial 6DoF localization typically relies on precise GNSSsignals or radiometrically rich 3D reconstructions, limiting scalability andon-board deployment. We propose SemCityLoc, a semantic–geometricalignment system that reframes aerial pose estimation as structuredsurface registration between foundation-model-derived visual priors andstandardized LoD-compliant 3D city models. Instead of matching sparsecontours or dense texture, our method aligns semantic surfaces and monoc-ular depth with lightweight semantic 3D building models, increasing pose Corresponding authordiscriminability in repetitive and occluded urban environments. To enableaccurate evaluation, we introduce SemCityLockeD, the first real-worldbenchmark combining centimeter-accurate UAV poses with standardizedLoD1–LoD3 semantic city models and challenging low-altitude imagery.Experiments demonstrate substantial improvements over existing map-based approaches, improving recall by up to 36% and reducing meanpositional error from 9.89 m to 2.62 m in challenging urban canyons. Ourresults indicate that semantically structured geometry provides sufficientand scalable constraints for high-precision aerial localization withoutradiometric scene reconstructions. The code and data are available athttps://albertchen98.github.io/SemCityLoc.

Abstract:
Cross-modal image registration is essential for multi-sensorperception but remains fundamentally challenging due to severe non-linear radiometric discrepancies and geometric distortions. Existing de-terministic matching methods lack uncertainty awareness, struggling tonavigate the resulting highly non-convex optimization landscape and fre-quently accumulating errors in ambiguous regions. In this paper, wepropose RBE-Flow, a novel framework that reformulates dense cross-modal flow estimation as a closed-loop recurrent Bayesian estimationproblem on learned feature manifolds. Diverging from standard feed-forward regression, RBE-Flow establishes a robust self-correcting mech-anism by deeply coupling feature-metric non-linear optimization withprobabilistic state updates. Specifically, a Recurrent Manifold Optimiza-tion (RMO) block iteratively generates flow observations and their as-sociated uncertainties, which are then optimally assimilated into theprior state via an Uncertainty-Adaptive Probabilistic Update (UAPU)using deterministic sigma-point projection. Crucially, the resulting cal-ibrated posterior covariance is fed back to adaptively regularize thedamping of subsequent optimization steps, allowing the system to mod-ulate its convergence based on predictive confidence. To ensure stableprobabilistic training, we introduce a hybrid supervision scheme featur-ing a geometry-aware rectified NLL loss that structurally prevents vari-ance collapse. Extensive experiments on challenging OSdataset, WHU-OPT-SAR, and RoadScene benchmarks demonstrate that RBE-Flowconsistently achieves state-of-the-art performance, outperforming exist-ing methods by a significant margin, particularly under strict sub-pixelcriteria. Project page: https://github.com/NEU-Liuxuecong/RBE-Flow

Abstract:
LiDAR-based Human Mesh Reconstruction (HMR) is crucialfor understanding human activities in real-world environments. How-ever, the inherent sparsity of LiDAR point clouds often causes severeloss of anatomical details, limiting reconstruction accuracy. Althoughrecent methods have made promising progress, most still follow a decou-pled pipeline that first estimates skeletal joints and then reconstructsthe mesh, leading to error propagation under sparse observations. Toaddress this issue and fully exploit both the topological guidance of theskeleton on the mesh and the spatial constraints of the surface mesh onthe skeleton, we propose SynHMR, a Synergistic Joint-Mesh Modelingframework for robust LiDAR-based HMR. Specifically, we first initializeboth the skeletal joints and the mesh vertices as queries equipped withpositional information for co-optimization. We then design a Geometry-Aware Transformer that leverages point cloud features to apply the at-tention mechanism between joint and vertex queries, enabling their it-erative refinement through mutual information exchange. Furthermore,we introduce a Noise-Augmented Learning strategy that injects per-turbed joint queries during training. This reduces the impact of noisewhile encouraging bidirectional association between joints and mesh. Ex-tensive experiments on LiDARHuman26M, SLOPER4D, and Human-M3 demonstrate that SynHMR achieves state-of-the-art performanceamong LiDAR-based HMR approaches. The project page is availableat https://github.com/ShiRui1208/SynHMR.

Abstract:
Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory-based approaches often condition generation on globally reconstructed 3D scenes by rendering anchor videos from the reconstructed geometry in the history. However, reconstructing a global 3D scene from multiple views inevitably introduces cross-view misalignment, as pose and depth estimation errors cause the same surfaces to be reconstructed at slightly different 3D locations across views. When fused, these inconsistencies accumulate into noisy geometry that contaminates the conditioning signals and degrades generation quality. We introduce AnchorWeave, a memory-augmented video generation framework that replaces a single misaligned global memory with multiple clean local geometric memories and learns to reconcile their cross-view inconsistencies. To this end, AnchorWeave performs coverage-driven local memory retrieval aligned with the target trajectory and integrates the selected local memories through a multi-anchor weaving controller during generation. Extensive experiments demonstrate that AnchorWeave significantly improves long-term scene consistency while maintaining strong visual quality, with ablation and analysis studies further validating the effectiveness of local geometric conditioning, multi-anchor control, and coveragedriven retrieval. We provide more qualitative demonstration videos in the supplementary material via an anonymized local webpage.

Abstract:
With the rapid advancement of vision–language models, anincreasing number of studies have explored their potential for SVG gener-ation tasks. Although existing approaches improve performance by con-structing large-scale SVG datasets and introducing SVG-specific tokens,they still suffer from limited generalization, redundant paths in code out-puts, and a lack of explicit reasoning. In this work, we present CTRL-S(Chain-of-Thought Reinforcement Learning for SVG), a unified frame-work that introduces a chain-of-thought mechanism to explicitly exposethe model’s reasoning process during SVG generation. To support thisstructured reasoning, we construct SVG-Sophia, a high-quality datasetcontaining 145K samples across SVG code refinement, Text-to-SVG, andImage-to-SVG tasks. By training the model to generate group-level struc-tured SVG code, CTRL-S significantly improves structural coherenceand visual fidelity. Furthermore, we adopt the GRPO algorithm and de-sign a multi-reward optimization framework, incorporating DINO, im-age–text similarity, format, and code efficiency rewards. Through jointmulti-reward optimization and multi-task training, our approach system-atically enhances overall generation capabilities. Extensive experimentsshow that CTRL-S outperforms existing methods, achieving higher tasksuccess rates, superior SVG code quality, and exceptional visual fidelity.Our code is available at https://github.com/hmwang2002/CTRL-S.

Abstract:
Hairstyle transfer has practical applications such as virtual try-on, yet remains challenging when the source and reference exhibit large head-pose discrepancies. We propose H-Adapter, which improves pose robustness by training with a region-specific loss that disentangles hair and non-hair objectives and thereby induces spatially disentangled cross-attention, from which a source-aligned hair edit mask is derived to guide diffusion-based inpainting. Experiments on pose-agnostic and posedifferent subsets demonstrate strong quantitative results, including the best FID, FID , and CLIP-I under pose differences, while maintaining CLIP competitive non-hair preservation and improving qualitative fidelity to fine-grained reference hairstyle details. Beyond source-conditioned transfer, H-Adapter supports practical extensions including reference-guided text-to-image generation, auxiliary prompt-based hair color control, and compatibility with an identity-preserving IP-Adapter variant. We also introduce a VLM-as-a-judge protocol and observe consistent gains in hairstyle faithfulness, non-hair preservation, and artifact quality.

Abstract:
Recent breakthroughs of transformer-based diffusion models, particularly with Multimodal Diffusion Transformers (MMDiT) driven models like FLUX and Qwen Image, have facilitated thrilling experiences in visual generation. However, these models rely only on the interactions between textual conditions and visual features to produce semantically aligned images. Once the interactions fail to reflect the nuanced compositional structure of the prompt, the generated images might be unsatisfactory. Thus, a comprehensive understanding of how different blocks and their interactions with textual conditions is crucial for better understanding the intrinsic attributes and for enhancing their interactions accordingly to strengthen the prompts adherence. In this paper, we first develop a systematic pipeline to comprehensively investigate each block’s functionality by removing, disabling, and enhancing textual hidden-states at corresponding blocks. Our analysis reveals that 1) semantic information appears in earlier blocks and finer details are rendered in later blocks, 2) removing specific blocks is usually less disruptive than disabling text conditions, and 3) enhancing textual conditions in selective blocks improves semantic attributes. Building on these observations, we propose TexTailor, a novel inference-time method for tailoring block-wise textual guidance. Our approach not only improves text-image alignment but also enables a range of downstream applications, including precise editing and inference acceleration. Extensive experiments demonstrated that our method outperforms various baselines and remains flexible across text-to-image generation, image editing, and inference acceleration. Our method improves T2I-Combench from 56.92% to 63.00% and GenEval from 66.42% to 71.63% on SD3.5, without sacrificing synthesis quality. These results advance understanding of MMDiT models and provide valuable insights to unlock new possibilities for further improvements. Our code is available at https://github.com/phil329/TexTailor.

Abstract:
While autoregressive (AR) Multimodal Large Language Mod-els (MLLMs) excel at complex document-to-markup generation, theirsequential decoding causes severe latency. Conversely, existing paralleland diffusion-based methods accelerate inference but often struggle tomaintain strict structural dependencies, resulting in significantly higherparsing errors. To bridge this gap, we propose DARL (Diffusion AutoRe-gression with Look-ahead), a novel hybrid decoding framework. DARLintroduces a Sliding Diffusion Block (SDB) that maintains a verifiedAR prefix for strict syntactic correctness while speculatively generat-ing a localized window of future tokens in parallel. To optimize thesediscrete diffusion trajectories, we introduce Online Monte Carlo Tra-jectory Generation (OMTG) and Diffusion Trajectory Preference Opti-mization (DTPO). OMTG dynamically samples candidate paths basedon the model’s real-time state, effectively mitigating the exposure biasinherent in static heuristics. DTPO integrates immediate and look-aheadrewards to optimize current accuracy and facilitate future correctness,ensuring stable and rapid convergence of the predictive trajectory space.Experiments on OmniDocBench-1.5 and olmOCR-Bench demonstratethat DARL establishes a new Pareto frontier in document parsing. Itachieves up to a 2.3× speedup while matching or exceeding the state-of-the-art accuracy of AR baselines. The code and model are available athttps://github.com/SCUT-DLVCLab/DARL.

Abstract:
As virtual try-on (VTON) systems become increasingly im-portant in fashion e-commerce, there is a growing need for reliable reference-free evaluation methods, since ground-truth images of the same personwearing the target garment are typically unavailable in real-world scenar-ios. To address this challenge, we propose VTON-IQA, a reference-freeframework for human-aligned image quality assessment without requir-ing ground-truth images. To model human perceptual judgments, we con-struct VTON-QBench, a large-scale human-annotated benchmark com-prising 62,688 try-on images generated by 14 representative VTON mod-els and 431,800 quality annotations collected from 13,838 qualified anno-tators. To the best of our knowledge, this is the largest dataset to date forhuman subjective evaluation in VTON. Extensive experiments show thatVTON-IQA achieves reliable human-aligned image quality assessment.Moreover, we conduct a comprehensive benchmark evaluation of 14 rep-resentative VTON models using VTON-IQA. The dataset and sourcecode are released at https://github.com/litelightlite/VTON-IQA.

Abstract:
Existing latent diffusion models excel at visual generationand editing tasks, employing autoencoders to project RGB images andvideos into latent spaces. However, their low-compression latents pro-duce redundant and excessive visual tokens, limiting scalability to high-resolution content such as 4K images and 2160p videos. One promis-ing approach for acceleration is to leverage deeply compressed latentspaces produced by deep compression autoencoders. However, trainingindustrial-scale models from scratch remains expensive and often fails tomatch the performance of pre-trained models. Alternatively, directly fine-tuning existing pre-trained models on deeply compressed latent spacesis appealing, but it introduces a critical challenge: a representation gapbetween the original latent space and the target compressed space, whichcan result in suboptimal convergence and even training instability. DC-Gen offers the first solution to tackle this challenge. Specifically, we in-troduce a novel embedding alignment process to bridge the representa-tion gap. Once the embedding spaces are aligned, only lightweight LoRAfine-tuning is required to unlock the pre-trained model’s full generativecapability. We validate the effectiveness of DC-Gen across tasks rangingfrom image and video generation to image editing. The resulting DC-Genmodels achieve quality comparable to pre-trained models while deliveringsubstantial speedups. For example, DC-Gen-FLUX reduces the latencyof 4K image generation by 53.8× on a single NVIDIA H100 GPU. Codeis available at https://github.com/dc-ai-projects/DC-Gen.

Abstract:
Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason orcreate within it. We challenge this paradigm by asking whether all modal-ities, including textual descriptions, spatial layouts, and editing instruc-tions, can be unified into a single visual representation. We presentFlowInOne, a framework that reformulates multimodal generation as apurely visual flow, converting all inputs into visual prompts and enablinga clean image-in, image-out pipeline governed by a single flow matchingmodel. This vision-centric formulation naturally eliminates cross-modalalignment bottlenecks, noise scheduling, and task-specific architecturalbranches, unifying text-to-image generation, layout-guided editing, andvisual instruction following under one coherent paradigm. To supportthis, we introduce VisPrompt-5M, a large-scale dataset of 5 millionvisual prompt pairs spanning diverse tasks including physics-aware forcedynamics and trajectory prediction, alongside VP-Bench, a rigorouslycurated benchmark assessing instruction faithfulness, spatial precision,visual realism, and content consistency. Extensive experiments show thatFlowInOne achieves state-of-the-art performance across unified generationtasks, outperforming open-source models and rivaling competitive com-mercial systems. These results establish FlowInOne as a strong foundationfor fully vision-centric generative modeling, where perception and creationare unified within a single continuous visual space. Code is available atcsu-jpg.github.io/FlowInOne.github.io.

Abstract:
We propose OTCache, a training-free framework for ac-celerating diffusion sampling via caching schedule prediction. Existinggraph-based caching methods reduce redundant computation by opti-mizing shortest-path objectives, but rely on an additive independenceassumption, which often breaks down in the low NFE regime. To addressthis issue, OTCache models caching schedules across inference budgetsas a smooth evolution in policy space, inspired by Optimal Transport(OT). The framework consists of three stages: (1) obtaining a high-fidelity reference schedule using a graph-based caching method un-der a conservative budget; (2) performing a lightweight anchor searchunder an extreme low-budget setting via Optuna optimization with anend-to-end perceptual objective; and (3) predicting schedules for tar-get budgets via quantile interpolation between the reference and an-chor policies using continuous warping representations. Experiments onFLUX.1 [dev], Qwen-Image, and HunyuanVideo show that OTCacheachieves 4.5×, 4.7×, and 3.66× acceleration, respectively, while consis-tently improving generation fidelity over state-of-the-art caching base-lines. This work provides a new perspective on accelerating diffusionmodels through Optimal-Transport-inspired schedule modeling. Code:https://github.com/UnicomAI/OTCache

Abstract:
Recent text-to-image models such as DALL·E-3 excel at following diverse prompts yet remain blind to individual aesthetic preferences. We study personalized image generation, where models must align outputs with a user’s implicit visual preferences based on a few historically preferred images and a short prompt. To this end, we introduce PIPBench, the first profile-inclusive benchmark for evaluating personalized image generation. We further propose a novel data construction pipeline that leverages psychological and demographic profiling dimensions for both real-user data collection and scalable agentbased data generation. Using PIPBench, we conduct a thorough evaluation of representative line of methods. Our experiments reveal key limitations in existing methods, suggesting new challenges and opportunities for personalized text-to-image synthesis. Project page: https: //wuyuhang05.github.io/PIPBench/

Abstract:
While instruction-based image editing, enabled by multi-modal generative models, has advanced significantly, existing bench-marks lack comprehensive evaluation of physics-based reasoning—a criti-cal capability for handling real-world scenarios. To address this, we intro-duce PhyEditBench, a benchmark designed to assess the physical under-standing of editing models. Guided by a hierarchical taxonomy, we estab-lish 4 primary classes and 12 subclasses. It comprises 238 high-quality,high-resolution, real-world instances—meticulously extracted fromvideos to capture authentic physical dynamics, alongside 35 syntheticAnti-Physics instances. Our empirical analysis of current SOTA editingmethods exposes substantial limitations in their physics-based reasoning.We further propose a training-free baseline named PhyWorld that usestest-time scaling and a latent reduction strategy. PhyWorld outperformscomparable models and suggests that the video generation process caneffectively serve as a reasoning mechanism for image editing. The projectpage is available at https://github.com/Previsior/PhyEditBench.

Abstract:
Diffusion models have shown promise in drag-style editing.Previous works mainly focus on point-based drag, which is inherentlyambiguous. This paper focuses on region-based drag and introduces anovel In-Context Region-based Drag (ICRDrag) method. Under the in-context learning framework, ICRDrag consumes a source image, a sourceregion mask, and a target region mask, producing the target draggedimage. Built upon the basic in-context learning model, we introduce twonovel attention regularization: 1) image-mask attention consistency toensure that a target region attends to similar source regions for image andmask modalities; 2) source-target attention correspondence to ensure themutual correspondence between source and target regions. To facilitateregion-based drag, we also construct Paired Region Dataset (PRD), alarge-scale dataset with paired masks and images. Extensive experimentsshow that ICRDrag significantly outperforms existing methods in bothquantitative metrics and user studies, achieving superior editing accuracyand visual fidelity. The dataset, code, and model are available at https://github.com/bcmi/ICRDrag-Region-Drag-Editing.

Abstract:
Di x001B usion models have achieved remarkable success in im-age and video generation, yet the high computational cost of iterativesampling remains a critical bottleneck for practical deployment. Featurecaching has emerged as a promising acceleration paradigm by reusingor predicting intermediate features across timesteps. However, existingtraining-free methods apply uniform prediction strategies that cannotadapt to the heterogeneous feature dynamics, causing signi x001C cant qualitydegradation under high acceleration ratios. We propose LinCa , a fea-ture caching framework based on learnable invertible networks. LinCadecomposes cached features into sub-components with distinct conti-nuity properties via a lightweight invertible network and applies dif-ferentiated prediction orders matched to each component. The strictinvertibility guarantees lossless reconstruction back to the original fea-ture space, forming a uni x001C ed Decompose-Predict-Reconstruct pipeline.By training separate predictors for di x001B erent models and timestep seg-ments, LinCa adapts to heterogeneous feature dynamics. Experiments onFLUX, Qwen-Image, and HunyuanVideo demonstrate that LinCa, withless than 0.2% additional parameters, signi x001C cantly outperforms exist-ing methods and maintains near-lossless quality at 5-7× speedup. Code:https://github.com/QHR69/LinCa.

Abstract:
Text-to-image diffusion models power everyday creative tasks,but they still reproduce the demographic biases in their training data. Oncommon prompts such as “a photo of a nurse,” “a photo of a CEO”, theyskew their outputs toward one gender, driven by the statistics of trainingdata rather than anything in the text. Existing debiasing methods showpromise in narrow settings but require retraining, batch-level control, orprompt-specific tuning, limiting their scalability. We propose EquiSteer,a training-free method that works per sample by steering cross-attention(CA) activations at inference time. For each target attribute, EquiSteerprecomputes steering vectors from contrastive prompts. Then at gener-ation time, a prompt-aware gate leaves attribute-specific prompts un-touched, while for neutral ones it clears existing attribute signals fromthe CA activations and injects a target attribute. Across SD-1.5, SD-2.1,SDXL, and SANA, EquiSteer reduces the average parity gap by up to87%, with minimal effect on image quality and text-image alignment.Code is available at https://github.com/Atmyre/EquiSteer.

Abstract:
Joint video super-resolution and deblurring (VSRDB) requires both efficient long-range temporal modeling and robustness to frame-wise exposure-duration variation, which changes the extent of motion blur across video frames. We propose FMA-Net++, a non-recurrent, sequence-level framework built from Hierarchical Refinement with Bidirectional Aggregation (HRBA) blocks. By stacking HRBA blocks, FMANet++ processes video frames in parallel while hierarchically expanding the temporal receptive field, avoiding the limited temporal receptive field of sliding-window designs and the sequential bottleneck of recurrent ones. To handle exposure-duration-dependent blur, we introduce an Exposure Time-aware Modulation (ETM) layer that conditions HRBA features on exposure embeddings from an Exposure Time-aware Feature Extractor (ETE). The conditioned features guide an exposure-aware flow-guided dynamic filtering module to predict motionand exposureaware degradation kernels. FMA-Net++ decouples degradation learning from restoration: the former predicts degradation priors and the latter exploits them for efficient high-resolution restoration. To evaluate VSRDB under controlled exposure-duration variation, we introduce the REDS-ME (multi-exposure) and REDS-RE (random-exposure) benchmarks. Trained solely on synthetic data, FMA-Net++ achieves stateof-the-art accuracy and temporal consistency on these benchmarks. It further shows strong out-of-distribution performance on GoPro and challenging real-world videos, while outperforming recent methods in both restoration quality and inference speed.

Abstract:
In this work, we propose a source-agnostic framework that dynamically refines a binary mask throughout the reverse diffusion process by computing the discrepancies of a pretrained diffusion model’s prediction for each latent time step. Rather than relying on a fixed threshold, our method introduces a time-dependent statistical thresholding scheme derived from the empirical mean and standard deviation of prediction discrepancies across the latent noisy images from the target distribution. This allows the mask to adapt to the model’s varying predictive confidence at different noise levels, effectively isolating domain-specific regions while preserving global structural coherence. Experimental results on the AFHQ and Celeba-HQ datasets demonstrate that our approach outperforms state-of-the-art unsupervised Image-to-Image methods in both realism (FID, KID) and faithfulness (SSIM, LPIPS). By requiring only a pretrained model of the target domain, our approach enables precise, automated localization and seamless translation across diverse source distributions without any specialized training. The project source code is available at: https://github.com/dtoma95/PM-Edit

Abstract:
Diffusion models excel in Real-World Image Super-Resolution (Real-ISR) due to their powerful generative priors but suffer from slow iterative sampling. Although existing one-step distillation methods accelerate inference, they typically require auxiliary teacher models that inflate training memory and restrict scalability to large-scale architectures. Furthermore, these fixed-step models lack the flexibility to trade off speed for quality. In this paper, we propose TEASR, a training-efficient anystep diffusion framework for Real-ISR that enables both one-step and multi-step restoration within a unified model. Our key idea is to perform self-adversarial distillation within a single diffusion model, eliminating the need for auxiliary teachers or discriminators. Specifically, we propose a timestep-aware rectification strategy that stabilizes one-step generation across noise levels. These two designs further enables the distillation of 20B-parameter diffusion models on a single GPU, significantly improving training efficiency. Moreover, we introduce a dual-branch diffusion transformer with decoupled timestep condition to separate the current noise state and the denoising target to enhance sampling quality. Extensive experiments demonstrate that TEASR supports seamless any-step sampling and consistently outperforms state-of-the-art methods across multiple datasets. Code: https://github.com/MediaX-SJTU/TEASR.

Abstract:
While recent flow-matching 3D generative models (e.g., Vec-Set) adopt structured representations, their tokens share global context,causing conventional training-free editing to suffer from semantic arti-facts such as collapsed preserved regions or incomplete transformations.To address this, we propose TanGO, a training-free framework thatenables adaptive per-token steering in the tangent space of generativedynamics. To realize this selective control, we formulate a one-step opti-mal control rule and determine the strength of each token’s control signalusing a von Mises-Fisher inspired directional discrepancy derived fromthe source and target velocity fields. Experiments show that TanGOsubstantially reduces structural artifacts and achieves state-of-the-artperformance, outperforming existing 3D editing baselines. The code ispublicly available at https://github.com/siw00-lim/TanGO.

Abstract:
Diffusion prior-based methods have shown impressive resultsin real-world image super-resolution (ISR), yet two key challenges per-sist: balancing pixel-level fidelity with semantic quality, and adapting todiverse degradations. Existing dual-branch approaches freeze the pixelmodule during semantic training, but the semantic branch can still ex-pand capacity within the pixel subspace, precluding genuine perceptualimprovement. Moreover, using a single static adapter cannot generalizeacross heterogeneous real-world corruptions. To address both issues, wepropose FreqOrtho-SR, which comprises: Frequency-guided Mixture ofLoRA Experts (FreqMoE), it routes inputs to specialized experts via anon-parametric FFT-based degradation-feature extractor that encodesfrequency-domain signatures, enabling stable and interpretable special-ization across corruption types; and Orthogonal Gradient Projection(OGP), which reframes the dual-objective optimization as a subspace-constrained problem: by extracting the pixel-fidelity subspace via SVDon combined expert weight deltas and projecting semantic gradients ontoits null space, OGP guarantees orthogonality between the two objectives,enabling genuinely complementary learning without mutual interference.Experiments show that FreqOrtho-SR achieves competitive overall per-formance and a strong fidelity-perception trade-off across multiple bench-marks with efficient single-step inference. The source code of our methodcan be found at sonhm3029/FreqOrtho-SR.

Abstract:
Image restoration aims to recover high-quality images fromdegraded observations, yet existing methods struggle with diverse degra-dation types. In this work, we propose SLAIR, an all-in-one image restora-tion framework integrating latent space separation and deterministictransport. We introduce a latent-space separation approach that ensuresstable, distinct representations of degraded and clean images, facilitat-ing effective flow matching. By learning continuous transport flow in alatent space via an ordinary differential equation (ODE), our methodenables deterministic restoration without relying on degradation labelsor stochastic sampling. While the restoration is performed in the la-tent space, a high-frequency refinement module is introduced into thelatent decoder to handle detail distortion during decoding. Experimentsdemonstrate that SLAIR outperforms existing approaches in terms ofboth perceptual quality and computational efficiency, making it a robustsolution for scalable, degradation-agnostic image restoration. Our projectpage is available at https://fightforql.github.io/SLAIR-page.

Abstract:
Underwater computer vision tasks, such as detection, restora-tion, and segmentation, are limited by the scarcity of large-scale and di-verse training data. We introduce WaterGen, a method for generatinglarge-scale, realistic, and diverse underwater images that provides inde-pendent control of the scene and water medium conditions. Our approachtreats underwater image generation as the decoupled control of two fac-tors: realistic and diverse scene content (what is in the image), and accu-rate and controllable water medium effects (what the water does to theimage). Existing methods generally achieve only part of this objective:they either provide controllability with limited realism or diversity, orgenerate realistic scenes without accurately and independently modelingwater-medium effects. Our key insight, that allows us to avoid this com-promise, is that scene generation and medium modeling can be decoupledwithin a latent diffusion framework, enabling diverse scene generation to-gether with accurate and controllable underwater appearance. To do this,we decompose underwater image synthesis into two stages. First, we fine-tune the latent diffusion U-Net using degradation-free underwater imagesso that it learns to generate diverse and realistic latent embeddings ofunderwater scene content without medium-induced degradation. Second,we formulate the physically accurate medium degradation synthesis asa conditional decoding process applied to these latent embeddings. Thisdecoupled design allows our model to generate diverse scenes with fullcontrol of underwater appearance. We leverage WaterGen to build large-scale synthetic underwater datasets that are diverse in scene structuresand accurate in water effects and pseudo-labels. We demonstrate that oursynthetic data consistently improve downstream performance in under-water restoration and semantic segmentation. Code and model weightsare available at https://github.com/jiayi-wu-umd/WaterGen.

Abstract:
Diffusion models typically suffer from error accumulationduring iterative sampling, commonly referred to as exposure bias. Wereveal systematic frequency-dependent discrepancies between trainingand inference, which can be interpreted as frequency-dependent SNRerror. Crucially, the direction of this mismatch varies across modelsand timesteps, indicating that fixed correction rules do not generalize.We propose Spectral Alignment (SPA), a lightweight, guidance-basedmethod that calibrates the power spectrum of intermediate predictionsto a pre-computed prior. Our approach consists of two stages: (1) of-fline fitting of a parametric spectrum model from training data, and (2)inference-time guidance via efficient FFT-based gradient computation.SPA introduces minimal computational overhead (3-4%) and is com-plementary to Classifier-Free Guidance (CFG). We demonstrate consis-tent improvements across diverse architectures, from pixel-space mod-els (DDPM, ADM) to latent diffusion models (SD2.0, SDXL) and flow-matching models (SD3.5, FLUX).

Abstract:
Diffusion Transformers have become the dominant paradigmin generative AI, but their high computational costs severely hinderreal-time applications. Prediction-based feature caching is widely usedto accelerate diffusion transformers; however, as the number of stepsincreases, the deviation between its predictions and the reference full-compute trajectory gradually grows. An intuitive idea is to use an onlineregression model to dynamically correct this deviation, but it faces theissue of label data being unavailable during the acceleration process.This paper presents a statistical observation that the residuals betweenthe features of full computation steps using caching methods and refer-ence full-compute trajectory locally exhibit a zero-mean Gaussian dis-tribution. By treating the features of full computation steps as noisyobservations of reference features, the data acquisition problem is re-solved. Based on this observation, a plug-and-play GP-Refiner correctionframework is proposed. This method utilizes Gaussian Process Regres-sion for correction and, leveraging the properties of GPR, introducesan uncertainty-adaptive computation strategy that triggers necessaryfull-computation calibration by monitoring the posterior variance in realtime. Experiments demonstrate significant improvements across differentmodels when combined with various state-of-the-art methods. Integrat-ing the proposed framework with TaylorSeer reduces the computationalload by 19.3% while improving PSNR by 0.9 dB and reducing LPIPSfrom 0.46 to 0.29. Code is available in https://github.com/Aredstone/GP-Refiner.

Abstract:
While 10B-level industrial foundation models have pushedthe boundaries of image inpainting, their prohibitive computational costsseverely hinder practical deployment. Constructing a highly optimizedtask-specific specialist offers a promising solution; however, extreme struc-tural compression inevitably triggers a severe representation bottleneck.To conquer this, we propose Moebius, a highly efficient lightweight in-painting framework. We systematically reconstruct the diffusion back-bone by introducing the Local-λ Mix Interaction (LλM I) block. Com-prising Local-λ and Interactive-λ modules, it elegantly summarizes spa-tial contexts and global semantic priors into fixed-size linear matrices,preserving complex latent interactions while drastically shedding param-eters. Furthermore, to unlock the full representational capacity of thishighly compact architecture, we synergistically pair it with an adap-tive multi-granularity distillation strategy. Operating strictly within thelatent space to avoid expensive pixel-space decoding, this strategy dy-namically balances multiple gradient-based losses to achieve high-fidelityalignment. Extensive experiments across natural and portrait bench-marks demonstrate that this optimal synergy enables Moebius to rival oreven surpass the generation quality of the 10B-level industrial generalistFLUX.1-Fill-Dev. Remarkably, Moebius achieves this using less than 2%of the parameters (0.22B vs. 11.9B) while delivering a > 15× accelerationin total inference time, setting a new efficiency standard for high-fidelityinpainting. Project page at https://hustvl.github.io/Moebius.

Abstract:
Diffusion Transformers (DiTs) are a dominant backbone forhigh-fidelity text-to-image generation due to strong scalability and align-ment at high resolutions. However, quadratic self-attention over densespatial tokens leads to high inference latency and limits deployment. Weobserve that denoising is spatially non-uniform with respect to aestheticdescriptors in the prompt. Regions associated with aesthetic tokens re-ceive concentrated cross-attention and show larger temporal variation,while low-affinity regions evolve smoothly with redundant computation.Based on this insight, we propose AccelAes, a training-free frameworkthat accelerates DiTs through aesthetics-aware spatio-temporal reduc-tion while improving perceptual aesthetics. AccelAes builds AesMask, aone-shot aesthetic focus mask derived from prompt semantics and cross-attention signals. When localized computation is feasible, SkipSparsereallocates computation and guidance to masked regions. We further re-duce temporal redundancy using a lightweight step-level prediction cachethat periodically replaces full Transformer evaluations. Experiments onrepresentative DiT families show consistent acceleration and improvedaesthetics-oriented quality. On Lumina-Next, AccelAes achieves a 2.11×speedup and improves ImageReward by +11.9% over the dense baseline.Code is available at https://github.com/xuanhuayin/AccelAes.

Abstract:
We propose LF-MultiDiffusion, a training-free panorama gen-eration method that extends MultiDiffusion to support linear projectionsbetween target and reference image spaces. Our key idea is to reformulatelatent aggregation as a regularized least-squares problem and solve itefficiently with a Krylov-based iterative solver inside the denoising loop.This formulation enables denser and more natural mappings than priortraining-free methods, yielding more stable generation with far fewerperspective views. As a result, LF-MultiDiffusion reduces the number ofimage generator evaluations during denoising and significantly improvesinference efficiency. Experiments show that LF-MultiDiffusion achievesbetter visual quality, text alignment, and panoramic consistency thanthe strongest training-free baseline, while providing a 15.36× speedup.Our project page is available at: https://ahykw.github.io/lfmd.

Abstract:
In diffusion and flow-matching generative models, guidance techniques are widely used to improve sample quality and consistency. Classifier-free guidance (CFG) is the de facto choice in modern systems and achieves this by contrasting conditional and unconditional samples. Recent work explores contrasting negative samples at inference using a weaker model, via strong/weak model pairs, attention-based masking, stochastic block dropping, or perturbations to the self-attention energy landscape. While these strategies refine the generation quality, they still lack a reliable control over the granularity or difficulty of the negative samples, and target-layer selection is often fixed. We propose Exponential Moving Average Guidance (EMAG), a training-free mechanism that modifies attention at inference time in diffusion transformers, with a statistics-based, adaptive layer-selection rule. Unlike prior methods, EMAG produces harder, semantically faithful negatives (finegrained degradations), surfacing difficult failure modes, enabling the denoiser to refine subtle artifacts, boosting the quality and human preference score (HPS) by +0.54 over CFG. We further demonstrate that EMAG naturally composes with advanced orthogonal guidance techniques, such as APG and CADS, further improving HPS. Code is available at https://github.com/drkkgy/EMAG.

Abstract:
Colour is one of the most perceptually salient yet least con-trollable attributes in image generation. Although recent diffusion mod-els can modify object colours from user instructions, their results of-ten deviate from the intended hue, especially for fine-grained and localedits. Early text-driven methods rely on discrete language descriptionsthat cannot accurately represent continuous chromatic variations. Toovercome this limitation, we propose ColourCrafter, a unified diffusionframework that transforms colour editing from global tone transfer intoa structured, region-aware generation process. Unlike traditional colour-driven methods, ColourCrafter performs token-level fusion of RGB colourtokens and image tokens in latent space, selectively propagating colourinformation to semantically relevant regions while preserving structuralfidelity. A perceptual Lab-space Loss further enhances pixel-level pre-cision by decoupling luminance and chrominance and constraining ed-its within masked areas. Additionally, we build ColourfulSet, a large-scale dataset of high-quality image pairs with continuous and diversecolour variations. Extensive experiments demonstrate that ColourCrafterachieves state-of-the-art colour accuracy, controllability and perceptualfidelity in fine-grained colour editing. Our project is available at https://yangyuqi317.github.io/ColourCrafter.github.io/

Abstract:
Recently, diffusion models have been widely adopted in gen-erative modeling and have served as foundational models for many imagegeneration tasks. To control the generation without costly re-training orfine-tuning, many works seek inference-time guidance methods to steerthe latent via a differentiable objective at inference time. However, thesemethods cannot effectively preserve the original Gaussian distributionbecause they introduce distributional drift, thereby degrading the sam-ple quality. To address this gap, we propose DiffRGD, a distribution-aware guidance framework that explicitly preserves the latent Gaussianstructure. DiffRGD formulates each sampling step as a constrained op-timization problem on a spherical manifold induced by the latent Gaus-sian distribution, and solves it efficiently via Riemannian Gradient De-scent (RGD). DiffRGD is a plug-and-play method that can be seamlesslyintegrated into any pre-trained diffusion model. Extensive experimentsdemonstrate that DiffRGD outperforms previous methods in most im-age restoration and conditional generation tasks. Our project page isavailable at https://diffrgd.github.io/.

Abstract:
As the most perceptually powerful Face Video Super-Resolution (FVSR) method, existing works in Generative FVSR (GFVSR) mainly exploit the generative prior of pretrained diffusion models. However, viewed as full generation, they suffer from fixed sampling and expensive inference costs if without large-scale auxiliary training. Furthermore, an excessive pursuit of generic perceptual metrics often results in low fidelity. To address these issues, we present Dynamic Trajectory Initialization (DTI) paradigm for GFVSR, which reformulates GFVSR as an input-driven directional restoration. With a novel enhancement-andinjection conditioning mechanism for pretrained DiT backbone, fidelity of our model has been significantly improved without compromising perceptual quality. To dynamically set the starting sampling point, we propose a Discriminative Guide (DG) trained via objective Signal-to-Noise Ratio (SNR) alignment. With only minor model adaptation and finetuning, our method achieves a SOTA overall performance across diverse metrics and benchmarks. An analysis of relationship between actual comprehensive quality and common metrics is also conducted, which demonstrates the perception-distortion trade-off and that the LPIPS is the most convincing metric in our case. Code: https://github.com/MediaXSJTU/DTI

Abstract:
Di!usion and flow-matching models dominate conditionalimage generation, yet inference-time scaling for these models is far lessdeveloped than for autoregressive language models. Because final qual-ity is highly sensitive to the initial noise seed, many approaches spendextra compute on seed search or resampling under a black-box reward,but typically maintaining a constant memory footprint throughout in-ference. We show that relaxing this constraint enables an underexploredinference-time scaling axis: by front-loading exploration, evaluating manyseeds early, and pruning aggressively, we can use a fixed compute budgetmore e!ectively. Progressive Seed Pruning (PSP) scores intermediate de-noised estimates and progressively narrows the candidate set so that onlypromising trajectories are fully denoised, while keeping the total num-ber of model evaluations fixed. Across di!usion and flow-matching back-bones, PSP consistently improves reward-guided selection and achieveshigher GenEval scores (automated) and better human evaluation onprompt-alignment than best-of-N , importance-sampling, and tree-searchbaselines at matched compute. Project page: vision.caltech.edu/psp.Code: github.com/rogerioagjr/psp

Abstract:
Multimodal large language models (MLLMs) have becomethe dominant paradigm for chart understanding, yet they suffer from acritical vulnerability: sycophancy. When users embed false premises inqueries about charts, models override visual evidence to comply with theuser’s misconception. Existing mitigations incur a severe safety-utilitytrade-off : suppressing sycophantic compliance inevitably triggers over-refusal of valid queries, degrading general performance. To address thischallenge, we introduce Re-Check, a training-free inference frameworkthat follows a “Verify-then-Answer” workflow: it first decomposes userqueries into atomic claims, then verifies each claim against the chartimage, and finally routes the generation through an adaptive three-way mechanism. To ensure verification reliability, we propose the Con-trastive Visual Dependency Score (CVDS), an information-theoreticmetric that measures the KL divergence between the model’s predic-tions with and without visual input, filtering out textual-bias-drivenmisjudgments. Through extensive experiments on diverse benchmarks,Re-Check improves overall accuracy by 15.01% (from 57.49% to 72.50%with Qwen3-VL-8B) on sycophancy scenarios, while maintaining compet-itive performance on general benchmarks such as ChartQAPro. Theseresults demonstrate a superior safety-utility trade-off. Code is availableat: https://github.com/X1Wang/ReCheck-code.

Abstract:
Existing image forgery detection (IFD) methods either ex-ploit low-level, semantics-agnostic artifacts or rely on multimodal largelanguage models (MLLMs) with high-level semantic knowledge. Althoughnaturally complementary, these two information streams are highly hetero-geneous in both paradigm and reasoning, making it difficult for existingmethods to unify them or effectively model their cross-level interactions.To address this gap, we propose ForenAgent, a multi-round interactiveIFD framework that enables MLLMs to autonomously invoke, execute,and iteratively refine Python-based low-level tools around the detectionobjective, thereby achieving more flexible and interpretable forgery analy-sis. ForenAgent adopts a two-stage training pipeline with Cold Start andReinforcement Fine-Tuning to progressively improve tool interaction andreasoning adaptability. We design a human-inspired dynamic reasoningloop with global perception, local focusing, iterative probing, and holisticadjudication, and implement it as both a data-sampling strategy anda task-aligned process reward. To support training and evaluation, webuilt FABench, a heterogeneous agent-forensics dataset with 100k imagesand about 200k agent-interaction question-answer pairs. Experimentsshow that ForenAgent exhibits emergent tool-use competence and re-flective reasoning on challenging IFD tasks when assisted by low-leveltools, charting a promising route toward general-purpose IFD. The codeis available at https://github.com/zfr00/ForenAgent.

Abstract:
While high-quality preference data is vital for fine-grainedvision-language alignment, its creation remains bottlenecked by the highcost and subjective noise of human annotation. We propose a self-supervisedframework that derives solid, highly discriminative supervisory signalsthrough deterministic reconstruction feedback. By transforming abstractsemantic matching into concrete visual reconstruction, our approach pro-vides reliable consistency signals that overcome the input limits of tra-ditional models. A reward model trained on our generated data achievescompetitive accuracy across multiple zero-shot preference benchmarksusing only 20% of the typical training data volume. Furthermore, fine-tuning VLMs via Direct Preference Optimization (DPO) based on thisdata significantly improves performance in handling complex, high-densitydescriptions. Our results demonstrate the potential of reconstruction-driven self-correction for low-cost, high-precision alignment of multi-modal models. Our code of the scoring model is released at https://github.com/XC0053/iterative-perceptual-alignment.

Abstract:
Unified Multimodal Models (UMMs) integrate image under-standing and generation within a single architecture, yet how the twotasks interact remains understudied. We investigate transferability inUMMs: whether training a capability on one task improves the samecapability on the other without explicit supervision. Through controlledexperiments, we empirically find that transferability depends on archi-tecture—models with fully shared transformer backbone and a unifiedvisual encoder exhibit consistent cross-task transfer, while loosely cou-pled designs show little or none. Leveraging this transferability, we pro-pose a practical training strategy. The most straightforward way to im-prove a target generative capability (e.g., counting) is to fine-tune gen-eration directly, but this can degrade visual quality due to distributionshift. Instead, we train the corresponding understanding task and letit transfer into generation, which improves capability-specific genera-tive performance while minimizing distribution shift. We validate thisacross three capabilities—counting, spatial relation, and text recogni-tion/generation—showing that cross-task transferability can be system-atically exploited in UMMs.

Abstract:
Current Large Multimodal Models (LMMs) struggle withhigh-resolution visual inputs during the reasoning process, as the num-ber of image tokens increases quadratically with resolution, introducingsubstantial redundancy and irrelevant information. A common practiceis to identify key image regions and refer to their high-resolution counter-parts during reasoning, typically trained with external visual supervision.However, such visual supervision cues require costly grounding labelsfrom human annotators. Meanwhile, it remains an open question how toenhance a model’s grounding abilities to support reasoning without rely-ing on additional annotations. In this paper, we propose High-resolutionAnnotation-free Reasoning Technique (HART), a closed-loop frameworkthat enables LMMs to focus on and self-verify key regions of high-resolution visual inputs. HART incorporates a post-training paradigmin which we design Advantage Preference Group Relative Policy Opti-mization (AP-GRPO) to encourage accurate localization of key regionswithout external visual annotations. Notably, HART provides explain-able reasoning pathways and enables efficient optimization of localiza-tion. Extensive experiments on MME-RealWorld-Lite, TreeBench, VBench, HR-Bench-4K/8K, and MMStar demonstrate that HART im-proves performance across a wide range of high-resolution visual tasks,consistently outperforming strong baselines. Code will be available athttps://github.com/RL-MIND/HART.

Abstract:
Image Quality Assessment (IQA) in open-world environmentsremains challenging due to limited generalization and interpretability.Recent approaches based on multimodal large language models (MLLMs)introduce textual reasoning for quality prediction, yet their judgmentsrely heavily on semantically biased internal representations, making theminsensitive to low-level perceptual degradations. We propose IQA-T1, atool-based visual evidence reasoning framework that augments MLLMreasoning with explicit perceptual observations. During inference, themodel autonomously invokes specialized analysis tools to generate struc-tured visual evidence, such as noise residual maps, gradient statistics,and frequency spectra, which are progressively integrated into the rea-soning process. To support this paradigm, we construct Q-Tool, a datasetcontaining 11k multimodal reasoning chains grounded in tool-generatedevidence. Extensive experiments on seven IQA benchmarks show thatIQA-T1 achieves the best overall performance across datasets while pro-ducing interpretable and evidence-grounded quality assessments. Codeand dataset are available at https://github.com/zibuyu-02/IQA-T1.

Abstract:
Knowledge-based Visual Question Answering (KB-VQA) re-quires models to combine image understanding with external knowledge.Most prior methods use a fixed retrieve-then-generate pipeline with a pre-selected retriever and a static top-k setting, which is not adaptive duringreasoning. We propose ProMSA, a progressive multimodal search agentfor KB-VQA. Given an image-question pair, the agent iteratively choosesimage search, text search, or stop, under explicit tool-call budgets andwith deduplication to avoid redundant retrieval. For training, we first userejection-sampling SFT to learn valid tool-use formats, then optimize theagent with TN-GSPO, a sequence-level RL objective that normalizesupdates by both generation length and tool-interaction depth. Experi-ments on E-VQA and InfoSeek show consistent gains over strong RAGand agent baselines, and improved retrieval and end-to-end accuracy.Thecode is available at https://github.com/DingWu1021/Promsa.

Abstract:
Unified models (UMs) combine visual understanding (I2T)and generation (T2I) in a single framework, alongside broader unimodalcapabilities. We focus on the cross-modal pair T2I and I2T, where cross-consistency—what a model understands, it should be able to generate—is a core promise of unification and a practical necessity in applicationsthat compose both capabilities. Yet, existing benchmarks evaluate themin isolation: FID/GenEval for T2I; MME/MMBench for I2T. We showthat this gap is consequential: models that score competitively on thesebenchmarks can fail severely when their own understanding and gener-ation are composed, progressively losing core entities, attributes, spa-tial relations, and counts, resulting in semantic drift. To quantify drift,we introduce the Semantic Drift Protocol (SDP), inspired by the Tele-phone Game: starting from a caption or image, we iteratively alternateI2T and T2I over multiple generations and measure how faithfully se-mantics are preserved. We propose two complementary metrics: MeanCumulative Drift (MCD), an embedding-based measure of overall con-tent retention across three representation spaces, and Multi-GenerationGenEval (MGG), which extends GenEval’s object-level compliance scor-ing across generations. To stress-test models beyond COCO-style data,we create a benchmark of 400 image-text pairs sampled from NoCapsand DOCCI that emphasizes novel objects and fine-grained descriptions.Applying SDP to seven recent unified models reveals that drift behaviorvaries dramatically and is not predicted by single-pass scores: BAGELretains high semantic fidelity over multiple generations, while VILA-Uand Janus variants collapse within five generations, despite comparableisolated metrics. We identify six recurring failure modes and find thatdegradation is typically catastrophic rather than gradual: once a criticalerror occurs, subsequent generations compound it. Overall, SDP exposesfailure modes that single-pass benchmarks miss, enabling a more faithfulassessment of unified model reliability. Code and benchmark resourcesare available at https://github.com/mollahsabbir/telephone-game-semantic-drift.

Abstract:
Text and formulas constitute the core informational components of many documents. Accurately and efficiently recognizing both is crucial for developing robust and generalizable document parsing systems. Recently, vision-language models (VLMs) have achieved impressive unified recognition of text and formulas. However, they are largesized and computationally demanding, restricting their usage in many applications. In this paper, we propose UniRec-0.1B, a unified recognition model with only 0.1B parameters. It is capable of performing text and formula recognition at multiple levels, including characters, words, lines, paragraphs, and documents. To implement this task, we first establish UniRec40M, a large-scale dataset comprises 40 million text, formula and mixed samples, enabling the training of a powerful yet lightweight model. Secondly, we identify two challenges when building such a lightweight but unified expert model. They are: structural variability across levels and semantic entanglement between textual and formulaic content. To tackle these, we introduce a hierarchical supervision training that explicitly guides structural comprehension, and a semantic-decoupled tokenizer that separates text and formula representations. Finally, we develop a comprehensive evaluation benchmark covering Chinese and English documents from multiple domains and with multiple levels. Experimental results on this and public benchmarks demonstrate that UniRec-0.1B outperforms both general-purpose VLMs and leading document parsing expert models, while achieving 2-9× speedup, validating its effectiveness and efficiency. Codebase and Dataset: https://github.com/Topdu/OpenOCR.

Abstract:
Theearlydetectionandpreventionofcolorectalcancer(CRC)ishighly dependentontheaccuratepolypsegmentationfromendoscopicvideos.However, existing video polyp segmentation (VPS) methods are largely hampered by the scarcity of large-scale, high-quality pixel-level annotations. While some semisupervised VPS models have been proposed, their performance is limited by the unreliability of pseudo-labels produced in the training process. In addition, inherent challenges of endoscopic videos, such as low contrast, significant inter-frame variations, and consecutive low-quality frames, further exacerbate the above issues. To address these shortcomings, we propose a unified semi-supervised video polyp segmentation framework, dubbed synergistic confidence–dispersion learning (SCDL), which aims to simultaneously improve pseudo-label reliability, reduce regional supervision bias, and alleviate ambiguities caused by low-contrast features. Our framework has three innovative components: (1) a new spectral convex optimization separation (SCOS) algorithm, which employs convex optimization in a confidence–dispersion space for robust pseudo-label selection, (2) contextual consistency perturbation (CCP), a novel augmentation strategy that reduces regional supervision bias by forcing semantic reconstruction from unreliable areas; and (3) a prototype-guided adaptive refinement (PAR) module that leveragesprototype-drivenaggregationandobject-awarefusiontosharpenfeature discrimination, suppress background clutter, and strengthen temporal coherence. Extensive experiments demonstrate that our model consistently achieves SOTA performance on the SUN-SEG dataset across all labeled-data ratios, and exhibits zero-shot generalization capability on the unseen CVC-ClinicDB dataset. Code are available at https://github.com/Yuanqin-He/SCDL.

Abstract:
Millimeter-wave (mmWave) radar provides privacy-preserving sensing and is valuable for human action recognition (HAR). Existing mmWave point cloud datasets are limited in scale and mostly collected under homogeneous single-source settings, preventing current methods from handling real-world distribution shifts caused by heterogeneous radar sources, such as different devices and frequency bands. To address this, we introduce UniMM-HAR, the largest and first mmWave point cloud HAR dataset for heterogeneous multi-source scenarios, standardizing three distinct radar configurations to realistically evaluate crosssource generalization. We further propose the Doppler-aware Point Cloud Network (DAP-Net) to tackle heterogeneity challenges. DAP-Net enhances intra-modal representations and performs cross-modal alignment to learn source-invariant action semantics. Leveraging action-consistent spatio-temporal Doppler patterns as anchors, the Dual-space Doppler Reparameterization (D²R) module performs sample-adaptive geometric densification and Doppler-guided feature recalibration, while the Text Alignment Module (TAM) provides stable semantic anchors via a pretrained textual space. Experiments show that DAP-Net significantly outperforms existing methods under heterogeneous radar settings, achieving state-of-the-art accuracy and strong cross-source robustness. Code and dataset are publicly available at https://github.com/jolin830/DAP-Net.

Abstract:
Assessing AI-generated images remains a signi x001C cant chal-lenge because both visual quality and prompt-image alignment are vital.Existing metrics prioritize visual quality, but the exploration of semanticalignment with user prompts remains limited. To address this issue, wepropose a novel cyclic mutual information maximization framework forAI-generated image quality assessment (CMIM-AIGIQA). Unlike exist-ing methods, our framework focuses on promoting e x001B ective multimodalinformation fusion in the deep feature domain, which has cyclic mutualinformation maximization phases. In the forward phase, we maximize themutual information between text and image features to generate re x001C ned,cross-aware representations. The re x001C ned features are then integratedthrough a fusion network. In the backward phase, the mutual informationbetween the joint embedding and every single modality is maximized toensure that the joint embedding retains critical semantic and visual cues.Experiments on three datasets demonstrate that our method e x001B ectivelybridges the gap between visual quality and prompt-image alignment. Thecode is available at https://github.com/Buka-Xing/CMIM-AIGIQA.

Abstract:
Traditional image stitching methods estimate warps from hand-crafted geometric features, whereas recent learning-based solutions leverage semantic features from neural networks instead. These two lines of research have largely diverged along separate evolution, with virtually no meaningful convergence to date. In this paper, we take a pioneering step to bridge this gap by unifying semantic and geometric features with UniStitch, a unified image stitching framework from multimodal features. To align discrete geometric features (i.e., keypoint) with continuous semantic feature maps, we present a Neural Point Transformer (NPT) module, which transforms unordered, sparse 1D geometric keypoints into ordered, dense 2D semantic maps. Then, to integrate the advantages of both representations, an Adaptive Mixture of Experts (AMoE) module is designed to fuse geometric and semantic representations. It dynamically shifts focus toward more reliable features during the fusion process, allowing the model to handle complex scenes, especially when either modality might be compromised. The fused representation can be adopted into common deep stitching pipelines, delivering significant performance gains over any single feature. Experiments show that UniStitch outperforms existing state-of-the-art methods with a large margin, paving the way for a unified paradigm between traditional and learning-based image stitching. Our project page is available at https://mmelodyy.github.io/projects/unistitch/.

Abstract:
Infrared-visible image fusion (IVIF) is pivotal for multimodalperception, yet reconciling the inherent information disparity betweenthermal and textural features remains a fundamental challenge. Exist-ing prior-guided methods often rely on static constraints that induceoptimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.g., CLIP/DINO), which frequently fail toexploit the intrinsic modality characteristics essential for high-fidelityfusion. To address these issues, we propose P²Fusion, a prior-guideddistillation-based framework that reformulates IVIF via dual intrinsicprompts. Instead of imposing hard-coded penalties, we distill image-intrinsic priors, thermal saliency and spatial quality—into learnable,dynamic regulators. Specifically, a Teach-to-Fuse mechanism providesdual-granularity progressive guidance, coupled with a Gated DynamicExpert Recalibration (GDER) module for decoupled feature refinement.This design enables the network to adaptively mediate modal competi-tion through expert specialization. Extensive experiments demonstratethat P²Fusion achieves state-of-the-art performance across five main-stream datasets. Notably, our framework demonstrates consistent per-formance advantages in fusion quality, achieving state-of-the-art resultsin 14 out of 20 key evaluation metrics across 5 benchmarks. Further-more, it effectively contributes to the robustness of downstream percep-tion, such as +3.2% mAP on MSRS, +0.5% mAP on M3FD and +0.9%mAP on DroneVehicle for object detection. Our code will be available athttps://github.com/YiShi99/P2Fusion.

Abstract:
Existing advanced video frame interpolation (VFI) methods strug-gle to achieve accurate per-pixel motion or object-level motion estimation. Thereasons lie in that pixel-level motion estimation allows for infinite possibilities,making it challenging to guarantee global motion consistency. Conversely, lo-cal pixel-level motion tends to be discontinuous whereas object-level motionestimation is typically optimized for global motion. Therefore, a hierarchicalmotion learning scheme is imperative to enhance the consistency and continu-ity of motion prediction in VFI. To this end, we marry the object-level motionto the pixel-level motion to construct a hierarchical motion estimation frame-work. This approach elaborately incorporates object priors derived from open-world knowledge models, such as Segment Anything Model (SAM), to facili-tate latent object-level motion learning. In particular, a hybrid contextual fea-ture extraction module (HCE) is employed to aggregate both pixel-wise and se-mantic representations. This is further reinforced by hierarchical motion loss(HML) and hierarchical feature loss (HFL), which respectively simulate the con-sistent and continuous motion patterns for individual objects. Our frameworksupports two configurations: a full variant with HCE, HML, and HFL for higheraccuracy, and an efficient variant with only HML and HFL, which introducesno additional inference overhead. Extensive experiments demonstrate consistentimprovements over state-of-the-art VFI methods across multiple benchmarks.https://github.com/hhhhhumengshun/SAM-VFI

Abstract:
Prototypical part-based models provide explainable predic-tions by comparing input regions to learned prototypes. However, currentapproaches are burdened by complex, multi-stage training pipelines andheavily rely on auxiliary regularization to prevent prototype collapse. Toovercome these limitations, we introduce Orthonormal Prototype Align-ment Learning (OPAL), a single-stage, end-to-end framework that sim-plifies interpretable classification. Our approach anchors the latent spaceusing predefined orthonormal bases, embedding each class within a dedi-cated subspace spanned by fixed part-prototypes. To achieve precise partlocalization, OPAL enforces spatial competition across feature maps.This mechanism isolates sparse, discriminative regions, directing eachprototype to consistently attend to the same semantic concept across dif-ferent images. By framing classification as a direct representation align-ment task, our method eliminates the need for auxiliary losses. Extensiveexperiments on fine-grained benchmarks demonstrate that OPAL outper-forms both its non-interpretable counterparts and state-of-the-art part-prototype methods, delivering granular visual explanations by explicitlyrevealing the specific image regions driving every prediction. Code isavailable at https://github.com/ilancarretero/OPAL.

Abstract:
Slot Attention, an approach that binds different objects in ascene to a set of "slots", has become a leading method in unsupervisedobject-centric learning. Most methods assume a fixed slot count K, andto better accommodate the dynamic nature of object cardinality, a fewworks have explored K-adaptive variants. However, existing K-adaptivemethods still suffer from two limitations. First, they do not explicitlyconstrain slot-binding quality, so low-quality slots lead to ambiguousfeature attribution. Second, adding a slot-count penalty to the recon-struction objective creates conflicting optimization goals between reduc-ing the number of active slots and maintaining reconstruction fidelity.As a result, they still lag significantly behind strong K-fixed baselines.To address these challenges, we propose Quality-Guided K-AdaptiveSlot Attention (QASA). First, we decouple slot selection from recon-struction, eliminating the mutual constraints between the two objectives.Then, we propose an unsupervised Slot-Quality metric to assess per-slotquality, providing a principled signal for fine-grained slot–object binding.Based on this metric, we design a Quality-Guided Slot Selection schemethat dynamically selects a subset of high-quality slots and feeds them intoour newly designed gated decoder for reconstruction during training. Atinference, token-wise competition on slot attention yields a K-adaptiveoutcome. We conduct experiments on both object discovery and objectproperty prediction. Results show that QASA substantially outperformsexisting K-adaptive methods on both real and synthetic datasets. More-over, on real-world datasets, QASA surpasses K-fixed methods. The codeis available at https://github.com/ouyangtianran/QASA-tianran.Fig. 1: mBOi vs. slot count K on COCO [25]. K-fixed methods, such as SPOT [20]and DINOSAUR [33], show large performance fluctuations as K varies. By contrast,K-adaptive methods do not rely on a carefully tuned K. Our method substantiallyoutperforms existing K-adaptive baselines, including MetaSlot [26] and AdaSlot [16],and even surpasses strong K-fixed baselines.

Abstract:
Open-vocabulary 3D object detection aims to localize andrecognize objects beyond a fixed training taxonomy. In multi-view RGBsettings, recent approaches often decouple geometry-based instance con-struction from semantic labeling, generating class-agnostic fragments andassigning open-vocabulary categories post hoc. While flexible, such de-coupling leaves instance construction governed primarily by geometricconsistency, without semantic constraints during merging. When geomet-ric evidence is view-dependent and incomplete, this geometry-only merg-ing can lead to irreversible association errors, including over-mergingof distinct objects or fragmentation of a single instance. We proposeGroup3D, a multi-view open-vocabulary 3D detection framework thatintegrates semantic constraints directly into the instance constructionprocess. Group3D maintains a scene-adaptive vocabulary derived from amultimodal large language model (MLLM) and organizes it into semanticcompatibility groups that encode plausible cross-view category equiva-lence. These groups act as merge-time constraints: 3D fragments are asso-ciated only when they satisfy both semantic compatibility and geometricconsistency. This semantically gated merging mitigates geometry-drivenover-merging while absorbing multi-view category variability. Group3Dsupports both pose-known and pose-free settings, relying only on RGBobservations. Experiments on ScanNet and ARKitScenes demonstratethat Group3D achieves state-of-the-art performance in multi-view open-vocabulary 3D detection, while exhibiting strong generalization in zero-shot scenarios. The project page is available at https://ubin108.github.io/Group3D/.

Abstract:
Small Object Detection (SOD) is a fundamental yet chal-lenging problem in computer vision due to its limited spatial resolu-tion and weak visual cues. Although recent approaches have achievedremarkable advances, the background distractors in different frequencyspectra still degrade the performance. In this paper, we propose a novelsmall object detection framework termed SFDNet, which is capableof detecting small objects via efficient spectrum-aware feature disentan-glement. Specifically, we propose an Adaptive Spectrum Disentangle-ment (ASD) module that decomposes backbone features into multiplecomplementary spectral components, aiming to construct discriminativeobject-relevant representations by discarding the background distrac-tors for each component. Afterwards, to strengthen the semantic con-sistency of the similar objects in the same class, we propose a Class-Wise Prototype Distillation (CPD) procedure, which establishes classprototypes for the object instances and enforces the compact repre-sentation by efficient prototype distillation. Extensive experiments onmultiple challenging benchmarks show that SFDNet outperforms exist-ing state-of-the-art methods by a large margin. Our code is available athttps://github.com/ManOfStory/SFDNet.

Abstract:
RGB–Thermal (RGB-T) salient object detection leveragescomplementary cues from visible and thermal modalities to improve ro-bustness in challenging environments. However, in real-world scenarios,the reliability of each modality is inherently unstable: RGB images de-grade under low illumination, motion blur, and noise, while thermal im-agery often suffers from contrast compression and sensor artifacts. Suchdegradation introduces unreliable perceptual evidence that can misleadcross-modal fusion and significantly deteriorate detection performance.To address this challenge, we propose RA-SOD, a reliability-aware RGB-T salient object detection framework that explicitly models modality re-liability and integrates it into feature learning and cross-modal fusion.First, we introduce a reliability-conditioned representation that adap-tively compensates degraded modality features while preserving struc-tural cues. Second, an uncertainty-guided dual-stream refinement strat-egy progressively corrects cross-modal representations while suppress-ing unreliable evidence. Finally, we propose a pixel-wise modality com-petition mechanism that dynamically selects modality cues accordingto spatial reliability for fine-grained fusion. Extensive experiments onfour benchmarks (VT821, VT1000, VT5000, and VT-IMAG) demon-strate that RA-SOD achieves state-of-the-art performance and exhibitsstrong robustness under severe modality degradation. Code and modelsare available at https://github.com/zaoxienian/RA-SOD.

Abstract:
Domain adaptive object detection (DAOD) is essential fordeploying reliable detectors in real-world environments with inevitabledomain shifts. While recent approaches rely on image-to-image transla-tion, they often suffer from instability inherent in pixel-level domaintransformation. To address these limitations, we propose DA-F2F, anovel framework that directly aligns domains within the feature space.DA-F2F introduces a style-aware feature modulation (SFM) module thatextracts style statistics from the target domain to dynamically modulatesource representations. Furthermore, to improve adversarial learning, wepropose a soft-weighted proposal alignment (SPA) module that stablyseparates foreground and background regions in the target domain. Ex-tensive experiments demonstrate that DA-F2F significantly outperformsexisting state-of-the-art methods across diverse adaptation scenarios,achieving 60.5% mAP on clear-to-adverse weather (Foggy Cityscapes),46.9% mAP on small-to-large-scale datasets (BDD100K), and 69.9%mAP on synthetic-to-real adaptation (SIM10K). Code is available athttps://github.com/ohhotaek/DA-F2F.

Abstract:
Small object detection (SOD) remains a challenging task inreal-world applications. Despite recent advances, existing detectors re-main limited by rigid processing that entangle spatial aggregation withimplicit frequency aliasing and truncation, leading to inadequate preser-vation of high-frequency components for SOD. To tackle these limita-tions, we propose a Frequency-Spatial Domain Collaborative DetectionTransformer (FSDC-DETR), a novel collaborative framework that ex-plicitly models complementary spatial and frequency representations.Specifically, we first introduce Dual-Branch Frequency-Spatial AdaptiveFusion (DBFSAF) to enhance frequency diversity and adaptively cap-ture frequency-spatial domain discriminative representations. Buildingon these representations, a frequency-spatial interaction scheme is fur-ther explored within the hybrid encoder to enable progressive featurepropagation to the decoder. In particular, structure-aware frequency-spatial aggregation is achieved through Shunt Frequency-Spatial Fea-ture Fusion (SFS-FF), establishing bidirectional interaction and pro-gressive cross-scale propagation between frequency and spatial repre-sentations for coherent discriminative modeling. Meanwhile, informativehigh-frequency responses are preserved during scale transitions throughFrequency-Spatial Dynamic Downsampling (FSD-Down), thereby mini-mizing frequency degradation throughout multi-scale fusion for the pre-cise SOD. Experimental results demonstrate that FSDC-DETR achievesstate-of-the-art performance, improving AP by 6.4 on VisDrone-DET2019and 6.6 on AITODv2, with gains of 6.8 and 6.9 AP for small objects. Thecode is available at https://github.com/nevereverinsomnia/FSDC-DETR.

Abstract:
Offline-trained detectors and matchers often degrade in de-ployment when encountering previously unknown targets, since offlinetraining cannot anticipate the appearance and structural variations ofunknown targets. We propose EVEE, a feature adaptation frameworkthat leverages temporally accumulated event evidence as test-time proxysupervision for adapting detection and matching to previously unseentargets. Rather than performing fully asynchronous event-level match-ing or using events merely as auxiliary inputs, EVEE constructs event-derived proxy supervision to drive label-free online updates. EVEE con-tains three components. (i) Training Warehouse (TraW) accelerates con-vergence within a limited observation window via multi-threaded updatesand best-weight broadcasting. (ii) Event Voting Bank (EVB) accumu-lates bidirectional positive/negative votes to separate foreground frombackground, thereby reducing the impact of background noise on themodel. (iii) ReWeight improves correspondence reliability by rescoringcandidates using appearance similarity, geometric consistency, and EVBpriors. Experiments on public datasets and a real RGB–event platform,show that EVEE achieves 1.9% higher average pose ACC/AUC on un-known targets than strong baselines. Meanwhile, it runs at 51.3 FPSfor matching and 14.52 FPS for online adaptation. The code is publiclyavailable at https://github.com/ZeJZhao/EVEE.

Abstract:
Unsupervised Anomaly Detection (UAD) excels in real-worldapplications especially with rare anomalies due to the elimination of la-beled defective samples. Most existing UAD algorithms adopt a compar-ison process where the features of the test image are compared againsttheir normal “prototypes”. Such a paradigm relies primarily on raw fea-tures from pre-trained models and is inefficient due to the excessive num-ber of prototypes. To enhance UAD performance, recent algorithms aimto establish a mapping from the test features to their respective proto-types . However, the proposed mapping models, typically based on distil-lation or attention mechanisms, perform the “reconstruction” implicitly,making the process unnecessarily complex and difficult to explain. In thispaper, we propose a simple yet effective scheme, termed Fast DynamicPrototype (FDP), to rapidly generate high-quality reconstructed proto-types for Multi-class UAD. First, a raw prototype bank is obtained viasimply clustering the deep features of normal samples. Secondly, withinthe proposed Dynamic Bank Adapter module, the raw bank is dynam-ically condensed and adapted based on the information of the currenttest image. The reconstructed prototype of a given test feature is thendefined as the weighted average of its nearest bank members. Finally,an auxiliary FDP utilizing a different backbone model is introduced tosuccessfully mitigate the impact of artifacts present in the primary FDP.The study’s extensive experiments demonstrate the superiority of theproposed algorithm, which outperforms all compared SOTA methods onthree well-known UAD datasets (MVTec-AD, MVTec-3D, and ViSA),while attaining a speed up to 477 fps. Our implementation is publiclyavailable at https://github.com/lmlpy/FDP.git.

Abstract:
Recent advances in large-scale image generative models en-able photorealistic scene synthesis with controllable attributes. Beyonddata augmentation, their potential as diagnostic tools for trained visionsystems remains unexplored in the aerial and remote sensing domains.We introduce a synthetic diagnostic framework for aerial-view vehicle de-tection that combines text-guided generation, attribute-controlled edit-ing, and automated attribute verification to construct a controllable syn-thetic testbed. This enables fine-grained evaluation of pretrained detec-tors under diverse scene types and environmental conditions that aredifficult to isolate in real datasets. Across three detection architecturesand three real aerial datasets, synthetic scene-wise performance trendsclosely match real-world weaknesses. Guided by these diagnostics, tar-geted supplementation with small real datasets from the identified weakcategories yields improvements of up to 13% AP50 while requiring sub-stantially fewer additional samples than non-targeted augmentation. Ourresults show that controlled synthetic probing can predict real-domainperformance gaps and guide efficient data collection. The proposed diag-nostic framework is modular and can incorporate alternative generativeor vision-language models as capabilities evolve. Our code and datasetsare available here: humansensinglab.github.io/AVODDiag/

Abstract:
Point-supervised crowd counting and localization enable end-to-end training by casting prediction as set matching with one-to-oneHungarian assignment. However, point annotations provide no explicitscale information, making distance-based assignment prone to neighbor-induced ambiguity in congested regions, where nearby ground-truth pointscompete for similar proposals and corrupt supervision. We propose LocalSpacing-Aware Hungarian Matching (SAH-matcher), a drop-in replace-ment that derives a local spacing prior from k-nearest-neighbor distancesand performs per-target rescaling of the geometric cost, inducing a moreselective effective matching region in dense areas while remaining toler-ant in sparse ones. To quantify assignment behavior, we introduce Com-petitive Ambiguity Score (CAS) and Hijacking Rate (HR) for within-epoch ambiguity and severe hijacking failures, and combine them withInstability Rate (IR) to measure cross-epoch consistency. Experiments onmultiple benchmarks show improved assignment reliability and trainingstability, with particularly clear counting and localization gains in denseor locally ambiguous settings, modest overhead, and favorable transfer-ability across the evaluated point-based frameworks. Code is available athttps://github.com/kaijiang77/SAHCC.

Abstract:
Offset learning has recently demonstrated remarkable prowessin capturing the structural diversity of objects. Consequently, it ap-pears to be a natural fit for modeling the discriminative features ofvisual targets in tracking. Yet existing leading offline offset-based meth-ods struggle to cope with the ever-changing appearance and motionof the target during tracking. To address this limitation, we presentODONet, a Transformer-based tracker that performs online dynamicoffset network. Its Dynamic Visual–Motion Feature Interaction (DV-MFI) method fuses target appearance with historical box information,yielding appearance features and motion-aware embeddings. These arethen fed to an Online Dynamic Offset Propagation (ODOP) methodthat explicitly captures inter-frame motion trends, augments them withoffline offsets, and steers deformable attention toward an adaptive re-ceptive field. To our knowledge, ODONet is the first tracker to prop-agate dynamic offsets online, with excellent performance on publiclyavailable datasets, demonstrating its robustness to appearance and mo-tion drift. Code, models, and additional information are available athttps://github.com/WhiteButterflies/ODONet.

Abstract:
Real-time object detection on edge platforms is constrainedby inference latency due to limited resources. Existing runtime controllersadapt detector configurations using coarse frame-level feedback, but failto account for object-level accuracy degradation caused by detection tem-poral mismatch. This leads to suboptimal scheduling decisions under dy-namic workloads. We propose a lightweight per-object IoU decay modelthat predicts accuracy loss and derives closed-form deadline estimatesfor each detected object. These deadline predictions provide fine-grained,deadline-aware feedback that can be integrated into runtime control poli-cies. We integrate the model into a new closed-loop detector selectionframework and evaluate it under streaming conditions on Argoverse-HDand MOT17 across heterogeneous hardware platforms. Experiments onreal hardware testbeds show that our model and control algorithm im-prove deadline prediction accuracy by 56% and streaming AP by 39% onaverage across 71 video scenarios, while incurring acceptable overhead.The code is available at https://github.com/3Eerfan/OLAP-ECCV2026.

Abstract:
Pixel-level annotation remains a major bottleneck in medicalimage segmentation, making weak supervision an attractive yet under-constrained alternative. We propose OBBSeg, an intermediate super-vision paradigm guided by Oriented Bounding Boxes (OBBs) thatbridges the gap between full and weak supervision. By jointly encodingspatial extent and orientation, OBBs provide compact geometric super-vision that better aligns with elongated or anisotropic lesions, reducingthe ambiguity of coarse box annotations. To mitigate the inherent rect-angular bias of OBBs, we introduce a Mask-to-OBB loss, a differen-tiable formulation that enforces geometric consistency between predictedmasks and OBB regions. Furthermore, we incorporate prompt-drivensemantic guidance through two complementary modules—PAFE andDBFE—which enhance foreground representation and suppress back-ground interference. Extensive experiments on 13 datasets across 5imaging modalities show that OBBSeg not only outperforms existingweakly supervised methods but also achieves performance comparableto fully supervised approaches, demonstrating its potential for efficientand scalable medical image segmentation. The code is available athttps://github.com/StarLxc3/OBBSeg.

Abstract:
Real-world visual perception demands joint handling of low-level image degradation and high-level semantic understanding, yet thesetwo objectives inherently conflict: restoration seeks fine-grained texturerecovery while segmentation prioritizes semantic contrast. We proposethe nested unfolding network (NUN), a principled framework that re-solves this conflict by nesting one deep unfolding network (DUN) in-side another. NUN embeds a degradation-resistant unfolding network(DeRUN) within each stage of a segmentation-oriented unfolding network(SODUN), enabling both tasks to optimize in their own subspaces whileinteracting in a controlled manner. DeRUN handles unknown degrada-tion through proximal gradient unfolding with learnable operators thatimplicitly approximate it, while SODUN performs reversible foreground-background estimation. A bi-directional unfolding interaction mechanismuses IQA to select optimal DeRUN outputs, and a cross-stage consis-tency loss ensures robust predictions under varying restoration quality.Theoretically, under local assumptions, we show that NUN reduces di-rect parameter-level gradient conflict through disjoint parameter setsand achieves a degradation-sensitivity bound, where degradation affectssegmentation only through the inner-loop optimization and restorationapproximation errors. We instantiate NUN on concealed object segmen-tation and demonstrate consistent superiority over SOTA alternativesacross 12 benchmarks. The code is available at https://github.com/ChunmingHe/NUN.

Abstract:
Unsupervised outlier detection that automatically identifies whether visual systems involve anomalous images is a significant research topic. However, most approaches are limited to natural images because they rely on frozen and pre-trained feature extractors. So that their performance cannot be maintained in practical scenarios, especially for industrial inspection and medical imaging. In this work, we first revisit this task and then introduce a versatile unsupervised outlier detection framework to enrich the application domains. The core idea of this framework is to improve feature discriminativess via exploiting intrinsic distribution priors. Evaluated on 3 domains and 14 benchmark datasets, our proposed solution achieves state-of-the-art performance and significantly outperforms existing methods. More importantly, we show its plug-andplay property that can be integrated into diverse visual applications to improve their robustness, such as image classification and 3D reconstruction. Our code is available at https://github.com/zhliu-uod/VUOD.

Abstract:
Real-time monocular 3D object detection remains challeng-ing due to severe depth ambiguity, viewpoint shifts, and the high compu-tational cost of 3D reasoning. Existing approaches either rely on LiDARor geometric priors to compensate for missing depth or sacrifice efficiencyto achieve competitive accuracy. We introduce LeAD-M3D, a monocu-lar 3D detector that achieves state-of-the-art accuracy and real-time in-ference without extra modalities. Our method is enabled by three keycomponents. Asymmetric Augmentation Denoising Distillation (A2D2)transfers geometric knowledge from a clean-image teacher to a MixUp-noised student via a quality- and importance-weighted depth-feature loss,enabling stronger depth reasoning without LiDAR. 3D-aware ConsistentMatching (CM3D ) improves prediction-to-ground truth assignment by in-tegrating 3D MGIoU into the matching score, yielding stable and precisesupervision. Finally, Confidence-Gated 3D Inference (CGI3D ) acceleratesinference by restricting expensive 3D regression to confident regions. To-gether, these contributions set a new Pareto frontier for monocular 3Ddetection: LeAD-M3D achieves state-of-the-art accuracy on KITTI andWaymo, and the best reported car AP on Rope3D, while running up to3.6 × faster than prior high-accuracy models (e.g., MonoDiff). LeAD-M3D demonstrates that high fidelity and real-time monocular 3D de-tection is simultaneously attainable, without LiDAR, stereo, or stronggeometric assumptions.

Abstract:
Personalized segmentation and personalized retrieval bothaim to identify the same physical object across different images. Whilethe former localizes the object within a target image, the latter retrievesimages where it appears. Despite this shared instance-level objective, thetwo tasks have largely evolved separately and are addressed with distinctsolutions. In this work, we introduce FoundYou, a unified framework builton the observation that Segment Anything 2 (SAM 2), trained to pre-serve object identity across video frames, inherently captures instance-level cues. We leverage this property to match objects across independentimages, enabling segmentation and retrieval to emerge as two outcomesof the same instance alignment process. This unified view unlocks newcapabilities beyond traditional benchmarks, including few-shot person-alized retrieval and promptable personalized segmentation with flexibleprompts. Extensive experiments show consistent gains over unified andtask-specific methods, including +18.4 mIoU on PerMIS and +17.8 mAPon ILIAS. Performance scales with additional references and remainsrobust to weaker prompts. Beyond personalization, FoundYou achievesstate-of-the-art results on category-level retrieval benchmarks. Notably,our approach keeps the SAM 2-small model entirely frozen and addsonly 5.9 M trainable parameters, yielding a 52 M-parameter model thatis over 75× faster and 20× smaller than the only prior unified solution.

Abstract:
In Multiple Object Tracking (MOT), Re-identification (ReID) features are widely employed as a powerful cue for object association. However, they are often wielded as a one-size-fits-all hammer, applied uniformly across all videos through simple similarity metrics. We argue that this overlooks a fundamental truth: MOT is not a general retrieval problem, but a context-specific task of discriminating targets within a single video. To this end, we advocate for the adjustment of visual features based on the context specific to each video sequence for better adaptation. In this paper, we propose a history-aware feature transformation method that dynamically crafts a more discriminative subspace tailored to each video’s unique sample distribution. Specifically, we treat the historical features of established trajectories as context and employ a tailored Fisher Linear Discriminant (FLD) to project the raw ReID features into a sequence-specific representation space. Extensive experiments demonstrate that our training-free method dramatically enhances the discriminative power of features from diverse ReID backbones, resulting in marked and consistent gains in tracking accuracy. Our findings provide compelling evidence that MOT inherently favors context-specific representation over the direct application of generic ReID features. We hope our work inspires the community to move beyond the naive application of ReID features and towards a deeper exploration of their purposeful customization for MOT.

Abstract:
We propose a self-supervised framework designed to enhance the capability of scene text detectors in identifying and recognizing text in scenarios where instances are shown at significant distances, typically small, blurred, and frequently missed by conventional models. Our approach leverages the high-fidelity performance of existing text spotting models on large, clear text as a foundational supervisor. By temporally back-tracking these high-confidence detections through video sequences, we automatically synthesize pseudo-labels for preceding frames where the distant text is still visually degraded or undersized. These pseudolabels enable training a student model specialized for early text detection, without requiring any manual annotation. The success of this approach depends on accurate pseudo-label generation, for which we develop a dedicated scene text tracker capable of maintaining consistent text identities across challenging video sequences. In addition, we propose SceneText50, a diverse multilingual outdoor dataset to facilitate training and evaluation. Experiments show that our framework significantly improves early detection accuracy and robustness across varied scenes and languages. Code and data are at https://github.com/trid2912/BackTrackingText.

Abstract:
Interpreting the decisions of complex computer vision modelsis crucial to establish trust and accountability, especially in safety-criticaldomains. An established approach to interpretability is generating visualattribution maps that highlight regions of the input most relevant to themodel’s prediction. However, existing methods face a three-way trade-off. Propagation-based approaches are efficient, but they can be biasedand architecture-specific. Meanwhile, perturbation-based methods arecausally grounded, yet they are expensive and for vision transformersoften yield coarse, patch-level explanations. Learning-based explainersare fast but usually optimize surrogate objectives or distill from heuris-tic teachers. We propose a learning scheme that instead optimizes dele-tion and insertion metrics directly. Since these metrics depend on non-differentiable sorting and ranking, we frame them as permutation learn-ing and replace the hard sorting with a differentiable relaxation usingGumbel-Sinkhorn. This enables end-to-end training through attribution-guided perturbations of the target model. During inference, our methodproduces dense, pixel-level attributions in a single forward pass with op-tional, few-step gradient refinement. Our experiments demonstrate con-sistent quantitative improvements and sharper, boundary-aligned expla-nations, particularly for transformer-based vision models. Code and pre-trained models are available at https://github.com/dschinagl/AHA.

Abstract:
Deep neural networks often show poor generalization whentrained on biased datasets presenting spurious relations between sam-ples and target labels. Unsupervised debiasing methods aim at mitigat-ing models’ dependency on bias, without relying on bias information.Bias shortcuts are typically learned early in training, with the modelrapidly overfitting the few samples not sharing the same related at-tributes. Avoiding bias conflicting memorization for mining bias infor-mation useful for mitigation (e.g., in the form of pseudo-labels) is still afundamental open issue in this context, with existing approaches propos-ing to learn for a few epochs or to rely on unbiased validation sets for earlystopping. In this work, we propose to exploit intermediate layers’ featuresfor achieving bias mitigation robust to the memorization problem, replac-ing the when to stop with a where to look paradigm. Specifically, we pro-pose AracNet, an unsupervised debiasing framework for self-mitigation.Attaching one linear layer to each network’s block, which we refer to asShallow Monitor, we obtain a useful debiasing signal capable of miti-gating bias dependency in the same model when randomly reinitialized.Our results on typical benchmark datasets with single and multiple biasesconfirm the effectiveness of the proposed approach, paving the way forfurther research in understanding how bias propagates in trained models.Code is available at https://github.com/Malga-Vision/AracNet

Abstract:
Implicit neural representation (INR) has emerged as a pow-erful prior for multi-dimensional data (e.g., multispectral images andvideos). However, most INR methods employing periodic activation func-tions (e.g., Sine) predominantly rely on function composition. This mech-anism introduces optimization instability as network depth increases,thereby limiting their performance. Meanwhile, these methods fail to in-corporate proper physical priors to effectively alleviate spectrum bias. Toaddress these issues, inspired by the commonalities between deep periodicnetworks and generalized Fourier series, we propose a novel CalibratedHarmonic Overlaid Implicit Neural Representation (CHOIR). Specifi-cally, we utilize Coordinated Harmonic Superposition (CHS) to replacethe conventional function composition used in most INRs, thereby en-suring optimization stability when scaling network depth. Furthermore,we introduce a Perceptual Spectrum Calibration (PSC) to mitigate spec-trum bias. This calibration embeds the ubiquitous power-law spectrumprior of natural images and adjusts the globally fixed spectrum towards aphysically plausible log-uniform distribution. Extensive experiments onvarious multidimensional data recovery problems demonstrate that ourmethod achieves superior performance over state-of-the-art approaches.Code is available at https://github.com/chorl0229/CHOIR.

Abstract:
We study dynamic Gaussian Splatting from monocular videos.While recent advancements in dynamic Gaussian Splatting offer a promis-ing foundation for modeling dynamic scenes, they often overfit to thetraining views and fail under occlusion or complex scene motion due tothe lack of reliable regularization signals in under-constrained regions.We propose Semantic Motion Graph (SMG), a novel approach that mod-els the Gaussian motion as the semantic motion. Our key insight is thatthe real-world scene motion is often structured by semantic coherence:regions that are spatially close and semantically related tend to exhibitconsistent dynamics. To leverage this prior, we build SMG to modelstructured motion of the scene. The Gaussian motion is driven by the mo-tion of SMG nodes. We further observe that the uncertainty of Gaussianmotion arises from both unreliable off-the-shelf priors and weakly con-strained regions during optimization. SMG addresses this by using reli-able graph nodes to guide the motion of nearby unreliable nodes. To eval-uate dynamic Gaussian Splatting under challenging real-world scenarios,we introduce a new multiview dataset collected under an ego-exo setup.Extensive experiments demonstrate that SMG achieves state-of-the-artperformance on monocular dynamic Gaussian Splatting across challeng-ing real-world benchmarks. Project: https://smg-gaussian.github.io/.

Abstract:
Reconstructing non-rigid objects with physical plausibilityremains a significant challenge. Existing approaches leverage differen-tiable rendering for per-scene optimization, recovering geometry anddynamics but requiring expensive tuning or manual annotation, whichlimits practicality and generalizability. To address this, we propose Re-conPhys, the first feedforward framework that jointly learns physicalattribute estimation and 3D Gaussian Splatting reconstruction from asingle monocular video. Our method employs a dual-branch architecturetrained via a self-supervised strategy, eliminating the need for ground-truth physics labels. Given a video sequence, ReconPhys simultaneouslyinfers geometry, appearance, and physical attributes. Experiments ona large-scale synthetic dataset demonstrate superior performance: ourmethod achieves 21.64 PSNR in future prediction compared to 13.27by state-of-the-art optimization baselines, while reducing Chamfer Dis-tance from 0.349 to 0.004. Crucially, ReconPhys enables fast inference(<1 second) versus hours required by existing methods, facilitating rapidgeneration of simulation-ready assets for robotics and graphics. The codeis available at https://github.com/chuanshuogushi/ReconPhys.

Abstract:
Physical principles are fundamental to realistic visual sim-ulation, but remain a significant oversight in video generation. Thisgap highlights a critical limitation in rendering rigid body motion, acore tenet of classical mechanics. While computer graphics and physics-based simulators can easily model such dynamics using Newton for-mulas, modern video generative models discard the concept of objectrigidity during pixel-level global denoising. Existing methods attemptto tackle this problem through physical data augmentation, dynamicspre-simulation, or reinforcement learning with VLM ratings, but noneof these approaches accurately reflect physical principles or enable themodel to internalize physical knowledge. Motivated by these considera-tions, we introduce reinforcement learning with physically verifiable re-wards. We design a quantitatively verifiable metric that combines Trajec-tory Offset and Collision Detection, which can accurately capture rigid-body motion states and assess the quality of generated samples. Subse-quently, we extend this paradigm to a unified post-training framework,termed Mimicry-Discovery Cycle, which enables stable training onout-of-distribution scenarios while improving overall model performance.To validate our approach, we construct new benchmark PhysRVGBenchand perform extensive qualitative and quantitative experiments to thor-oughly assess its effectiveness. The code and demo can be found athttps://lucaria-academy.github.io/PhysRVG/.

Abstract:
Traditional video highlight detection relies on a narrow, event-centric definition of saliency, which often fails to generalize to uncon-strained personal videos where highlights are heterogeneous and perspective-dependent. To address this, we introduce TRINITY, a multi-perspectivebenchmark that decomposes highlight saliency into three complemen-tary dimensions, Event, Emotion, and Nature, within a unified tempo-ral framework. Leveraging this multi-faceted view, we propose a shared-backbone multi-branch architecture designed for parallel multi-perspectiveprediction via view-specific experts. Comprehensive experiments demon-strate that our method significantly outperforms state-of-the-art base-lines, achieving gains of +7.15/ + 3.62 mAPρ=15%/50% on Mr. HiSumand +10.82 mAP on YouTube Highlights. These results validate thatmulti-perspective modeling provides a more robust and comprehensiveformulation of video saliency, especially for complex real-world scenar-ios. The benchmark and relevant codes will be released upon acceptance.The benchmark is available at https://huggingface.co/datasets/vanilladucky/TRINITY.

Abstract:
Generating realistic 3D hand-object interactions (HOI) is afundamental challenge in computer vision and robotics, requiring bothtemporal coherence and high-fidelity physical plausibility. Existing meth-ods remain limited in their ability to learn expressive motion repre-sentations for generation and perform temporal reasoning. In this pa-per, we present HO-Flow, a framework for synthesizing realistic hand-object motion sequences from texts and canonical 3D objects. HO-Flowfirst employs an interaction-aware variational autoencoder to encode se-quences of hand and object motions into a unified latent manifold byincorporating hand and object kinematics, enabling the representationto capture rich interaction dynamics. It then leverages a masked flowmatching model that combines auto-regressive temporal reasoning withcontinuous latent generation, improving temporal coherence. To furtherenhance generalization, HO-Flow predicts object motions relative to theinitial frame, enabling effective pre-training on large-scale synthetic data.Experiments on the GRAB, OakInk, and DexYCB benchmarks demon-strate that HO-Flow achieves state-of-the-art performance in both phys-ical plausibility and motion diversity for interaction motion synthesis.

Abstract:
Music-to-dance generation aims to translate auditory sig-nals into expressive human motion. Despite promising progress, existingmethods remain underexplored in achieving high-quality generation un-der a strict efficiency budget, due to (i) generative strategies that sup-port high-fidelity few-step sampling, and (ii) per-step model architecturesthat are optimized for efficient yet refined long-horizon motion synthesis,thereby degrading downstream user experience in terms of responsive-ness and visual fidelity. Thus, we propose FlowerDance, which not onlygenerates refined motion with physical plausibility and artistic expres-siveness, but also achieves significant generation efficiency on inferencespeed and memory utilization. Specifically, FlowerDance combines Mean-Flow with Physical Consistency Constraints, which enables high-qualitymotion generation with only a few sampling steps. Moreover, Flower-Dance leverages a simple but efficient model architecture with BiMamba-based backbone and Channel-Level Cross-Modal Fusion, which generateslong-horizon dance with efficient non-autoregressive manner. Meanwhile,FlowerDance supports motion editing, enabling users to interactively re-fine dance sequences. Extensive experiments on AIST++ and FineDanceshow that FlowerDance achieves state-of-the-art results in both motionquality and generation efficiency. Code is available at https://sun-happy-ykx.github.io/FlowerDance/.

Abstract:
Autoregressive video diffusion models have enabled the gener-ation of arbitrarily long videos by removing conditioning on future frames,thus greatly improving computational efficiency. Yet, they suffer from er-ror accumulation over time, as the denoised sequence gradually drifts awayfrom the conditioning distribution seen during training. Recent advancesattempt to reduce this error by anchoring each generated frame to thelearned manifold of real ones. However, even when all generated individualframes lie close to the real manifold, there are trajectories which the modellacks sufficient knowledge to continue without exiting it, thus reachinga terminal point. To prevent the model from being trapped in terminalpoints, we start from the hypothesis that for well-modeled future trajec-tories the distribution of the predicted noise should match the one of theforward noising process. To enforce such a prior at test time, we introduceTerminal points Avoidance through Noise Guided Optimization (TANGO),which uses the diffusion model as a critic of its own outputs, by predictingone step forward and requiring an isotropic Gaussian noise prediction. Weuse the deviation from this expected noise distribution to search for an al-ternative trajectory that does not lead to a terminal point. Our approachachieves a 3.1% absolute improvement on VBench over state-of-the-art,while reducing Fréchet Video Distance by 28.3% on average across 15 svideos. Our code is available on https://mever-team.github.io/tango.

Abstract:
Developing physically aware video generation models remainsa significant challenge due to the difficulty in capturing diverse physicalphenomena, such as thermal dynamics, mechanics, and optics. In thiswork, we introduce PhysRAG, a novel pipeline that enhances physicalawareness in video generation through Retrieval-Augmented Generation(RAG). To address the issue of limited high-quality data, we design atwo-stage data filtering pipeline based on the WISA-80K dataset, result-ing in a curated set of 7K high-quality videos for training. Furthermore,we construct a physical video database and develop a mechanism to injectphysical knowledge into a video diffusion model using learnable queries.Our method achieves state-of-the-art performance in both visual qualityand physical rule compliance, surpassing existing models in benchmarkssuch as PhyGenBench and VBench. We conduct extensive ablation stud-ies to validate the effectiveness of our key components, including the datafiltering pipeline, RAG mechanism, and method for physical informationextraction. To facilitate future research, our code, data, and models areprepared for release at https://github.com/sediment1024/PhysRAG.

Abstract:
High-fidelity and expressive controllable human animation isessential for content creation and digital avatar applications. However,existing methods face a dilemma between expressiveness and disentangle-ment. Mainstream 2D pose-conditioned approaches suffer from "motion-shape entanglement", leading to the leakage of the driving subject’s bodyshape. Conversely, methods relying on 3D priors (e.g., SMPL) achievegeometric disentanglement but struggle to capture facial expressions andcomplex gestures, resulting in rigid animations. To this end, we proposeEMOSH, a novel framework for high-fidelity controllable human videogeneration. First, an Expressive Human Model (EHM) is introduced asthe core control representation. By explicitly disentangling shape andpose parameters, we fundamentally resolve the body shape leakage issue.Alongside this, a robust motion tracker is designed to accurately estimateEHM parameters from video. Second, we propose a Coarse-to-Fine Hy-brid Motion Injection strategy, enabling more fine-grained control overexpressions and gestures. Furthermore, we introduce a Spatially-AlignedConditioning mechanism to bridge the domain gap between training and∗ †Intern at WeChat Vision. Corresponding authors.inference, improving identity consistency. Extensive experiments demon-strate that EMOSH outperforms previous methods in both self-drivenand cross-driven scenarios, producing high-fidelity videos with vivid ex-pressions while maintaining shape disentanglement. Video demos andadditional results are available at our Project page.

Abstract:
Although existing video editing methods are generally feasi-ble, they often require many costly iterations and still struggle to deliverhigh-quality yet satisfying editing results. We attribute this limitationto the prevalent data-to-data paradigm, which is less compatible withmodern generative models than noise-to-data generation. To address thisgap, we revisit video editing from a noise-to-data perspective and pro-pose Streaming-Generation-based Video Editing (StreamEdit), whichpreserves few-step sampling while seamlessly injecting source-video con-ditions. Built on pre-trained streaming generation models, StreamEditintroduces dual-branch fast sampling with a self-attention bridge andcross-attention grounding/boosting to satisfy both sampling and con-ditioning requirements. We further propose source-oriented guidance toimprove target-generation quality, and a visual prompting strategy to en-hance editing flexibility and practicality. The method is effective, robust,and generalizable across different models. Extensive experiments on di-verse video editing tasks show that StreamEdit consistently outperformsexisting approaches, even in few-step settings with minimal time cost.Code and results are available at: dsl-lab.github.io/StreamEdit/.

Abstract:
We present FFAvatar, a Transformer-based 3D Gaussian framework for fast construction of high-quality and animatable 4D head avatars from one or more reference portrait images. Unlike existing feed-forward approaches that require a fixed number of input views, FFAvatar supports incremental reconstruction, progressively refining the avatar representation as additional reference images become available. At the core of our method is an alternating attention mechanism that disentangles identity appearance from expression and viewpoint variations, enabling the reconstruction of a canonical 3D appearance that remains consistent across poses and facial expressions. To balance visual fidelity and computational efficiency, we introduce a sparse-to-dense learning paradigm. Coarse appearance features are first learned using sparse primitives anchored to the FLAME vertex level and are subsequently densified in the UV domain to capture fine-grained geometric and texture details. We further propose a plug-and-play motion refinement module that enables subject-specific dynamic personalization by modeling residual motion beyond parametric deformation. Extensive experiments demonstrate that FFAvatar efficiently produces high-fidelity and controllable 4D head avatars, achieving superior flexibility, driving efficiency, and identityconsistent rendering across diverse expressions and viewpoints. Project Page: https://jj-yao.github.io/ffavatar/

Abstract:
Long-trajectory video generation is a crucial yet challeng-ing task for world modeling, primarily due to the limited scalability ofexisting video diffusion models (VDMs). Autoregressive models, whileoffering infinite rollout, suffer from visual drift and poor controllability.To address these issues, we propose DCARL, a novel divide-and-conquer,autoregressive framework that effectively combines the structural stabil-ity of the divide-and-conquer scheme with the high-fidelity generationof VDMs. Our approach first employs a dedicated Keyframe Generatortrained without temporal compression to establish long-range, globallyconsistent structural anchors. Subsequently, an Interpolation Generatorsynthesizes the dense frames in an autoregressive manner with overlap-ping segments, utilizing the keyframes for global context and a singleclean preceding frame for local coherence. Trained on a large-scale inter-net long trajectory video dataset, our method achieves superior perfor-mance in both visual quality (lower FID and FVD) and camera adher-ence (lower ATE and ARE) compared to state-of-the-art autoregressiveand divide-and-conquer baselines, demonstrating stable and high-fidelitygeneration for long trajectory videos up to 32 seconds in length.

Abstract:
Precise human mesh recovery (HMR) from multi-view im-ages remains challenging: end-to-end methods produce entangled errorshard to localize, while x001C tting-based methods rely on sparse keypointsthat provide limited surface constraints. We observe that the true bot-tleneck lies in the quality of intermediate representations, and that densepixel-to-surface correspondences can be e x001B ectively generated by repur-posing pre-trained di x001B usion models with rich visual priors. We proposeDi x001B Proxy, a Stable-Di x001B usion-based framework trained on large-scalesynthetic data with pixel-perfect annotations. A multi-conditional proxygenerator predicts dense correspondences from multi-view images, pro-viding uniform surface constraints that enable precise x001C tting. Hand re- x001C nement feeds enlarged hand crops alongside full-body images for x001C ne-grained detail, while test-time scaling exploits di x001B usion stochasticity toestimate per-pixel uncertainty. Trained only on synthetic data, Di x001B Proxyachieves state-of-the-art results on x001C ve diverse real-world benchmarks.Project page: https://wrk226.github.io/DiffProxy.html

Abstract:
Training embodied AI agents depends critically on the visual fidelity of simulation environments and the ability to model dynamic humans. Current simulators predominantly rely on mesh-based rasterization, for which photorealistic assets are costly to author at scale, and their support for dynamic human avatars is largely constrained to mesh representations, hindering agent generalization to human-populated realworld scenarios. We present Habitat-GS, a navigation-centric embodied AI simulator extended from Habitat-Sim that integrates 3D Gaussian Splatting scene rendering and drivable gaussian avatars while maintaining full compatibility with the Habitat ecosystem. Our system implements a 3DGS renderer for real-time photorealistic rendering and supports scalable 3DGS asset import from diverse sources. For dynamic human modeling, we introduce a gaussian avatar module that enables each avatar to simultaneously serve as a photorealistic visual entity and an effective navigation obstacle, allowing agents to learn human-aware behaviors in realistic settings. Experiments on point-goal navigation demonstrate that agents trained on 3DGS scenes achieve stronger cross-domain generalization. Evaluations on avatar-aware navigation further confirm that gaussian avatars enable effective human-aware navigation, while performance benchmarks validate the system’s scalability. Code is available at https://github.com/zju3dv/habitat-gs.

Abstract:
Effective and generalizable control in video generation re-mains a significant challenge. While many methods rely on ambiguousor task-specific signals, we argue that a fundamental disentanglement of"appearance" and "motion" provides a more robust and scalable path-way. We propose FlexAM, a unified framework built upon a novel 3Dcontrol signal. This signal represents video dynamics as a point cloud,introducing three key enhancements: multi-frequency positional encodingto distinguish fine-grained motion, depth-aware encoding, and a flexiblecontrol signal for balancing precision and generalization. This represen-tation allows FlexAM to effectively disentangle appearance and motion,enabling a wide range of tasks including I2V/V2V editing, camera con-trol, and spatial object editing. Extensive experiments demonstrate thatFlexAM achieves superior performance across all evaluated tasks. Codesare available at https://github.com/IGL-HKUST/FlexAM.

Abstract:
We present OctWorld, a video diffusion framework with 3Dmemory for generating explorable, world-consistent, and high-fidelity vi-sual scenes. Given a single image, OctWorld enables stable autoregressiveworld generation along user-specified camera trajectories. We focus onthe problem of long spatial range generation, characterized by long cam-era path and wide viewing angle coverage, where maintaining spatialconsistency – particularly when revisiting previously generated regions –becomes challenging. To address this, we first build upon a pretrained au-toregressive video diffusion model that supports longer-horizon contextmodeling. We adapt this model to RGB-D generation and introduce adepth normalization scheme for inter-chunk consistency. Next, and moreimportantly, we propose an extensible and spatially-adaptive 3D memory,termed OctMap. OctMap constructs a global 3D representation of thegenerated scene by progressively fusing the generated RGB-D frames.To accommodate diverse scene scales in the open world setting, it ap-plies adaptive spatial resolution dynamically determined by image evi-dence with a sparse octree hierarchy, which preserves effective geometricand appearance details while maintaining low memory overhead. Experi-ments demonstrate that OctWorld can generate long-range and spatiallyconsistent videos, outperforming prior methods on existing benchmarksas well as in longer-range generation cases.

Abstract:
Large-scale video diffusion models achieve impressive visualquality, yet often fail to preserve geometric consistency. Prior approachesimprove consistency either by augmenting the generator with additionalmodules or applying geometry-aware alignment. However, architecturalmodifications can compromise the generalization of internet-scale pre-trained models, while existing alignment methods are limited to staticscenes and rely on RGB-space rewards that require repeated VAE de-coding, incurring substantial compute overhead and failing to general-ize to highly dynamic real-world scenes. To preserve the pretrained ca-pacity while improving geometric consistency, we propose VGGRPO(Visual Geometry GRPO), a latent geometry-guided framework forgeometry-aware video post-training. VGGRPO introduces a Latent Ge-ometry Model (LGM) that stitches video diffusion latents to geometryfoundation models, enabling direct decoding of scene geometry from thelatent space. By constructing LGM from a geometry model with 4D re-construction capability, VGGRPO naturally extends to dynamic scenes,overcoming the static-scene limitations of prior methods. Building onthis, we perform latent-space Group Relative Policy Optimization withtwo complementary rewards: a camera motion smoothness reward thatpenalizes jittery trajectories, and a geometry reprojection consistencyreward that enforces cross-view geometric coherence. Experiments onboth static and dynamic benchmarks show that VGGRPO improvescamera stability, geometry consistency, and overall quality while elim-inating costly VAE decoding, making latent-space geometry-guided re-inforcement an efficient and flexible approach to world-consistent videogeneration. Our project page is here.

Abstract:
Real-world dynamics are inherently compositional: multiple entities move simultaneously within a shared scene, each exhibiting distinct motion patterns. Yet most existing video representations encode motion globally, without explicitly capturing localized motion for individual entities. Crucially, motion is defined relative to a global reference frame, including camera motion and scene layout. However, localized embeddings are often computed from cropped images or obtained by masking features after encoding, discarding the context needed to interpret motion. To address this, we introduce a promptable localized motion representation that produces persistent embeddings for user-specified regions defined by spatial masks. Rather than cropping the input or masking features, our model processes the full video and conditions motion encoding directly on the queried region. This yields temporally consistent, region-addressable embeddings that isolate local dynamics while retaining the global context required for disambiguation. We demonstrate object-level motion transfer, enabling controlled composition of dynamic scenes. Beyond generative control, our embeddings support localized action classification in multi-actor videos. Across both tasks, our approach improves controllability and outperforms global representations localized through cropping or post-hoc masking. Project page: https://compvis.github.io/WhatMoves

Abstract:
With the advancement of video generation foundation mod-els (VGFMs), customized generation, particularly subject-to-video (S2V),has attracted growing attention. However, a key challenge lies in balanc-ing the intrinsic priors of a VGFM, such as motion coherence, visualaesthetics, and prompt alignment, with its newly derived S2V capa-bility. Existing methods often neglect this balance by enhancing oneaspect at the expense of others. To address this, we propose Libra-Gen, a novel framework that views extending foundation models forS2V generation as a balance game between intrinsic VGFM strengthsand S2V capability. Specifically, guided by the core philosophy of “Rais-ing the Fulcrum, Tuning to Balance,” we identify data quality as thefulcrum and advocate a quality-over-quantity approach. We constructa hybrid pipeline that combines automated and manual data filteringto improve overall data quality. To further harmonize the VGFM’s na-tive capabilities with its S2V extension, we introduce a Tune-to-Balancepost-training paradigm. During supervised fine-tuning, both cross-pairand in-pair data are incorporated, and model merging is employed toachieve an effective trade-off. Subsequently, two tailored direct prefer-ence optimization (DPO) pipelines, namely Consis-DPO and Real-FakeDPO, are designed and merged to consolidate this balance. During in-ference, we introduce a time-dependent dynamic classifier-free guidancescheme to enable flexible and fine-grained control. Experimental resultsdemonstrate that LibraGen outperforms both open-source and commer-cial S2V models using only thousand-scale training samples. ProjectPage: https://github.com/Phantom-video/LibraGen

Abstract:
Human motion generation has been widely studied across di-verse input modalities — text, music, and video — and recent efforts haveunified these into single multimodal frameworks. However, while morpho-logical factors such as gender and body shape are known to produce dis-tinct kinematic signatures, no existing unified framework incorporatesthis into generation, treating all subjects as morphologically equiva-lent. We present Odoriko, the first unified multimodal motion generationframework that reflects subject bio-morphological information directly insynthesized motion output. Rather than averaging over subject variation,Odoriko generates motion that is consistent with who is moving, not justwhat they are asked to do — across text, music, and video conditionswithin a single model. When explicit morphological information is un-available, Odoriko additionally recovers subject morphology alongsidemotion, unifying estimation and generation in one framework. Extensiveexperiments across text-to-motion, music-to-dance, and video-to-motionbenchmarks demonstrate that Odoriko matches or exceeds prior special-ized models on standard metrics, while enabling morphology-consistentgeneration that no existing unified framework supports. Project page:https://dsshim0125.github.io/odoriko.github.io/

Abstract:
Despite rapid advances in video generative models, robust metrics for evaluating visual and temporal correctness of complex human actions remain elusive. Critically, existing pure-vision encoders and Multimodal Large Language Models (MLLMs) are strongly appearancebiased, lack temporal understanding, and thus struggle to discern intricate motion dynamics and anatomical implausibilities in generated videos. We tackle this gap by introducing a novel evaluation metric derived from a learned latent space of real-world human actions. Our method first captures the nuances, constraints, and temporal smoothness of real-world motion by fusing appearance-agnostic human skeletal geometry features with appearance-based features. We posit that this combined feature space provides a robust representation of action plausibility. Given a generated video, our metric quantifies its action quality by measuring the distance between its underlying representations and this learned real-world action distribution. For rigorous validation, we develop a new multi-faceted benchmark specifically designed to probe temporally challenging aspects of human action fidelity. Through extensive experiments, we show that our metric achieves substantial improvement of more than 68% compared to existing state-of-the-art methods on our benchmark, performs competitively on established external benchmarks, and has a stronger correlation with human perception. Our in-depth analysis reveals critical limitations in current video generative models and establishes a new standard for advanced research in video generation. Code is available at https://xthomasbu.github.io/video-gen-evals/

Abstract:
This paper presents DualCamCtrl, a novel architecture forcamera-controlled video generation that internalizes geometric reason-ing into the diffusion process. Existing methods rely solely on ray-basedcamera conditioning, which entangles appearance and geometry model-ing within a single representation, or resort to external 3D cache andmulti-stage pipelines that are hard to jointly optimize. DualCamCtrladdresses this by generating depth as a co-evolving modality alongsideRGB through a dual-branch framework, enabling geometry and appear-ance to inform each other throughout denoising. To govern this cross-modal interaction, we propose the SemantIc Guided Mutual Alignment(SIGMA) mechanism, which schedules RGB–depth fusion in a stage-aware manner based on our empirical finding that depth and cameraposes exert asymmetric influence across the denoising trajectory. To-gether with a two-stage training strategy, these designs enable DualCam-Ctrl to disentangle and jointly evolve appearance and geometry, gener-ating videos that faithfully adhere to specified camera trajectories. Ex-tensive experiments demonstrate that DualCamCtrl achieves more con-sistent camera-controlled video generation with over 40% reductionon camera rotation errors compared with prior methods. Project Page:https://soyouthinkyoucantell.github.io/dualcamctrl-page.

Abstract:
Recent generative models can produce high-fidelity videos, yet they often exhibit 3D spatial geometric inconsistencies. Existing evaluation methods fail to accurately characterize these inconsistencies: fidelity-centric metrics like FVD are insensitive to geometric distortions, while consistency-focused benchmarks often penalize valid foreground dynamics. To address this gap, we introduce SGC, a metric for evaluating 3D Spatial Geometric Consistency in dynamically generated videos. We quantify geometric consistency by measuring the divergence among multiple camera poses estimated from distinct local regions. Our approach first separates static from dynamic regions, then partitions the static background into spatially coherent sub-regions. We predict depth for each pixel, estimate a local camera pose for each subregion, and compute the divergence among these poses to quantify geometric consistency. Experiments on real and generative videos demonstrate that SGC robustly quantifies geometric inconsistencies, effectively identifying critical failures missed by existing metrics.

Abstract:
Human-Object Interaction (HOI) video generation aims to synthesize realistic videos of humans manipulating diverse objects, serving as a promising avenue for AI-driven live streaming e-commerce. A primary obstacle in this domain lies in the complexity of modeling fine-grained physical dynamics and the intricate spatial-temporal coordination between human hands and objects. Existing approaches to this problem typically rely on dense temporal guidance, e.g., frame-wise hand-object pose sequences, to strictly control the interaction process. However, such dense guidance incurs high annotation costs and affects motion synthesis diversity. To overcome these limitations, we introduce SparseCtrl-HOI, a novel sparse temporal control framework for HOI video generation. It requires only a few keyframes that capture interaction states at designated timestamps. Specifically, we employ a TimeControlled Rotary Positional Embedding (TiRoPE) mechanism to temporally anchor these keyframes while preserving their spatial integrity. Subsequently, to govern the dynamics across intermediate frames, we propose a Motion Prior Injection Module that leverages Multimodal Large Language Models (MLLMs) to extract high-level motion priors. This empowers the model to hallucinate logically and physically plausible transitions. Furthermore, we build SparseHOI-5K, a high-quality and richly annotated dataset for HOI video generation with sparse temporal control. Comprehensive evaluations confirm that our method substantially reduces annotation overhead while synthesizing superior live-streaming e-commerce videos. Both our code and dataset are publicly available at https://mpi-lab.github.io/SparseCtrl-HOI.

Abstract:
Human image animation, which aims to generate a video of a reference subject following a provided action sequence, has received increasing research interest. With the development of diffusion-based/flowbased video foundation models, existing animation works have began to upgrade the guidance information from 2D skeleton/pose to 3D modeling conditions. Despite achieving reasonable results, these approaches face challenges in synthesizing trajectory-controllable human motion within natural scene under changed camera views. In this work, we present a scene-adaptive human image animation framework that controls both human motion and camera trajectories within a reconstructed 3D environment for video generation. To achieve this, we first develop a groundadaptive 3D motion retargeting approach to enable user-friendly motion trajectory control adapting to the changes of elevations of ground and orientations automatically. Then we design a viewpoint-adaptive latent fusion mechanism to inject point-cloud geometric priors through scenevisibility masking into the generative process, providing precise guidance of viewpoint changes under camera control. Experiments on two standard human image animation benchmark datasets demonstrate remarkable improvements of our method over the state of the arts in related video generation metics. Project page: https://robinhood256100.github.io/webdisp

Abstract:
Multimodal Large Language Models (MLLMs) often struggle with fine-grained perception, such as identifying small objects in high-resolution images or detecting key moments in long videos. Existing methods typically rely on complex, task-specific fine-tuning, which reduces generalizability and increases system complexity. In this work, we propose an effective, training-free framework that uses an MLLM’s intrinsic uncertainty as proactive guidance. Our core insight is that a model’s uncertainty decreases when provided with relevant visual information. We introduce a unified mechanism that scores candidate visual inputs by response uncertainty, enabling the model to autonomously focus on the most informative data. We apply this simple principle to three challenging visual tasks: Visual Search, Long Video Understanding, and Temporal Grounding, allowing off-the-shelf MLLMs to achieve performance competitive with specialized, fine-tuned systems. Our results demonstrate that leveraging intrinsic uncertainty is a powerful strategy for improving fine-grained multimodal performance. Code is available at https://github.com/ExplainableML/ug-framework.

Abstract:
Autoregressive (AR) diffusion offers a promising framework for generating videos of theoretically infinite length. However, a major challenge is maintaining temporal continuity while preventing the progressive quality degradation caused by error accumulation. To ensure continuity, existing methods typically condition on highly denoised contexts; yet, this practice propagates prediction errors with high certainty, thereby exacerbating degradation. In this paper, we argue that a highly clean context is unnecessary. Drawing inspiration from bidirectional diffusion models, which denoise frames at a shared noise level while maintaining coherence, we propose that conditioning on context at the same noise level as the current block provides sufficient signal for temporal consistency while effectively mitigating error propagation. Building on this insight, we propose HiAR, a hierarchical denoising framework that reverses the conventional generation order: instead of completing each block sequentially, it performs causal generation across all blocks at every denoising step, so that each block is always conditioned on context at the same noise level. This hierarchy naturally admits pipelined parallel inference, yielding a ∼1.8× wall-clock speedup in our 4-step setting. We further observe that self-rollout distillation under this paradigm amplifies a low-motion shortcut inherent to the mode-seeking reverse-KL objective. To counteract this, we introduce a forward-KL regulariser in bidirectional-attention mode, which preserves motion diversity for causal inference without interfering with the distillation loss. On VBench (20s generation), HiAR achieves the best overall score and the lowest temporal drift among all compared methods.

Abstract:
Vision-Language-Action (VLA) systems have shown strong potential for language-driven robotic manipulation. However, scaling them to long-horizon tasks remains challenging. Existing pipelines typically separate data collection, policy learning, and deployment, resulting in heavy reliance on manual environment resets and brittle multi-policy execution. We present RoboClaw, an agentic robotics framework that unifies data collection, policy learning, and task execution under a single VLM-driven controller. At the policy level, RoboClaw introduces Entangled Action Pairs (EAP), which couple forward manipulation behaviors with inverse recovery actions to form self-resetting loops for autonomous data collection. This mechanism enables continuous on-policy data acquisition and iterative policy refinement with minimal human intervention. During deployment, the same agent performs high-level reasoning and dynamically orchestrates learned policy primitives to accomplish long-horizon tasks. By maintaining consistent contextual semantics across collection and execution, RoboClaw reduces mismatch between the two phases and improves multi-policy robustness. Experiments in real-world manipulation tasks demonstrate improved stability and scalability compared to conventional open-loop pipelines, while significantly reducing human effort throughout the robot lifecycle, achieving a 25% improvement in success rate over baseline methods on long-horizon tasks and reducing human time investment by 53.7%. Code is available at: https://github.com/RoboClaw-Robotics/RoboClaw.

Abstract:
Video emotion analysis is typically framed as a static clas-sification problem, treating each clip as an independent labeled unit.However, such a formulation overlooks a key psychological fact: emotionschange as a result of cumulative reactions to consecutive causal events. Tobridge this gap, we introduce DAR (Dynamic Affective Reasoning), thefirst large-scale benchmark for viewer-centric affect transitions and causalreasoning over consecutive video events. DAR contains 15,087 videos and36,908 event-aligned affective segments annotated with 27 emotion cat-egories. Unlike existing video-based emotion datasets, DAR presents anew viewer-centric perspective on fine-grained emotional expressions andtransitions, and provides dense, temporally grounded, and causally ex-plicit reasoning chains. Based on DAR, we formally define three challeng-ing tasks: affective segmentation, fine-grained emotion classification, andaffective reasoning. Complementing this benchmark, we propose DAR-R1, a two-stage framework that combines supervised fine-tuning withGroup Relative Policy Optimization. Experiments across 10+ MLLMsshow that DAR-R1 sets a new state-of-the-art for dynamic affective rea-soning, in terms of both emotional localization and affective reasoning.Project page: https://github.com/Zhang-Zhiyan/DAR.

Abstract:
Analyzing fine-grained skill activities (e.g., sports, surgery) requires not only recognizing visual patterns but also performing stepby-step visual reasoning that leads to the final judgment. While recent advances in action quality assessment have achieved remarkable progress in evaluating performance, existing models remain black boxes, where they lack the ability to explicitly reveal the reasoning processes underlying their judgments. To address this limitation, we propose Latent Visual Diffusion Reasoning (LVDR), a novel framework that integrates keypoint-guided Monte Carlo Tree Search (MCTS) to model and visualize the latent visual reasoning process. LVDR not only produces more accurate skill assessments but also uncovers the critical visual reasoning sequences that contribute to the final evaluation. Extensive experiments across four datasets spanning diverse sports and surgical domains demonstrate that LVDR achieves competitive quantitative performance while providing interpretable visual reasoning trajectories leading to the final predictions. Source codes and models can be found through the following link: https://github.com/XiruiTeng/LVDR_Official.git.

Abstract:
Sign language generation (SLG), also known as text-to-signgeneration, aims to bridge the communication gap between signers andnon-signers. Unlike many other generative tasks, SLG must satisfy twofundamental linguistic constraints. First, sign language expresses mean-ing through a sequence of gestures aligned with word-like units calledglosses, and therefore requires correct lexical ordering to preserve in-tended meaning. Second, each gesture should faithfully reflect the in-tended gloss (semantic accuracy). Despite recent progress, existing SLGmethods frequently produce signs with incorrect lexical order and lowsemantic accuracy. A common limitation of prior approaches stems fromglobally fused conditioning strategies, which weaken temporal ground-ing, the temporal correspondence between glosses and their realized signsegments. This often leads to incorrect lexical order and semanticallyambiguous signs. To address this limitation, we propose SIGNER, aSIGN language generation framework with timE-Resolved conditioningto ensure temporal grounding, leveraging a temporal-gloss condition andlocal temporal fusion (LTF). SIGNER constructs a temporal-gloss con-dition by estimating a gloss sequence and its durations from input text,and assigning gloss semantics across the temporal dimension. We thenintroduce LTF, a temporally grounded fusion module that integratesthe temporal-gloss condition within a constrained temporal window dur-ing denoising. By enforcing temporal locality in condition fusion, LTFpreserves temporal grounding, leading to correct lexical ordering andclearer per-gloss semantics. Experiments on Phoenix-2014T and CSL-Daily demonstrate state-of-the-art performance, further supported bymotion-smoothness analysis. The project page is available here.

Abstract:
We introduce EgoSAT, the first comprehensive benchmarkfor egocentric video reasoning in streaming settings, designed to evaluatethe capabilities of modern vision–language models (VLMs). The bench-mark targets streaming interaction understanding, where video framesarrive sequentially and models must continuously interpret evolving vi-sual context. EgoSAT unifies several previously distinct tasks within asingle streaming framework. In this formulation, queries about completedevents correspond to retrospective reasoning, queries about ongoing ac-tivities require online understanding, and queries about future actionsinvolve prospective anticipation. This unified setting requires models toreason about the past, present, and future while operating under theconstraint that only previously observed frames are available. EgoSATcontains 1,997 unique videos spanning 165 hours of egocentric footageand around 4,800 high-quality question–answer pairs, carefully designedto probe reasoning across varying temporal contexts. Using this bench-mark, we evaluate a diverse set of both open-weight and closed-weightVLMs, providing a systematic assessment of their ability for streaminginteraction understanding. By distinguishing answerability and conduct-ing diagnostics on confidence of models, we find existing models not onlystruggle with prospective and retrospective modeling, but also exhibit se-vere mis-calibration: confidence often fails to track inherent answerabil-ity, leading to dangerous “confidently wrong” behaviors. Project page:https://leiyj23.github.io/EgoSAT/

Abstract:
Recent multimodal large language models (MLLMs) havesubstantially advanced video understanding, yet long-form video QAremains challenging under fixed input token budgets, where uniform sam-pling can be inefficient for evidence localization. We propose ReQuest, an uncertainty-driven, question-adaptive keyframe selection pipelinethat aligns question intent with relevant video content through selec-tive computation. ReQuest integrates (i) a lightweight question-awareselector distilled from MLLM-generated supervision, (ii) Re-thinkingRouting that triggers additional inference only when the model is uncer-tain with a length-adaptive criterion, and (iii) uncertainty-guided adap-tive non-maximum suppression that selects temporally diverse frameswhile adjusting spacing based on question difficulty. As a plug-and-play method, ReQuest improves long-video QA without modifyingor fine-tuning the underlying MLLM. Experiments on Video-MME,MLVU, and LongVideoBench demonstrate consistent accuracy gainswith competitive computational cost, with particularly strong improve-ments in medium and long video regimes. The code is available athttps://geppa.github.io/ReQuest

Abstract:
While Image-to-Video models unlock new creative possibil-ities, they also raise risks of unauthorized adaptation, deepfakes, andscenario manipulation, making robust authentication and traceability so-lutions urgent for image owners. While image watermarking offers a solu-tion, existing methods expose a critical vulnerability: due to the inherentsemantic drift of generated videos, images watermarks survive only in ini-tial frames. This allows attackers to easily evade detection via temporalcropping—discarding early frames to obtain watermark-free derivativevideos. Consequently, in an era of pervasive I2V models, long-term ro-bustness is imperative for image watermarking. Since simply increasingforward embedding strength fails against severe generative divergence,we introduce LoT-Pass, a watermarking framework built on a new in-sight: only one-directional robustness enhancement is hard to achievelong-term robustness. We propose a two-way strategy: (1) expand therobustness distance by training under simulated temporal evolution inI2V generation, and (2) recover unextractable frames by reversing themthrough a temporal inversion module. This shift, from merely resistingdistortions to actively rewinding the video to a watermark-friendly state,enables LoT-Pass to maintain extractability even under temporal drift.Experiments on mainstream open-source and commercial I2V modelsdemonstrate that LoT-Pass achieves stronger long-term robustness whilepreserving imperceptibility, offering a new paradigm for image copyrightprotection in the era of widespread I2V adoption. Code

Abstract:
We present ActionPlan, a unified motion diffusion frame-work that bridges real-time streaming with high-quality offline genera-tion within a single model. The core idea is to introduce a per-frameaction plan: the model predicts frame-level text latents that act as densesemantic anchors throughout denoising, and uses them to denoise thefull motion sequence with combined semantic and motion cues. To sup-port this structured workflow, we design latent-specific diffusion steps,allowing each motion latent to be denoised independently and sampledin flexible orders at inference. As a result, ActionPlan can run in ahistory-conditioned, future-aware mode for real-time streaming, whilealso supporting high-quality offline generation. The same mechanism fur-ther enables zero-shot motion editing and in-betweening without addi-tional models. Experiments demonstrate that our real-time streaming is5.25× faster while also achieving 18% motion quality improvement overthe best previous method in terms of FID.∗ †Equal contribution. Corresponding author

Abstract:
To adapt billion-parameter Vision Transformers for long-video understanding, recent methods freeze the backbone and train light-weight convolutional modules. While effective for parameter-efficient trai-ning, existing adapters do not reduce inference-time computation, leav-ing scalability with respect to video length largely unaddressed. Tokenselection can reduce attention cost by pruning redundant tokens, butit breaks the spatial grid structure required by convolutional adapters.This forces an expensive dense reconstruction, nullifying much of thepotential speedup. We address this by introducing native sparse 2D con-volutions, a primitive that allows these adapters, for the first time, tooperate directly and efficiently on dynamically pruned token sets. Weintegrate this primitive into SV-TAD, an adapter framework for tem-poral action detection, reducing VideoMAEv2-L computation by up to64% and achieving 2.2× faster inference, while maintaining state-of-the-art accuracy on THUMOS-14 and ActivityNet-1.3. When scaled toInternVideoNext-L, our approach surpasses the previous state of the artat roughly half its computational cost. Moreover, the sparse formulationnaturally supports auxiliary task tokens, which improves fine-grained as-sembly detection on ATTACH. Code and trained models are available athttps://github.com/pcr-upm/eccv26_tad.

Abstract:
Online Video Large Language Models (VideoLLMs) playa critical role in supporting responsive, real-time interaction. Existingmethods focus on streaming perception, lacking a synchronized logicalreasoning stream. However, directly applying test-time scaling methodsincurs unacceptable response latency. To address this trade-off, we pro-pose Video Streaming Thinking (VST), a novel paradigm for streamingvideo understanding. It supports a thinking-while-watching mechanism,which activates reasoning over incoming video clips during streaming.This design improves timely comprehension and coherent cognition whilepreserving real-time responsiveness by amortizing LLM reasoning latencyover video playback. Furthermore, we introduce a comprehensive post-training pipeline that integrates VST-SFT, which structurally adapts theoffline VideoLLM to causal streaming reasoning, and VST-RL, whichprovides end-to-end improvement through self-exploration in a multi-turn video interaction environment. Additionally, we devise an auto-mated training-data synthesis pipeline that uses video knowledge graphsto generate high-quality streaming QA pairs, with an entity–relationgrounded streaming Chain-of-Thought to enforce multi-evidence reason-ing and sustained attention to the video stream. Extensive evaluationsshow that VST-7B performs strongly on online benchmarks, e.g. 79.5%on StreamingBench and 59.3% on OVO-Bench. Meanwhile, VST remainscompetitive on offline long-form or reasoning benchmarks. Comparedwith Video-R1, VST responds 15.7× faster and achieves +5.4% improve-ment on VideoHolmes, demonstrating higher efficiency and strong gen-eralization across diverse video understanding tasks. Code, data, andmodels have been released at https://github.com/1ranGuan/VST.

Abstract:
Referring Video Object Segmentation (RefVOS) seeks to seg-ment target objects in videos guided by natural language descriptions,demanding both temporal reasoning and fine-grained visual comprehen-sion. Existing sampling strategies for LLM-based approaches typicallyrely on either handcrafted heuristics or external keyframe models. Theformer often overlooks essential temporal cues, while the latter increasessystem complexity. To address this, we propose a unified frameworkthat jointly optimizes Temporal Sentence Grounding (TSG) and Re-fVOS, naturally incorporating key moment grounding capability. Duringtraining, we introduce a novel TSG paradigm that employs a dedicated[FIND] token for key moment identification through temporal token sim-ilarity matching, thereby avoiding the need for external timestamp en-codings. For inference, we design a Moment-Centric Sampling (MCS)strategy that densely samples informative moments while sparsely sam-pling non-essential frames, preserving both motion details and globalcontext. To further enhance tracking stability, we develop BidirectionalAnchor-updated Propagation (BAP), which leverages the most relevantmoment as start point for high-quality mask initialization and dynam-ically updates at sampled points to mitigate accumulated errors. Codewill be released at https://github.com/Dmmm1997/MomentSeg.

Abstract:
Current token-sequence-based Large Language Models (LLMs)struggle to directly process 3D Boundary Representation (B-rep) mod-els that contain complex geometric and topological information. To thisend, we propose BrepLLM, the first multimodal framework that enablesLLMs to directly parse and reason over raw B-rep data. BrepLLM adoptsa two-stage training pipeline: cross-modal alignment pre-training andtwo-stage LLM fine-tuning. In the first stage, we design an adaptiveUV sampling strategy to convert B-reps into graph representations thatintegrate geometric and topological information. Subsequently, we con-struct a hierarchical BrepEncoder to extract features from geometricelements (faces and edges) and topology, generating a global token anda sequence of node tokens. Then, via contrastive learning, we conduct aninitial alignment between this global token and the text embeddings of afrozen CLIP text encoder (ViT-L/14). In the second stage, we integratethe pre-trained BrepEncoder into the LLM and employ a two-stage pro-gressive strategy to align the sequence of node tokens: (1) training anMLP-based semantic mapping network that utilizes the prior knowledgeof a 2D-VLM to align the B-rep representation to the 2D visual semanticspace; (2) utilizing LoRA for parameter-efficient fine-tuning of the Q-Former and the LLM backbone network to achieve the final 3D-languagegeneration capability. Furthermore, we construct the Brep2Text dataset,which contains 269,444 B-rep and text question-answer pairs. Experi-ments demonstrate that BrepLLM achieves SOTA performance on 3Dobject classification and captioning tasks.The project page is availableat https://user-deng.github.io/BrepLLM/.

Abstract:
Spatial reasoning is foundational for Vision-Language Mod-els (VLMs), particularly when deployed as Vision-Language-Action (VLA)agents in physical environments. However, existing benchmarks predomi-nantly focus on elementary, single-hop relations, neglecting the multi-hopcompositional reasoning and precise visual grounding essential for real-world scenarios. To address this, we introduce MultihopSpatial, o!er-ing three key contributions: (1) A comprehensive benchmark designedfor multi-hop and compositional spatial reasoning, featuring 1- to 3-hopcomplex queries across diverse spatial perspectives. (2) Acc@50IoU,a complementary metric that simultaneously evaluates reasoning andvisual grounding by requiring both answer selection and precise bound-ing box prediction—capabilities vital for robust VLA deployment. (3)MultihopSpatial-Train, a dedicated large-scale training corpus to fos-ter spatial intelligence. Extensive evaluation of 37 state-of-the-art VLMsyields eight key insights, revealing that compositional spatial reasoningremains a formidable challenge. Finally, we demonstrate that reinforce-ment learning post-training on our corpus enhances both intrinsic VLMspatial reasoning and downstream embodied manipulation performance.

Abstract:
While traditional image restoration focuses on perceptualquality, Task-Driven Image Restoration (TDIR) aims to maximize theperformance of downstream high-level vision tasks. Recent approachesleveraging generative priors have shown promise for TDIR; however,they typically suffer from computational inefficiency and potential se-mantic alteration by indiscriminately updating all latent tokens. In thispaper, we posit that not all visual information is equally important formachine perception. Through an analysis of the latent token space, weobserve that task-relevant cues are unevenly distributed across the to-ken sequence, exhibiting index-wise specialization. This suggests thatselectively refining a subset of tokens can be sufficient for task-drivenobjectives. Leveraging this insight, we propose TaskTok, a novel frame-work that selectively restores only task-relevant tokens via a learnabletoken switch and a lightweight token refinement module. Extensive ex-periments across image classification, semantic segmentation, and objectdetection demonstrate that TaskTok significantly enhances task perfor-mance with high computational efficiency. The source code is availableat our project page https://github.com/jimmy9704/TaskTok.

Abstract:
CLIP (Contrastive Language-Image Pre-training) has be-come a de facto paradigm for image-text alignment, but it struggleswith long-context descriptions (> 77 tokens) due to absolute positionalencoding and pretraining on short captions. In long contexts, sentencesare often reordered, summarized, or partially omitted. Although priorworks extend CLIP with longer positional encodings, they often sufferfrom degraded image-text alignment under such text perturbations. Weattribute this limitation to the Euclidean contrastive objective, whichenforces strict one-to-one matching and lacks explicit mechanisms formodeling hierarchical relationships between global context and its con-stituent elements. To address this issue, we propose HyFL-CLIP, a hy-perbolic fine-tuning framework that distills the well-established text-image alignment learned in Euclidean CLIP into hyperbolic space viacross-manifold similarity distillation, leveraging its geometry to capturehierarchical and entailment relations. Our method models hierarchicalsemantics by linking summarized token-wise features, long-context de-scriptions, constituent short textual components, and images, capturingpart–whole relationships via hyperbolic entailment with Einstein mid-point aggregation. Experiments on diverse benchmarks, including long-context cross-modal retrieval, cross-modal retrieval with caption pertur-bations, intra-modality retrieval, and short-text cross-modal retrieval,show that HyFL-CLIP achieves more robust long-context understand-ing. In particular, it yields up to 19.5% improvement in long-text cross-modal retrieval under textual perturbations over the best prior method.We also show HyFL-CLIP can be seamlessly integrated into other modelframeworks by applying it to Stable Diffusion XL (SDXL). The projectpage is available at https://janeyeon.github.io/hyflclip.

Abstract:
Cancer segmentation models can fail silently, generating plausible but incorrect masks that risk missed findings or unnecessary biopsies. A critical question arises: Do AI models “know” when they are wrong, and if so, can we use the signal to predict their own failures? Humans do have a “Feeling of Error” (FOE): a spontaneous sense of unease that flags a potential error during thinking. We investigate whether cancer segmentation models exhibit an analogous internal signal. Unlike output-level cues (e.g., prediction confidence or uncertainty), which offer no insight into why a failure occurs and suffer from a sensitivity–quality tradeoff where high detection sensitivity could degrade overall segmentation quality. We instead propose to capture the model’s FOE from its inner workings. Using mechanistic interpretability tools, specifically Sparse Autoencoders, we decompose internal neural activations into a dictionary of human-interpretable concepts and show that failure cases exhibit a distinct latent signature: fewer active concepts with lower activation magnitudes compared to successful segmentation. By training a classifier on these concept activations, we achieve accurate failure detection along with explanations for the model’s mistakes. Experiments on prostate, pancreatic, and brain cancer segmentation demonstrate that our approach outperforms output-based methods in failure detection while preserving segmentation quality. Code is abailable at https://github.com/deep-real/CancerSegFailure.

Abstract:
Recent advancements have successfully adapted autoregressive language models to process multimodal signals, such as images and actions. Since raw action signals are continuous, effective tokenization is essential to map high-dimensional inputs into compact discrete tokens for autoregressive processing. However, existing discrete action tokenizers often suffer from high reconstruction loss, failing to preserve the finegrained dynamics required for precise control. This “discretization bottleneck” significantly limits the performance ceiling of downstream VisionLanguage-Action (VLA) models. To address this, we propose M2Tok, a Multi-head Multi-codebook Action Tokenizer designed to minimize reconstruction error and enhance policy performance. Our approach introduces two key structural innovations: (1) we decompose the latent action features into multiple heads, enabling the model to implicitly align specific heads with distinct action dimensions; (2) we assign independent codebooks to each head for quantization. By leveraging the combinatorial nature of multiple codebooks, we significantly expand the representational expressivity of the tokenizer, leading to substantially lower reconstruction loss compared to previous methods. We evaluate the M2Tok-based VLA on the RoboTwin, Simpler-Env, and 3 zero-shot real-world tasks. Experimental results demonstrate our method not only achieves superior reconstruction fidelity but also significantly boosts the success rate of VLA models. Comprehensive ablation studies further confirm the effectiveness of the multi-head and multi-codebook mechanisms. Code is available at https://github.com/cpaaax/M2Tok.

Abstract:
Scienti x001C c research based on multimodal clinical data (includ-ing medical imaging) requires coordinating clinical, radiological, pro-gramming, and biostatistical expertise, a fragmented process that bottle-necks discovery. We present Veritas (Veri x001C able Epistemic Reasoningfor Image-Derived Hypothesis Testing via Agentic Systems), a clinicalco-scientist: a multi-agent system that autonomously tests natural-language hypotheses and produces a fully auditable evidence trail, trac-ing every conclusion through executable outputs from analysis plan tosegmentation masks to statistical code to x001C nal verdict. Unlike prior AI-scientist systems, which mainly operate on tabular or text data, Veritasgrounds autonomous discovery directly in medical images. It decomposesthe work x001D ow into four phases handled by role-specialized agents, and in-troduces an epistemic evidence label framework that mechanically classi- x001C es outcomes as Supported, Refuted, Underpowered, or Invalid byjointly evaluating signi x001C cance, e x001B ect direction, and study power. This dis-tinction is critical in medical imaging, where non-signi x001C cant results oftenre x001D ect insu x001E cient sample size rather than absent e x001B ects. We construct atiered benchmark of 64 hypotheses spanning six complexity levels acrosscardiac and brain glioma MRI datasets. Veritas reaches 81.4% verdictaccuracy with frontier models and 71.2% with locally-hosted open-weightmodels (8 x0015 30B), outperforming all single-model baselines in both classes.It also produces the highest rate of independently veri x001C able statisticaloutputs (86.6%), so even its failures remain diagnosable through arti-fact inspection. Structured multi-agent decomposition thus substitutesfor model scale while preserving the veri x001C ability that scienti x001C c discoverydemands. We release code, hypothesis bank, and evaluation pipeline athttps://github.com/LucZot/veritas.

Abstract:
Although Multimodal Large Language Models (MLLMs) havemade impressive progress in understanding videos—such as recognizingobjects, actions, and events—they still struggle with spatial reasoning.In particular, they have difficulty forming a consistent 3D understandingof a scene from separate 2D video frames. Unlike humans, who can natu-rally infer depth, distance, object size, and their own movement throughspace, these models often lack the ability to accurately reconstruct a co-herent 3D environment. This limitation makes it challenging for them toestimate real-world measurements, such as how far an object is or howmuch the camera has moved, when relying only on 2D image sequences.To bridge these gaps, we propose VoxAnchor, a framework for explicitspatial-semantic grounding. Specifically, we reify the 2D video sequenceinto a spatiotemporally continuous representation by unprojecting visualtokens, geometric features, and view-consistent semantics into a unified3D voxel grid. To facilitate precise spatial reasoning, we propose a Voxel-Aware Attention mechanism, which constrains feature interactions withinphysically consistent volumetric units. This approach enables the modelto effectively aggregate fragmented observations and infer metric-awarespatial relationships from monocular video. VoxAnchor sets a new state-of-the-art on VSI (66.8%) and VSTI (66.3%) benchmarks, outperform-ing significantly more parameter-intensive models and confirming thatexplicit geometric grounding effectively bridges the 2D-to-3D gap. Codewill be available at https://github.com/Embrace-Arch/VoxAnchor.

Abstract:
Models trained via Contrastive Language-Image Pretrain-ing (CLIP) serve as the foundational vision encoders for many modernLarge Vision Language Models (LVLMs). Despite their widespread adop-tion, CLIP models exhibit a critical yet underexplored failure mode:irrelevant text appearing within images confounds visual representa-tions, biasing them toward lexical meaning rather than true visual se-mantics. This robustness issue, commonly described as a TypographicAttack (TA), exposes a vulnerability that poses a significant risk tosafety-critical applications such as autonomous driving. To achieve in-terpretable and effective robustness against TA, we propose a novel,training-free mechanistic interpretability method. Our method providessampling-based interpretations of hidden state representations and quan-titatively attributes semantic versus lexical focus to individual attentionheads. Through probabilistic analysis and circuit mining, we isolate spe-cific Vision Transformer (ViT) components that disproportionately en-code lexical information, thereby identifying the mechanistic source ofTA. We further show that simple interventions applied directly to theidentified circuits, without any additional training, can substantially im-prove robustness against Typographic Attacks in object classification.These interventions, such as selective adjustment of attention weights,outperform both supervised and training-free defense methods. Our ex-periments also demonstrate that applying the proposed intervention tothe vision encoders of several state-of-the-art LVLMs yields substantialgains in Visual Question Answering accuracy under Typographic At-tack interference on RIO-Bench. These results confirm both the efficacyand the generalizability of our mechanistic approach. Code is released athttps://github.com/Liu-524/SamplingTAR.

Abstract:
Vision-language models (VLMs) can follow complex textualinstructions, yet they struggle to reason from purely visual context. Inparticular, current models fail to infer shared concepts from sets of exam-ple images and apply them to new inputs. We introduce Visual ConceptInference from Sets (VICIS), a task that evaluates this capability. Givena small context set of images sharing a concept and a query image, themodel must generate new images that preserve the context-defined conceptwhile remaining consistent with the query. We show that state-of-the-artVLMs perform poorly on this task, often ignoring the visual contextor defaulting to biased generations. To address this gap, we propose atraining framework and architecture that learn to infer visual conceptsfrom image sets and extract concept-specific embeddings from queries.Experiments on synthetic data and large-scale ImageNet/WordNet datashow that our model generates more accurate and diverse outputs andgeneralizes to unseen concepts and modalities such as sketches.

Abstract:
The rapid advancements of Text-to-Image (T2I) models haveushered in a new phase of AI-generated content, marked by their growingability to interpret and follow user instructions. However, existing T2Imodel evaluation benchmarks fall short in limited prompt diversity andcomplexity, as well as coarse evaluation metrics, making it difficult to eval-uate the fine-grained alignment performance between textual instructionsand generated images. In this paper, we present TIIF-Bench (Text-to-Image Instruction Following Benchmark), aiming to systematicallyassess T2I models’ ability in interpreting and following intricate tex-tual instructions. TIIF-Bench comprises 5,000 prompts organized alongmultiple dimensions and categorized into three levels of difficulty andcomplexity. To rigorously evaluate robustness to prompt length, eachprompt is provided in both short and long versions with identical coresemantics. We further propose a novel Global Normalized Edit Distance(GNED) metric for text rendering and provide aspect-ratio-diverse ref-erence images for each prompt to assess style control. In addition, wecollect 100 high-quality designer-level prompts covering diverse scenariosfor comprehensive evaluation. To enable scalable and fine-grained evalua-tion, we explore the best paradigm for leveraging the world knowledgeencoded in large Vision-Language Models (VLMs) as automated binaryevaluators. Through extensive ablations, we develop a fully reproducibleevaluator that provides interpretable reasoning and reliable verification,enabling our benchmark to discern subtle variations in T2I model out-puts. Through comprehensive benchmarking of mainstream T2I modelson TIIF-Bench, we analyze the strengths and weaknesses of current T2Isystems and reveal the limitations of existing evaluation benchmarks.

Abstract:
3D visual grounding aims to locate objects based on naturallanguage descriptions in 3D scenes. Existing supervised methods are lim-ited by generalization and recent zero-shot methods typically rely on apredefined Object Lookup Table (OLT) to query Visual Language Mod-els (VLMs) for reasoning about object locations via a single step ground-ing, which limits the applications in scenarios with undefined targets andcomplex queries. To address these problems, we present OpenGround, anovel zero-shot framework for open-world 3D visual grounding that re-mains compatible with recent zero-shot methods. OpenGround integratesTask-Chain Planning to decompose a query into a plan of context-to-target sub-goals for progressive grounding, and Context-Guided Percep-tion to perceive novel objects online under context guidance from thetask chain. We also propose a new dataset named OpenTarget, whichcontains over 7000 object-description pairs to mimic open-world eval-uation. Extensive experiments demonstrate that OpenGround achievescompetitive performance on Nr3D, state-of-the-art on ScanRefer, anddelivers a substantial 17.6% improvement on OpenTarget.

Abstract:
Understanding and generating 3D objects as compositionsof meaningful parts is fundamental to human perception and reasoning.However, most text-to-3D methods overlook the semantic and functionalstructure of parts. While recent part-aware approaches introduce de-composition, they remain largely geometry-focused, lacking semanticgrounding and failing to model how parts align with textual descriptionsor their inter-part relations. We propose DreamPartGen, a frameworkfor semantically grounded, part-aware text-to-3D generation. Dream-PartGen introduces Duplex Part Latents (DPLs) that jointly modeleach part’s geometry and appearance, and Relational Semantic Latents(RSLs) that capture inter-part dependencies derived from language. Asynchronized co-denoising process enforces mutual geometric and seman-tic consistency, enabling coherent, interpretable, and text-aligned 3Dsynthesis. Across multiple benchmarks, DreamPartGen delivers state-of-the-art performance in geometric fidelity (→60% Chamfer Distance) andtext–shape alignment (↑↓20% CLIP/ULIP), while producing composi-tionally consistent and controllable parts.PLAN Lab https://plan-lab.github.io/dreampartgen

Abstract:
Visual Autoregressive Models (VAR) generate images throughnext-scale prediction, producing all tokens within each scale in paral-lel. We show that this parallel decoding constitutes a mean-field-styleapproximation that discards spatial dependencies among same-scale to-kens, causing locally incoherent samples regardless of backbone capacity– a limitation of the decoding rule. Addressing this limitation, we intro-duce the Logit Refiner, a lightweight autoregressive module that restoresintra-scale dependencies by sequentially sampling tokens conditioned onfrozen backbone features. Adding only ∼10% parameters and less than5% of the base model’s training compute, it plugs into any pretrainedVAR checkpoint without retraining. Controlled ablations isolate jointintra-scale sampling – rather than additional capacity or training – asthe critical ingredient. Across backbones from 310M to 2B parameterson class-conditional ImageNet 256×256, the refiner consistently improvesgeneration quality, enabling a 1.1B-parameter model to surpass one twiceits size. The approach further generalizes to text-to-image generation,confirming that the mean-field bottleneck persists across VAR variantsand is effectively alleviated by our method.Project page: https://compvis.github.io/logit-refiner/.

Abstract:
Recent progress in image and video synthesis has inspiredtheir use in advancing 3D scene generation. However, we observe thattext-to-image and -video approaches struggle to maintain scene- andobject-level consistency beyond a limited environment scale without apersistent, explicit geometric representation. We thus present a geometry-first approach that decouples this complex problem of large-scale 3Dscene synthesis into its structural composition, represented as a meshscaffold, and realistic appearance synthesis, which leverages powerful im-age synthesis models conditioned on the mesh scaffold. From an inputtext description, we first construct a mesh capturing the environment’sgeometry (walls, floors, etc.), and then use image synthesis, segmentationand object reconstruction to populate the mesh structure with objects inrealistic layouts. This mesh scaffold is then rendered to condition imagesynthesis, providing a structural backbone for consistent appearance gen-eration. This enables scalable, arbitrarily-sized 3D scenes of high objectrichness and diversity, combining robust 3D consistency with photoreal-istic detail. We believe this marks a significant step toward generatingtruly environment-scale, immersive 3D worlds.

Abstract:
We propose triangular consistency as a first-principled con-straint for optical flow, which is agnostic to network architecture, super-vision type, and dataset, and applies to both image-pair and multi-framesettings. This simple but powerful constraint is to compose two flows toinduce a third flow and enforce consistency among the three. The com-posed flows may arise from (i) image pairs, yielding cycle consistency; (ii)multiple video frames, producing longer-range motion through temporalchaining; or (iii) image pairs combined with controlled synthetic transfor-mations, which becomes data augmentation. This triangular consistencyintroduces negligible computational overhead and requires no additionalannotations. Since it is derived directly from the geometry of opticalflow, it does not rely on model-specific assumptions and serves as a “uni-versal” plug-and-play component for optical flow training. Experimentsshow consistent improvement across supervised, unsupervised, and trans-fer learning settings. Code: https://github.com/lsuvision/tri-flow.

Abstract:
Diffusion-based face generation commonly combines iden-tity cues and edit conditions through Euclidean mixing at the condi-tioning interface. This becomes fragile when facial attributes are cou-pled: naively adding feature vectors ignores their interactions and canlead to identity drift and artifacts. We present FlowFace, a geometry-aware identity-conditioning framework that rectifies the Stable Diffusionconditioning geometry during training without modifying the inferencesampler. FlowFace includes (i) a dual-stream manifold encoder inspiredby fiber bundles to disentangle geometry-related variation from seman-tic identity, (ii) a Lie-algebraic composition module based on a trun-cated BCH expansion to introduce an explicit interaction term betweencoupled edits, and (iii) a geodesic-consistency regularizer that learns alocal SPD metric that encourages Euclidean operations to better cor-relate with a geodesic-consistent surrogate under the learned geome-try. Experiments show that FlowFace outperforms strong baselines inidentity preservation, text alignment, and perceptual quality, while re-taining adapter-level inference efficiency. The code will be available athttps://github.com/SunFly0/Flowface.

Abstract:
Diffusion models typically reconstruct coarse, low-frequency structure before recovering high-frequency detail during the reverse denoising. However, the standard forward process injects Gaussian white noise that corrupts all frequencies uniformly, creating a mismatch between forward corruption and reverse denoising and hindering the recovery of fine detail. We address this mismatch with an adaptive noise covariance schedule that evolves along the geodesic on the Symmetric Positive Definite (SPD) manifold under the Bures-Wasserstein (BW) metric. Specifically, we evolve the covariance along the BW geodesic from a blue-noise endpoint to a white-noise endpoint, so the noise power spectrum transitions smoothly from high-frequency emphasis to uniform. This transition better aligns forward corruption with reverse denoising and enhances textural detail. We provide theoretical and quantitative analyses showing that this transition path yields smoother noise power spectrum evolution and lower noise power fluctuations. In addition, we adapt the transition speed to the characteristics of each image, yielding finer forward-reverse alignment and improved perceptual quality. Experiments on multiple datasets show consistent gains over deterministic diffusion baselines in FID and KID. Our code and pretrained models are available at https://github.com/BolinDeng/RMDM.

Abstract:
We propose a novel framework for modeling, synthesis, and rendering of mesoscale appearances based on 3D Gaussian Splatting (3DGS) for better quality and performance. The key problem is that synthesis of 3D Gaussians is difficult due to their explicit nature. Therefore, the core of our method is a multi-layer Gaussian texture representation to support high-quality and efficient synthesis of 3D Gaussians. Each layer of the texture contains Gaussians with similar heights to the object surface, which is compatible with 2D image texture synthesis approaches. Through by-example autocovariance-preserving synthesis, we achieve the run-time synthesis of mesoscale appearances with their structures preserved. Due to the deformation of mapping from texture to arbitrary objects and the baking of lighting, directly applying the Gaussian texture for rendering may cause visual artifacts. Thus, we further introduce two components: a deformation-aware mapping and a material-lighting decomposition. The mapping is based on barycentric UV coordinates with a scaling constraint of Gaussians to reduce artifacts. The decomposed materials are represented by latent features to maintain the consistency of the material attributes during synthesis. Consequently, our approach achieves a faithful representation of mesoscale appearances modeled from multi-view images, which supports synthesis and rendering on arbitrary objects in real time, achieving about 500× acceleration compared to the latest NeRF-Texture. Our code is available at: https://github.com/ChenXiang0810/multilayer-gaussian-texture.

Abstract:
Volumetric video seeks to model dynamic scenes as tem-porally coherent 4D representations. While recent Gaussian-based ap-proaches achieve impressive rendering fidelity, they primarily emphasizeappearance but are largely agnostic to instance-level structure, limit-ing stable tracking and semantic reasoning in highly dynamic scenarios.In this paper, we present Director, a unified spatio-temporal Gaussianrepresentation that jointly models human performance, high-fidelity ren-dering, and instance-level semantics. Our key insight is that embeddinginstance-consistent semantics naturally complements 4D modeling, en-abling more accurate scene decomposition while supporting robust dy-namic scene understanding. To this end, we leverage temporally alignedinstance masks and sentence embeddings derived from Multimodal LargeLanguage Models to supervise the learnable semantic features of eachGaussian via two MLP decoders, enabling language-aligned 4D repre-sentations and enforcing identity consistency over time.To enhance temporal stability, we bridge 2D optical flow with 4D Gaus-sians and finetune their motions, yielding reliable initialization and re-ducing drift. For the training, we further introduce a geometry-awareSDF constraints, along with regularization terms that enforces surfacecontinuity, enhancing temporal coherence in dynamic foreground model-ing. Experiments demonstrate that Director achieves temporally coher-ent 4D reconstructions while simultaneously enabling instance segmen-tation and open-vocabulary querying.

Abstract:
Recent advances in 3D Gaussian Splatting have enabled im-pressive photorealistic novel view synthesis. However, to transition froma pure rendering engine to a reliable spatial map for autonomous agentsand safety-critical applications, knowing where the representation is un-certain is as important as the rendering fidelity itself. We bridge thiscritical gap by introducing a lightweight, plug-and-play framework forpixel-wise, view-dependent predictive uncertainty estimation. Our post-hoc method formulates uncertainty as a Bayesian-regularized linear least-squares optimization over reconstruction residuals. This architecture-agnostic approach extracts a per-primitive uncertainty channel withoutmodifying the underlying scene representation or degrading baseline vi-sual fidelity. Crucially, we demonstrate that providing this actionablereliability signal successfully translates 3D Gaussian splatting into atrustworthy spatial map, further improving state-of-the-art performanceacross three critical downstream perception tasks: active view selection,pose-agnostic scene change detection, and pose-agnostic anomaly detec-tion. Code is available at github.io/3DGS-Uncertainty.

Abstract:
High dynamic range (HDR) novel view synthesis (NVS) aimsto reconstruct HDR scenes from multi-exposure low dynamic range (LDR)images. Existing HDR pipelines heavily rely on known camera poses,well-initialized dense point clouds, and time-consuming per-scene op-timization. Current feed-forward alternatives overlook the HDR prob-lem by assuming exposure-invariant appearance. To bridge this gap, wepropose InstantHDR, a feed-forward network that initializes 3D HDRscenes from uncalibrated multi-exposure LDR collections in a fast sin-gle forward pass. Specifically, we design a geometry-guided appearancemodeling for multi-exposure fusion, and a meta-network for generaliz-able scene-specific tone mapping. Due to the lack of HDR scene data,we build a pre-training dataset, called HDR-Pretrain, for generalizablefeed-forward HDR models, featuring 168 Blender-rendered scenes, di-verse lighting types, and multiple camera response functions. Compre-hensive experiments show that our InstantHDR delivers a single-forwardHDR initialization at ∼ 700× the speed of SoTA optimization-basedmethods, and reaches comparable quality in real settings after lightweightpost-optimization while remaining ∼ 20× faster. All code, models, anddatasets: https://github.com/Bugjudger/InstantHDR.

Abstract:
Indoor scene relighting demands photorealism, precise spa-tial control, and strict multi-view consistency. While diffusion-based im-age editing models enable semantic lighting manipulation via text prompts,enforcing exact 3D light placement often disrupts their generative pri-ors. We propose Lume-Palette, a progressive framework that leveragessemantic lighting priors for spatially controllable multi-view indoor re-lighting. The approach decouples relighting into two stages: (1) illumi-nation distillation, which extracts canonical illumination palettes froma pretrained diffusion model to preserve realistic material–light interac-tions, and (2) illumination casting, which explicitly maps target spatiallighting conditions defined from coarse 3D geometry. To efficiently handledense multi-view and multi-modal inputs, we introduce an asymmetricmulti-view conditioning strategy that selectively injects essential spatialcontext. Experiments on diverse synthetic scenes and real-world scenesdemonstrate that Lume-Palette produces photorealistic, spatially con-trollable, and multi-view consistent relighting results.

Abstract:
The e x001E cient spatial allocation of primitives serves as thefoundation of 3D Gaussian Splatting, as it directly dictates the synergybetween representation compactness, reconstruction speed, and render-ing x001C delity. Previous solutions, whether based on iterative optimizationor feed-forward inference, su x001B er from signi x001C cant trade-o x001B s between thesegoals, mainly due to the reliance on local, heuristic-driven allocationstrategies that lack global scene awareness. Speci x001C cally, current feed-forward methods are largely pixel-aligned or primitive-aligned. By un-projecting pixels into dense, view-aligned primitives, they bake redun-dancy into the 3D asset. As more input views are added, the represen-tation size increases and global consistency becomes fragile. To this end,we introduce GlobalSplat, a framework built on the principle of align x001C rst, decode later. Our approach learns a compact, global, latent scenerepresentation that encodes multi-view input and resolves cross-view cor-respondences before decoding any explicit 3D geometry. Crucially, thisformulation enables compact, globally consistent reconstructions withoutrelying on pretrained pixel-prediction backbones or reusing latent fea-tures from dense baselines. Utilizing a coarse-to- x001C ne training curriculumthat gradually increases decoded capacity, GlobalSplat natively preventsrepresentation bloat. On RealEstate10K and ACID, our model achievescompetitive novel-view synthesis performance while utilizing as few as16K Gaussians, signi x001C cantly less than required by dense pipelines, ob-taining a light 4MB footprint. Further, GlobalSplat enables signi x001C cantlyfaster inference than the baselines, operating under 78 milliseconds in asingle forward pass.

Abstract:
3D Gaussian Splatting (3DGS) has revolutionized fast novelview synthesis, yet its opacity-based formulation makes surface extrac-tion fundamentally difficult. Unlike implicit methods built on SignedDistance Fields or occupancy, 3DGS lacks a global geometric field, forc-ing existing approaches to resort to heuristics such as TSDF fusion ofblended depth maps. Inspired by the Objects as Volumes framework [38],we derive a principled occupancy field for Gaussian Splatting and showhow it can be used to extract highly accurate watertight meshes of com-plex scenes. Our key contribution is to introduce a learnable orientednormal at each Gaussian element and to define an adapted attenuationformulation, which leads to closed-form expressions for both the normaland occupancy fields at arbitrary locations in space. We further intro-duce a novel consistency loss and a dedicated densification strategy toenforce Gaussians to wrap the entire surface by closing geometric holes,ensuring a complete shell of oriented primitives. We modify the differen-tiable rasterizer to output depth as an isosurface of our continuous model,and introduce Primal Adaptive Meshing for Region-of-Interest mesh-ing at arbitrary resolution. We additionally expose fundamental biasesin standard surface evaluation protocols and propose two more rigor-ous alternatives. Overall, our method Gaussian Wrapping sets a newstate-of-the-art on DTU and Tanks and Temples, producing complete,watertight meshes at a fraction of the size of concurrent work—recoveringthin structures such as the notoriously elusive bicycle spokes. Our projectpage is available here.

Abstract:
Building one-shot 3D animatable head avatars is an impor-tant yet challenging problem. Existing methods generally collapse underlarge camera pose variations, compromising the realism of 3D avatars.In this work, we propose a new framework to tackle the novel settingof one-shot 3D full-head animatable avatar reconstruction in a singleforward pass via inpainted UV-space Gaussian modeling, enabling 360◦rendering views and real-time animation. To facilitate efficient animationcontrol, we model 3D head avatars with Gaussian primitives embeddedon the surface of a parametric face model within the UV space, andproject the input image features to the UV space, resulting in incom-plete local UV feature maps. To inpaint the missing regions, we obtainknowledge of full-head geometry and textures from rich 3D full-head pri-ors within a pretrained 3D generative adversarial network (GAN) forglobal full-head feature extraction and multi-view supervision. Specifi-cally, to enhance the fidelity of 3D reconstruction during inpainting, wetake advantage of the symmetric nature of the UV space and humanfaces to fuse incomplete yet detailed local UV feature maps with theextracted global full-head textures, resulting in inpainted UV Gaussianattribute maps for avatar modeling. Extensive experiments demonstratethat our method is the first to achieve high-quality 3D full-head animat-able avatar modeling, significantly improving side and back views whileoutperforming state-of-the-art animation approaches, thereby improvingthe realism of 3D animatable avatars. The project page is available athttps://shaelynz.github.io/fhavatar/.

Abstract:
Large view synthesis models synthesize novel views throughcross-view attention without explicit 3D representations, and recent stud-ies have shown that they learn accurate spatial correspondence fromRGB supervision alone. We observe that this correspondence generalizesbeyond appearance. When non-photorealistic signals such as binary en-coded panoptic labels are passed through the model, they are propagatedto novel views with consistent spatial structure. These results indicatethat the correspondence learned for RGB view synthesis can also propa-gate view-independent per-pixel labels. From this observation, we studyhow large view synthesis models can be extended beyond appearancerendering to 3D scene understanding. We propose a panoptic segmen-tation pipeline that reuses a frozen view synthesis model to propagatepanoptic labels from input views to novel views, without 3D reconstruc-tion or any segmentation-specific training of the view synthesis model.Given panoptic labels on the input views, we encode them into binarychannel representations and pass them through the same model to rendertarget-view segmentation. On ScanNet, our method achieves segmenta-tion quality on par with Gaussian based approaches requiring explicit3D reconstruction, while outperforming them in novel view synthesis bymore than 7 dB. The label propagation also transfers across datasets,surpassing these approaches on Replica without any fine-tuning.

Abstract:
Generative video models have significantly advanced the photorealistic synthesis of adverse weather for autonomous driving; however, they consistently demand massive datasets to learn rare weather scenarios. While 3D-aware editing methods alleviate these data constraints by augmenting existing video footage, they are fundamentally bottlenecked by costly per-scene optimization and suffer from inherent geometric and illumination entanglement. In this work, we introduce AutoWeather4D, a feed-forward 3D-aware weather editing framework designed to explicitly decouple geometry and illumination. At the core of our approach is a G-buffer Dual-pass Editing mechanism. The Geometry Pass leverages explicit structural foundations to enable surface-anchored physical interactions, while the Light Pass analytically resolves light transport, accumulating the contributions of local illuminants into the global illumination to enable dynamic 3D local relighting. Extensive experiments demonstrate that AutoWeather4D achieves comparable photorealism and structural consistency to generative baselines while enabling finegrained parametric physical control, serving as a practical data engine for autonomous driving.

Abstract:
Reconstructing physically plausible 3D human-scene interactions (HSI) from a single image currently presents a trade-off: optimization based methods offer accurate contact but are slow (∼20s), while feed-forward approaches are fast yet lack explicit interaction reasoning, producing floating and interpenetration artifacts. Our key insight is that geometry-based human–scene fitting can be amortized into fast feed-forward inference. We present GRAFT (Geometric Refinement And Fitting Transformer), a learned HSI prior that predicts Interaction Gradients: corrective parameter updates that iteratively refine human meshes by reasoning about their 3D relationship to the surrounding scene. GRAFT encodes the interaction state into compact body-anchored tokens, each grounded in the scene geometry via Geometric Probes that capture spatial relationships with nearby surfaces. A lightweight transformer recurrently updates human meshes and re-probes the scene, ensuring the final pose aligns with both learned priors and observed geometry. GRAFT operates either as an end-to-end reconstructor using image features, or with geometry alone as a transferable plug-and-play HSI prior that improves feed-forward methods without retraining. Experiments show GRAFT improves interaction quality by up to 122% over state-of-the-art feed-forward methods and matches optimization-based interaction quality at ∼100× lower runtime, while generalizing seamlessly to in-the-wild multi-person scenes and being preferred in 64.8% of threeway user study. Project page: https://pradyumnaym.github.io/graft.

Abstract:
Feed-forward foundation models for multi-view 3-dimensional(3D) reconstruction have been trained on large-scale datasets of perspec-tive images; when tested on wide field-of-view images, e.g., from a fish-eye camera, their performance degrades. This degradation arises fromchanges in spatial arrangements of pixels induced by the non-linear pro-jection model that maps 3D points onto the 2D image plane. While onemay surmise that training on fisheye images would resolve this problem,there are far fewer fisheye images with ground truth than perspectiveimages, which limits generalization. To enable inference on imagery ex-hibiting high radial distortion, we propose Fisheye3R, a novel adaptationframework that extends these multi-view 3D reconstruction foundationmodels to natively accommodate fisheye inputs without performance re-gression on perspective images. To address the scarcity of fisheye imagesand ground truth, we introduce flexible learning schemes that supportself-supervised adaptation using only unlabeled perspective images andsupervised adaptation without any fisheye training data. Extensive ex-periments across three foundation models, including VGGT, π 3 , and Ma-pAnything, demonstrate that our approach consistently improves camerapose, depth, point map, and field-of-view estimation on fisheye images.Code is available at https://github.com/android-xr/fisheye3r.

Abstract:
Current visual generation models are capable of producinghigh-quality content, yet they lack a coherent perception of the spatialstructure. Existing generative novel view synthesis methods typically in-troduce explicit geometry priors, which enforce spatial consistency butinherently restrict generalization in large view changes. In contrast, re-cent interactive generative methods favor implicit scene modeling, offer-ing greater flexibility at the cost of precise camera control and geome-try consistency. In this paper, we propose MetaView, a diffusion-basedmonocular novel view synthesis framework that enables rendering underlarge view changes from a single image. Our key insight is to combineimplicit geometry modeling with minimal yet essential explicit 3D cues:we incorporate implicit geometry priors from a feed-forward geometryperception network to regularize structure without imposing restrictivereconstruction pipelines, while leveraging metric depth to anchor the gen-eration to a metric scale. This design allows MetaView to achieve bothgeometry consistency and precise controllability. Extensive experimentsdemonstrate that, under challenging monocular large viewpoint changes,MetaView significantly outperforms existing methods and exhibits supe-rior generalization. Our code will be made publicly available.

Abstract:
3D Gaussian Splatting has revolutionized novel view synthe-sis with real-time rendering and photorealistic quality. However, standard3DGS methods fail in aerial scenes due to sparse anisotropic UAV captures.Two critical issues emerge: incomplete triangulated points in texturelessregions that destabilizes Gaussian initialization and view-aligned degen-eracies where Gaussians overfit limited observations rather than respecttrue geometry. We present StratoSplat, a framework that exploits the in-herent layered structure of aerial scenes for robust sparse-view 3DGS. Ourkey insight is that buildings, terrain, and roads naturally stratify alonggravity, providing strong geometric priors to regularize this problem. Weaddress point sparsity through layered memory guided initialization thatenforces multi-view consistency. To prevent optimization degeneracies, weintroduce neural multi-plane Gaussians where virtual primitives anchor tolearned parallel planes for geometric regularization. Extensive experimentsdemonstrate state-of-the-art performance, surpassing the best baseline byover 3dB. Code available at https://github.com/keloee/StratoSplat.

Abstract:
Generating animation from a single 2D drawing is challenging because the output must preserve character appearance while remaining plausible and temporally coherent under motion. Existing drawingbased 3D animation pipelines often use sample-wise 2D refinement to align animated renderings with the input image, but such optimization tends to overfit to the observed view and fails to correct projectioninduced artifacts in novel views. To address this limitation, we introduce SPECSIA-15K, a paired stylization dataset containing 14,980 artifactcorrupted projection/refinement-target pairs from 1,498 3DBiCar characters. We further present DraViE (Drawing-based View Enhancement), a lightweight plug-and-play module trained with data-level priors to remove novel-view artifacts while preserving style and motion plausibility. Experiments show consistent gains in novel-view fidelity and temporal coherence with lower per-character adaptation cost than sample-wise fine-tuning. Project page: https://rbdnjs7830.github.io/SPECSIA/

Abstract:
Despite recent advancements in 3D generation, models oftenstruggle to produce assets with high visual fidelity. To bridge this gap,we propose OREO, an alignment framework that enhances the realismof 3D generators by leveraging rich 2D diffusion priors. Instead of relyingon static datasets, OREO establishes a dynamic optimization loop thatproduces on-the-fly pseudo ground truths. At its core, OREO combinestwo components: (i) Reinforced Editing, which uses an image editor torefine rendered views of the 3D output, improving the visual fidelity ofrendered views while preserving geometric structure; and (ii) ContrastiveDistillation, which treats the edited views as positive and the originalrenderings as negative anchors, distilling the fidelity gap into the 3Dgenerator via a latent contrastive objective. Experiments demonstratethat OREO effectively improves upon pre-trained baselines, producing3D assets with enhanced visual realism. Our project page is at https://theericma.github.io/oreo/.

Abstract:
While 3D Gaussian Splatting (3DGS) has revolutionized 3Dreconstruction and novel-view synthesis, scenarios with limited inputviews often lead to poor reconstruction quality and artifacts in ren-dered novel views. Recent efforts attempt to utilize powerful diffusionpriors, yet they typically process rendered and reference views concate-nated along an additional dimension in a single network. These methodsoverlook an inherent nature that different views should maintain ap-pearance similarity but differ in structure due to view shifts, leadingto blur caused by conflicts between the two properties. In this paper,we propose DualDiff, a novel pipeline that leverages dual diffusion pri-ors with a Structure-Appearance Attention (SAA) module to introduce∗ Equal contributionB Corresponding author: zhangjian.sz@pku.edu.cnreference guidance for refining low-quality novel views rendered fromflawed 3D representations. Specifically, we retain one diffusion branchto focus on extracting structural information from the low-quality novelviews, while introducing another branch to ensure appearance consis-tency with reference views. Furthermore, we present a 3D reconstructionframework named DualDiff3D, which integrates a reliability-enhancedRender-Refine-Optimize (RRO) loop to progressively and robustly incor-porate the refined novel views, yielding more accurate 3DGS. Extensiveexperiments demonstrate that our approach outperforms state-of-the-art methods even in the inference-only setting, with further performancegains achievable through training. Our code and pre-trained weights willbe publicly released.

Abstract:
While 3D Vision Foundation Models (3DVFMs) have demon-strated remarkable zero-shot capabilities in visual geometry estimation,their direct application to generalizable novel view synthesis (NVS) re-mains challenging. In this paper, we propose AirSplat, a novel train-ing framework that effectively adapts the robust geometric priors of3DVFMs into high-fidelity, pose-free NVS. Our approach introduces twokey technical contributions: (1) Self-Consistent Pose Alignment (SCPA),a training-time feedback loop that ensures pixel-aligned supervision toresolve pose-geometry discrepancy; and (2) Rating-based Opacity Match-ing (ROM), which leverages the local 3D geometry consistency knowl-edge from a sparse-view NVS teacher model to filter out degraded prim-itives. Experimental results on large-scale benchmarks demonstrate thatour method significantly outperforms state-of-the-art pose-free NVS ap-proaches in reconstruction quality. Our AirSplat highlights the potentialof adapting 3DVFMs to enable simultaneous visual geometry estimationand high-quality view synthesis.

Abstract:
High-fidelity semantic 3D scene representations are crucialfor numerous applications, including robotics, autonomous driving, andsimulation. Beyond this, the ability to edit such representations enablesdevelopers to adapt these applications more easily to specific target sce-narios. Current approaches provide limited support for controllable edit-ing. We introduce TASE, a method that projects pretrained 2D seman-tic features into a truncation-aware embedding space to enable flexi-ble 3D scene editing. Our method explicitly optimizes a feature spacein which progressively reducing feature channels yields increasingly ab-stract semantic representations, while retaining more channels preservesfine-grained detail. Additionally, we improve multi-view consistency ofthe features using a scale- and translation-consistency loss. The result-ing truncation-aware embedding space enables text-driven edits to 3Dscenes, providing explicit control over how strongly edits adhere to theoriginal scene content and allowing more substantial modifications thanprior methods. Moreover, we propose a finetuning stage for the edit-ing image generation model to mitigate artifacts caused by geometricchanges. Experimental results demonstrate competitive performance in3D scene editing, substantially outperforming prior methods on editsinvolving large geometric modifications.

Abstract:
Simulation provides a low-cost, scalable pathway to largescale robotic manipulation data collection. However, existing 3D scene generation methods can rarely be applied directly to manipulation data synthesis, as their generated scenes often lack instance-level interactivity and physical plausibility. Focusing on tabletop manipulation, we propose TabletopGen, a training-free and automated tabletop scene generation and interactive simulation engine. Starting from text or a single image, we first obtain independent 3D object models via generative instance extraction. Second, we introduce a novel pose and scale alignment approach that recovers a collision-free scene layout using a Differentiable Rotation Optimizer and a Top-View Spatial Alignment mechanism. Finally, we assemble the generated scene in a physics simulator with collision geometry, yielding a stable, interactable environment for synthesizing multimodal manipulation data. Extensive experiments and user studies demonstrate that TabletopGen achieves state-of-the-art performance in visual fidelity, layout accuracy, and physical plausibility. Furthermore, we validate the executability of the collected trajectories on a real robotic arm via zeroshot real-to-sim-to-real policy transfer, indicating that TabletopGen can serve as a reliable data engine for robotic manipulation data synthesis.

Abstract:
Previous works that leverage video models for image-to-3Dscene generation often suffer from geometric distortions and blurry con-tent. Using video generation models to implicitly maintain geometricconsistency according to a single-frame input is ineffective. In this paper,we present a two-stage method, named GeoWorld, that renovates theimage-to-3D scene generation pipeline by providing full-frame geometryfeatures. The first-stage video generation model, followed by a multi-viewgeometry model, produces full-frame geometry features, which are thenused as a mental draft of geometric conditions to aid the second-stagevideo-generation model. A geometric loss is proposed to impose real-worldgeometric constraints, and a geometry adaptation module is introducedto ensure the effective utilization of geometry features. Thanks to full-frame geometric modeling, the two smaller video models in our two-stagemethod can generate higher-fidelity 3D scenes than SOTA methods, whilebeing even faster, e.g. 7.5× faster than Hunyuan-Voyager. Project page:https://peaes.github.io/GeoWorld.

Abstract:
Streaming 3D reconstruction maintains a persistent latentstate that is updated online from incoming frames, enabling constant-memory inference. A key failure mode is the state update rule: aggressiveoverwrites forget useful history, while conservative updates fail to tracknew evidence, and both behaviors become unstable beyond the traininghorizon. To address this challenge, we propose FILT3R, a training-freelatent filtering layer that casts recurrent state updates as stochastic stateestimation in token space. FILT3R maintains a per-token variance andcomputes a Kalman-style gain that adaptively balances memory reten-tion against new observations. Process noise – governing how much thelatent state is expected to change between frames – is estimated onlinefrom EMA-normalized temporal drift of candidate tokens. Using exten-sive experiments, we demonstrate that FILT3R yields an interpretable,plug-in update rule that generalizes common overwrite and gating poli-cies as special cases. Specifically, we show that gains shrink in stableregimes as uncertainty contracts with accumulated evidence, and risewhen genuine scene change increases process uncertainty, improving long-horizon stability for depth, pose, and 3D reconstruction, compared to theexisting methods.

Abstract:
Online 3D scene graph generation builds a persistent, struc-tured representation of a scene by incrementally fusing 2D observationsinto a global 3D graph. Existing online methods treat this fusion asa fully deterministic pipeline, where we identify three sources of un-certainty that are overlooked: observation, 2D model, and 3D repre-sentation. We propose PUF: a Plug-and-play, Uncertainty-aware, andtraining-free Fusion framework. Scene graph node association is refor-mulated as a probabilistic likelihood over semantic and spatial factors,replacing binary accept/reject gates. Dirichlet evidence accumulation dis-tributes class and relationship evidence across plausible candidates pro-portional to association likelihood. An optional class-conditional priorcompletes edges for sparsely or never co-observed object pairs. We instan-tiate PUF with both a 3D Gaussian and a 3D voxel backend and observeconsistent improvements, demonstrating its ability to generalize acrossdifferent representations. Experiments on the 3DSSG and ReplicaSSGbenchmarks show that our method substantially outperforms existingapproaches while maintaining real-time latency. These results establishuncertainty-aware fusion as a principled and effective paradigm for on-line 3D scene understanding. The source code is publicly available athttps://github.com/yyyyangyi/PUF.

Abstract:
Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet its accuracy still lags far behind descriptor-based pipelines. We identify this gap to insufficient geometric discriminability in geometryonly matching. Without visual appearance, current methods underutilize local geometry cues, lack the global context among keypoints, and overfit to a single keypoint detector. We further observe that descriptorfree matching naturally enables multi-detector training, as heterogeneous keypoints can be optimized in a shared geometry-only space without aligning descriptor spaces. Building on these insights, we propose GeoMix, a descriptor-free 2D-3D matching framework that strengthens geometric discriminability at three levels. Locally, directional and distance-aware embeddings enrich neighborhood aggregation with finegrained spatial structure. Globally, learnable context nodes aggregate and redistribute scene-wide information via cross-attention to resolve ambiguities beyond local receptive fields. At the training level, MixTraining exploits this detector-agnostic geometry space to learn representations across multiple keypoint detectors. Extensive experiments on MegaDepth, Cambridge Landmarks, 7Scenes, and Aachen Day-Night show that GeoMix sets a new state of the art among descriptor-free methods, reducing 75th-percentile rotation error by 89% and translation error by up to 90% over the previous best, while generalizing zero-shot to unseen detectors and narrowing the gap to descriptor-based pipelines. Code is available at https://github.com/YejunZhang/Geomix.

Abstract:
Transformer-based models have made remarkable progress in image restoration (IR) tasks. However, the quadratic complexity of self-attention in Transformer hinders its applicability to high-resolution images. Existing methods mitigate this issue with sparse or windowbased attention, yet inherently limit global context modeling. Linear attention, a variant of softmax attention, demonstrates promise in global context modeling while maintaining linear complexity, offering a potential solution to the above challenge. Despite its efficiency benefits, vanilla linear attention suffers from a significant performance drop in IR, largely due to the low-rank nature of its attention map. To counter this, we propose Rank Enhanced Linear Attention (RELA), a simple yet effective method that enriches feature representations by integrating a lightweight depthwise convolution. Building upon RELA, we propose an efficient and effective Vision Transformer, named LAformer. LAformer eliminates hardware-inefficient operations such as softmax and window shifting, enabling efficient processing of high-resolution images. Extensive experiments across 7 IR tasks and 21 benchmarks demonstrate that LAformer outperforms SOTA methods and offers significant computational advantages. Furthermore, we extend LAformer to diffusion-based and flow-based visual generation, showcasing its strong potential as a competitive alternative to DiT and SiT. Code and models are available at https://github.com/shallowdream204/LAformer.

Abstract:
Robust medical image segmentation across modalities remains challenging due to severe domain shifts and the lack of targetdomain labels. While diffusion models have been explored for crossdomain generation and augmentation, target-domain conditional diffusion training typically relies on highly noisy pseudo masks; naively conditioning on a single Arg-Max pseudo-label can corrupt diffusion training and downstream segmentation. We propose UPDiff-UDA, a unified UDA framework whose core is an uncertainty-guided training objective for target-domain conditional diffusion. Given an imperfect sourcetrained segmenter, we use its per-pixel softmax distribution to form ranked pseudo-label maps (Arg-Max, Arg-2nd, Arg-3rd, ...). Each map yields a conditional score estimate, and we aggregate them via pixel-wise confidence weighting to obtain an uncertainty-reweighted score for score matching, improving robustness to pseudo-label noise while leveraging alternative plausible labels in uncertain regions. We further provide a theoretical justification showing that confidence-weighted aggregation follows a minimum-MSE convex-combination principle under the segmenterinduced surrogate label distribution. To improve pseudo-condition quality, we also introduce a feature-guided, low-degree-of-freedom Bézier curve adaptation to reduce appearance gaps. Experiments on multiple public datasets and modality shifts show that UPDiff-UDA generates highfidelity labeled target-style samples for augmentation and consistently outperforms strong UDA baselines. The code for this project is available at: https://github.com/superlc1995/Multi-Channel-UncertaintyDiffusion-UDA

Abstract:
Chest X-ray report generation (CXR-RG) has the potential to substantially alleviate radiologists’ workload. However, conventional autoregressive vision–language models (VLMs) suffer from high inference latency due to sequential token decoding. Diffusion-based models offer a promising alternative through parallel generation, but they still require multiple denoising iterations. Compressing multi-step denoising to a single step could further reduce latency, but often degrades textual coherence due to the mean-field bias introduced by token-factorized denoisers. To address this challenge, we propose ECHO, an efficient diffusion-based VLM (dVLM) for chest X-ray report generation. ECHO enables stable one-step-per-block inference via a novel Direct Conditional Distillation (DCD) framework, which mitigates the mean-field limitation by constructing unfactorized supervision from on-policy diffusion trajectories to encode joint token dependencies. In addition, we introduce a Response-Asymmetric Diffusion (RAD) training strategy that further improves training efficiency while maintaining model effectiveness. Extensive experiments demonstrate that ECHO surpasses state-of-the-art autoregressive methods, improving RaTE and SemScore by 64.33% and 60.58%, respectively, while achieving up to an 8× inference speedup with negligible degradation in clinical accuracy. Code, data, and models are publicly available at https://echo-midea-airc.github.io/.

Abstract:
Capturing high-speed polarization dynamics is crucial forrevealing physical cues like stress variations and surface structures thatare invisible in conventional intensity imaging. While snapshot compres-sive imaging (SCI) enables high-speed video reconstruction from a singlemeasurement, extending it to division-of-focal-plane (DoFP) polarizationsensing introduces a fundamentally more ill-posed inverse problem due tothe coupling between temporal multiplexing and spatial polarization mo-saicing. To address this challenge, we propose Polarization Coded Aper-ture Compressive Temporal Imaging (Pol-CACTI), a hardware–softwareco-designed framework for polarized video SCI. First, we build an opticalsystem that encodes DoFP polarization dynamics into a single snapshotthrough spatiotemporal modulation and integration. Second, we con-struct a large-scale high-quality polarized video dataset with carefullydenoised Stokes-domain supervision, enabling supervised learning of po-larization video SCI reconstruction models. Third, we develop a physics-driven reconstruction network that jointly performs temporal recoveryand polarization demosaicing, where the Polarization-Aware LearnableBack-Projection (PA-LBP) module is proposed to enforce measurementconsistency with the raw DoFP mosaic and mitigates error accumula-tion caused by decoupled reconstruction pipelines. Extensive experimentsdemonstrate that our method significantly improves both intensity fi-delity and polarization estimation accuracy. The dataset and code arereleased to https://github.com/kaeoqxhailesxya/Pol-CACTI.

Abstract:
Recently, Vision Transformer (ViT)-based models have exhibited remarkable performance in image super-resolution. However, the quadratic computational complexity of ViTs with respect to spatial resolution severely constrains their efficiency, leading to high latency and massive memory consumption. To alleviate this, various window-based attention mechanisms have been proposed; yet, they inherently compromise the long-range dependency modeling that is the primary advantage of ViTs. To overcome these limitations, we propose the Clustered Unitlevel Similarity Transformer (CUST), a novel architecture that efficiently integrates global and local information. Specifically, CUST enables each patch to aggregate and attend to similar patches within a broadened regional scope outside its local window, thereby capturing extensive contextual understanding. Furthermore, it employs overlapping attention windows to capture local dependencies, while explicitly extracting highfrequency details by computing the residual difference between the original features and their downsampled-upsampled counterparts. Comprehensive experiments demonstrate that our proposed model achieves a practical balance between computational efficiency and restoration performance. It achieves a lower memory footprint and faster inference speed compared to recent global context or lightweight models under realistic constraints. Code is available at https://github.com/jwgdmkj/CUST.

Abstract:
Monocular 3D object understanding has largely been cast asa 2D RoI-to-3D box lifting problem. However, emerging downstream ap-plications require image-plane geometry (e.g., projected 3D box corners)which cannot be easily obtained without known intrinsics, a problem forobject detection in the wild. We introduce MoCA3D, a Monocular,Class-Agnostic 3D model that predicts projected 3D bounding box cor-ners and per-corner depths without requiring camera intrinsics at in-ference time. MoCA3D formulates pixel-space localization and depthassignment as dense prediction via corner heatmaps and depth maps.To evaluate image-plane geometric fidelity, we propose Pixel-AlignedGeometry (PAG), which directly measures image-plane corner anddepth consistency. Extensive experiments under an oracle-2D box pro-tocol demonstrate that MoCA3D achieves state-of-the-art performance,improving image-plane corner PAG by 22.8% while remaining compa-rable on 3D IoU, using up to 57× fewer trainable parameters. Finally,we apply MoCA3D to downstream tasks which were previously imprac-tical under unknown intrinsics, highlighting its utility beyond standardbaseline models. Project page: https://jeoncwcw.github.io/moca3d/.

Abstract:
Recent depth foundation models trained on perspective im-agery achieve strong performance, yet generalize poorly to 360∘ imagesdue to the substantial geometric discrepancy between perspective andpanoramic domains. Moreover, fully fine-tuning these models typicallyrequires large amounts of panoramic data. To address this issue, wepropose RePer-360, a distortion-aware self-modulation framework formonocular panoramic depth estimation that adapts depth foundationmodels while preserving powerful pretrained perspective priors. Specifi-cally, we design a lightweight geometry-aligned guidance module to derivea modulation signal from two complementary projections (i.e., ERP andCP) and use it to guide the model toward the panoramic domain withoutoverwriting its pretrained perspective knowledge. We further introduce aSelf-Conditioned AdaLN-Zero mechanism that produces pixel-wise scalingfactors to reduce the feature distribution gap between the perspectiveand panoramic domains. In addition, a cubemap-domain consistency lossfurther improves training stability and cross-projection alignment. Byshifting the focus from complementary-projection fusion to panoramic do-main adaptation under preserved pretrained perspective priors, RePer-360surpasses standard fine-tuning methods while using only 1% of the train-ing data. Under the same in-domain training setting, it further achievesan approximately 20% improvement in RMSE. The code is available athttps://github.com/munimo/RePer360.

Abstract:
Monocular depth estimation has seen remarkable progressthrough foundation models achieving robust zero-shot generalization, yettheir computational demands place them far beyond the reach of em-bedded and mobile platforms. Lightweight alternatives exist, but havebeen developed almost exclusively within single-domain, self-supervisedparadigms, failing silently under domain shift. We present ZipDepth, acompact monocular depth network that bridges this gap by combining anefficient reparameterizable encoder-decoder with large-scale knowledgedistillation from a foundation model over a large multi-domain train-ing set. Comprising just 6.1M parameters, ZipDepth runs at real-timerates from server GPUs to power-constrained devices, achieving the besttrade-off between zero-shot accuracy and deployment efficiency amonglightweight models across five benchmarks, taking a significant step to-wards the accuracy of foundation models with 50× more parameters.

Abstract:
Domain adaptive visual object tracking under adverse weatherconditions has garnered significant attention in recent years. Despitethe impressive performance, existing methods heavily rely on the large-scale video frames from both source and target domains, which is im-practical under rigid resource constraints where source data is unavail-able. To overcome this limitation, we propose SFDATrack, a general-ized source-free domain adaptive tracker that merely leverages adverseweather samples from the target domain for robust state estimation.Specifically, SFDATrack first employs a mean-teacher backbone withDual Interactive Mamba (DIM) blocks to distill the candidate targettokens that are resilient to weather variations from classified, augmentedsamples. Afterwards, we introduce a hyperspherical prototype projec-tion (HPP) module to project these tokens onto multi-domain proto-types within a latent hyperspherical space. By enforcing both domain-specific and domain-invariant properties of the multi-domain prototypes,SFDATrack can be seamlessly adapted to diverse weather conditionswith powerful generalizability. Extensive experiments evaluated on vari-ous benchmarks demonstrate that SFDATrack achieves superior perfor-mance compared to state-of-the-art approaches. The code is available athttps://github.com/watcherBR0/sfdatrack.

Abstract:
Cloud removal (CR) is essential for optical remote sensing,serving as a prerequisite for reliable downstream interpretation, suchas semantic segmentation and change detection. However, existing CRapproaches often prioritize visual realism while overlooking their im-pact on subsequent analytical tasks, leading to semantic drift and de-graded downstream performance. To address this issue, we propose Geo-Anchored Cloud Removal (GACR), a unified framework that jointlyensures faithful reconstruction and robust interpretability. At its core,GACR incorporates Observation-Anchored Residual Flow (OAR-Flow),which reformulates CR as a physically grounded residual inversion pro-cess. By anchoring the generative trajectory to the cloudy observationrather than pure noise, OAR-Flow enables fast, stable, and faithful re-construction. To further preserve semantic structures critical for down-stream interpretation, GACR integrates Geo-Contextual Prior Align-ment (GCPA) to constrain the reconstruction within a semantic manifoldinduced by a Vision Foundation Model (VFM). Consequently, GACRstrictly maintains the spatial-semantic integrity of complex landscapes.Extensive experiments across six CR datasets and twelve downstreamtasks demonstrate that GACR produces superior reconstruction qual-ity while consistently improving downstream task accuracy. The code isavailable at https://github.com/wzy6055/GACR.

Abstract:
Deep neural networks often rely on spurious features to makepredictions, which makes them brittle under distribution shift and onsamples where the spurious correlation does not hold (e.g., minority-group examples). Recent studies have shown that, even in such settings,the feature extractor of an Empirical Risk Minimization (ERM)-trainedmodel can learn rich and informative representations, and that muchof the failure may be attributed to the classifier head. In particular,retraining a lightweight head while keeping the backbone frozen cansubstantially improve performance on shifted distributions and minor-ity groups. Motivated by this observation, we propose a bilevel meta-learning method that performs augmentation directly in feature space toimprove spurious correlation handling in the classifier head. Our methodlearns support-side feature edits such that, after a small number of inner-loop updates on the edited features, the classifier achieves lower loss onhard examples and improved worst-group performance. By operating atthe backbone output rather than in pixel space or through end-to-endoptimization, the method is highly efficient and stable, requiring only afew minutes of training on a single GPU. We further validate our methodwith CLIP-based visualizations, showing that the learned feature-spaceupdates induce semantically meaningful shifts aligned with spurious at-tributes. Our code is available at: https://github.com/ArianYp/HSFM.

Abstract:
Remote photoplethysmography (rPPG) enables contactlessphysiological monitoring by capturing subtle skin-color variations fromfacial videos. However, most existing methods predominantly rely ontime-domain modeling, making them vulnerable to motion artifacts andillumination fluctuations, where weak physiological clues are easily over-whelmed by noise. To address these challenges, we propose FreqPhys,a frequency-guided rPPG framework that explicitly leverages physio-logical frequency priors for robust signal recovery. Specifically, Freq-Phys first applies a Physiological Bandpass Filtering module to sup-press out-of-band interference, and then performs Physiological Spec-trum Modulation together with adaptive spectral selection to empha-size pulse-related frequency components while suppress residual in-bandnoise. A Cross-domain Representation Learning module further fusesthese spectral priors with deep time-domain features to capture infor-mative spatial–temporal dependencies. Finally, a frequency-aware con-ditional diffusion process progressively reconstructs high-fidelity rPPGsignals. Extensive experiments on six benchmarks demonstrate that Fre-qPhys yields significant improvements over state-of-the-art approaches,particularly under challenging motion conditions. It highlights the im-portance of explicitly modeling physiological frequency priors. Our codeis available at the https://github.com/WeiQian98/FreqPhys.

Abstract:
We present MeshFM, an efficient feedforward framework for extracting rich features from 3D inputs. Our method distills 2D features from visual foundation models into 3D. We train a feedforward network to directly predict 3D features without requiring optimization during inference. The approach utilizes a two-stage training strategy. First, we optimize a feature field in 3D using only 2D feature supervision. Second, we train a network to regress this feature field. The entire procedure requires no 3D annotation, instead relying on the powerful information in 2D foundation models. We demonstrate that our learned features can be immediately applied to downstream tasks, including part segmentation, dense correspondence, and mesh deformation. Extensive experiments show that MeshFM, trained solely with 2D supervision, performs on par with methods trained explicitly with 3D supervision, even without task-specific fine-tuning. Moreover, our model is trained to be robust to extreme rotations of the input objects. Project page: https://threedle.github.io/MeshFM/

Abstract:
Arbitrary-scale image super-resolution (ASISR) aims to recon-struct high-resolution images from low-resolution inputs over a continuousrange of upscaling factors. While traditional pixel-regression approachesoften produce overly smooth results that lack realistic details, recent dif-fusion methods can produce sharper and more realistic textures. However,these diffusion techniques frequently introduce the risk of structural hal-lucinations. To address these issues, we propose Fidelity- and Perception-Aware Local Implicit Attention (FPLIA), a framework that effectivelyintegrates fidelity-oriented features into a diffusion pipeline to produce re-alistic and faithful reconstructions for ASISR. We introduce a Fidelity andPerception Attention Module (FPAM), which applies both self-attentionand cross-attention to fidelity-oriented and perceptual features to en-hance representational capacity. To further exploit their complements,we design a Fidelity and Perception Select Module (FPSM) that adap-tively selects the most representative features for RGB values prediction.We conduct extensive experiments to validate the effectiveness of thesecomponents. Both qualitative and quantitative results show that FPLIAdelivers superior perceptual realism while maintaining reconstructionaccuracy on standard ASISR benchmarks. The source code is accessibleat the following repository: https://github.com/XUSean0118/FPLIA.

Abstract:
High-resolution 3D radar data is scarce. Commodity mmWavesensors use small antenna arrays that limit angular resolution to several de-grees, and existing datasets provide only 2D range–azimuth maps or sparsepoint clouds rather than raw analog-to-digital converter (ADC) signals.Hardware scaling is expensive, synthetic-aperture scanning is impracticalat fleet scale, and learned synthesis methods are bottlenecked by the verydata shortage they aim to address. We present mmIR, an open-sourcedifferentiable frequency-modulated continuous-wave (FMCW) radar in-verse renderer that fits a physics-based forward model to real capturesand re-renders from dense virtual apertures to synthesize high-resolution3D radar data. Because radar resolution is too coarse to recover geom-etry directly, mmIR performs LiDAR-assisted inverse rendering: usingLiDAR-derived meshes as a geometric scaffold, mmIR optimizes per-vertex International Telecommunication Union (ITU) physics materials,vertex normals, and antenna beam patterns through end-to-end auto-matic differentiation of a phase-coherent multiple-input multiple-output(MIMO) forward model with multi-bounce propagation, polarization,and free-space diffraction. On seven outdoor and six indoor ColoRadarscenes, mmIR achieves 0.914 mean Pearson correlation on range–azimuthmaps versus 0.307 for Sionna-RT. Scenes trained on a cascaded imag-ing radar transfer to a co-located single-chip radar without re-training(0.554 correlation), and dense virtual arrays (100×100 elements) pro-duce single-frame 3D occupancy validated against LiDAR. Project page:https://mmwave-inverse-rendering.github.io/.

Abstract:
Skeleton-based action recognition has achieved remarkablesuccess by exploiting joint coordinates and their topological connec-tions, yet prevailing methods overwhelmingly assume complete and cleanskeleton inputs. In real-world deployments, such as egocentric vision,crowded surveillance, wearable devices, or edge robotics, limited field-of-view (FoV) frequently causes substantial joint visibility dropout, leadingto severe performance degradation that existing models are largely un-prepared to handle. To bridge this critical yet underexplored gap, weintroduce PartialVisGraph, a novel hypergraph framework tailored forrobust skeleton action recognition under constrained FoV. We first con-struct highly expressive hypergraphs by introducing learnable virtualhyperedges that form a soft incidence matrix, capturing flexible high-order dependencies beyond conventional pairwise graphs. We then pro-pose the Single-Head Sample-Adaptive Transformer, which adaptivelyaggregates joint features onto hyperedges while explicitly incorporatinga visibility prior. This prior selectively gates information flow, preventingoccluded or out-of-view joints from corrupting reliable feature propaga-tion. We further establish rigorous evaluation protocols with realisticFoV simulation benchmarks on NTU RGB+D 60 and 120. Extensive ex-periments demonstrate that PartialVisGraph consistently achieves state-of-the-art accuracy under partial visibility, with gains of up to 68.8% onsubsets with severe FoV restrictions compared to recent strong base-lines, while remaining superior on full-visibility settings. Our approachoffers a principled and practical pathway toward deployable skeleton-based action understanding in unconstrained environments. Resourcesrelated to the constrained FoV setting used in this work are available at:https://github.com/yaa1haa1/PartialVisGraph.

Abstract:
Surface geometry and multispectral reflectance estimation are essential tools for reliable physical scene understanding in applications such as industrial inspection, cultural heritage analysis, and remote sensing. To obtain dependable spectral and geometric information, conventional pipelines often require multiple multispectral images captured across viewpoints, or multispectral photometric stereo with repeated, sequential illuminations. These approaches are frequently bandwidthintensive and slow to acquire due to their staged capture protocols. We propose EventSpecPS, an event-based multispectral photometric stereo system. It comprises a Spectrally-Angularly Multiplexed Encoding Device that encodes geometric and spectral cues into the event stream, which are then decoded by an Event-based Spectral-Angular Decoder to jointly recover surface normals and absolute multispectral reflectance. This enables single-stage, high-speed, and low-bandwidth acquisition and reconstruction, while remaining robust to cast shadows and specularities in practice. Extensive experiments on both synthetic and real data demonstrate state-of-the-art performance over existing methods. Code and data are available at https://github.com/Wujingqian/EventSpecPS.

Abstract:
Detecting and localizing objects in space is a fundamentalcomputer vision problem. While much progress has been made to solve2D object detection, 3D object localization is much less explored and farfrom solved, especially for open-world categories. To address this researchchallenge, we propose Boxer, an algorithm to estimate static 3D boundingboxes (3DBBs) from 2D open-vocabulary object detections, posed images,and optional depth represented either as a sparse point cloud or densedepth. At its core is BoxerNet, a transformer-based network that lifts2D bounding box (2DBB) proposals into 3D, followed by multi-viewfusion and geometric filtering to produce globally consistent de-duplicated3DBBs in metric world space. Boxer leverages the power of existing 2DBBdetection algorithms (e.g., DETIC [52], OWLv2 [29], SAM3 [8]) to localizeobjects in 2D. This allows the main BoxerNet model to focus on liftingto 3D rather than detecting, ultimately reducing the demand for costlyannotated 3DBB training data. Extending the CuTR [20] formulation, weincorporate an aleatoric uncertainty for robust regression, a mean depthpatch encoding to support sparse depth inputs, and large-scale trainingwith over 1.2 million unique 3DBBs. BoxerNet outperforms state-of-the-art baselines in open-world 3DBB lifting, including CuTR in egocentricsettings without dense depth (0.532 vs. 0.010 mAP) and on CA-1M withdense depth available (0.412 vs. 0.250 mAP). Project page and code areavailable at https://facebookresearch.github.io/boxer.

Abstract:
Video action models (VAMs) have emerged as a promising paradigm for robot learning, owing to their powerful visual foresight for complex manipulation tasks. However, current VAMs, typically relying on either slow multi-step video generation or noisy one-step feature extraction, cannot simultaneously guarantee real-time inference and high-fidelity foresight. To address this limitation, we propose S-VAM, a shortcut video-action model that foresees coherent geometric and semantic representations via a single forward pass. Serving as a stable blueprint, these foreseen representations significantly simplify the action prediction. To enable this efficient shortcut, we introduce a novel self-distillation strategy that condenses structured generative priors of multi-step denoising into one-step inference. Specifically, vision foundation model (VFM) representations extracted from the diffusion model’s own multi-step generated videos provide teacher targets. Lightweight decouplers, as students, learn to directly map noisy one-step features to these targets. Extensive experiments in simulation and the real world demonstrate that our S-VAM outperforms state-of-the-art methods, enabling efficient and precise manipulation in complex environments. Our project page is https://haodong-yan.github.io/S-VAM/.

Abstract:
V2X collaborative object detection features overcoming thelimitations of single-vehicle systems by aggregating environmental fea-tures from multiple collaborative agents. However, existing mainstreamV2X perception methods mainly focus on 2D BEV object detection.When 3D detection task is concerned, inferior results are obtained be-cause they ignore the 3D spatial misalignment caused by differing heightand attitude among the collaborators. In this paper, we propose a novelcollaborative 3D object detection framework called CoGoal3D, whichextracts and refines the 3D feature gradually in a two-stage pipeline. Inthe first stage, a multiscale 3D-aware global fusion module is designedto mitigate the 3D spatial misalignment. The resulting proposals arethen refined in the second stage with an auxiliary task of 3D point re-construction. An effective multi-agent collaborative data augmentationstrategy is further proposed to enrich the training data while minimizinginformation loss. Extensive experiments on public real-world datasetsdemonstrate that our CoGoal3D achieves new state-of-the-art perfor-mance, with 3D AP@0.7 improvements of 10.86%, 10.34%, and 10.18%on the DAIR-V2X, V2V4Real, and V2X-Real datasets, respectively.

Abstract:
Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventional 3D human keypoints estimation methods primarily focus on the whole-body reconstruction quality relative to the root joint. However, in practical human-robot interaction (HRI) scenarios, robots are more concerned with the precise metric-scale spatial localization of task-relevant body parts under the egocentric camera 3D coordinate. We propose TAIHRI, the first Vision-Language Model (VLM) tailored for close-range HRI perception, capable of understanding users’ motion commands and directing the robot’s attention to the most taskrelevant keypoints. By quantizing 3D keypoints into a finite interaction space, TAIHRI precisely localize the 3D spatial coordinates of critical body parts by 2D keypoint reasoning via next token prediction, and seamlessly adapt to downstream tasks such as natural language control or global space human mesh recovery. Experiments on egocentric interaction benchmarks demonstrate that TAIHRI achieves superior estimation accuracy for task-critical body parts. We believe TAIHRI opens new research avenues in the field of embodied human-robot interaction. Code is available at: https://github.com/Tencent/TAIHRI.

Abstract:
Fitting an underlying body model to 3D clothed human assets has been extensively studied, yet most approaches focus on either single-modal inputs such as point clouds or multi-view images alone, often requiring known metric scale—a constraint that is frequently unavailable, particularly for AIGC-generated assets where scale distortion is prevalent. We propose OmniFit, where “Omni” signifies our method’s ability to seamlessly handle diverse multi-modal inputs (e.g., full scans, partial depth, image captures) while remaining scale-agnostic across both real and synthetic assets. Our key innovation is a simple yet effective conditional transformer decoder that directly transforms surface points into dense body landmarks, which are subsequently used for SMPL-X parameter fitting. Additionally, an optional plug-and-play image adapter enriches geometric details with visual cues to address potential incompleteness. We further introduce a dedicated scale predictor to resize subjects into canonical proportions. Remarkably, OmniFit substantially outperforms state-of-the-art methods by 57.1%–80.9% across daily and loose clothing scenarios, making it the first body fitting method to surpass multi-view optimization baselines and the first to achieve millimeter-level accuracy on CAPE and 4D-DRESS benchmarks.

Abstract:
End-to-end autonomous driving models based on Vision-Language-Action (VLA) architectures have shown promising results bylearning driving policies through behavior cloning on expert demonstra-tions. However, imitation learning inherently limits the model to repli-cating observed behaviors without exploring diverse driving strategies,leaving it brittle in novel or out-of-distribution scenarios. Reinforcementlearning (RL) offers a natural remedy by enabling policy explorationbeyond the expert distribution. Yet VLA models, typically trained onoffline datasets, lack directly observable state transitions, necessitating alearned world model to anticipate action consequences. In this work, wepropose a unified understanding-and-generation framework that lever-ages world modeling to simultaneously enable meaningful explorationand provide dense supervision. Specifically, we augment trajectory pre-diction with future RGB and depth image generation as dense worldmodeling objectives, requiring the model to learn fine-grained visual andgeometric representations that substantially enrich the planning back-bone. Beyond serving as a supervisory signal, the world model furtheracts as a source of intrinsic reward for policy exploration: its imageprediction uncertainty naturally measures a trajectory’s novelty rela-tive to the training distribution, where high uncertainty indicates out-of-distribution scenarios that, if safe, represent valuable learning op-portunities. We incorporate this exploration signal into a safety-gatedreward and optimize the policy via Group Relative Policy Optimiza-tion (GRPO). Experiments on the NAVSIM and nuScenes benchmarksdemonstrate the effectiveness of our approach, achieving a state-of-the-art PDMS score of 93.7 and an EPDMS of 88.8 on NAVSIM. The codeis available at https://zihaosheng.github.io/ExploreVLA/.

Abstract:
Embodied agents perceive the world as continuous streams,yet prevailing benchmarks evaluate spatial reasoning offline, presuppos-ing global spatial omniscience and overlooking real-time spatial intelli-gence. We present StreamSpatialBench, a testbed for streaming 3Dvisual-spatial reasoning built on three pillars: fine-grained online tempo-ral perspectives (past / current / future anchored to the query times-tamp), dynamic multi-agent interactions, and comprehensive 3D spatialreasoning. It comprises 5,280 millisecond-grounded QA pairs over 2,003egocentric/scene videos across 10 sub-tasks. Benchmarking 18 represen-tative MLLMs against humans reveals a substantial gap, rooted in threedeficiencies: limited spatiotemporal memory, absent incremental updat-ing, and poor adaptation to dynamic, multi-agent scenes. To close thisgap, we propose Stream3D-Mem, which (i) maintains a bounded mem-ory buffer with selective consolidation via round-decayed compressionto prune redundancy while preserving recent context, and (ii) distillscognitive-map knowledge into compact implicit latent tokens, bypassinglatency-heavy explicit map generation. Together they offer a rigoroustestbed and a practical step toward continuous, human-like 3D spatialintelligence for robotics and AR/VR. Code: https://github.com/OPPO-Mente-Lab/StreamSpatial.

Abstract:
The increasing diversity and density of traffic participantshave made modern driving environments highly complex, posing greatchallenges to reliable perception in autonomous driving. Single-vehicleperception is fundamentally limited by sensor range and occlusions, whilevehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) cooperationonly partially mitigate these issues due to their fixed or ground-levelviewpoints. In contrast, aerial platforms offer global and flexible sens-ing perspectives that can dynamically adjust their positions to providebroader spatial coverage and reduce blind zones. These advantages mo-tivate the exploration of aerial-ground cooperative perception (AGCP).However, progress in this area is hindered by the lack of public datasetsand standardized benchmarks. To bridge this gap, we introduce VVSim,a large-scale dataset for AGCP that provides synchronized multimodaldata and state information from both vehicles and unmanned aerial ve-hicles (UAVs). VVSim consists of about 61k fully annotated frames span-ning diverse interaction scenarios (e.g., cut-in, lane change), 5 weatherconditions (sunny, foggy, rainy, cloudy, snowy), and 11 scenes (e.g., city,town, university, highway, mountain). In total, it comprises 255k Li-DAR sweeps and 3.5M images (RGB, semantic segmentation, and depth)collected from vehicles and UAVs, along with detailed annotations for2D/3D bounding boxes, object trajectories, and agent states. To supportunified evaluation, we further propose VVFormer, a novel architecturethat integrates multi-agent and multimodal features from UAVs and ve-hicles. Extensive experiments on VVSim demonstrate that VVFormerachieves superior performance, significantly outperforming strong V2Vand V2I cooperative baselines on 3D perception tasks. The code is avail-able at https://github.com/LOTEAT/vvdetection3d.

Abstract:
The ultimate goal of spatial intelligence is to enable embod-ied agents to actively interact with the physical world. However, exist-ing benchmarks adopt a “Spectator View”—relying on single images orpassive video—neglecting the necessity of resolving geometric ambigu-ity through active viewpoint transformation. To address this, we presentWalkerBench, a global-scale interactive benchmark built on real-worldstreet-view and map data spanning 161 cities, with two categories ofhierarchically structured spatial tasks. Our empirical evaluation revealsa critical “representation misalignment”: VLMs’ one-dimensional linearcontext structures are fundamentally incompatible with the topologicalnature of 3D environments, causing inevitable spatial forgetting duringnavigation. To overcome this, we propose Spatial-IDE, which breaks fromlinear dialogue history via two mechanisms: (1) State Externalization,transforming implicit observations into an Explicit Topological Memory(ETM); and (2) Cognitive Decoupling, disentangling visual understand-ing into Goal-Directed Perception and Spatial Reasoning, letting theVLM focus on pure high-level decision-making. Spatial-IDE achieves sub-stantial improvements on WalkerBench and successfully generalizes zero-shot to real-world urban navigation on the Unitree humanoid robot. Theproject is available on https://github.com/lalayang123456- ctrl/WalkerBench.

Abstract:
Visual signals play a crucial role in policy learning by en-abling models to capture object motion and interaction dynamics. Just ashumans reason about actions using both past experience and anticipatedoutcomes, effective policies should integrate past interactions with futurepredictions. However, existing visuomotor policies typically model eitherhistorical context or future dynamics in isolation, lacking a unified tem-poral representation of interaction dynamics. In this work, we introduceChronoFlow, a temporally unified representation that captures past,current, and future interaction dynamics through sparse 3D keypointsof both objects and the gripper. Based on this representation, we proposeChronoFlow-Policy, a diffusion-based visuomotor policy that jointlylearns ChronoFlow and action sequences through a co-training objective.Experiments on 14 simulated tasks and 5 real-world manipulation tasksdemonstrate that ChronoFlow-Policy consistently outperforms strongdiffusion-policy baselines and improves robustness in long-horizon andnon-Markovian manipulation scenarios. Code and models will be releasedat https://github.com/The-kamisato-Sii/ChronoFlow-Policy.

Abstract:
Situated reasoning often relies on active exploration, yet inmany real-world scenarios such exploration is infeasible due to phys-ical constraints of robots or safety concerns of visually impaired users.Given only a limited observation, can an agent mentally simulate a futuretrajectory toward a target situation and answer spatial “what-if” ques-tions? We introduce WanderDream, the first large-scale dataset designedfor the emulative simulation of mental exploration, enabling models toreason without active exploration. WanderDream-Gen comprises 15.8Kpanoramic videos across 1,088 real scenes from HM3D, ScanNet++,and real-world captures, depicting imagined trajectories from currentviewpoints to target situations. WanderDream-QA contains 158K ques-tion–answer pairs, covering starting states, paths, and end states alongeach trajectory to comprehensively evaluate exploration-based reason-ing. Extensive experiments with world models and MLLMs demonstrate(1) that mental exploration is essential for situated reasoning, (2) thatworld models achieve compelling performance on WanderDream-Gen, (3)that imagination substantially facilitates reasoning on WanderDream-QA, and (4) that WanderDream data exhibit remarkable transferabilityto real-world scenarios. The source code and all data are released athttps://github.com/RuipingL/WanderDream.

Abstract:
LiDAR has increasingly been integrated into traffic cam-eras to expand coverage and mitigate occlusion in roadside cooperativeperception. However, how unimodal and camera–LiDAR fusion archi-tectures behave under variations in LiDAR point sparsity induced bysensor configurations and scene-dependent sensing conditions remainsunderexplored. We introduce RESOLVE, a large-scale real-world bench-mark dataset featuring multi-resolution roadside LiDAR and synchro-nized camera-LiDAR sensing for systematic evaluation of unimodal andfusion-based architectures in roadside 3D detection and tracking. RE-SOLVE contains over 100k images and 26k point cloud frames with 220kmanually annotated bounding boxes, captured at a real-world urban in-tersection across diverse lighting and weather conditions and spanning10 classes of traffic participants. In particular, RESOLVE enables con-trolled evaluation across three LiDAR resolution levels while keeping allother sensing and environmental factors fixed. This allows fair cross-architecture comparisons under point cloud distribution shifts resultingfrom resolution variations, sensing distance, and training–inference res-olution mismatches. Results from extensive benchmark experiments re-veal insights into how multimodal fusion can compensate for LiDARpoint sparsity, offering clues for designing cost-efficient roadside multi-modal perception. The dataset and benchmark codes are available athttps://github.com/ASU-Suo-Lab/RESOLVE.

Abstract:
End-to-end multi-modal planning has been widely adopted to model the uncertainty of driving behavior, typically by scoring candidate trajectories and selecting the optimal one. Existing approaches generally fall into two categories: scoring a large static trajectory vocabulary, or scoring a small set of dynamically generated proposals. While static vocabularies often suffer from coarse discretization of the action space, dynamic proposals provide finer-grained precision and have shown stronger empirical performance on existing benchmarks. However, it remains unclear whether dynamic generation is fundamentally necessary, or whether static vocabularies can already achieve comparable performance when they are sufficiently dense to cover the action space. In this work, we start with a systematic scaling study of Hydra-MDP, a representative scoring-based method, revealing that performance consistently improves as trajectory anchors become denser, without exhibiting saturation before computational constraints are reached. Motivated by this observation, we propose SparseDriveV2 to push the performance boundary of scoring-based planning through two complementary innovations: (1) a scalable vocabulary representation with a factorized structure that decomposes trajectories into geometric paths and velocity profiles, enabling combinatorial coverage of the action space, and (2) a scalable scoring strategy with coarse factorized scoring over paths and velocity profiles followed by fine-grained scoring on a small set of composed trajectories. By combining these two techniques, SparseDriveV2 scales the trajectory vocabulary to be 32× denser than prior methods, while still enabling efficient scoring over such super-dense candidate set. With a lightweight ResNet-34 as backbone, SparseDriveV2 achieves 92.0 PDMS and 90.1 EPDMS on NAVSIM, with 89.15 Driving Score and 70.00 Success Rate on Bench2Drive. Code and model are released at https://github.com/swc-17/SparseDriveV2.

Abstract:
Modeling dexterous hand–object interactions is challengingas it requires understanding how subtle finger motions influence theenvironment through contact with objects. While recent world mod-els address interaction modeling, they typically rely on coarse actionspaces that fail to capture fine-grained dexterity. We, therefore, intro-duce DexWM, a Dexterous Interaction World Model that predicts futurelatent states of the environment conditioned on past states and dexterousactions. To overcome the scarcity of finely annotated dexterous datasets,DexWM represents actions using finger keypoints extracted from ego-centric videos, enabling training on over 900 hours of human and non-dexterous robot data. Further, to accurately model dexterity, we find thatpredicting visual features alone is insufficient; therefore, we incorporatean auxiliary hand consistency loss that enforces accurate hand config-urations. DexWM outperforms prior world models conditioned on text,navigation, or full-body actions in future-state prediction and demon-strates strong zero-shot transfer to unseen skills on a Franka Panda armwith an Allegro gripper, surpassing Diffusion Policy by over 50% on av-erage across grasping, placing, and reaching tasks. Codes and dataset areavailable on the project page: https://raktimgg.github.io/dexwm/.

Abstract:
We present 360CityArena, a benchmark for evaluating theurban exploration capabilities of Embodied Agents within a photorealis-tic environment constructed from 360° videos. Existing outdoor bench-marks either lack sufficient photorealism or complexity, resulting in aconsiderable gap from real-world urban environments. 360CityArena isbuilt on a realistic reconstruction of the Akihabara district in Tokyo,Japan, using 602 360° video segments covering 85 streets, and consistsof 175 meticulously human-crafted tasks. It encompasses three task cat-egories: Environment Understanding, Path Reasoning, and Spatial Rea-soning, covering fundamental abilities required for urban exploration,such as localization, landmark search, path planning, and relational spa-tial reasoning, thereby enabling comprehensive evaluation in realisticurban scenes. Our evaluation using state-of-the-art LMM-based agentsshows that even the strongest model, Gemini-2.5 Flash, performs far be-low human level (human: 77.3% vs. Gemini-2.5 Flash: 17.1%), revealingsubstantial challenges that remain in city-scale embodied navigation andreasoning. 360CityArena provides a necessary and challenging testbed forphotorealistic urban-district navigation and spatial reasoning.

Abstract:
Existing imitation learning methods for end-to-end autonomousdriving predominantly learn from successful demonstrations by minimiz-ing geometric deviations from expert trajectories. This paradigm implic-itly assumes that spatial proximity implies behavioral safety, leading toa critical objective mismatch: trajectories with nearly identical imita-tion losses may exhibit drastically different safety outcomes, where oneremains recoverable while the other results in collision. To address thislimitation, we propose BeyondDrive, a failure-aware imitation learn-ing framework that jointly learns from successful and failed driving be-haviors. First, we introduce a flow matching-based negative trajectory† Corresponding author. Code link: https://github.com/wjl2244/BeyondDrive.This work was supported by Beijing Natural Science Foundation-Xiaomi InnovationJoint Fund L253007, by Beijing Nova Program (202604841268) and Beijing NaturalScience Foundation under Grant 4242052.generator that synthesizes safety-critical yet expert-proximate trajecto-ries, enabling explicit modeling of safety asymmetry. Second, we developa diversity-aware sampling strategy that mitigates mode collapse andimproves coverage of diverse failure modes during negative trajectorygeneration. Third, we propose a Repulsive Distance Loss that simultane-ously attracts predictions toward expert demonstrations while repellingthem from hard negative trajectories, thereby establishing discrimina-tive safety boundaries in trajectory space. Applied to the uni-modalbaseline Latent TransFuser, BeyondDrive achieves 89.7 PDMS on theNAVSIMv1 closed-loop benchmark, outperforming prior state-of-the-artmethods. Moreover, BeyondDrive generalizes effectively across differentautonomous driving architectures, including multi-modal planners, andfurther demonstrates strong zero-shot transferability on the HUGSIMbenchmark.

Abstract:
Vision-Language-Action (VLA) models have made signifi-cant strides in embodied intelligence by integrating the powerful repre-sentations of pre-trained Vision-Language Models (VLMs). However, themassive parameter scale of VLAs imposes a heavy computational burden,and these models exhibit extreme sensitivity to parameter pruning. Cur-rent paradigms often treat the resulting performance degradation as in-evitable, relying on fine-tuning or low-rank corrections to recover efficacy.We challenge this convention by questioning whether the removed param-eters are truly redundant if VLA pruning necessitates performance recov-ery to be effective, or if this paradigm masks the indiscriminate pruningof critical parameters. We revisit parameter redundancy through the lensof VLM-to-VLA adaptation, first quantifying the spatial distribution ofparameter divergence during adaptation to reveal structured patternsacross different modules. Subsequently, we introduce controlled pruningas a diagnostic probe: by comparing the direct impact of removing dif-ferent parameter subsets on VLA performance without any fine-tuning,we establish a causal link between adaptation-induced divergence signalsand functional contributions. Based on the discovered modular hetero-geneities, we design a multi-module joint pruning scheme. Evaluationson the LIBERO benchmark demonstrate that our approach reduces theparameters of OpenVLA and π0.5 by 12%–30% while maintaining approx-imately 90% of the original performance without any post-pruning recov-ery. In contrast, existing parameter pruning criteria result in total perfor-mance collapse when evaluated under the same recovery-free constraints.Our study reveals the parameter evolution mechanism in VLA adapta-tion and provides a new path for deploying efficient, robust robotic poli-cies in resource-constrained environments. Code is available at https://github.com/Niannnnnn/VLA Parameter Redundancy_VLM2VLA.

Abstract:
Multi-task model merging aims to consolidate several task-specific experts into a unified model, yet static merging consistently suf-fers from parameter interference. While dynamic merging models aimto bridge this gap, many works rely on the costly storage and loadingof redundant expert components at inference. In this work, from theperspective of task expert, we view parameter interference as parame-ter perturbation introduced to each expert during merging process. Weshow that such parameter perturbations can be modeled as affine trans-formation, which can be approximated as additive offsets. Motivatedby these, we propose Recover Task eXpert (ReTeX), a frameworkthat predicts those offsets, in order to undo parameter interference andrecover task-expert performance from a single merged checkpoint. Torecover the appropriate expert when task identity is unknown, we in-troduce a router-free task identifier based on SVD subspace signaturescomputed offline before inference. At inference, the identifier selects thetask whose subspace yields the smallest projection residual for a giveninput. As a result, ReTeX recovers over 95% of individual-expert per-formance in both vision and NLP domains, while significantly improvinggeneralization to unseen tasks. Crucially, we also show that the param-eter offset prediction leads to emergent adaptive interpolation of expertknowledge for out-of-distribution (OOD) tasks. ReTeX adaptively in-terpolates seen expert knowledge to handle unseen tasks. Our code isavailable at https://github.com/BAIKLAB/ReTeX

Abstract:
Federated learning (FL) enables collaborative learning ofcomputer vision models, where privacy and regulatory constraints pre-vent centralizing data across devices or organizations. However, prac-tical FL deployments often exhibit severe class imbalance and labelskew, causing standard aggregation protocols to overfit dominant clientsand degrade minority-class performance. We propose a data-free, class-wise contribution estimation and aggregation framework based on logitmaximization (CELM) that does not require raw data, client metadata,or auxiliary public datasets at the server. The FL server probes clientupdates to obtain class-wise evidence scores and assembles a cross-clientevidence matrix, which quantifies both per-class competence and classcoverage. Using this matrix, we compute contribution weights that up-weight clients providing discriminative evidence for underrepresentedclasses. The resulting aggregation is stable due to simplex constraints andmomentum smoothing, and remains compatible with standard FL train-ing pipelines. We evaluate the approach on representative vision bench-marks under controlled non-IID and pathological label splits, demon-strating that CELM-based aggregation improves robustness to imbalanceand statistical heterogeneity, while yielding better performance with-out requiring any additional data exchange. The code is available at:https://github.com/asimukaye/celm.

Abstract:
Model merging has emerged as a promising paradigm for enabling multi-task capabilities without additional training. However, traditional basic merging methods often experience performance degradation due to parameter conflicts, even when applied to similar tasks. While recent personalized merging frameworks successfully preserve taskspecific information to maintain performance, they typically incur storage overhead. In this paper, we propose Decomposition, Thresholding, and Scaling (DTS), an approximation-based personalized merging framework that pushes task-specific storage efficiency. DTS first applies singular value decomposition to the task-specific information and retains only a small subset of singular values and vectors. It then introduces a novel thresholding strategy that partitions singular vector elements into groups and assigns a scaling factor to each group. To enable generalization to unseen tasks, we further extend DTS with a variant that fuses task-specific information in a data-free manner based on the semantic similarity of task characteristics. Extensive experiments demonstrate that DTS consistently outperforms state-of-the-art baselines while requiring only 1% extra storage per task. Furthermore, experiments on unseen tasks show that the DTS variant achieves significantly better generalization performance. Our code is available at https://github.com/krumpguo/DTS.

Abstract:
Foundation Models (FMs) are increasingly integrated into remote sensing (RS)pipelines for applications such as environmental monitoring, disaster assessment, and land-usemapping. These models include unimodal vision encoders trained in a single data modalityand multimodal architectures trained in multiple sensor modalities, such as synthetic aper-ture radar (SAR), multispectral, and hyperspectral imagery, or jointly in image-text pairs invision-language settings. FMs are adapted to diverse perception tasks, such as semantic seg-mentation, image classification, change detection, and visual question answering, dependingon their pretraining objectives and architectural design. However, selecting the most suit-able remote sensing foundation model (RSFM) for a specific task remains challenging dueto scattered documentation, heterogeneous formats, and complex deployment constraints.To address this, we first introduce the RSFM Database (RS-FMD), the first structuredand schema-guided resource covering over 160 RSFMs trained on various data modalities,spanning different spatial, spectral, and temporal resolutions, considering different learn-ing paradigms. Built upon RS-FMD, we further present Remsa (Remote-sensing ModelSelection Agent), a constraint-aware agent that enables automated RSFM selection from nat-ural language queries. Remsa combines structured FM metadata grounding with task-awareorchestration for retrieval, clarification, ranking, and explanation. In detail, it interprets userinput, clarifies missing constraints, ranks models via in-context learning, and provides trans-parent justifications. Our system supports various RS tasks and data modalities, enablingpersonalized, reproducible, and efficient FM selection. To evaluate Remsa, we constructa benchmark of 100 expert-verified RS query scenarios. Each query is evaluated across 4systems and 3 LLM backbones, with the top-3 selected models assessed by domain expertsusing a fixed suitability rubric. This results in 3,000 expert-scored task–system–model con-figurations under our rubric-based expert evaluation protocol. Remsa outperforms multiplebaselines, including naive agent-based method, dense retrieval, and unstructured retrievalaugmented generation approaches, showing its practical utility in real decision-making ap-plications. Remsa operates entirely on publicly available metadata of open source RSFMs,without accessing private or sensitive data. Our code and data are publicly available at:https://github.com/be-chen/REMSA.

Abstract:
Even modern AI models often remain vulnerable to multimodal queries in which harmful intent is embedded in images. A widely used approach for safety alignment is training with extensive multimodal safety datasets, but the costs of data curation and training are often prohibitive. To mitigate these costs, inference-time alignment has recently been explored, but they often lack generalizability across diverse multimodal jailbreaks and still incur notable overhead due to extra forward passes for response refinement or heavy pre-deployment calibration procedures. Here, we identify insufficient visual attention to safety-critical image regions as one of the key causes of multimodal safety failures. Building on this insight, we propose Multimodal Risk-Adaptive Steering (MoRAS), which enhances safety-critical visual attention via concise visual contexts for accurate multimodal risk assessment. This risk signal enables risk-adaptive steering for direct refusals, reducing inference overhead while remaining generalizable across diverse multimodal jailbreaks. Notably, MoRAS requires only a small calibration set to estimate multimodal risk, substantially reducing pre-deployment overhead. We conduct various empirical validations across multiple benchmarks and MLLM backbones, and observe that the proposed MoRAS consistently mitigates jailbreaks, preserves utility, and reduces computational overhead compared to state-of-the-art inference-time defenses. Code is available at https://github.com/snumprlab/moras.

Abstract:
Test-Time Adaptation (TTA) has emerged as an effectivesolution for adapting Vision Transformers (ViT) to distribution shiftswithout additional training data. However, existing TTA methods oftenincur substantial computational overhead, limiting their applicability inresource-constrained real-world scenarios. To reduce inference cost, plug-and-play token aggregation methods merge redundant tokens in ViTsto lower computation demands. Albeit efficient, it suffers from signif-icant performance degradation when directly integrated with existingTTA methods. We formalize this problem as Efficient Test-Time Adap-tation (ETTA), seeking to preserve the adaptation capability of TTAwhile reducing inference latency. In this paper, we start by providingan analysis showing that token aggregation inherently leads to informa-tion loss, which cannot be fully mitigated by conventional norm-tuning-based TTA methods. Guided by this insight, we propose to NeutralizeToken Aggregation via Information Augmentation (NAVIA). Specif-ically, we propose an information augmentation mechanism that em-ploys an input-level [CLS] embedding augmentation to compensate fordomain-level information loss, and a feature-level [CLS] bias augmen-tation for fine-grained layerwise information compensation. Theoreticalanalysis provides a principled motivation that augmenting a global infor-mation carrier before aggregation can improve the learnable-informationupper bound, with entropy minimization serving as a practical surro-gate objective. Extensive experiments across various out-of-distributionbenchmarks and model backbones demonstrate that NAVIA outperformsall competing methods. Notably, NAVIA improves performance by upto 3.1% while achieving 14% to 26% wall-clock speedup over the TTAstate-of-the-art, effectively addressing the ETTA challenge. We provideour official implementation at https://github.com/Bostoncake/NAVIA.

Abstract:
Out-of-Distribution (OOD) detection is a critical safety requirement for deploying deep neural networks in open-world environments. While recent advances increasingly rely on more computationally intensive training methods involving synthetic outliers, contrastive objectives, or specialized loss functions, their gains often come with substantial computational overhead and implementation complexity. In this work, we revisit the fundamentals of OOD detection and uncover a key flaw in common distance-based detectors: sensitivity to feature magnitude. We show that low-norm OOD samples can appear closer to in-distribution (ID) class centroids than actual ID samples, evading detection. To this end, we introduce Angular Separation Learning (ASL), a simple and highly effective strategy that applies ℓ -normalization to features before 2 the final classification layer. This modification compels the network to optimize for angular separation, achieving robust feature learning without additional regularization mechanisms, synthetic samples, or costly negative mining. Through extensive experiments on diverse benchmarks, we demonstrate that ASL not only matches but often surpasses state-of-theart methods, especially in challenging near-OOD scenarios, while maintaining training efficiency. Our results indicate that a minimalist rethink of standard training can achieve superior OOD performance, prompting a re-evaluation of the complexity-to-performance trade-off in OOD detection. Codes are available at https://github.com/HAIV-Lab/ASL.

Abstract:
The effectiveness of multimodal instruction tuning dependsnot only on dataset scale, but also critically on whether training samplesgenuinely require visual reasoning. However, existing instruction datasetsoften contain a substantial portion of visually redundant samples (solv-able from text alone), as well as multimodally misaligned supervisionthat can degrade learning. To address this, we propose VisNec (VisualNecessity Score), a principled data selection framework that measuresthe marginal contribution of visual input during instruction tuning. Bycomparing predictive loss with and without visual context, VisNec iden-tifies whether a training instance is vision-critical, redundant, or mis-aligned. To preserve task diversity, we combine VisNec with semanticclustering and select high-necessity samples within each cluster. Across10 downstream benchmarks, training on only 15% of the LLaVA-665Kdataset selected by VisNec achieves 100.2% of full-data performance. Onthe smaller Vision-Flan-186K dataset, our selection not only further re-duces data size but surpasses full-data training by 15.8%. These resultsdemonstrate that measuring and leveraging visual necessity provides aneffective solution for both efficient and robust multimodal instructiontuning. Project Page: https://dmk041218.github.io/VisNec/.

Abstract:
While test-time adaptation (TTA) empowers vision-languagemodels to adapt without costly retraining, it remains highly vulnerable toout-of-distribution (OOD) outliers prevalent in real-world applications.This discrepancy motivates Noisy TTA (NTTA), an online task to fil-ter noisy OOD samples on the fly while maximizing in-distribution (ID)classification accuracy. Existing zero-shot NTTA approaches typicallyrely on test-time discriminative training, leading to overconfident mis-classifications and significantly degraded inference efficiency. To addressthese limitations, we propose a novel framework named Dual Distribu-tion Estimation (DDE), shifting the zero-shot NTTA paradigm frominstance-level learning to training-free Gaussian distribution modeling.DDE incorporates two novel modules: Positive Feature Distribution Es-timation (PFDE) and Negative Label Distribution Estimation (NLDE).PFDE explicitly models class-wise inclusion and exclusion Gaussian dis-tributions to formulate a calibrated contrastive score, robustly enhancingID accuracy. In parallel, NLDE improves OOD identification by explicitlymodeling the negative label distribution to mine highly discriminative la-bels, effectively mitigating spurious correlations. Extensive experimentsshow that on the large-scale ImageNet benchmark, DDE achieves an im-provement of 3.70% in harmonic mean accuracy and reduces the FPR95for OOD detection by 6.20%, while ensuring highly scalable and effi-cient online inference. Furthermore, DDE is zero-shot and training-free,demonstrating remarkable robustness in data-scarce scenarios. Codes areavailable at https://github.com/PolyU-VCLab/OpenOOD-VLM.

Abstract:
We investigate a challenging task of dynamic scene geometry estimation, which requires representing both spatial and temporal features. Typically, existing methods align two features into a unified latent space to model scene geometry. However, this unified paradigm suffers from potentially mismatched representations due to the heterogeneous nature between spatial and temporal features. In this work, we propose 4D-VGGT, a general foundation model with divide-and-conquer spatiotemporal representation for dynamic scene geometry. Our model is divided into three aspects: 1) Multi-setting input. We design an adaptive visual grid that supports input sequences with arbitrary numbers of views and time steps. 2) Multi-level representation. We propose a cross-view global fusion for spatial representation and a cross-time local fusion for temporal representation. 3) Multi-task prediction. We append multiple task-specific heads to spatiotemporal representations, enabling a comprehensive visual geometry estimation for dynamic scenes. Under this unified framework, these components enhance the feature discriminability and application universality of our model for dynamic scenes. In addition, we integrate multiple geometry datasets to train our model and conduct extensive experiments to verify the effectiveness of our method across various tasks on multiple dynamic scene geometry benchmarks. Our code is released at https://github.com/Haonan-Wang-aurora/4D-VGGT.

Abstract:
Reliable 3D dynamic perception requires models that can anticipatemotion beyond predefined categories, yet progress is hindered by the scarcityof dense, high-quality motion annotations. While self-supervision on unlabeledreal data offers a path forward, empirical evidence suggests that scaling unla-beled data fails to close the performance gap due to noisy proxy signals. In thispaper, we propose learning robust real-world motion priors entirely from scal-able simulation. We introduce SynFlow, a data generation pipeline for large-scalesynthetic LiDAR scene flow. Unlike prior works that prioritize sensor-specificrealism, SynFlow employs a motion-oriented strategy to synthesize diverse kine-matic patterns across 4,000 sequences (∼940k frames), termed SynFlow-4k. Thisrepresents a 34× scale-up in annotated volume over existing real-world bench-marks. Our experiments demonstrate that SynFlow-4k provides a highly domain-invariant motion prior. In a zero-shot regime, models trained only on our syntheticdata generalize across multiple real-world benchmarks, comparable to in-domainsupervised baselines on nuScenes and outperforming state-of-the-art methodson TruckScenes by 31.8%. Furthermore, SynFlow-4k serves as a label-efficientfoundation: fine-tuning with only 5% of real-world labels surpasses models trainedfrom scratch on the full available budget. We open-source the pipeline and datasetto facilitate research in generalizable 3D motion estimation. More detail can befound at https://kin-zhang.github.io/SynFlow.

Abstract:
3D object detection from LiDAR point clouds faces a fun-damental dilemma: voxel-based methods achieve efficiency at the costof geometric quantization, while point-based methods preserve fidelitybut suffer from prohibitive computational bottlenecks. Specifically, point-based architectures are crippled by slow downsampling strategies (e.g.,FPS) and expensive dynamic neighbor queries (e.g., k-NN) coupled withcostly continuous interactions. To tackle these systemic inefficiencies, wepropose PointLAM, a highly efficient and powerful point-based architec-ture driven by two synergistic innovations. First, to resolve the downsam-pling bottleneck, we develop the Laplacian Point Sampler (LPS). LPSemploys an implicit discrete Laplacian high-pass filter and Doubly SortedSampling to achieve fast, structure-aware foreground preservation. Sec-ond, to overcome local modeling latency, we design the Local HadamardAggregator (LHA). LHA decouples spatial indexing from feature repre-sentation using transient grids, and replaces complex continuous interac-tions with a Hadamard Gating mechanism for topology-aware, attentivemodulation. By coupling this local gating with Bi-Directional Mamba(BDM) layers for global sequence modeling, we formulate the Local At-tentive Mamba (LAM) block. Powered by this architecture, PointLAMachieves competitive performance on nuScenes and Waymo for point-based detectors. It rivals highly optimized voxel competitors while re-quiring a fraction of the computational footprint, demonstrating markedsuperiority in detecting small instances and handling extreme sparsity.Project page: https://pointlam.github.io/.

Abstract:
Understanding 3D objects from images is fundamental to robotics and AR/VR applications. While recent work has made progress in category-level pose estimation, current representations fail to capture the fine-grained semantics needed for reasoning about object parts, functions, and interactions. In this work, we study category-level 3D correspondence in camera space—predicting, from a single image, 3D locations that remain consistent across instances within a category—and show that it can emerge without explicit correspondence supervision by learning a shared morphable object prior. To enable research in this direction, we introduce HouseCorr3D, the first large-scale benchmark for single-view category-level 3D correspondence with 178k images across 50 household object categories, 280 unique instances, and 3D keypoint annotations directly on CAD models. Crucially, HouseCorr3D provides amodal correspondence labels for occluded regions and explicit symmetry annotations, addressing key limitations of existing datasets. We further propose Morpheus, a method that learns morphable category-level shape priors by disentangling canonical shape, deformation, and object pose. Through this shared canonical grounding, semantically meaningful 3D correspondences in camera space emerge implicitly. These emerging 3D correspondences set a new state of the art on HouseCorr3D, demonstrating that semantic 3D object understanding can arise without direct correspondence supervision. Data and code: §/GenIntel/HouseCorr3D.

Abstract:
Indoor scene generation is crucial for robot simulation andmodern interior design. However, complex layouts together with scarce3D scene data make learning-based generation challenging. Existing meth-ods often rely on hand-crafted rules or focus on isolated sub-tasks (e.g.,floorplan synthesis or single-room furnishing), producing whole-homescenes that lack global coherence, realism, and simulation readiness. Tomitigate these limitations, we propose a unified hierarchical frameworkthat decomposes indoor scene synthesis into controllable stages. First, wecurate a large-scale dataset of 300K real residential floorplans to traina large language model for whole-home floorplan generation. With de-tailed descriptions and a K-D tree–based representation, our method en-ables fine-grained, controllable whole-home floorplan generation. Build-ing upon the generated whole-home floorplan, we leverage image gener-ation models to draft furniture layouts from multi-level roaming view-points, and then generate the layouts of small manipulable objects ondifferent supporting surfaces (e.g. cabinets, desks, and dining tables) forembodied AI simulation. During furniture and object layout generation,a VLM-based refiner iteratively corrects furniture and object placement,and a 3D generative model enables flexible replacement of individualassets. We further attach basic physical attributes and simple surfacetexture and lighting setups to complete the pipeline for embodied AIuse. Experiments and user studies demonstrate that our pipeline pro-duces indoor spaces with greater layout diversity and stronger 3D designappeal, outperforming prior methods on both quantitative and qualita-tive metrics. Finally, alongside our generation pipeline, we will releasethe floorplan dataset and 5K fully furnished scenes to the community.Project page: https://kairos-homeworld.github.io/.

Abstract:
Estimation of Human-Object Interactions in 3D (3D HOI)is a fundamental problem in 3D computer vision with applications inAR/VR, robotics, and embodied AI. However, reconstructing these in-teractions in 3D remains challenging due to depth ambiguities, occlu-sions, and object shape variability. Existing approaches are primarilyconcerned with reprojection and contact constraints, fitting parametrichuman models and object templates to 2D images. In this paper, weexplore a different avenue. We present MILO, a framework that lever-ages the visual capabilities of Large Reconstruction Models (LRMs) torecover detailed 3D human-object interactions from a single image. Ourkey observation is that LRMs provide a powerful geometric scaffold thatpreserves relative human-object arrangement and proximity cues. Thissignificantly simplifies the reconstruction procedure, reframing the prob-lem as interpreting the LRM mesh: we segment it into human and objectcomponents, fit a parametric body model to the human part, and op-tionally align an object template to the object part (if such a templateis available). MILO achieves strong reconstruction accuracy and out-performs existing baselines across multiple benchmarks and interactionscenarios. Our code is available at https://ac5113.github.io/MILO.

Abstract:
Neural Parametric Head Models (NPHMs) are a recent ad-vancement over mesh-based 3d morphable models (3DMMs) to facilitatehigh-fidelity geometric detail. However, fitting NPHMs to visual inputsis notoriously challenging due to the expressive nature of their under-lying latent space, which heavily limits NPHM’s practical use. To thisend, we propose Pix2NPHM, a vision transformer (ViT) network that di-rectly regresses NPHM parameters, given a single image as input, finallybridging the gap from theoretical to practical use. Compared to exist-ing 3DMM regressors, the neural parametric space allows our methodto reconstruct more recognizable facial geometry and accurate facial ex-pressions. For broad generalization, we exploit domain-specific ViTs asbackbones, which are pretrained on geometric prediction tasks. We trainPix2NPHM on a mixture of 3D data, including a total of over 100KNPHM registrations, and large-scale 2D video datasets, for which nor-mal estimates serve as pseudo ground truth. While Pix2NPHM runs atinteractive frame rates, it is possible to improve geometric fidelity by asubsequent optimization against estimated surface normals and canoni-cal point maps. As a result, we achieve unprecedented face reconstructionquality that can run at scale on in-the-wild data.

Abstract:
Predicting how a scene may evolve from partial observationsrequires reasoning about multiple possible futures rather than committingto a single trajectory. Existing approaches either generate appearance-dominated video predictions or sample a small number of trajectorieswithout explicitly modeling the distribution of possible motion. We intro-duce Goal Aware Representations of Future kInEmatic Latent Distribu-tions (GARFIELD), a probabilistic model of scene kinematics that learnsa structured spatio-temporal latent representation of the distribution overpossible futures given an image and optional spatio-temporally sparseconstraints. The same latent representation enables both joint samplingof all trajectories and direct access to the underlying motion distributionthrough an efficient deterministic density decoder. As a result, uncer-tainty about future motion can be localized to specific scene elementsand timesteps and progressively refined through additional constraints.Experiments demonstrate strong motion planning performance competi-tive with large video generation models while sampling trajectories 97×faster. Our method further estimates motion densities two orders of mag-nitude faster than Monte-Carlo sampling from motion generation models,enabling interactive exploration and uncertainty-aware planning.Project page: https://compvis.github.io/schroedingers_cat/

Abstract:
Traditional high-quality 3D scanning and reconstruction typ-ically relies on human labor to plan the scanning procedure. With therapid development of embodied systems such as drones and robots, thereis a growing demand of performing accurate 3D scanning and recon-struction in an fully automated manner. We introduce Auto3R, a data-driven uncertainty quantification model that is designed to automatethe 3D scanning and reconstruction of scenes and objects, includingobjects with non-lambertian and specular materials. Specifically, in aprocess of iterative 3D reconstruction and scanning, Auto3R can makeefficient and accurate prediction of uncertainty distribution over poten-tial scanning viewpoints, without knowing the ground truth geometryand appearance. Through extensive experiments, Auto3R achieves supe-rior performance that outperforms the state-of-the-art methods, partic-ularly on challenging viewpoints. We also deploy Auto3R on a robotarm equipped with a camera and demonstrate that Auto3R can beused to effectively digitize real-world 3D objects and delivers ready-to-use and photorealistic digital assets. Our code is available at https://tomatoma00.github.io/auto3r.github.io/.

Abstract:
Multi-exposure fusion (MEF) brings the dynamic range ofconventional cameras closer to that of human vision, producing imageswith rich scene content. Given the large variability in scene luminance,exposure strategies often require different numbers of frames to capturethe full radiance range faithfully. However, conventional MEF techniquesare typically designed for a fixed number of inputs, forcing deploymentsystems to maintain separate models for different frame-count require-ments, which undermines deployment efficiency. To address this limita-tion, we propose FreeMEF, the first flexible-frame transformer for MEFthat seamlessly accommodates varying numbers of input exposures with-out retraining or architectural changes. The proposed approach consistsof two key modules. First, we introduce a recurrent state space mod-ule (RSSM) that sequentially fuses features from arbitrary sequencesvia adaptive alignment and state-space recurrent modeling, thereby pro-viding global information guidance for the subsequent restoration. Sec-ond, we devise a global feature guided block (GFGB) incorporating anextremity-aware hybrid attention (EAHA) and an affine-injection feed-forward network (AFFN), which effectively resolves the similarity para-dox while simultaneously optimizing contrast and brightness regulation.Extensive experiments on three benchmark datasets demonstrate the ef-fectiveness of our method, which performs favorably against state-of-the-art methods both quantitatively and qualitatively. The code is availableat https://github.com/qulishen/FreeMEF.

Abstract:
Omnidirectional depth estimation from multi-fisheye camerarigs is complicated by visibility conflicts: wide baselines cause differentcameras to observe different portions, or even different faces, of the sameobject, so aggregating their features into a unified equirectangular (ERP)representation under fixed projection produces ambiguous matching ev-idence near occlusion boundaries and thin structures. Although existingmethods mitigate this by down-weighting unreliable views, they do notresolve the underlying discrepancy because context formation and cross-view fusion remain tied to rigid fisheye-to-ERP sampling. We presentOmniDS, an iterative depth refinement framework that replaces rigidaggregation by combining dynamic context fusion with consensus-awaremulti-view similarity. A dual-stream encoder pairs a lightweight CNNfor geometric detail with a frozen DINOv3 for semantic priors; their fea-tures are reprojected into ERP space at each refinement step via learnedview weighting and deformable cross-attention with geometric distortionbias. In parallel, a multi-view consensus volume captures global cross-camera agreement through group-wise correlation and feature variance,regularized by a 3D U-Net. For efficient deployment, we distill the dual-stream representation into a single MobileNet-based encoder. OmniDSachieves state-of-the-art performance on the OmniThings, OmniHouse,and Sunny benchmarks while maintaining competitive inference speed.Project page and codes are available here.

Abstract:
Diffusion-based text-to-motion models synthesize realistic hu-man motions but often exhibit semantic drift from the input text. Mo-tion is inherently temporal, especially in compositional and long-durationsequences that require semantic consistency across multiple action seg-ments and smooth kinematic transitions throughout the trajectory. Weposit that the initial noise is central to this consistency: within the Gaus-sian noise space, certain instances, i.e. winning noise tickets, carry latentstructure that biases denoising toward particular motion semantics, evenunder null prompts. We propose WInning Noise Retrieval and Optimiza-tion (WINRO), a training-free, model-agnostic framework that improvestext–motion alignment by selecting and refining such tickets before diffu-sion sampling. WINRO maps random noises to motion features generatedunder null prompts, retrieves the best-aligned noise for a given text, andrefines it via a KL-regularized objective that reduces the residual seman-tic gap while preserving the Gaussian prior. An optional LoRA-basedadapter amortizes this refinement into a single forward pass. WINROconsistently improves text–motion fidelity across different base models,MDM and MotionLCM, on HumanML3D without retraining, improvestemporal robustness on the MTT benchmark, and generalizes to appli-cations such as motion stylization and spatial constraint satisfaction.

Abstract:
Current instruction-guided video editing models struggle tosimultaneously balance precise semantic modifications with faithful mo-tion preservation. While existing approaches rely on injecting explicitexternal priors (e.g., VLM features or structural conditions) to mitigatethese issues, this reliance severely bottlenecks model robustness and gen-eralization. To overcome this limitation, we present SAMA (factorizedSemantic Anchoring and Motion Alignment), a framework that factor-ize video editing into semantic anchoring and motion modeling. First, weintroduce Semantic Anchoring which establish a reliable visual anchorby jointly predicting semantic tokens and video latents at sparse an-chor frames, enabling purely instruction-aware structural planning. Sec-ond, Motion Alignment pre-trains the same backbone on motion-centricvideo restoration pretext tasks (cube inpainting, speed perturbation, andtube shuffle), enabling the model to internalize temporal dynamics di-rectly from raw videos. SAMA is optimized with a two-stage pipeline: afactorized pre-training stage that learns inherent semantic-motion rep-resentations without paired video-instruction editing data, followed bysupervised fine-tuning on paired editing data. Remarkably, the factorizedpre-training alone already yields strong zero-shot video editing ability,validating the proposed factorization. SAMA achieves state-of-the-artperformance among open-source models and is competitive with leadingcommercial systems (e.g. Kling-Omni). Code, models, and datasets arereleased in https://cynthiazxy123.github.io/SAMA.

Abstract:
Text-conditioned diffusion models have advanced image andvideo super-resolution by using prompts as semantic priors, and modernsuper-resolution pipelines typically rely on latent tiling to scale to highresolutions. In practice, a single global caption is used with the latenttiling, often causing prompt misguidance. Specifically, a coarse globalprompt often misses localized details (errors of omission) and provideslocally irrelevant guidance (errors of commission) which leads to substan-dard results at the tile level. To solve this, we propose Tiled Prompts,a unified framework for image and video super-resolution that generatesa tile-specific prompt for each latent tile and performs super-resolutionunder locally text-conditioned posteriors to resolve prompt misguidancewith minimal overhead. Our experiments on high resolution real-worldimages and videos show that tiled prompts bring consistent gains in per-ceptual quality and fidelity, while reducing hallucinations and tile-levelartifacts that can be found in global-prompt baselines. Project Page:https://bryanswkim.github.io/tiled-prompts/.

Abstract:
Large-scale text-to-image di x001B usion models have become thebackbone of modern image editing, yet text prompts alone do not o x001B eradequate control over the editing process. Two properties are especiallydesirable: disentanglement, where changing one attribute does not un-intentionally alter others, and continuous control, where the strengthof an edit can be smoothly adjusted. We introduce a method for dis-entangled and continuous editing through token-level manipulation oftext embeddings. The edits are applied by manipulating the embeddingsalong carefully chosen directions, which control the strength of the tar-get attribute. To identify such directions, we employ a Sparse Autoen-coder (SAE), whose sparse latent space exposes semantically isolateddimensions. Our method operates directly on text embeddings withoutmodifying the di x001B usion process, making it model agnostic and broadlyapplicable to various image synthesis backbones. Experiments show thatit enables intuitive and e x001E cient manipulations with continuous controlacross diverse attributes and domains.

Abstract:
AI-generated image (AIGI) attribution presents a pressingchallenge that goes beyond mere AIGI detection, aiming to identify thesource model or technique responsible for a synthetic image. However,most previous source attribution methods operate in a closed-set manner,which necessitates retraining to recognize any novel category, preventingadaptation to the rapid evolution of image generation. In this work, wepropose a new paradigm for synthetic image attribution, termed few-shotattribution. This paradigm targets the reliable identification of unseengenerators using only limited samples, making it highly suitable for real-world applications. To facilitate this work, we construct OmniFake, alarge-scale, well-categorized synthetic image dataset that contains 1.17million images from 45 distinct generators. We further introduce Om-niDFA (Omni Detector and Few-shot Attributor), a few-shot attributionbaseline that not only assesses the authenticity of images but also deter-mines their synthesis origins. Experiments demonstrate that OmniDFAexhibits excellent capability in few-shot attribution and achieves state-of-the-art generalization performance in AIGI detection. Our dataset andcode are available at https://github.com/teheperinko541/OmniDFA.

Abstract:
Video generation is central to AI-powered content creation.The alignment with human preferences is one of the key metrics for mea-suring the quality of the generated videos. Despite significant progress invisual quality, three key challenges remain: 1) The reliability of rewardsignals is constrained by the quality of human preference data, which isoften corrupted by subjective noise and bias. 2) Standard scalar rewardmodels collapse multi-aspect human preference into a single value, lead-ing to the loss of dynamic trade-offs across multiple dimensions of humanpreference. 3) In policy optimization, the widely adopted KL-divergenceimposes only local constraints, failing to capture holistic human pref-erence. To address these challenges, we propose a unified, preference-aware learning framework for video generation. First, we propose elite-guided filtering to calibrate preference data and construct reliable su-pervision for reward-model training. We then model video quality asa multidimensional reward distribution to capture the uncertain na-ture of human preference, and use the Wasserstein distance to alignit with the empirical human preference distribution. Finally, we intro-duce Wasserstein-based distributional alignment in GRPO, guiding thepolicy’s video generation to match the global structure of human videopreference. Experiments on reward modeling and video generation showthat our approach improves the reliability of reward signals and theperceptual consistency of generated videos. Our code is available athttps://github.com/alignhs26/ahs.

Abstract:
Text-guided video editing with diffusion models is imprac-tically slow, hindered by costly multi-step sampling and inversion. Wepresent OSVE, the first framework to successfully adapt one-step Text-to-Image (T2I) models for high-quality video editing, addressing the corechallenges of inversion, editability, and temporal consistency. To by-pass slow iterative inversion, we train a learnable encoder that pre-dicts the initial noise for each frame in a single forward pass. This en-coder is trained with a novel Structure-Aware Editing (SAE) loss on acurated dataset of structurally-aligned image pairs, teaching it to pre-serve the source video’s geometry during edits. For temporal coherence,we introduce Unified-Frame Editing (UFE), a technique that concate-nates frame latents to facilitate cross-frame attention in a single gen-eration step. Furthermore, for long videos, a sliding-window strategywith an anchor frame maintains global consistency. Our extensive ex-periments demonstrate that OSVE achieves editing quality compara-ble or superior to state-of-the-art multi-step methods, while operatingapproximately 155–171 times faster. This breakthrough paves the wayfor practical, real-time video editing applications. Code is available athttps://github.com/KU-VGI/OSVE.

Abstract:
Text-to-image diffusion models have achieved remarkable fi-delity in synthesizing images from explicit text prompts, yet exhibita critical deficiency in processing implicit prompts that require deep-level world knowledge, ranging from natural sciences to cultural com-monsense, resulting in counter-factual synthesis. This paper traces theroot of this limitation to a fundamental dislocation of the underlyingknowledge structures, manifesting as a chaotic organization of implicitprompts compared to their explicit counterparts. In this paper, we pro-pose EruDiff, which aims to refactor the knowledge within diffusionmodels. Specifically, we develop the Diffusion Knowledge DistributionMatching (DK-DM) to register the knowledge distribution of intractableimplicit prompts with that of well-defined explicit anchors. Furthermore,to rectify the inherent biases in explicit prompt rendering, we employthe Negative-Only Reinforcement Learning (NO-RL) strategy for fine-grained correction. Rigorous empirical evaluations demonstrate that ourmethod significantly enhances the performance of leading diffusion mod-els, including FLUX and Qwen-Image, across both the scientific knowl-edge benchmark (i.e., Science-T2I) and the world knowledge bench-mark (i.e., WISE), underscoring the effectiveness and generalizability.Our code is available at https://github.com/xiefan-guo/erudiff.

Abstract:
LangDriveCTRL is a natural-language-controllable framework for editing real-world driving videos to synthesize diverse traffic scenarios. It represents each video as an explicit 3D scene graph, decomposing the scene into a static background and dynamic object nodes. To enable fine-grained editing and realism, it introduces a feedback-driven agentic pipeline. An Orchestrator converts user instructions into executable graphs that coordinate specialized multi-modal agents and tools. An Object Grounding Agent aligns free-form text with target object nodes in the scene graph; a Behavior Editing Agent generates multiobject trajectories from language instructions; and a Behavior Reviewer Agent iteratively reviews and refines the generated trajectories. The edited scene graph is rendered and harmonized using a video diffusion tool, and then further refined by a Video Reviewer Agent to ensure photorealism and appearance alignment. LangDriveCTRL supports both object node editing (removal, insertion, and replacement) and multi-object behavior editing from natural-language instructions. Quantitatively, it achieves nearly 2× higher instruction alignment than the previous SoTA, with superior photorealism, structural preservation, and traffic realism.

Abstract:
In-Image Machine Translation (IIMT) aims to translate scenetext in an image and render the translated text back into the original re-gions while preserving the overall visual appearance. Recent unified mul-timodal models provide a promising solution by combining visual-textunderstanding and image generation within a single framework. However,directly adapting such models to IIMT remains challenging. In particu-lar, they often suffer from understanding-generation conflicts, where thetranslation inferred during understanding is inconsistent with the textsupervision used in generation, and spatial position misalignment, wherethe rendered text does not accurately match the target text regions.To address these issues, we present UniTranslator, a unified multimodalframework for IIMT that tightly couples translation understanding andtext editing. Specifically, we introduce an Understand-Generation Align-ment Module (UGAM) to bridge the representation gap between un-derstanding and generation, encouraging semantic consistency betweentranslated content prediction and text rendering. We further propose aSpatial Mask Decoder (SMD) with pixel-level supervision over text re-gions to improve spatial grounding, geometric alignment, and layout con-trollability during generation. Extensive experiments on multiple bench-marks demonstrate that UniTranslator achieves state-of-the-art perfor-mance across diverse language directions and complex real-world layouts.Moreover, our results reveal a strong mutual reinforcement effect betweentranslation understanding and image generation, highlighting the advan-tage of unified translation multimodal learning. Code is available here.

Abstract:
Recent advances in motion-aware large language models haveshown remarkable promise for jointly learning motion understandingand generation knowledge. However, these models typically treat un-derstanding and generation separately, limiting the mutual benefits thatcould arise from interactive feedback between tasks. In this work, wereveal that motion assessment and refinement tasks can act as crucialbridges to enable knowledge flow from motion understanding to genera-tion. Specifically, we propose Interleaved Reasoning for Motion Genera-tion (IRMoGen), a novel paradigm that tightly couples motion genera-tion with assessment and refinement through iterative text-motion dia-logue. To realize this, we introduce IRG-MotionLLM, the first model thatseamlessly interleaves motion generation, assessment, and refinement toimprove the alignment between generated motion and goal text. IRG-MotionLLM is developed progressively with a novel three-stage train-ing scheme, initializing and subsequently enhancing native IRMoGencapabilities. To facilitate this development, we construct an automateddata engine to synthesize interleaved reasoning annotations from existingtext-motion datasets. Extensive experiments demonstrate the propertiesbrought by IRMoGen training, and the advanced cross-benchmark andcross-evaluator performance of IRG-MotionLLM. Code and models areavailable at https://github.com/HumanMLLM/IRG-MotionLLM.

Abstract:
Despite remarkable progress in text-guided image editing, gen-erative models frequently fail to preserve visual object consistency, definedas the preservation of a subject’s key attributes throughout the editingprocess. We address this limitation through three contributions. First, weintroduce ABO-Edit, a dataset specifically designed to study object con-sistency, comprising over 12,000 triplets of source images, editing prompts,and high-quality target images rendered from artist-designed 3D assets,with multi-view coverage and human-verified quality control. Second, weuncover an overlooked property of image-editing rectified flow models: theconditioning embedding space, not directly supervised during training,encodes a prediction of the final generated image even at high noise levels.Third, exploiting this finding, we propose FlowMirror, a parameter-freeauxiliary loss that supervises this conditioning embedding space. Withoutarchitectural changes, our method improves generation quality acrossseveral metrics over baselines. Page: francescotaioli.github.io/DiTailed

Abstract:
Multimodal large language models (MLLMs) struggle withalignment due to the limitations of existing reward models (RMs), whichare predominantly vision-centric, dependent on costly human labels, andprovide opaque scalar scores that fail to capture nuanced reasoning,leading to brittle alignment. We present Omni-RRM, an Omni-modalRubric-grounded Reward Model that generates multi-dimensional re-ward signals across text, image, video, and audio. To overcome the highcost and inherent inconsistency of human-centric evaluation in multi-dimensional reasoning, we introduce Omni-Preference, a high-qualitydataset constructed via automatic rubric-grounded preference synthe-sis. In this pipeline, teacher models reconcile raw preferences into ex-plicit justifications, ensuring that the synthesized supervision is bothhigh-fidelity and interpretable. Omni-RRM is trained using a progres-sive SFT + GRPO regimen, specifically optimized to sharpen rewarddiscrimination on low-margin, hard preference pairs. It achieves state-of-the-art accuracy on video (80.2% on ShareGPT-Video) and audiobenchmarks (66.8% on Audio-HH-RLHF and 65.0% on TA2T), yield-ing a five-benchmark Overall accuracy of 70.4% and a +17.0% relativegain over its backbone. Furthermore, Omni-RRM effectively guides Best-of-N selection and exhibits robust transfer to text-only alignment. Allresources, including the dataset, training and inference code, and modelcheckpoints are available at https://tmfk418.github.io/Omni-RRM.

Abstract:
In recent years, diffusion stochastic differential equation (SDE)inversion and inversion-free methods have become prevalent for training-free image editing, as they can achieve faithful reconstruction withouttuning. However, existing approaches remain inefficient, exhibit limitedplasticity, and struggle to accurately preserve unedited regions. To ad-dress these issues, we propose MIEdit, a training-free editing frame-work based on SDE inversion. MIEdit introduces a predictor–correctormulti-history-step scheme to achieve superior editing quality with fewersteps. We further mitigate heterogeneity and conflict between the multi-conditioned noise residuals and gradient terms during sampling, improv-ing stability and editing plasticity under large edits. MIEdit also includesInversion-Time Automatic Semantic Angle Masking (IASM); it leveragesclassifier-free guidance to automatically generate semantic angle masksduring inversion and applies them throughout the sampling process forregional constraints, without extra user inputs. We additionally constructEditEval++ (30 fine-grained tasks, 1,000+ image–text–mask triplets) forcomprehensive evaluation; experiments show that MIEdit outperformsstate-of-the-art techniques. Project page: https://whywwwzzzg.github.io/MIEdit/.

Abstract:
Diffusion models have achieved impressive results in gener-ative tasks such as text-to-image synthesis, yet they often struggle tofully align outputs with nuanced user intent and maintain consistentaesthetic quality. Existing preference-based training methods such asDiffusion Direct Preference Optimization help address these issues, butobtain their supervision targets from the forward process q(xt−1 | xt , x0 )derived from terminal samples, which is not directly aligned with themodel’s actual backward denoising transitions at each step. In this work,we introduce Direct Diffusion Score Preference Optimization (DDSPO),which defines stepwise preference supervision directly over backward de-noising transitions through a contrastive policy pair, rather than relyingon forward-process approximations from terminal samples. We proposetwo practical instantiations of the contrastive policy pair: training sepa-rate winning and losing models on preference data, and inducing a con-trastive policy pair without additional training by using a pretrainedreference model conditioned on an original prompt and a semanticallydegraded variant, requiring neither reward modeling nor manual annota-tions. Empirical results show that contrastive-policy-pair supervision ismore effective than forward-process-based supervision across text–imagealignment and aesthetic-quality tasks. Our implementation is availableat: https://dohyun-as.github.io/DDSPO

Abstract:
Diffusion models have significantly advanced video super-resolution (VSR) but remain largely constrained to fixed upsamplingscales. Conversely, while coordinate-based arbitrary-scale VSR methodsoffer scale flexibility, they inherently suffer from severe over-smoothingat large scaling factors. Integrating generative priors with continuous de-coding is promising but currently hindered by severe temporal flickeringcaused by the stochasticity of diffusion sampling. To address this, wepropose AVSR-Diff (Arbitrary-scale Video Super-Resolution with Dif-fusion), a novel decoupled framework that separates scale-agnostic la-tent denoising from continuous coordinate rendering, effectively avoid-ing computationally heavy resolution-specific sampling. Our approachintroduces a Temporally-Gated Feature Recurrence (TGFR) module toextract strictly aligned, temporally consistent latent priors. Furthermore,we design a continuous video VAE decoder incorporating a Scale-AwareFourier Refinement (SAFR) module to dynamically adapt frequencycomponents to any target scale. Extensive experiments demonstrate thatAVSR-Diff consistently preserves high-frequency details and strong tem-poral stability across various scales, surpassing state-of-the-art arbitrary-scale baselines. Remarkably, our framework outperforms recent fixed-scale generative models even on their native resolution.

Abstract:
Text-to-image (T2I) diffusion models often fail to faithfullyrender explicit textual descriptions, instead defaulting to strongly learnedvisual priors due to a phenomenon referred to as concept association bias.We show that such bias is particularly strong for one-and-only (OAO)objects, entities that exist in a single canonical form, such as celestialbodies, landmarks, and artworks. The deeply ingrained visual identity forthese concepts often resists modification through prompting alone. Ad-dressing this challenge, we first identify through an information-theoreticanalysis that the final text embedding discards concept-level informationpresent in the intermediate-layer text representations, reducing the mu-tual information available to the subsequent denoising process. We thenpropose Intermediate Text Representation (IR)-guided diffusion, whichinjects intermediate hidden states of the text encoder into the condi-tioning signal during early denoising steps, recovering suppressed con-cepts without any additional training, optimization, or external models.To systematically evaluate the challenging task of aligning generativeoutputs with unusual prompts for OAO objects, we introduce OAO-AttackBench, a benchmark comprising counterfactual prompts that di-rectly conflict with the core visual identity of OAO objects. Experi-ments on four benchmarks, including OAO-AttackBench, show that ourmethod achieves up to a 19.1 percentage-point improvement in VQAS-core while preserving generation fidelity and human preference. Projectpage: https://soyoun-won.github.io/one-and-only-ir-guidance/.

Abstract:
Diffusion-based decoding has recently emerged as an appealing alternative to autoregressive (AR) generation, offering the potential to update multiple tokens in parallel and reduce latency. However, diffusion vision language models (dVLMs) still lag significantly behind mainstream autoregressive vision language models. This is due to the scarcity and weaker performance of base diffusion language models (dLLMs) compared with their autoregressive counterparts. This raises a natural question: Can we build high-performing dVLMs directly from existing powerful AR models, without relying on dLLMs? We propose DiffusionVL, a family of dVLMs obtained by translating pretrained AR models into the diffusion paradigm via an efficient diffusion finetuning procedure that changes the training objective and decoding process while keeping the backbone architecture intact. Through an efficient diffusion finetuning strategy, we successfully adapt AR pretrained models into the diffusion paradigm. This approach yields two key observations: (1) The paradigm shift from AR-based multimodal models to diffusion is remarkably effective. (2) Direct conversion of an AR language model to a dVLM is also feasible, achieving performance comparable to that of the same AR model finetuned with standard autoregressive visual instruction tuning. To enable practical open-ended generation, we further integrate block decoding, which supports arbitrarylength outputs and KV-cache reuse for faster inference. Our experiments demonstrate that despite training with less than 5% of the data required by prior methods, DiffusionVL achieves a comprehensive performance improvement, with a 34.4% gain on the MMMU-Pro (vision) benchmark and 37.5% gain on the MME (Cog.) benchmark, alongside a 2× inference speedup. The model and code are released at https://github.com/hustvl/DiffusionVL.

Abstract:
We present a novel paradigm for ultra-low-bitrate imagecompression (ULB-IC) that exploits the “temporal” evolution in gener-ative image compression. Specifically, we define an explicit intermediatestate during decoding: a compact anchor frame, which preserves the scenegeometry and semantic layout while discarding high-frequency details.We then reinterpret generative decoding as a virtual temporal transitionfrom this anchor to the final reconstructed image. To model this progres-sion, we leverage a pretrained video diffusion model (VDM) as a temporalprior: the anchor frame serves as the initial frame and the original imageas the target frame, transforming the decoding process into a next-frameprediction task. In contrast to image diffusion-based ULB-IC models, ourdecoding proceeds from a visible, semantically faithful anchor, which im-proves both fidelity and realism for perceptual image compression. Ex-tensive experiments demonstrate that our method achieves superior rate-distortion performance. On the CLIC2020 test set, our method achievesover 50% bitrate savings across LPIPS, DISTS, FID, and KID com-pared to DiffC, while also delivering a significant decoding speedup of upto ×5. Code will be released at https://github.com/UnoC-727/NeFIC.

Abstract:
Text spotting requires both accurate text recognition and precise spatial localization. Current specialised spotters excel at predicting tight bounding boxes in natural scenes, but falter on complex or artistic text, whereas multimodal large language models (MLLMs) possess strong recognition capabilities yet remain weak at localisation. To equip the text spotter with general and powerful recognition capabilities and to maximize its localization ability, we explore two MLLM-based fine-tuning methods: Supervised Fine-Tuning (SFT) and reinforcement learning finetuning based on Group Relative Policy Optimisation (GRPO). An interesting finding is that SFT is less effective than GRPO at enhancing recognition, while GRPO is less effective than SFT at enhancing detection. To compensate for each other’s shortcomings, we introduce a joint training strategy, SupGRPO, which simultaneously optimizes the model using both SFT and GRPO. SupGRPO employs the specially designed reward functions and develops a matching-based online SFT applied solely to coordinate tokens. It both mitigates the reward sparsity problem of GRPO and avoids the instance order dependency problem of SFT. To evaluate particularly challenging cases, we curate ATS, a dataset for artistic text spotting. Experiments demonstrate that SupGRPO improves both text recognition and detection, and attains superior performance. Our code and dataset will be released at https://github.com/Psycho-9/SupGRPO.

Abstract:
Text-to-image diffusion models have achieved unprecedented success but still struggle to produce high-quality results under limited sampling budgets. Existing training-free sampling acceleration methods are typically developed independently, leaving the overall performance and compatibility among these methods unexplored. In this paper, we bridge this gap by systematically elucidating the design space, and our comprehensive experiments identify the sampling time schedule as the most pivotal factor. Inspired by the geometric properties of diffusion models revealed through the Frenet-Serret formulas, we propose constant total rotation schedule (TORS), a scheduling strategy that ensures uniform geometric variation along the sampling trajectory. TORS outperforms previous training-free acceleration methods and produces highquality images with 10 sampling steps on Flux.1-Dev and Stable Diffusion 3.5. Extensive experiments underscore the adaptability of our method to unseen models, hyperparameters, and downstream applications. Code is available at https://github.com/zju-pi/TORS.

Abstract:
Event vision exploration holds significant achievements in various vision tasks, such as object recognition, action recognition, etc. However, improving the model’s robustness against overfitting remains a critical challenge. To address this, we propose augmenting event data from the noise rather than applying fixed data transformations. Specifically, we first decouple the quadruplet event data across spatial, temporal, and polarity dimensions to construct a representation, breaking existing frame-tied manners. Then, an event diffusion decoder is integrated with a condition extractor to gradually optimize the noise to the event data (N2E). The condition extractor aims to yield guidance vectors from the original data, which are fed into each optimization step of the diffusion process. During data augmentation, we can directly generate new event streams by adjusting the noise, diffusion parameters (e.g., number of steps or noise intensity), or conditioning vectors, thereby boosting the performance of downstream event vision models. Extensive experiments on two downstream tasks among five event vision models demonstrate that N2E achieves greater improvements than existing augmentation baselines. Code is available at https://github.com/rfww/n2e.

Abstract:
Diffusion models achieve state-of-the-art generative performance but are fundamentally bottlenecked by their slow, iterative sampling process. While diffusion distillation techniques enable high-fidelity, few-step generation, traditional objectives often restrict the student’s performance by anchoring it solely to the teacher. Recent approaches have attempted to break this ceiling by integrating Reinforcement Learning (RL), typically through a simple summation of distillation and RL objectives. In this work, we propose a novel paradigm by re-conceptualizing distribution matching as a reward, denoted as R . This unified perspective bridges the algorithmic gap between Diffusion Matching Distillation (DMD) and RL, providing several primary benefits: (1) Enhanced Optimization Stability: We introduce Group Normalized Distribution Matching (GNDM), which adapts standard RL group normalization to stabilize R estimation. By leveraging group-mean statistics, GNDM establishes a more robust and effective optimization direction. (2) Seamless Reward Integration: Our reward-centric formulation inherently supports adaptive weighting mechanisms, allowing for the fluid combination of DMD with external reward models. (3) Improved Sampling Efficiency: By aligning with RL principles, the framework readily incorporates Importance Sampling (IS), leading to a significant boost in sampling efficiency. Extensive experiments demonstrate that GNDM outperforms vanilla DMD, reducing the FID by 1.87. Furthermore, our multi-reward variant, GNDMR, surpasses existing baselines by striking an optimal balance between aesthetic quality and fidelity, achieving a peak HPS of 30.37 and a low FID-SD of 12.21. Ultimately, R provides a flexible, stable, and efficient framework for real-time, high-fidelity synthesis. Code is available at: https://github.com/Flq2002/Rdm.

Abstract:
Image-guided style transfer aims to apply the artistic characteristics of a style image to a content image while preserving its semantic structure and layout. Despite advances in diffusion-based methods, existing approaches often face challenges in disentangling content and style, particularly when independently optimized adapters are naively combined, causing conflicts between adapters and limiting controllability over the content-style balance in inference. We further demonstrate that training-free structural guidance directly derived from the content image through the internal attention of pre-trained model outperforms a dedicated content LoRA adapter in terms of structural fidelity and computational efficiency. Building on these observations, we propose AnyStyle, a streamlined framework for image-guided style transfer. The framework adopts a unified single-adapter paradigm for coherent style capture from the style image and incorporates training-free structural guidance from the content image, thus avoiding complex entanglement between multiple adapters and improving controllability and stability. Extensive experiments show that our method delivers competitive quantitative performance and significantly improved perceptual quality. Code is available at https://github.com/Yvan1001/AnyStyle.

Abstract:
Image outpainting extends an image beyond its original borders, requiring seamless style integration and globally coherent scene completion. Building on the success of diffusion models, recent methods have achieved substantial improvements in visual quality. In practice, however, high-resolution outpainting is commonly performed via progressive expansion around a fixed source image, particularly in artwork scenarios. Despite this progress, existing approaches still suffer from three key limitations: (i) the absence of a reliable global planning mechanism, which leads to structural instability and error accumulation at high resolutions; (ii) limited spatial controllability beyond text prompts, making it difficult to place objects at user-specified locations; and (iii) high inference latency caused by inherently sequential patch generation. To address these issues, we propose a global blueprint-guided two-stage diffusion framework for layout-controllable high-resolution outpainting with efficient parallel synthesis. In Stage 1, we generate a low-resolution global blueprint using a layout adapter that injects bounding-box conditions into a Stable Diffusion inpainting backbone, producing a globally consistent structural plan while extracting global guidance features. In Stage 2, we synthesize high-resolution local patches in parallel by injecting the blueprint-derived global guidance and initializing each patch from the blueprint using the low-frequency preservation property of forward diffusion. This design eliminates sequential dependency while maintaining global coherence. Extensive experiments on large-scale artwork datasets demonstrate improved visual fidelity, stronger semantic consistency, and substantially reduced inference time compared to prior baselines, while uniquely supporting explicit layout control for artwork outpainting. Our code will be available at https://github.com/poohoh/BlueOut.

Abstract:
Autoregressive models in vision have gained widespread at-tention under the scalable next-token prediction paradigm, representingimages as sequences of discrete tokens. Yet their optimization remainschallenging due to the non-differentiability of discrete token samplingand the prohibitive memory costs of full-sequence fine-tuning. We pro-pose G3 AFT, a novel end-to-end fine-tuning framework for discrete-tokenautoregressive models. Within this framework, we introduce two com-plementary techniques, i.e. Global Scope Gradient Recording (GSGR)and Noise Suppressed Gradient Projection (NSGP). GSGR selectivelyupdates periodically sampled tokens by leveraging coarse global signalsthat summarize the overall layout and long-range structure of the image.This design alleviates the prohibitive memory overhead of full-sequencefine-tuning while maintaining long-range coherence. In parallel, NSGPenhances surrogate gradient flow by explicitly aligning updates withsalient sampling alternatives and filtering out spurious signals introducedby softmax relaxations. By suppressing noisy gradient components andreinforcing causally consistent updates, NSGP provides a more faithfulapproximation to discrete sampling, ensuring faithful gradient propaga-tion across long autoregressive trajectories. To the best of our knowl-edge, this is the first method under the next-token prediction paradigmfor discrete-token autoregressive image generation that enables fully dif-ferentiable optimization across the entire generation process. Extensiveexperiments validate that G3 AFT achieves superior image quality andquantitative performance, providing a practical framework for fully dif-ferentiable fine-tuning of autoregressive image generation models. Codeis available at https://github.com/Wotoosh/G3AFT.

Abstract:
We propose Discrete Diffusion Bridges (DDB), a novel frame-work designed to resolve the fundamental spatiotemporal misalignmentof standard discrete diffusion in image translation and generation. Bycorrupting data into a pure mask state via a random schedule, the con-ventional forward process induces a twofold misalignment: spatially, thispure-mask destination entirely discards the rich structural priors of thesource image; temporally, the random masking order inherently contra-dicts the “easy-first, hard-last” decoding mechanism used during infer-ence. To address this, DDB constructs a direct and efficient trajectorybetween domains. Spatially, we introduce a hybrid absorption mechanismthat redefines the absorbing state to a stochastic mixture of mask andsource tokens, effectively injecting source prior as spatial anchors into thelatent space. Temporally, we design an information-guided noise schedulethat quantifies semantic variation to prioritize the corruption of high-information regions at earlier timesteps. This ensures the model learnsto resolve difficult semantic changes using robust context from invariantregions. Extensive experiments validate the versatility and robustnessof our framework across diverse generative paradigms. DDB effectivelybalances edit alignment with structural fidelity across both text-guidedsemantic manipulation and pure structural image translation, while in-herently complementing text-to-image generation and guaranteeing ro-bust high-quality decoding under extremely low sampling steps. Codeand models are available at https://github.com/HKU-HealthAI/DDB.

Abstract:
Recent approaches in controllable novel view video generationoften rely on fine-tuning pre-trained Video Di!usion Models (VDMs).This dominant paradigm is computationally expensive and frequentlysu!ers from catastrophic forgetting of the model’s original generative pri-ors. To address this challenge, here we propose InverseCrafter, a VDMtraining-free framework that reformulates novel view video generationas an inpainting-based inverse problem in the latent space, eliminatingthe need for any annotated 4D training data. The core of our methodis to establish operator equivalence by employing a lightweight latentmask encoder to define a latent-domain masking operation via a con-tinuous, multi-channel representation. This principled representationfaithfully models the forward process in the latent domain, enablinge"cient, backpropagation-free solvers while bypassing the costly bottle-neck of repeated VAE operations. InverseCrafter achieves high-fidelity,spatio-temporally coherent novel view synthesis with near-zero additionalinference overhead and excels at general-purpose video inpainting andediting by fully preserving the pre-trained VDM’s generative capabilities.

Abstract:
Diffusion models generate conditional samples by progres-sively denoising Gaussian noise, yet the denoising trajectory can stall atvisually plausible but low-quality outcomes with conditional misalign-ment or structural artifacts. We interpret this behavior as local optimain a surrogate quality landscape: Once early denoising commits to a sub-optimal global structure, later steps mainly sharpen details and seldomcorrect the underlying mistake. While existing inference-time approachesexplore alternative diffusion states via re-noising with fixed strength ordirection, they exhibit limited capacity to escape steep quality plateaus.We propose Controlled Random Zigzag Sampling (Ctrl-Z Sampling), ascalable sampling strategy that detects plateaus in quality landscapevia a surrogate score, and allocates exploration only when a plateau isdetected. Upon detection, Ctrl-Z Sampling rolls back to noisier states,samples a set of alternative continuations, and updates the trajectorywhen a candidate improves the score, otherwise escalating the explo-ration depth to escape the current plateau. The proposed method ismodel-agnostic and broadly compatible with existing diffusion frame-works. Experiments show that Ctrl-Z Sampling consistently improvesgeneration quality over other inference-time scaling samplers across dif-ferent NFE budgets, offering a scalable compute-quality trade-off. Codeavailable at: https://github.com/ShunqiM/Ctrl-Z-Sampling.

Abstract:
Pixel-space diffusion has recently re-emerged as a strong al-ternative to latent diffusion, enabling high-quality generation withoutpretrained autoencoders. However, standard pixel-space diffusion mod-els receive relatively weak semantic supervision and are not explicitlydesigned to capture high-level visual structure. Recent representation-alignment methods (e.g., REPA) suggest that pretrained visual featurescan substantially improve diffusion training, and visual co-denoising hasemerged as a promising direction for incorporating such features intothe generative process. However, existing co-denoising approaches of-ten entangle multiple design choices, making it unclear which are trulyessential. We therefore present V-Co, a systematic study of visual co-denoising in a unified JiT-based framework. This controlled setting allowsus to isolate the ingredients that make visual co-denoising effective. Ourstudy reveals two main ingredients. First, co-denoising benefits from pre-serving feature-specific computation while enabling flexible cross-streaminteraction, which leads to a fully dual-stream architecture together witha structurally defined unconditional prediction for classifier-free guid-ance. Second, it requires both stronger semantic supervision and propercross-stream calibration, which we realize through a perceptual-driftinghybrid loss and RMS-based feature rescaling. Together, these findingsyield a simple recipe for visual co-denoising. Experiments on ImageNet-256 show that, at comparable model sizes, V-Co outperforms the un-derlying pixel-space diffusion baseline and strong prior pixel-diffusionmethods while using fewer training epochs, offering practical guidancefor future representation-aligned generative models.

Abstract:
Image generative models aim to sample data points fromthe underlying data manifold, a task that requires learning and decod-ing a dense, low-dimensional, and compact parameterization space. Toachieve this, we propose the Data Manifold-aware Image diffusioN moDel(MIND), a novel framework that explicitly models manifold geometry byintegrating discrete patch tokenization into the score function of a con-tinuous diffusion model. This approach successfully leverages both thestructural quantification capabilities of discrete tokens and the parallelgeneration flexibility of continuous diffusion. Moreover, we enable end-to-end differentiable training via a novel soft top-k aggregation mechanismand introduce dual-branch high-frequency feature embedding layers toalleviate the spectral bias of transformer backbones on low-dimensionalinputs. Furthermore, for inference, we design a multi-stage transitionsampling scheme that dynamically adjusts the sampling scheme based ontimestep. Extensive experiments on ImageNet 256×256 demonstrate theeffectiveness of MIND. After 80-epoch training, our base model achievesan FID of 22.73 without guidance, nearly halving the 43.47 FID of thevanilla DiT-B/2 baseline. The proposed method reduces FID by 15.95and 9.06 on average compared with the baselines DiT and SiT, respec-tively. For image generation on ImageNet 256×256 with guidance, theproposed MIND-B with only 130M parameters achieves an FID of 2.06,surpassing LlamaGen-3B with 3.1B parameters. Our MIND-XL with715M parameters further reduces the FID to 1.95. Our MIND intro-duces a fresh perspective on diffusion-based image generation, pavingthe way for future research and innovation in this community. The codeis available here: https://github.com/xddgit/MIND.

Abstract:
Multimodal Large Language Models (MLLMs) are critically hampered by hallucination—generating content inconsistent with the provided image. In this paper, we identify an internal signature of hallucination: progressive degradation of text-to-image cross-attention during generation, leading to specific failure patterns like unfocused or biased attention. Existing mitigation strategies are largely outcome-driven and do not explicitly target this failure mode. To address this problem, we propose ADAPT (Attention Dynamics Alignment with Preference Tuning), an attention-based framework that intervenes directly on text-toimage cross-attention dynamics. We propose ADAPT with three key contributions: a cross-attention visual anchor refined from early decoding to provide stable spatial grounding, an attention-supervised inference mechanism that detects and corrects attention drift online, and a Visual Attention Guidance DPO that aligns preferences toward visually grounded responses. Experiments show that each component of ADAPT contributes to hallucination reduction, and the full framework achieves new best results across multiple hallucination benchmarks, reducing hallucination rates by 40–60% across mainstream backbones while preserving general multimodal capabilities. Our work provides an attentionbased perspective on mitigating hallucinations by exploring the model’s internal text-to-image cross-attention behaviors. Code is available at: https://github.com/yao-ustc/ADAPT.

Abstract:
Vision Language Models (VLMs) face signi x001C cant challengeswith ultra-long, interleaved image-text sequences due to the quadraticcomplexity of self-attention. Current solutions either resort to aggres-sive token pruning, risking irreversible information loss, or adopt e x001E -cient but less precise architectures, while largely ignoring the equallyvital textual component. We introduce VLZip, a framework that uni- x001C es visual and textual compression for high- x001C delity reasoning within apure Transformer. At its core, VLZip hierarchically distills visual andtextual segments into compact, layer-speci x001C c "soft pre x001C xes" and injectsthem into each decoder layer's hidden states, drastically shortening theattention sequence while preserving x001C ne-grained global context. To ad-dress de x001C cient evaluations in the x001C eld, we also introduce LongVLBench,a new benchmark derived from video narratives that demands holistic,narrative-level reasoning. Extensive experiments show VLZip achievesleading performance on long-context multimodal reasoning, enabling train-ing up to 120K tokens x0016 a 6× increase over the baseline x0016 and inferencebeyond 280K tokens with signi x001C cantly reduced memory, while demon-strating the memory scalability to handle up to 2M tokens. By excellingat extreme context lengths where existing methods collapse, VLZip es-tablishes an e x001E cient and powerful new standard for long-context mul-timodal AI. Code is available at https://github.com/ShareLab-SII/VLZip.

Abstract:
Despite the advancements of Large Multimodal Models (LMMs)in RGB vision, their ability to generalize to unseen visual modalitiesremains a largely unexplored challenge. We argue that different visualmodalities are merely distinct samplings of the same physical world.Therefore, effective generalization requires models to possess both modality-agnostic perception of scene semantics and the adaptability to modality-specific characteristics. To achieve this, we propose a training frame-work, VVM-Tuning, to equip LMMs with these capabilities throughmodality synthesis and modality contexts. Specifically, we synthesize di-verse appearance-varied images from RGB scenes, training the model todisentangle invariant semantics from varying visual appearances, andalign these appearances with language for visual concepts decoupledfrom modalities. We then introduce modality contexts in the promptand use instruction tuning to assist the model in mapping these appear-ance variations back to modality-related attributes, enabling zero-shotadaptation to unseen modalities during inference. To facilitate researchin this direction, we introduce VVM-Bench, a comprehensive bench-mark featuring 6 real and synthetic modalities to evaluate semantic per-ception and modality understanding. Experiments demonstrate that, viaour training on synthetic modalities, 5 tested models exhibit consistentimprovements on both real-world and novel synthetic modalities withoutin-modality training. Source code and data will be publicly available athttps://github.com/Hunter-Will/VVM-Tuning.

Abstract:
Despite the remarkable prowess of Vision-Language Models(VLMs) in general multimodal tasks, they remain fundamentally “flat”when reasoning about the physical world. We argue that this spatialbottleneck stems from a profound dimensional mismatch: while VLMsare trained to interpret 2D projections, true spatial reasoning demandsthe recovery of latent 3D geometry and temporal continuity. To conquerthis high-dimensional complexity, we advocate a shift from monolithiclearning to a “divide and conquer” paradigm. We present FactoSR, afactorized reinforcement learning framework that explicitly interpret the Equal Contribution, † Project Lead, B Corresponding Authordimensions collapsed by visual projection. At its core, FactoSR decom-poses the monolithic problem of world-consistent reasoning into three or-thogonal, geometric sub-objectives: planar correspondence (XY ), depthconsistency (Z), and temporal reversibility (T ). By optimizing these ver-ifiable constraints within a unified policy learning mechanism, we effec-tively transform an ill-posed projection recovery problem into a series oftangible reasoning steps. Extensive evaluations on multi-view and videobenchmarks demonstrate that this elegant decomposition yields substan-tial gains in 3D and 4D reasoning, achieving a 5.9% boost on VSI-Benchand 4.5% on All-Angles-Bench. Our findings suggest that reinforcing ex-plicit, factorized 4D consistency is a critical step toward evolving VLMsinto robust, world-aware reasoners.Code: https://github.com/scott-yjyang/FactoSR

Abstract:
As video generation paradigms evolve from localized manip-ulation to full-scene synthesis, AI-generated video detection becomes in-creasingly challenging, as forgeries exhibit coherent global structure andhigh perceptual realism. However, existing benchmarks are biased towardperceptual fidelity and primarily evaluate detectors based on perceptualartifacts, providing limited coverage of scenarios that require reasoningabout violations of physical laws, structural coherence, or social logic.This dataset bias shapes current approaches and results in a Percep-tion–Reasoning Gap: artifact-centric models capture low-level statisticalirregularities yet lack semantic inference, whereas vision-language mod-els perform semantic reasoning but remain insensitive to fine-grainedforensic cues. To bridge this gap, we propose SafeGuard, a multi-agentframework that enables collaborative specialization between forensic per-ception and semantic reasoning. A hierarchical perceptual solver extractsfine-grained forensic evidence, while a self-reflective verifier enforces con-sistency between semantic inference and physical plausibility, forming aninterpretable evidence chain. To support evaluation, we introduce Safe-Vid, a novel AI-generated video detection benchmark comprising 20Kvideos spanning 10 social risk categories, designed to evaluate physicalplausibility, structural consistency, and the rationality of social behav-iors. Extensive experiments demonstrate the generalization of SafeGuard,improving accuracy on SafeVid by +18.7% and consistently outperform-ing prior methods across four public benchmarks. The code and datasetare publicly available at https://github.com/williamw99/SafeGuard.

Abstract:
Spatial tracing, as a fundamental embodied interaction ability for robots, is inherently challenging as it requires multi-step metricgrounded reasoning compounded with complex spatial referring and realworld metric measurement. However, existing methods struggle with this compositional task. To this end, we propose RoboTracer, a 3D-aware VLM that first achieves both 3D spatial referring and measuring via a universal spatial encoder and a regression-supervised decoder to enhance scale awareness during supervised fine-tuning (SFT). Moreover, RoboTracer advances multi-step metric-grounded reasoning via reinforcement fine-tuning (RFT) with metric-sensitive process rewards, supervising key intermediate perceptual cues to accurately generate spatial traces. To support SFT and RFT training, we introduce TraceSpatial, a largescale dataset of 30M QA pairs, spanning outdoor/indoor/tabletop scenes and supporting complex reasoning processes (up to 9 steps). We further present TraceSpatial-Bench, a challenging benchmark filling the gap to evaluate spatial tracing. Experimental results show that RoboTracer surpasses baselines in spatial understanding, measuring, and referring, with an average success rate of 79.1%, and also achieves SOTA performance on TraceSpatial-Bench by a large margin, exceeding Gemini- 2.5-Pro by 36% accuracy. Notably, RoboTracer can be integrated with various control policies to execute long-horizon, dynamic tasks across diverse robots (UR5, G1 humanoid) in cluttered real-world scenes. Please see the project page at https://zhoues.github.io/RoboTracer.

Abstract:
Despite recent progress in 3D-LLMs, they remain limitedin accurately grounding language to visual and spatial elements in 3Denvironments. This limitation stems in part from training data that fo-cuses on language reasoning rather than spatial understanding due toscarce 3D resources, leaving inherent grounding biases unresolved. Toaddress this, we propose 3D scene editing as a key mechanism to gener-ate visual counterfactuals that mitigate these biases through fine-grainedspatial manipulation, without requiring costly scene reconstruction orlarge-scale 3D data collection. Furthermore, to make these edits targetedand directly address the specific weaknesses of the model, we introduceDEER-3D, an error-driven framework that diagnoses grounding failuresand generates targeted counterfactual training supervision via a struc-tured “Decompose, Diagnose, Edit, and Retrain” loop. Specifically, givena grounding failure, DEER-3D first identifies the predicate-level error(e.g., attribute or spatial relation). It then performs minimal predicate-aligned scene edits, such as recoloring or repositioning, and constructsaligned question–answer pairs that explicitly target the failed predicate,forming targeted counterfactual training examples. We evaluate our edit-ing pipeline across multiple benchmarks for 3D grounding and scene un-derstanding tasks, consistently demonstrating improvements across allgrounding datasets through iterative refinement (4-6% gains). DEER-3D underscores the effectiveness of targeted, error-driven scene editingin bridging linguistic reasoning with spatial grounding in 3D LLMs.

Abstract:
Video understanding is a fundamental capability for multimodal intelligence, and recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance on Video Question Answering (VideoQA) benchmarks. However, existing benchmarks primarily evaluate whether models can perceive shallow visual cues, while rarely examining whether MLLMs can learn deeper knowledge or procedural skills from video tutorials and generalize them to downstream long-horizon agentic tasks. To address this gap, we introduce VG-GUIBench (Video-Guided GUI Benchmark), a new benchmark designed to evaluate whether MLLM-based GUI agents can follow video tutorials to complete corresponding GUI interactive tasks. Furthermore, we observe that the performance of models on both VideoQA and video-guided agentic tasks critically depends on effective keyframe extraction. Based on this observation, we propose TASKER (Task-driven And Scene-aware Keyframe searchER), a keyframe extraction algorithm that jointly considers task relevance and scene dynamics to identify informative frames. Experimental results demonstrate that TASKER achieves significant performance improvements on both VideoQA and video-guided agentic task benchmarks, outperforming the best baseline by 2.0% on the EgoSchema fullset and 1.8% on the NExT-QA dataset, respectively. These results further highlight the potential of generalized keyframe extraction methods for video understanding tasks. Our code and data are available at https://github.com/VG-GUI-TASKER/VG-GUI-TASKER.

Abstract:
Video Large Language Models (Video LLMs) incur high in-ference latency due to a large number of visual tokens provided to LLMs.To address this, training-free visual token pruning has emerged as asolution to reduce computational costs; however, existing methods areprimarily validated on Multiple-Choice Question Answering (MCQA)benchmarks, where coarse-grained cues often suffice. In this work, wereveal that these methods suffer a sharp performance collapse on fine-grained understanding tasks requiring precise visual grounding, such ashallucination evaluation. To explore this gap, we conduct a systematicanalysis and identify sink tokens–semantically uninformative tokens thatattract excessive attention–as a key obstacle to fine-grained video un-derstanding. When these sink tokens survive pruning, they distort themodel’s visual evidence and hinder fine-grained understanding. Moti-vated by these insights, we propose Sink-Token-aware Pruning (SToP),a simple yet effective plug-and-play method that introduces a sink scoreto quantify each token’s tendency to behave as a sink and applies thisscore to existing spatial and temporal pruning methods to suppress them,thereby enhancing video understanding. To validate the effectivenessof SToP, we apply it to state-of-the-art pruning methods (VisionZip,FastVid, and Holitom) and evaluate it across diverse benchmarks cover-ing hallucination, open-ended generation, compositional reasoning, andMCQA. Our results demonstrate that SToP significantly boosts perfor-mance, even when pruning up to 90% of visual tokens. Our code is avail-able at https://github.com/rlqja1107/SToP

Abstract:
Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the alignment between implicit perception and explicit reasoning. Unlike traditional black-box QA, CRISP utilizes metric 3D Scene Graphs and an oracle intervention protocol to decouple latent reasoning capabilities from perceptual bottlenecks. This granular diagnosis uncovers a systematic perception-reasoning disconnect. Crucially, we reveal that while proprietary models possess robust latent reasoning engines, they suffer from inaccurate metric estimation and a critical failure to leverage their implicit structural representations. Conversely, open-source models remain fundamentally bottlenecked by their lack of multi-hop compositional reasoning. By shifting the focus from merely “guessing correctly” via language priors to genuinely “perceiving, verifying, and reasoning,” CRISP offers a rigorous roadmap for multimodal alignment beyond end-to-end post-training. The code and dataset are available at https://github.com/iiyamayuki/CRISP-Bench.

Abstract:
The recent surge in vision-language models (VLMs) has revolutionized open-world perception, yet transferring this success to 3D scene understanding remains impeded by the scarcity of large-scale point-text pairs and the computational overhead of existing frozen-VLM approaches. To bridge this gap, we present ORACLE-3D (Open-world Region-Aligned Cross-modal LEarning for Label-efficient 3D Scene Understanding), a unified framework that effectively co-embeds point clouds, images, and text into a shared latent space without relying on labor-intensive 3Dtext pair construction. ORACLE-3D leverages the image modality as a semantic bridge, employing a novel cross-modal distillation strategy that comprises logit distillation and feature distillation. These components transfer the rich, open-vocabulary capabilities of pre-aligned 2D VLMs directly to the 3D domain. Furthermore, to mitigate the semantic noise arising from inevitable calibration errors and motion artifacts during 3D-2D projection, we propose a vision-point matching module that dynamically rectifies point-pixel misalignments. To ensure robust convergence amidst the gradient bias inherent in heterogeneous multimodal learning, we introduce a two-stage optimization strategy coupled with four task-specific weighted losses. Extensive evaluations on the nuScenes, Waymo, SemanticKITTI, and ScanNet datasets demonstrate the superiority of our paradigm. Specifically, ORACLE-3D outperforms state-of-the-art methods by an average of 11.6 and 14.8 in mIoU on base-annotated and annotation-free tasks, respectively. Project Page: https://ocean-luna.github.io/ORACLE.github.io/.

Abstract:
Leveraging Vision Foundation Models (VFMs) for camera-toLiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD). However, current approaches typically treat VFMs as black-box teachers, relying exclusively on frame-wise feature similarity. Consequently, they do not fully exploit the teacher’s layer-wise semantic structure and global context, as well as the rich spatiotemporal information inherent in LiDAR sequences. We propose HilDA, a self-supervised pre-training framework for LiDAR backbones that better captures the semantic what and geometric where needed for driving tasks. HilDA combines hierarchical distillation comprising multi-layer distillation for progressive semantic alignment and global context distillation for scene-level semantics, with a temporal occupancy diffusion objective promoting spatiotemporal consistency. Models pre-trained with HilDA achieve state-of-the-art results on cross-modal distillation benchmarks and outperform models trained via prior distillation approaches on 3D object detection, scene flow, and semantic occupancy prediction. Code available at: https://maxiuw.github.io/hilda.

Abstract:
Multi-modal object Re-Identification (ReID) benefits from complementary information across heterogeneous imaging modalities. To further enrich semantic representation, text descriptions have recently been incorporated as an additional modality. However, recent visionlanguage approaches often treat text descriptions as clean, deterministic signals and overlook their inherent noise, including modality-mismatched phrases and semantically ambiguous expressions. Moreover, prevailing methods lack explicit mechanisms to reconcile fine-grained structural discrepancies between modalities, even after high-level semantic alignment. To address these challenges, we propose a novel framework centered on Positive-Incentive Noise (π-noise) and structured prompt modulation. First, the Semantic Cross-Modal Modulator harnesses task-aware π-noise, sampled from a distribution conditioned on both visual and text inputs, to perturb global tokens and enable semantics-guided crossmodal compensation. Second, the Structure-Aware Prompt Adapter injects learnable geometric priors via prompts to enhance spatial consistency. Third, the Context-Aware Sparse Fusion module distills structural context to guide adaptive fusion while shielding identity features from noisy local details. Experiments on three multi-modal ReID benchmarks demonstrate the effectiveness and robustness of our approach. The code is available at https://github.com/zw-absin/INSPI.

Abstract:
Cross-domain Few-shot Segmentation (CD-FSS) aims to tra-nsfer knowledge learned from source domain to distinct target domains,segmenting unseen target classes with only a few annotated samples.Although existing methods have made significant progress, they stillrely on training or fine-tuning processes, which incur high computationalcosts and risk overfitting. We observe that when powerful and general-purpose vision foundation models are incorporated into these methods,their performance shows only marginal improvement or even degradesdue to overfitting. To address this, we eliminate trainable parameters andpropose a training-free framework to avoid both training overhead andoverfitting. Built upon the self-supervised vision encoder DINOv3, ourframework addresses cross-domain challenges through three core mod-ules. First, the Semantic-aware Feature Re-fusion (SAFR) module iden-tifies and re-fuses features that emphasize semantic patterns, generatingrepresentations with enhanced semantic discriminability. Additionally,the Adaptive Support Enhancement (ASE) module narrows semanticgaps between support and query through robust query information ag-gregation. Finally, the Hybrid Prototype Matching (HPM) module inte-grates matching results from diverse prototypes to adapt to varying se-mantic complexity across domains. Extensive experiments on four targetdomain datasets demonstrate that our method achieves state-of-the-artperformance in CD-FSS without any training. Our code is available athttps://github.com/Sparkling-Water/RSRM.

Abstract:
Multi-Modal LLMs (MLLMs) demonstrate strong visualgrounding capabilities on popular object detection benchmarks likeOdinW-13 and RefCOCO. However, state-of-the-art models still struggleto generalize to out-of-distribution classes, tasks and imaging modalitiesnot typically found in their pre-training. While in-context promptingis a common strategy to improve performance across diverse tasks, wefind that it often yields lower detection accuracy than prompting withclass names alone. This suggests that current MLLMs cannot yet effec-tively leverage few-shot visual examples and rich textual descriptionsfor object detection. Since frontier MLLMs are typically only accessi-ble via APIs, and state-of-the-art open-weights models are prohibitivelyexpensive to fine-tune on consumer-grade hardware, we instead exploreblack-box prompt optimization for few-shot object detection. To this end,we propose Detection Prompt Optimization (DetPO), a gradient-freetest-time optimization approach that refines text-only prompts by max-imizing detection accuracy on few-shot visual training examples whilecalibrating prediction confidence. Our proposed approach yields consistentimprovements across generalist MLLMs on Roboflow20-VL and LVIS,outperforming prior black-box approaches by up to 9.7 mAP. Our codeand optimized prompts are available on our project page.

Abstract:
Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention: given partially la-belled data, the goal is to correctly recognize known classes while discov-ering coherent novel categories from unlabelled samples. Recent GCDmethods typically adapt foundation models by jointly optimizing su-pervised classification and unsupervised discovery objectives on mixedlabelled–unlabelled data. While effective, this coupled training can en-tangle closed-set recognition and open-set discovery, leading to objectiveconflict and biased predictions, and may disturb the semantic geome-try of pretrained representations under limited labels and noisy pseudo-labels. We propose CloSeR, a simple plug-and-play framework that in-jects Closed-Set Relational knowledge into GCD training. CloSeR firstbuilds a domain-adapted closed-set teacher by tuning lightweight block-wise adapters on labelled known-class data while keeping the founda-tion model backbone frozen, thereby preserving pretrained priors at lowtraining cost. It then transfers the teacher’s knowledge to downstreamGCD via Unified Relational Distillation (URD), which distills comple-mentary global sample-to-prototype relations to anchor known-class se-mantics and local sample-to-sample relations to preserve neighborhoodstructure, using separate feature pathways to reduce optimization in-terference. CloSeR is head-agnostic and readily integrates with bothparametric and non-parametric GCD methods. Extensive experimentswith DINO and DINOv2 backbones on six benchmarks (CIFAR-10/100,ImageNet-100, CUB, Stanford-Cars, and FGVC-Aircraft) show consis-tent gains over GCD baselines, achieving state-of-the-art performance.Project page: https://visual-ai.github.io/closer/

Abstract:
Micro-Action Recognition (MAR) aims to identify transient and subtle bodily movements that occur during interpersonal communication. Existing MAR methods attempt to emphasize low-amplitude motion by injecting skeletal priors and have achieved certain progress. However, they (1) rely on additional modalities and costly pose annotations, and (2) employ coarse joint graphs that fail to capture sub-joint micro-movements and fine-grained appearance cues (e.g., finger tremors). In contrast, raw RGB videos preserve such subtle cues, and pretrained VideoMAE provides strong spatiotemporal priors without requiring additional supervision. Motivated by this, we first establish a strong MAR baseline by task-adaptively fine-tuning VideoMAE. To further enhance its capacity to perceive subtle motion cues, we propose A3-MAE, a VideoMAE-based holistic-subtle collaborative framework that integrates Amplification, Aggregation, and Adjustment in a unified design. Specifically, 1) we design a Temporal Gradient Local Amplification (TGLA) module to amplify subtle-motion regions directly from RGB inputs, forming a subtle-motion branch that complements the holistic-motion counterpart; 2) we develop a Holistic-Subtle Motion Aggregation (HSMA) module with dual cross-attention, enabling reciprocal conditioning between holistic and subtle branches while maintaining their complementarity; and 3) we introduce a Confidence-Aware Dynamic Adjustment (CADA) module to adaptively calibrate aggregation mismatch between the two branches in an instance-aware manner. Together, these components establish a unified amplify-aggregate-adjust paradigm for comprehensive micro-action understanding. Extensive experiments demonstrate that A3-MAE achieves new state-of-the-art performance on both MA-52 and iMiGUE benchmarks. Code is available at https://github.com/zy-hfut/A3MAE.

Abstract:
Camouflaged object detection (COD) segments objects thatintentionally blend with the background, so predictions depend on subtletexture and boundary cues. COD is often needed under tight on-devicememory and latency budgets, making low-bit inference highly desirable.However, COD is unusually hard to quantize aggressively. We studypost-training W4A4 quantization of Transformer-based COD and finda task-specific cliff: heavy-tailed background tokens dominate a sharedactivation range, inflating the step size and pushing weak-but-structuredboundary cues into the zero bin. This exposes a token-local bottleneck—remove cross-token range domination and bound the zero-bin mass under4-bit activations. To address this, we introduce COD-TDQ, a COD-aware Token-group Dual-constraint activation Quantization method.COD-TDQ addresses this token-local bottleneck with two coupled steps:Direct-Sum Token-Group (DSTG) assigns token-group scales to sup-press cross-token range domination, and Dual-Constraint Range Projection(DCRP) projects each token-group clip range to keep the step-to-dispersionratio and the zero-bin mass bounded. Across four COD benchmarksand two baseline models (CFRN and ESCNet), COD-TDQ consistentlyachieves an Sα score more than 0.12 higher than that of the state-of-the-art quantization method without retraining. The code is available athttps://github.com/MCG-NKU/nku-model-compre.

Abstract:
General Salient Object Detection (SOD) aims to identify andsegment visually interesting objects from uni-modality or multi-modalityscenes, recently advanced by cutting-edge State Space Models (SSMs).However, a critical limitation of current approaches is their neglect of theinherent spectral biases exhibited by different neural network paradigms.By digging to the dataset-level spectral analysis of Convolutional Neu-ral Networks (CNNs) and SSMs, their semantic representations are in-herently complementary based on their complementary frequency prefer-ences. Inspired by this, we harmonize heterogeneous representations fromSSMs and CNNs to bridge their spectral biases for general salient objectdetection. To this end, inspired by the dynamic information propaga-tion of Liquid Neural Networks (LNNs), we introduce a liquid fusion todynamically integrates features from two backbones, including VMambaand ConvNeXt, referred to Liquid Fusion Network (LFNet). Concretely,by treating the continuous VMamba features and ConvNeXt features asevolving states and exogenous stimulus, respectively, LFNet employs adynamic gating mechanism for content-aware feature aggregation. Cru-cially, this state-stimulus paradigm enables to scale to multi-modal cues,resulting in flexibility in general SOD. Besides, a Saliency-Guided Up-sampling (SGU) operator to propagate the features to the shallow layer,which leverages a spectral-spatial co-design to suppress upsampling ar-tifacts while preserving semantics. Extensive experiments across five di-verse tasks (RGB, RGB-D, RGB-T, VSOD, and VDT) demonstrate thatLFNet achieves state-of-the-art performance, offering a superior trade-offbetween detection accuracy and model efficiency. Code has been releasedat https://github.com/cke520/LFNet.

Abstract:
Recent deepfake detection methods leveraging vision foun-dation models (VFMs) like CLIP have made significant progress. How-ever, the abundant semantic information in large training datasets hasmade VFMs highly dependent on semantics. As a result, VFM-basedmethods perform well on images from the same category as the train-ing set but struggle with others. This bias limits generalization in real-world scenarios. To address this issue, we propose the Basis CorrectionNetwork (BCNet), which consists of two modules: the attention-guidedsemantic erasure (ASE), which adaptively identifies and erases seman-tic regions of the image by capturing the model’s semantic attention,and the normalized-gradient perturbation enhancement (NPE) scalesthe gradients of fake samples to concentrated values and adds them asa small perturbation to the original sample, helping the model recog-nize more forgery patterns and improving its ability to distinguish fakefrom real samples. This design ensures the model focuses on the coredistinction between real and fake categories rather than semantic infor-mation. Extensive experiments on 51 AI-generated datasets show thatBCNet achieves 96.7% generalization accuracy on WildRF (collectedfrom real social media) and outperforms other competitors by 8.7% onAIGI-Bench, offering a fresh perspective on semantic generalization indeepfake detection. The code is open-sourced and publicly available athttps://github.com/rstao-bjtu/BCNet.

Abstract:
Synthetic data mitigates the data scarcity problem in au-tonomous driving perception. However, the synthetic-to-real gap leads toperformance degradation, hindering real-world model generalization. Al-though current methods leverage diffusion models for photorealistic styletransfer to bridge this gap, they critically ignore a practical asymmetry:while synthetic data possesses perfect pixel-level annotations, real-worldstyle reference images generally lack corresponding labels. Consequently,existing methods relying on symmetric semantic guidance suffer fromeither prohibitive annotation costs or severe semantic misalignment. Toaddress this dilemma, we formally propose a novel task: AsymmetricStyle Transfer for Autonomous Driving (ASTAD), which requires se-mantically consistent transfer using only labeled synthetic content andunlabeled real-world references. We further introduce the ASTModel, atraining-free two-stage framework designed to bridge this domain gapunder asymmetric constraints. ASTModel first extracts a coarse seman-tic prior from the unlabeled target, followed by dynamic prior refinementand class-consistent style injection during the denoising process. Exten-sive experiments demonstrate that ASTModel significantly outperformsexisting methods in downstream perception utility and structural fidelity,while offering a 3.2× inference speedup. This work aligns synthetic-to-real adaptation with practical constraints, holding the potential to ac-celerate the scalable deployment of robust autonomous driving systems.Code: https://github.com/Dingyi-Yao/ASTAD.

Abstract:
Video polyp segmentation (VPS) is important for automatedcolorectal cancer screening, yet it is a very challenging task due to severecamouflage in mucosal scenes, complex intestinal anatomy, and substan-tial frame-to-frame appearance variations. Although Segment AnythingModel 3 (SAM 3) is able to provide strong representations for segmen-tation tasks, directly applying it to endoscopic videos usually cannotachieve satisfactory performance owing to domain shifts, boundary am-biguities, and unstable temporal propagation. To address these chal-lenges, we propose FST-SAM3, a novel parameter-efficient adaptationframework built on SAM 3 for VPS. FST-SAM3 introduces two inno-vative yet complementary components. First, we propose a Structure-Aware Frequency Enhancement (SAFE) module to strengthen structure-and boundary-sensitive cues under endoscopic appearance. Second, wedevelop a Spatio-Temporal Token Refinement Framework (STRF) torefine temporal information at the token level for more stable cross-frame segmentation. We freeze the SAM 3 backbone and learn only asmall set of additional parameters, keeping the inference characteristicsclose to SAM 3 while improving segmentation performance. We con-ducted extensive experiments on two benchmarking datasets (SUN-SEGand CVC-612) under standardized fully automatic (0-point) and first-frame prompted (1-point at t=0) protocols. Results demonstrate that ourmodel achieves consistent improvements over state-of-the-art approaches,including both task-specific baselines and representative SAM-based ap-proaches. Code is available at https://github.com/GavonW/FST-SAM3.

Abstract:
Incremental 3D object detection requires a detector to learnnovel object classes while remembering previously learned ones oversequentially arriving data. Previous methods, primarily based on pseudo-labeling, perform reasonably in short-incremental stages but still suf-fer from severe model forgetting when dealing with long-incrementalsequences. We investigate this failure and reveal a detrimental self-reinforcing cycle: data distribution shift of novel classes causes modelforgetting on old classes, which further produces accumulated error inpseudo-labeling that exacerbates model degradation. To address thisissue, we draw inspiration from the human learning process and proposethe Learning-Dynamics-driven Memory and Review (LDMR) framework.LDMR monitors per-class detection quality at periodic training check-points and uses these learning-dynamics signals to drive two innova-tive mechanisms, namely (i) human-like intra-stage review that divideseach incremental stage into multiple sub-stages’ training and concen-trates on remembering the most-forgotten objects, and (ii) scene-awarecross-stage memory evolution that evolves a memory bank to transferknowledge between two consecutive stages by jointly considering scenelearnability and diversity. Extensive experiments across multiple long-incremental protocols on indoor benchmarks SUN RGB-D and Scan-NetV2 show that LDMR substantially mitigates the model forgettingand outperforms all baselines by a clear margin. Code is available athttps://github.com/qianpeisheng/LDMR.

Abstract:
Developing effective dense crowd simulation is challengingdue to the scale gap between macroscopic collective coherence and mi-croscopic individual realism. Existing macroscopic (Eulerian) methodscapture global motion patterns but lack individual trajectories, whereasmicroscopic (Lagrangian) methods model individual behaviors yet oftenfail to preserve systemic consistency in dense scenarios. To address this,we propose the Eulerian–Lagrangian Hydrodynamics-Informed NeuralNetwork (ELHINN), a unified cross-scale framework that couples macro-scopic velocity evolution with microscopic trajectory refinement by usingevolved Eulerian velocity fields as physical priors to guide Lagrangian tra-jectories. For velocity evolution, we develop an enhanced Hydrodynamics-Informed Neural Network (HINN++) that incorporates a learnable gov-erning equation with Kolmogorov–Arnold Network (KAN) residual cor-rection and environmental boundary conditions, enabling accurate mod-eling of complex nonlinear dynamics under varying scenarios. For tra-jectory refinement, we use a Physics-Informed Neural Network (PINN)augmented with an entrance-aware resampling strategy (EARS) andcollision-avoidance constraints to ensure stability and fidelity. Experi-ments on two real-world crowd datasets demonstrate that ELHINN out-performs existing methods in simulating dense crowds across both scales.Codes are available at https://github.com/shanshan-zys/ELHINN.

Abstract:
Current anomaly detection methods primarily focus on struc-tural anomalies, while paying insu x001E cient attention to anomalies that vi-olate logical constraints. Conversely, top-performing logical anomaly de-tection approaches address this by modeling global semantic consistency,but perform poorly on subtle structural anomalies due to inadequate de-tection granularity. In this paper, we propose LogiCo , a uni x001C ed frame-work for Logi cal and structural anomaly detection via Co mponent-levelfeature reconstruction. Unlike existing methods that rely on explicitglobal semantic modeling, LogiCo employs a novel component-level fea-ture reconstruction technique to capture inter-component logical con-straints. Speci x001C cally, LogiCo maps pre-trained image features into a dis-crete component-level feature space and performs collaborative featurereconstruction at both component and patch levels, enabling it to ef-fectively detect both logical and structural anomalies. Furthermore, toaddress the speci x001C c challenge of count-related logical anomalies, we inte-grate a segmentation-map discriminator that extends the model's capa-bility to identify quantitative inconsistencies. LogiCo achieves state-of-the-art performance on both logical and structural anomaly detectionacross four benchmarks, including MVTec-LOCO, MVTec-AD, VisA,and Real-IAD, demonstrating its superiority and practical feasibility.The code is available at https://github.com/cnulab/LogiCo.

Abstract:
3D instance segmentation methods typically rely on high-quality point clouds or posed RGB-D scans, requiring complex multi-stage processing pipelines, and are highly sensitive to reconstructionnoise. While recent feed-forward transformers have revolutionized multi-view 3D reconstruction, they remain decoupled from high-level semanticunderstanding. In this work, we present SegVGGT, a unified end-to-endframework that simultaneously performs feed-forward 3D reconstructionand instance segmentation directly from multi-view RGB images. Byintroducing object queries that interact with multi-level geometric fea-tures, our method deeply integrates instance identification into the visualgeometry grounded transformer. To address the severe attention disper-sion problem caused by the massive number of global image tokens, wepropose the Frame-level Attention Distribution Alignment (FADA) strat-egy. FADA explicitly guides object queries to attend to instance-relevantframes during training, providing structured supervision without extrainference overhead. Extensive experiments demonstrate that SegVGGTachieves the state-of-the-art performance on ScanNetv2 and ScanNet200,outperforming both recent joint models and RGB-D-based approaches,while showing favorable generalization to ScanNet++. The code is avail-able at https://github.com/IDEA-Research/SegVGGT.

Abstract:
Skeleton-based Video Anomaly Detection (VAD) offers a ro-bust, privacy-preserving solution for identifying abnormal behaviors. Tomodel the distribution of normal static and moving poses, recent meth-ods train Energy-Based Models (EBMs) via Denoising Score Matching(DSM). However, directly injecting noise, required for training, into rawjoint coordinates creates physically impossible poses, and this structuralcollapse severely worsens as the temporal window expands. To addressthis, we introduce STEP, a simple framework that utilizes PrincipalComponent Analysis (PCA) to project pose sequences into a compact,whitened PC-space. Learning the data density within this well-behavedPC-space ensures that the injected noise translates into physically plau-sible variations, which allows the model to process longer video sequenceswithout the performance collapse of raw coordinate baselines. Addition-ally, to mitigate inherent pose estimation inaccuracies arising from occlu-sions or motion blur, we integrate a sequence-level weighting mechanismbased on the estimator’s confidence scores. Operating at real-time com-putational efficiency, our simple and lightweight framework outperformsthe previous skeleton-based state-of-the-art by 12.2% (90.1% AUROC)on the challenging UBnormal dataset and achieves highly competitiveresults by improving on the ShanghaiTech benchmark.

Abstract:
Multimodal large language models have significantly advancedzero-shot industrial anomaly detection, yet they remain highly constrainedwhen identifying logical anomalies. Fundamentally, logical anomalies arisefrom discrepancies between inspected products and their predefined con-straints. Current detection paradigms typically operate without accessto these explicit standards, forcing models to rely on pretrained com-mon sense rather than strict logical rules. To address this limitation,we introduce SCAN, a Systematic Criteria-driven ANomaly inspec-tion framework optimized through a two-stage post-training pipeline.We first employ supervised finetuning based on rule-by-rule Chain-of-Thought (CoT) reasoning to systematically verify explicit inspection cri-teria. We then apply reinforcement learning equipped with a rule-levelcredit assignment mechanism to enforce precise anomaly attribution andovercome severe errors caused by sparse feedback. However, such criteria-driven training demands large-scale logical anomaly data paired with ex-plicit inspection rules, yet existing datasets remain largely skewed towardstructural defects. To bridge this gap, we propose FLAW, a Fine-grainedLogical Anomaly dataset With criteria. This dataset leverages imagesynthesis and logical data retrieval to endow images with rich object-levelmetadata, enabling the construction of diverse inspection standards andlogical violations. Extensive experiments on MVTec LOCO demonstratethat our 8B-parameter SCAN model improves upon its base model by7.8% and establishes a new state of the art, outperforming GPT5.2-Chatand Gemini-3.1-Pro by 4.8% and 1.0%, respectively. Source code anddata will be publicly available at https://github.com/tzjtatata/SCAN.

Abstract:
We introduce Point-supervised Multi-Object Tracking (PS-MOT) as a cost-effective alternative to traditional bounding box su-pervision, shifting the focus from spatial fitting to topological center-driven representation. However, PS-MOT faces challenges, e.g., spatialambiguity and identity drift due to the lack of explicit geometric struc-ture and scale constraints. To address these, we propose PS-Track, ahierarchical pipeline transitioning from points to instances across data,model, and loss levels. At the data level, we introduce Temporal-FeedbackPrompting (TFP) to evolve points into temporally consistent pseudo-labels using negative spatial cues and motion priors. At the model level,we design the Point-Excited Wavelet Attention (PEWA) module, whichleverages semantic correlations to activate high-frequency components,“hallucinating” object boundaries. At the loss level, Uncertainty-GuidedGaussian Learning (UGL) models pseudo-labels as probabilistic distri-butions, dynamically calibrating supervision intensity. Experiments onDanceTrack, EmboTrack, SportsMOT, and JRDB demonstrate that PS-Track provides a feasible and effective point-supervised alternative acrossdiverse tracking scenarios, establishing a new state-of-the-art for point-supervised tracking. The source code is available at https://github.com/xifen523/PS-MOT.

Abstract:
3D shape assembly requires predicting 3D pose by estimatingrotation and translation separately to align objects or fractures. For 3Dpose prediction, prior works utilize the entangled features that capture3D pose information well, but this mixed representation impedes gen-eralization. To address this problem, we propose the SOT encoder thatdisentangles 3D pose into an SO(3)-equivariant (rotation) feature and aT(3)-equivariant (translation) feature. The SOT encoder consists of twobranches: 1) a rotation branch that suppresses translation by project-ing features onto the translation null space, and 2) a translation branchthat suppresses rotation by translation loss and directly subtracts theSO(3)-equivariant representation from its SE(3)-equivariant counter-part. Across a variety of assembly models and diverse datasets, the SOTencoder consistently demonstrates improved generalization performancein 3D shape assembly. Furthermore, in-depth analyses indicate that thedisentangled features show improved equivariant behavior with respectto the target factor while exhibiting invariant behavior with respect tothe other. These results indicate that explicitly encouraging factor dis-entanglement is a straightforward and effective approach for 3D shapeassembly. The code is available at https://github.com/maroo-sky/SOT.

Abstract:
Online Video Instance Segmentation (VIS) relies on per-frame instance queries for causal detection, segmentation, and tempo-ral association. However, in realistic videos with occlusion, blur, andclutter, these predictions often become noisy—exhibiting false positives,missed instances, or inaccurate masks. Such noisy queries not only desta-bilize cross-frame association but also contaminate contrastive supervi-sion and propagate drift through memory-based matching. Despite ad-vances in recent online VIS methods, none explicitly identify or addressnoisy query contamination, leaving a fundamental weakness unexam-ined. We introduce MiNQVIS, the first noise-aware framework for on-line VIS that stabilizes both training and inference. Our approach con-sists of three complementary components: (1) query-level gating that fil-ters unreliable predictions using classification-guided thresholding andembedding-norm stabilization; (2) video-level prototypical contrastivelearning that aggregates only reliable matches into stable, noise-robustprototypes; (3) dual-memory association that fuses momentum embed-dings with history-best retrieval to prevent drift from corrupted frames.Experiments on YouTubeVIS 2019/2021 and OVIS show consistent im-provements over strong online VIS baselines, with large gains in challeng-ing scenes. Project Page: https://nothing898.github.io/MiNQVIS.

Abstract:
Lifelong Person Re-Identification (LReID) aims to train aunified person retrieval model from a non-stationary data stream. Ex-isting LReID methods mainly focus on scenarios where the clothingof each person is consistent. Recently, the Cloth-Hybrid LReID (CH-LReID) where cloth-consistent and cloth-changing data alternately oc-cur, has emerged as a more practical and challenging scenario. Due to theconflict between clothing-relevant and clothing-irrelevant knowledge, thewell-known catastrophic forgetting problem is significantly exacerbatedin this task. To address this issue, we propose a SubDistribution-awareCOllaborative Knowledge REinforcing (SCORE) framework, where ourkey idea is explicitly modeling the intra-identity diversity to continu-ally consolidate distinct cloth-consistent and cloth-changing knowledge.Specifically, an Adaptive SubDistribution Modeling mechanism is devel-oped, where a set of distributional subprototypes is assigned to eachidentity to capture the intra-identity diversity, improving the compati-bility between cloth-consistent and cloth-changing knowledge. Then, aDistributional Knowledge Reinforcement scheme is introduced, wherethe knowledge of old distributional subprototypes is retained in the newones by a collaborative aligning mechanism. Extensive experiments showthat our SCORE achieves the state-of-the-art performance. Our code isavailable at https://github.com/zhoujiahuan1991/ECCV2026-SCORE.

Abstract:
We investigate the problem of identifying objects that havebeen added, removed, or moved between a pair of captures (imagesor videos) of the same scene at different times. Accurately identifyingverifiable changes is extremely challenging – some objects may appear tobe missing because they are occluded or out of frame, while others mayappear different due to large viewpoint changes. To study this problem, weintroduce the SceneDiff Benchmark, the first multiview change detectiondataset for scenes captured along different camera trajectories, comprising350 diverse video pairs with dense object instance-level annotations. Wealso introduce the SceneDiff algorithm, a training-free approach thatsolves for image poses, segments images into objects, and compares themusing semantic and geometric features. By building on pretrained models,SceneDiff generalizes across domains without retraining and naturallyimproves as the underlying models advance. Experiments on multiviewand two-view benchmarks demonstrate that our method outperformsexisting approaches by large margins (51.6% and 30.6% relative APimprovements). Project page: https://yuqunw.github.io/SceneDiff

Abstract:
Diffusion MRI is widely used to probe brain microstructure, with particular sensitivity to early cerebrovascular and neurodegenerative changes. Neurite Orientation Dispersion and Density Imaging (NODDI) decomposes the diffusion signal into three biophysically interpretable maps — neurite density index (NDI), orientation dispersion index (ODI), and free water fraction (FWF) — capturing neurite packing, fiber coherence, and extracellular fluid, respectively. These 3D maps provide a rich substrate for learning transferable microstructural representations for the human brain, yet effectively integrating them remains an open challenge: standard representation learning struggles to disentangle the unique information carried by each of the three brain microstructure maps (i.e., NDI, ODI, and FWF) from their shared and synergistic interactions. Here, we present BrainFIBRE (Brain Foundation Model via Information Decomposition for BRain MicrostructurE), the first foundation model for brain microstructure, pretrained on three NODDI-derived microstructure maps from the UK Biobank dataset (55,592 participants). To achieve this, we propose Self-supervised Partial Information Decomposition (SPID), which extends PID-guided multimodal learning to the self-supervised regime for the first time. A novel Counterfactual Candidate Construction (CCC) paradigm perturbs inter-modality alignment through modality dropping and swapping, providing the contrastive signal for a Mixture-of-Experts (MoE) architecture to disentangle unique, synergistic, and redundant information without any downstream label. Evaluated on both Caucasian and Asian cohorts, our model achieves state-of-the-art performance across diverse downstream tasks predicting age, sex, cerebrovascular disease (CeVD) and neurodegenerative markers, and cognitive performance, while yielding neurobiologically interpretable representations that reveal taskand cohort-specific interaction patterns among microstructural compartments. BrainFIBRE establishes a versatile foundation for neuroimaging analysis at the microstructural level. Code is available at https://github.com/hzlab/BrainFIBRE

Abstract:
Model reuse offers a solution to the challenges of segmentation in biomedical imaging, where high data annotation costs remain a major bottleneck for deep learning. However, although many pre-trained models are released through challenges, model zoos, and repositories, selecting the most suitable model for a new dataset remains diffcult due to the lack of reliable model ranking methods. We introduce the first blackbox-compatible framework for unsupervised and source-free ranking of semantic and instance segmentation models based on the consistency of predictions under perturbations. While ranking methods have been studied for classification and a few segmentation-related approaches exist, most target-related tasks such as transferability estimation or model validation and typically rely on labelled data, feature-space access, or specific training assumptions. In contrast, our method directly addresses the repository setting and applies to both semantic and instance segmentation, for zero-shot reuse or after unsupervised domain adaptation. We evaluate the approach across a wide range of biomedical segmentation tasks in both 2D and 3D imaging, showing that our estimated rankings strongly correlate with true target-domain model performance rankings. https://github.com/kreshuklab/model_ranking.

Abstract:
Safety-critical applications require classifiers that are bothrobust and reliable. Adversarial training is a widely adopted defense forimproving robustness in deep neural networks; however, its effect on thereliability of predictive uncertainty remains underexplored. We investi-gate this gap through the lens of selective classification, which has rarelybeen systematically analyzed alongside adversarial robustness. We in-troduce a unified benchmark for the robustness–uncertainty trade-off.It standardizes architectures, augmentations, threat models, and evalu-ation metrics across clean, adversarial, and common-corruption settings.Across a wide range of state-of-the-art adversarial training methods,we uncover a recurring failure mode: several approaches improve robustaccuracy while degrading uncertainty ranking, leading to poorer selec-tive behavior. To address this, we propose Evidential Adversarial Train-ing (EV-AT), which models uncertainty through a Dirichlet distributionand combines (i) an evidence-based loss promoting clean accuracy andreliable uncertainty with (ii) a robust evidence-alignment loss match-ing clean and adversarial predictions in log Dirichlet-parameter space.Extensive experiments show that EV-AT shifts the Pareto frontier ofrobustness–uncertainty trade-offs beyond prior state-of-the-art adversar-ial training methods. Our source code is publicly available at https://github.com/NicolasSournac/Robustness Meets Uncertainty.EV-AT.

Abstract:
Recent paradigms in Random Projection Layer (RPL)–basedcontinual representation learning have demonstrated superior performancewhen building upon a pre-trained model (PTM). These methods insert arandomly initialized RPL after a PTM to enhance feature representationin the initial stage. Subsequently, a linear classification head is used foranalytic updates in the continual learning stage. However, under severedomain gaps between pre-trained representations and target domains, arandomly initialized RPL exhibits limited expressivity under large do-main shifts. While largely scaling up the RPL dimension can improveexpressivity, it also induces an ill-conditioned feature matrix, therebydestabilizing the recursive analytic updates of the linear head. To this end,we propose the Stochastic Continual Learner with MemoryGuard Super-visory Mechanism (SCL-MGSM). Unlike random initialization, MGSMconstructs the projection layer via a principled, data-guided mechanismthat progressively selects target-aligned random bases to adapt the PTMrepresentation to downstream tasks. This facilitates the construction of acompact yet expressive RPL while improving the numerical stability ofanalytic updates. Extensive experiments on multiple exemplar-free ClassIncremental Learning (CIL) benchmarks demonstrate that SCL-MGSMachieves superior performance compared to state-of-the-art methods.Project Page: https://rlinl.github.io/SCL/

Abstract:
Transfer-based adversarial attacks craft adversarial examplesusing surrogate models to mislead black-box victim models. Beyond per-turbation generation, transferability is fundamentally governed by thecoupling of initialization, surrogate adaptation, and gradient dynam-ics. We revisit this challenge from a Bilevel-Minimax perspective andinstantiate it in BMAT (Bilevel-Minimax Adversarial Transfer). Thebilevel formulation captures the dependency between initialization andperturbation, while the inner minimax problem promotes surrogate ro-bustness for cross-architecture generalization. Algorithmically, we designan integrated bottom-up solver that combines a Soft Weight Modulatorand an Implicit Gradient Approximator for ternary coupling interaction.We further provide theoretical insights into the optimization dynamicsof the proposed bilevel-minimax framework. Extensive experiments onclassification and segmentation benchmarks show that BMAT surpasses10+ strong baselines across 30+ victim models, improving both intra-and cross-architecture transfer, and yielding up to 2× mIoU reduction.Code is available at https://github.com/callous-youth/BMAT.

Abstract:
The rapid progress of generative models has intensi x001C ed theneed for reliable and robust detection under real-world conditions. How-ever, existing detectors often over x001C t to generator-speci x001C c artifacts andremain highly sensitive to real-world degradations. As generative archi-tectures evolve and images undergo multi-round cross-platform shar-ing and post-processing (chain degradations), these artifact cues be-come obsolete and harder to detect. To address this, we propose Real-centric Envelope Modeling (REM), a new paradigm that shifts de-tection from learning generator artifacts to modeling the robust distribu-tion of real images. REM introduces feature-level perturbations in self-reconstruction to generate near-real samples, and employs an envelopeestimator with cross-domain consistency to learn a boundary enclosingthe real image manifold. We further build RealChain , a comprehensivebenchmark covering both open-source and commercial generators withsimulated real-world degradation. REM achieves an average improve-ment of 6.9% over state-of-the-art (SOTA) methods across 12 bench-mark evaluations. Notably, it maintains exceptional generalization on theseverely degraded RealChain benchmark, outperforming existing meth-ods by 18.4%. These results establish a solid foundation for reliable syn-thetic image detection under challenging real-world conditions. The codeand datasets are available at https://github.com/handsome-rich/REM.

Abstract:
Multi-perturbation adversarial training (MAT) aims to achieverobustness against multiple ℓp perturbations but suffers from robustnesstrade-offs between different threats. To address this, we employ a mixtureof experts (MoE) to route different threats through distinct model path-ways. However, naïve application of MoE encounters two critical chal-lenges: experts tend to overlook threat-specific features and redundantlycapture features shared across threats, and gating networks suffer fromthreat-agnostic routing where they learn nearly identical routing pat-terns across threats, thus preventing the construction of threat-specificmodel pathways. To this end, we propose Robust Mixture of Low-RankExperts (RoME), where each expert is a low-rank additive update tothe shared backbone, allowing it to capture threat-common featureswhile experts focus on threat-specific information. To address threat-agnostic routing, RoME introduces (i) dual-scale gating that exploitsthreat-discriminative signals from local and global level features, and(ii) threat-guided gating diversification that enforces diverse expert uti-lization across threats. Extensive experiments demonstrate that RoMEoutperforms existing state-of-the-art MAT in union robustness and nat-ural accuracy and improves robustness against unseen threats. Codes areavailable at https://github.com/wkim97/RoME.

Abstract:
Label noise is a major challenge in large-scale supervised face recognition, where weak or automatic annotations often introduce errors that mislead training. To address this, we propose a noise robust framework that performs noise detection and sample selection directly in cosine-similarity space. We observe that the non-target cosine similarities of clean samples share a highly consistent distribution profile with the target similarities of unfitted mislabeled samples. This phenomenon can be explained from a backpropagation perspective and provides a cue for monitoring label noise during training. Based on this observation, we formulate noise detection as boundary estimation in similarity space. Specifically, we track the upper bound of high-confidence clean nontarget similarities to determine the filtering threshold, without requiring prior knowledge of the noise rate or auxiliary networks. We further introduce a progressive rule during early training, where the threshold gradually increases from the estimated noise lower bound to the upper bound. This process discards unreliable samples while retaining hard but clean samples. Extensive experiments on eight synthetic and three real-world noisy datasets demonstrate that our method achieves superior noise detection and state-of-the-art recognition accuracy, with only about 0.3% average measured overhead. The filtered dataset produced by our method is also beneficial for subsequent training. Source code is available at https://github.com/wfl95/DDLN.

Abstract:
The rapid progress of visual generative models has madeAI-generated images increasingly difficult to distinguish from authen-tic ones, posing growing risks to social trust and information integrity.This motivates detectors that are not only accurate but also forensicallyexplainable. While recent multimodal approaches improve interpretabil-ity, many rely on post-hoc rationalizations or coarse visual cues, withoutconstructing verifiable chains of evidence, thus often leading to poor gen-eralization. We introduce REVEAL-Bench, a reasoning-enhanced multi-modal benchmark for AI-generated image forensics, structured aroundexplicit chains of forensic evidence derived from lightweight expert mod-els and consolidated into step-by-step chain-of-evidence traces. Basedon this benchmark, we propose REVEAL (Reasoning-enhanced Foren-sic Evidence Analysis), an explainable forensic framework trained withexpert-grounded reinforcement learning. Our reward design jointly pro-motes detection accuracy, evidence-grounded reasoning stability, and ex-planation faithfulness. Extensive experiments demonstrate significantlyimproved cross-domain generalization and more faithful explanations tobaseline detectors. All data and source codes are publicly available at:https://github.com/TrustMedia-zju/REVEAL.

Abstract:
Driven by the growing capacity and training scale, Text-to-Video (T2V) generation models have recently achieved substantialprogress in video quality, length, and instruction-following capability.However, whether these models can understand physics and generatephysically plausible videos remains a question. While Vision-LanguageModels (VLMs) have been widely used as general-purpose evaluators invarious applications, they struggle to identify the physically impossiblecontent from generated videos. To investigate this issue, we construct aPID (Physical Implausibility Detection) dataset, which consists of a testsplit of 500 manually annotated videos and a train split of 2,588 pairedvideos, where each implausible video is generated by carefully rewritingthe caption of its corresponding real-world video to induce T2V modelsproducing physically implausible content. With the constructed dataset,we introduce a lightweight fine-tuning approach, enabling VLMs to notonly detect physically implausible events but also generate textual expla-nations on the violated physical principles. Taking the fine-tuned VLMas a physical plausibility detector and explainer, namely PhyDetEx,we benchmark a series of state-of-the-art T2V models to assess their ad-herence to physical laws. Our findings show that although recent T2Vmodels have made notable progress toward generating physically plau-sible content, understanding and adhering to physical laws remains achallenging issue, especially for open-source models. Our dataset, train-ing code, and checkpoints are available at https://github.com/Zeqing-Wang/PhyDetEx.

Abstract:
Storyboarding is a core skill in visual storytelling for film,animation, and games. However, automating this process requires a sys-tem to achieve two properties that current approaches rarely satisfy si-multaneously: inter-shot consistency and explicit editability. While 2Ddiffusion-based generators produce vivid imagery, they often suffer fromidentity drift along with limited geometric control; conversely, traditional3D animation workflows are consistent and editable but require expert-heavy, labor-intensive authoring. We present StoryBlender, a grounded3D storyboard generation framework governed by a Story-centric Re-flection Scheme. At its core, we propose StoryBlender system, whichis built on a three-stage pipeline: (1) Semantic-Spatial Grounding, toconstruct a continuity memory graph to decouple global assets fromshot-specific variables for long-horizon consistency; (2) Canonical AssetMaterialization, to instantiate entities in a unified coordinate space tomaintain visual identity; and (3) Spatial-Temporal Dynamics, to achievelayout design and cinematic evolution through visual metrics. By or-chestrating multiple agents in a hierarchical manner within a verifica-tion loop, StoryBlender iteratively self-corrects spatial hallucinations viaengine-verified feedback. The resulting native 3D scenes support direct,precise editing of cameras and visual assets while preserving unwaver-ing multi-shot continuity. Experiments demonstrate that StoryBlendersignificantly improves consistency and editability over both diffusion-based and 3D-grounded baselines. Code and dataset are available athttps://engineeringai-lab.github.io/StoryBlender.

Abstract:
Recent video generation models have achieved remarkableprogress and are now deployed in film, social media production, and ad-vertising. Beyond their creative potential, such models also hold promiseas world simulators for robotics and embodied decision making. Despitestrong advances, current approaches still struggle to generate physicallyplausible object interactions and lack object-level control mechanisms. Toaddress these limitations, we introduce KineMask, an approach for videogeneration that enables realistic rigid body control, interactions, and ef-fects. Given a single image and a specified object velocity, our methodgenerates videos with inferred motions and future object interactions.We propose a two-stage training strategy that gradually removes futuremotion supervision via object masks. Using this strategy we train videodi!usion models (VDMs) on synthetic scenes of simple interactions anddemonstrate significant improvements and generalization to rigid bodyand hand-object interactions in real scenes. Furthermore, KineMask in-tegrates low-level motion control with high-level textual conditioning viapredicted scene descriptions, leading to support for synthesis of complexdynamical phenomena. Our experiments show that KineMask general-izes to di!erent VDMs and achieves strong improvements over recentmodels of comparable size. Ablation studies further highlight the com-plementary roles of low- and high-level conditioning in VDMs.Project Page: https://daromog.github.io/KineMask/

Abstract:
Current audio-driven 3D head generation methods primarily focus on single-speaker scenarios, struggling to model natural, bidirectional conversational dynamics. Furthermore, extending these models to dyadic interactions relies heavily on 3D pseudo-labels derived from tracking algorithms. These labels inherently contain severe noise and fail to capture fine-grained facial dynamics, particularly accurate lip closure. To address these limitations, we propose RealDyadic, a two-stage framework that leverages 2D photometric supervision to refine and correct 3D motion priors. In the first stage, a diffusion-based transformer equipped with a dual-audio interaction module generates synchronized 3D facial motions from multi-speaker audio streams. In the second stage, a 3D Gaussian Splatting renderer projects these motions into high-fidelity 2D frames, enabling image-level gradients to backpropagate and penalize the inaccuracies of the initial 3D pseudo-labels via an alternating training strategy. Additionally, we introduce Real-Dialog, a dataset comprising over 50 hours of aligned 2D-3D dyadic conversational data across 500+ identities. Extensive experiments demonstrate that integrating 2D supervision into 3D motion generation significantly outperforms existing baselines in both lip-sync accuracy and conversational realism, explicitly overcoming the performance ceiling dictated by pseudo-label errors. The code is available at https://github.com/Pixel-Talk/RealDyadic.

Abstract:
We present One4D, a unified framework for 4D generationand reconstruction that produces dynamic 4D content as synchronizedRGB frames and pointmaps. By consistently handling varying sparsitiesof conditioning frames through a Unified Masked Conditioning (UMC)mechanism, One4D can seamlessly transition between 4D generationfrom a single image, 4D reconstruction from a full video, and mixed gen-eration and reconstruction from sparse frames. Our framework adapts apowerful video generation model for joint RGB and pointmap generation,with carefully designed network architectures. The commonly used diffu-sion finetuning strategies for depthmap or pointmap reconstruction oftenfail on joint RGB and pointmap generation, quickly degrading the basevideo model. To address this challenge, we introduce Decoupled LoRAControl (DLC), which employs two modality-specific LoRA adapters toform decoupled computation branches for RGB frames and pointmaps,connected by lightweight, zero-initialized control links that graduallylearn mutual pixel-level consistency. Trained on a mixture of syntheticand real 4D datasets under modest computational budgets, One4D pro-duces high-quality RGB frames and accurate pointmaps across both gen-eration and reconstruction tasks. This work represents a step towardgeneral, high-quality geometry-based 4D world modeling using video dif-fusion models. Project page: https://mizhenxing.github.io/One4D.

Abstract:
We study the challenging problem of novel view video syn-thesis from single images or monocular videos. Existing methods, whichoperate under the assumption that pre-trained video models lack nativenovel view synthesis capability and enforce view alignment via cameraconditioning, task-specific fine-tuning, or stepwise hard denoising guid-ance, often suffer from artifacts and compromised global scene consis-tency. In this paper, we introduce NeoMap, a novel training-free frame-work designed to locate high-fidelity, view-consistent novel view solutionsfrom general pre-trained video models. The key to our approach is thecore insight that promising novel view solutions are inherently encodedwithin the natural video data manifold learned by pre-trained models,and the core challenge is simply to locate this optimal solution. We solvethis via our core mechanism: convergent manifold alternating projectioniterations that optimize the initial noise. Extensive experiments demon-strate that NeoMap significantly outperforms all existing methods across3 standard novel view synthesis benchmarks, including the challengingTanks-and-Temples, LLFF and DAVIS datasets, achieving state-of-the-art generation fidelity and top-tier view consistency. Our code and dataare available at https://github.com/vLAR-group/NeoMap

Abstract:
Tracking multi-person 3D human meshes from in-the-wildvideos is a highly challenging problem due to complex interactions, fre-quent occlusions, and severe truncation inherent in unconstrained envi-ronments. While recent approaches have improved robustness againstthese issues, they largely overlook the critical challenge prevalent inreal-world footage: frequent shot changes. These abrupt transitions incamera viewpoints often cause existing methods to lose track of humanidentities and fail in reconstructing temporally coherent trajectories. Al-though several recent works have explored 3D human mesh tracking un-der shot changes, they are still limited to single-person scenarios, mak-ing them inadequate for real-world videos where multiple people interactand appear simultaneously. To address this limitation, we propose Multi-THuMBS (Multi-person Tracking of 3D Human Meshes Beyond VideoShots) that leverages a state-of-the-art 3D scene prior to reconstructthe two boundary frames in a single shared 3D space. Human meshesare then registered within the shared 3D space, maintaining per-personidentity and motion consistency across shot changes. Extensive experi-ments demonstrate that our approach yields significant improvements in3D human mesh recovery, camera pose estimation, and identity track-ing, thereby ensuring high-fidelity motion reconstruction with consistentidentity preservation across shots compared to previous state-of-the-artmethods.

Abstract:
Understanding and forecasting audience reactions to videocontent are crucial for improving content creation, recommendation sys-tems, and media analysis. To enable audience reaction prediction andother content engagement applications, we introduce Video2Reaction,a multimodal dataset that maps short movie segments to a distributionof induced emotions of viewers in the wild, as expressed through socialmedia. Video2Reaction spans more than 10,000 videos and serves asa reliable benchmark as well as a training resource for audience reactionprediction. To enable cost-e!ective continuous annotations as reactionsmay change over time, we develop a two-stage multi-agent pipeline usingonly open-source LLMs, achieving 86% correctness under blind humanverification despite the inherently noisy and subjective nature of the task.We establish the first benchmark for video-to-reaction-distribution pre-diction in the wild and show that pretrained foundation video modelsfail in zero-shot settings, while finetuning transforms them into state-of-the-art predictors capable of modeling both full reaction distribu-tions and dominant responses from video alone. However, the task re-mains challenging: even the strongest methods achieve only 77% Top-3F1 in dominant reaction prediction (LLaVA-Next), highlighting a sub-stantial gap in modeling collective audience reaction. Dataset and codeare available at our project page: https://information-fusion-lab-umass.github.io/video2reaction-bench.github.io/.

Abstract:
Recent advances in generative video models have signifi-cantly improved visual realism in video object removal, yet evaluationprotocols still focus on masked-region fidelity, treating removal as lo-cal inpainting. In real scenes, object removal is a causal intervention:eliminating an object also requires removing its induced physical ef-fects, such as shadows, reflections, illumination changes, translucency,and dynamic traces. Existing benchmarks lack aligned clean referencesor remain limited to simplified synthetic settings, preventing systematicevaluation of causal consistency. We introduce BeyondMasks, a pairedbenchmark for causally consistent video object removal, consisting oftemporally aligned synthetic and real-world video pairs with clean back-ground references. The dataset spans diverse photometric, geometric,volumetric, and dynamic interactions, and supports both mask-basedand instruction-driven editing. We further propose CORE, a structuredvision–language model-based evaluation protocol that jointly measuresobject disappearance and after-effect consistency, aligning more closelywith human judgments than existing metrics. Benchmarking state-of-the-art methods reveals systematic failures in removing secondary physicaleffects despite high masked-region fidelity, exposing a gap between visualplausibility and causal correctness. BeyondMasks reframes video objectremoval as causal scene consistency rather than local reconstruction andprovides a unified framework for its evaluation.

Abstract:
Camera control has been extensively studied in conditional video generation; however, stable altering camera trajectories while faithfully preserving video content remains a challenging task. The mainstream approach to achieving camera control is warping a 3D representation according to the target trajectory. However, such methods fail to fully leverage the 3D priors of video diffusion models (VDMs) and often fall into the Inpainting Trap, resulting in subject inconsistency and degraded generation quality. To address this problem, we propose DepthDirector, a video re-rendering framework with stable camera controllability. Our key insight is to decompose the conditional injection of video content and camera trajectories by leveraging the depth video from explicit 3D representation as camera-control guidance. Specifically, we design a View-Content Dual-Stream Condition mechanism, enabling VDMs to comprehend camera movements and leverage their 3D understanding capabilities. Additionally, we construct a large-scale multi-camera synchronized dataset named MultiCamWarp Dataset using Unreal Engine 5. Extensive experiments show that DepthDirector outperforms existing methods. Our code and dataset will be publicly available on our project website: https://eleanor6725.github.io/DepthDirector/.

Abstract:
Diffusion-based methods have dominated the HOI genera-tion, as they enable critical contact fusions or signals to guide the dif-fusion process. However, they often result in high artifacts and unstableinteraction quality due to error accumulation during iterative denois-ing. In this work, we propose HOIMask, the first generative maskedframework for modeling HOI motion in discrete space. HOIMask firstencodes both motion sequences and contact-aware signals into discrete2D human and object token maps via HOI Vector Quantization (VQ),preserving fine-grained spatial-temporal structure beyond conventional1D representations. On this basis, a generative masked modeling frame-work is employed to jointly capture human-object interaction dynamics,leveraging a transformer architecture designed to model complex spatial-temporal and interaction dependencies. To generate more coherent andphysically plausible motions, we further introduce a novel contact-awarereconstruction guidance in discrete space during inference, which fusescontact signals to optimize HOI tokens that forces the generated mo-tion with higher spatio-temporal consistency. With craftily designed mo-tion interaction tokens, dedicated architecture and guidance strategy,HOIMask outperforms state-of-the-art diffusion-based methods, gener-ating more realistic and semantically aligned HOI motions. Please referto our project page for more results.

Abstract:
Choreographic motion generation poses unique challenges for AI, demanding precise semantic control over complex, temporally structured, and expressive full-body dynamics. While existing models can synthesize motion from music, they remain largely black boxes. Conversely, attempting to condition generation on both text and music frequently leads to modality collapse, where dense acoustic rhythms overwhelm sparse semantic text prompts, destroying user controllability. To resolve this spatial-temporal conflict, we propose STREAM (Structural-Temporal Rhythmic Energy-based Attention for Motion), a modality-decoupled diffusion transformer. STREAM strictly separates conditioning pathways: global text semantics dictate the kinematic structure via Adaptive Layer Normalization (AdaLN), while a novel Bimodal Energy-Based Attention Module (BEAM) routes these features to the musical beat without overwriting the semantics. We further introduce Motorica++, a newly curated dataset enriched with domain-specific dance vocabulary and frame-level semantic annotations from existing Motorica dataset. Additionally, to rigorously quantify zero-shot editability, we propose the Exchange Evaluation Protocol and Editable Dance Score (EDS). Through extensive experiments, STREAM achieves state-of-the-art alignment between motion and music while preserving choreographic semantics, positioning AI not merely as a reactive synthesizer, but as a controllable, collaborative partner for artistic direction. The source code and datasets are available at https://github.com/SeongJong-Yoo/STREAM.

Abstract:
Sign Language Production (SLP) faces a fundamental tradeoff: direct text-to-pose models suffer from regression-to-the-mean effects, while dictionary-retrieval methods produce disjointed transitions. To resolve this, we propose a novel training paradigm that leverages sparse keyframes to capture the underlying kinematic distribution of human signing. By generating dense motion from discrete anchors, our approach mitigates regression-to-the-mean while ensuring fluid articulation. To achieve this at scale, we introduce FAST, an ultra-efficient sign segmentation model that automatically mines precise temporal boundaries. We then present SignSparK, a Conditional Flow Matching (CFM) framework that utilizes these temporal anchors to synthesize 3D signing sequences. This keyframe-driven formulation also unlocks Keyframe-toPose (KF2P) generation, making precise spatiotemporal editing of signing sequences possible. Furthermore, SignSparK scales across four distinct sign languages, constituting the largest multilingual SLP framework to date, and integrates 3D Gaussian Splatting for photorealistic rendering. Extensive evaluations demonstrate that SignSparK achieves state-of-the-art across diverse SLP tasks and multilingual benchmarks. Our code is available at https://github.com/JianHe0628/SignSparK.

Abstract:
Video World Models are interactive video generation modelsthat predict future world states based on user actions and history videoframes. A critical challenge in video world models is the lack of memory,causing inconsistent generated scenes over extended durations. Previousmethods explored rule-based context frame retrieval as memory, but theyfail to generalize in scenarios with scene occlusions and dynamic objects.We propose MemLearner, a learning-based adaptive context querymethod using query tokens to bridge context and predicted tokens. Byleveraging the video generation model itself for context querying, Mem-Learner exploits pre-trained visual priors without training additionalmodules from scratch, and incorporates e x001E cient strategies for trainingand inference. We collect a dataset of long videos with scene occlusionsand dynamic objects, paired with camera pose annotations, and proposea multi-dataset training strategy leveraging both annotated renderedand unannotated real-world videos. Extensive experiments demonstratethat MemLearner signi x001C cantly outperforms prior video world modelsin terms of scene consistency and memory, particularly under challengingocclusion and dynamic scenarios.

Abstract:
Reconstructing a renderable 3D model from images is a useful but challenging task. Recent feedforward 3D reconstruction methods have demonstrated remarkable success in efficiently recovering geometry, but still cannot accurately model the complex appearances of these 3D reconstructed models. Recent diffusion-based generative models can synthesize realistic images or videos of an object using reference images without explicitly modeling its appearance, which provides a promising direction for object rendering, but lacks accurate control over the viewpoints. In this paper, we propose GO-Renderer, a unified framework integrating the reconstructed 3D proxies to guide the video generative models to achieve high-quality object rendering on arbitrary viewpoints under arbitrary lighting conditions. Our method not only enjoys the accurate viewpoint control using the reconstructed 3D proxy but also enables high-quality rendering in different lighting environments using diffusion generative models without explicitly modeling complex materials and lighting. Extensive experiments demonstrate that GO-Renderer achieves state-of-the-art performance across the object rendering tasks, including synthesizing images on new viewpoints, rendering the objects in a novel lighting environment, and inserting an object into an existing video. Project page: https://igl-hkust.github.io/GO-Renderer.

Abstract:
Estimating accurate camera poses, 3D scene geometry, and object mo-tion from in-the-wild videos is a long-standing challenge for classical structurefrom motion pipelines due to the presence of dynamic objects. Recent learning-based methods attempt to overcome this challenge by training motion estimatorsto filter dynamic objects and focus on the static background. However, their per-formance is largely limited by the availability of large-scale motion segmentationdatasets, resulting in inaccurate segmentation and, therefore, inferior structural 3Dunderstanding. In this work, we introduce the Dynamic Prior (D!"#%) to robustlyidentify dynamic objects without task-specific training, leveraging the powerfulreasoning capabilities of Vision-Language Models (VLMs) and the fine-grainedspatial segmentation capacity of SAM2. D!"#% can be seamlessly integrated intostate-of-the-art pipelines for camera pose optimization, depth reconstruction, and4D trajectory estimation. Extensive experiments on both synthetic and real-worldvideos demonstrate that D!"#% not only achieves state-of-the-art performanceon motion segmentation, but also significantly improves accuracy and robustnessfor structural 3D understanding. Code is available.

Abstract:
Egocentric AI assistants seek to infer user actions and intentions from long first-person videos to facilitate real-world tasks ranging from daily activities to industrial operations. However, collecting large-scale annotated egocentric videos that capture variations in objects, tools, environments and procedures is costly and impractical, motivating the need for data-efficient learning. Existing data augmentation approaches either offer limited visual diversity through simple transformations or rely on computationally expensive generative models that may introduce artifacts. We propose LogFA (Local-global Featurespace Augmentation), a novel framework that performs data augmentation directly in the feature space for Temporal Action Segmentation (TAS). LogFA efficiently generates semantically consistent variations without pixel-level synthesis by modifying pre-extracted video features at both local and global levels. Locally, LogFA leverages visionlanguage models with a Prompt-based Feature Enhancement strategy to create diverse action-level variations through text-guided feature modifications. Globally, LogFA constructs a Generalized Directed Acyclic Graph to model procedural dependencies and sample alternative action sequences. Experiments on egocentric video benchmarks show LogFA significantly improves model generalization to unseen environments while maintaining low computational and data collection costs. Code will be released at https://github.com/ZijiaLewisLu/ECCV2026- LogFA.

Abstract:
In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on o(cid:31)ine inference with global temporal sampling, which violates causality and prevents deployment in live surveillance streams. Conversely, general streaming video models satisfy causal access but dilute rare transient anomalies during memory compression and often invoke heavyweight MLLMs uniformly over long normal intervals. ReactVAU addresses this gap with three synergistic components: a lightweight Fast Detection Module based on Spatial Grid Folding (SGF) for continuous anomaly (cid:28)ltering; an Anomaly-Aware Persistent Memory (AAPM) that protects critical visual cues from temporal decay; and a heavyweight Slow Reasoning Module that remains dormant during normal streams and is awakened only by suspicious events for semantic veri(cid:28)cation and causal description. Extensive experiments on multiple benchmarks demonstrate that ReactVAU operates under strict streaming constraints while simultaneously achieving competitive performance in both anomaly detection and causal reasoning, alongside signi(cid:28)cantly enhanced computational e(cid:30)ciency by minimizing heavyweight MLLM invocations. Project page is available at https://huiyuiui.github.io/ReactVAU/ •

Abstract:
Video reasoning segmentation demands pixel-accurate object tracking across hundreds of frames under complex natural language queries, producing dense spatiotemporal tokens whose quadratic selfattention cost makes long-video processing prohibitive. Existing methods address this through token compression, yet typically operate on encoder features lacking temporal context, constraining selection before content redundancy can be reliably assessed. Informed compression requires contextual awareness, but acquiring that awareness at full resolution incurs the same quadratic cost compression aims to reduce. State-space models resolve this constraint, as their linear recurrence selectively conditions each token on temporal context at O(T) cost, producing representations where content redundancy becomes assessable. Building on this, Selective SpatioTemporal Aggregation and Compression (STAC) enriches features via decoupled bidirectional spatial and causal temporal scanning, leveraging recurrence-derived redundancy for hierarchical compression with adaptive thresholds optimised with segmentation objective. STAC achieves 85% token reduction and 1.8× speedup while surpassing compression-free baselines on reasoning segmentation benchmarks in a zero-shot streaming-compatible setting. Code is available here.

Abstract:
Vision Language Models (VLMs) have achieved strong per-formance across diverse video understanding tasks. However, their view-point invariant training limits their ability to understand egocentricproperties (e.g., human-object interactions) from exocentric video ob-servations. This limitation is critical for applications such as Activi-ties of Daily Living (ADL) monitoring, where understanding egocen-tric properties is essential yet egocentric cameras are impractical to de-ploy, making it impossible to simply collect egocentric data at test time.To address this challenge, we propose Ego2ExoVLM, a VLM frame-work that learns to infer egocentric properties from exocentric videos.Our key insight is that time-synchronized ego-exo video pairs can beleveraged during training, where the egocentric viewpoint provides priv-ileged supervision (rich egocentric signal available only at training time).Ego2ExoVLM achieves this through two components: Ego2Exo Se-quence Distillation, which transfers egocentric reasoning through alanguage-level sequence distillation objective, and Ego Adaptive Vi-sual Tokens, which encourages the model to surface relevant interac-tion cues within exocentric video representations. To measure this ca-pability, we introduce Ego-in-Exo Perception, a benchmark designedto evaluate the understanding of egocentric properties from exocentricvideos. Ego2ExoVLM is evaluated on 10 tasks across Ego-in-Exo Per-ception and existing ADL benchmarks, achieving state-of-the-art resultson the ADL-X benchmark suite and outperforming strong baselines onour proposed benchmark. Code, models, and data will be released athttps://github.com/dominickrei/EgoExo4ADL.

Abstract:
This study focuses on Text-to-Sounding-Video (T2SV) gen-eration, which aims to generate a video with synchronized audio fromtext, with both modalities aligned to the text conditions. Despite progressin joint audio-video training, two critical challenges remain: (1) text con-ditioning is a bottleneck—shared captions (TV = TA ) trigger modalinterference, while a gap persists between dense training captions andconcise inference user prompts, and (2) the optimal fusion mechanismfor cross-modal feature interaction remains unclear. To address the firstchallenge, we first propose the Cross-Referential Rewriter (CRR) cap-tion framework, a dual-agent pipeline where a Semantic Checker extractsgrounded Semantic Anchors and a Cross-Modal Rewriter generates dis-entangled caption pairs (TV and TA ), eliminating modal interference and1 →Equal contribution. Work done during an internship at Apple.2 †Project lead.3 ‡Corresponding author.bridging the training-inference gap via prompt expansion. For the sec-ond, we introduce BridgeDiT, a dual-tower di!usion transformer thatemploys Dual Cross-Attention (DCA) as a bidirectional bridge betweenvideo and audio streams, which we show through systematic compari-son to be the optimal fusion strategy for the dual-tower paradigm. Ex-tensive experiments on three benchmarks, supported by human evalu-ations, demonstrate state-of-the-art results on most metrics. Compre-hensive ablation studies further validate each component and o!er keyinsights for future T2SV systems. The codes and models are available athttps://bridgedit-t2sv.github.io/.

Abstract:
Affordance grounding aims to localize where to interact withan object, a fundamental capability for embodied agents. Yet progressis bottlenecked by data: manual annotation is prohibitively expensiveand confines existing datasets to a narrow set of predefined objectand affordance categories. We introduce Affogato, a framework foropen-vocabulary affordance grounding centered on Affogato-750K, alarge-scale dataset of 750K 3D affordance heatmaps paired with naturallanguage queries. We build it with a fully automated pipeline that or-chestrates foundation models to generate them at scale without humanlabeling. It covers significantly more diverse categories than any existingdataset. For reliable evaluation, we further provide 5K human-verified testpairs. We also present Espresso-3D and Espresso-2D, simple yet effec-tive models with a unified architecture across both modalities. Pretrainingon Affogato-750K improves both Espresso and prior methods and yieldsthe largest gains on unseen object and affordance categories, showingthat it provides broadly transferable supervision across architectures.

Abstract:
Omnimodal large language models (OmniLLMs) jointly process audio and visual streams, but the resulting long multimodal token sequences make inference prohibitively expensive. Existing compression methods typically rely on fixed window partitioning and attention-based pruning, which overlook the piecewise semantic structure of audio-visual signals and become fragile under aggressive token reduction. We propose Dynamic Audio-driven Semantic cHunking (DASH), a training-free framework that aligns token compression with semantic structure. DASH treats audio embeddings as a semantic anchor and detects boundary candidates via cosine-similarity discontinuities, inducing dynamic, variablelength segments that approximate the underlying piecewise-coherent organization of the sequence. These boundaries are projected onto video tokens as a soft temporally co-registered segmentation prior. Within each segment, token retention is determined by a tri-signal importance estimator that fuses structural boundary cues, representational distinctiveness, and attention-based salience, mitigating the sparsity bias of attentiononly selection. This structure-aware allocation preserves transition-critical tokens while reducing redundant regions. Extensive experiments on AVUT, VideoMME, and WorldSense demonstrate that DASH maintains competitive or superior accuracy while achieving higher compression ratios compared to prior methods. Code is available at: https://github.com/ laychou666/DASH.

Abstract:
We revisit the memory update mechanism in SAM2-basedvisual object tracking and identify confidence-only mask selection asthe dominant cause of drift under occlusion, rapid motion, and dis-tractors. We introduce SENTRY, a training-free, plug-and-play, refine-before-write module that validates each memory update for short-horizontemporal consistency before committing it. SENTRY aggregates diversesegmentation hypotheses per frame, backtracks them into short tracklets,and uses neighbor-aware cycle-consistent matching against recent trajec-tories to favor temporally and geometrically consistent masks. It leavesthe base architecture untouched, replacing confidence-driven writes withconsistency-validated ones. For fair evaluation, we re-evaluate major open-source SAM2-based trackers across all available scales and datasets, fill-ing gaps in prior reports. Integrated into five strong baselines, SENTRYdelivers consistent gains across nine benchmarks, achieving new zero-shotSOTA on LaSOT, LaSOText , GOT-10k, VOT20, VOT22, and DiDi. De-spite these checks, the SAM2-L version runs at 32.8 FPS on an A100, andacross compatible hosts adds only about 0.4–0.6 GB VRAM. Our resultsprovide the first unified all-scale evaluation of SAM2-based trackers andshow that enforcing temporal validity at write time stabilizes memory-augmented tracking without retraining.

Abstract:
Understanding hand-object interaction from egocentric vi-sion is essential for modeling how people physically engage with thesurrounding world. Yet reasoning about physically grounded interactionrequires estimating the forces acting on hands and objects, beyond lo-calizing contact. We present EgoPHI, the first method that jointly esti-mates dense contact maps and 3D force distributions on hand and objectmeshes from a single egocentric RGB image and object geometry. To ad-dress the lack of scalable ground-truth force annotations, we introducea physics-based simulation pipeline that augments existing hand-objectdatasets with dense per-vertex force supervision. EgoPHI then learnsdense 3D contact and force on interacting hand and articulated objectmeshes, extending vision-based force estimation beyond image-space orplanar settings. Our evaluation on in-distribution and out-of-distributionbenchmarks shows that EgoPHI improves force estimation over existingapproaches while generalizing to unseen datasets. To evaluate sim-to-realtransfer, we constructed two physical objects that capture dense objectcontact and force magnitude and used them to record a dataset of in-teractions from eight participants across diverse touch and grasp types.Our results demonstrate that EgoPHI recovers meaningful 3D contactand force distributions in simulated, out-of-distribution, and real-worldsettings, advancing egocentric hand-object understanding from contactlocalization toward physically grounded interaction reasoning.Code: https://github.com/eth-siplab/EgoPHI

Abstract:
Egocentric videos capture rich and diverse human–object in-teractions and have emerged as a fundamental resource for understand-ing human activities related to objects. In this context, Video ReferringExpression Comprehension (Video REC), the task of localizing the tem-poral and spatial extent of a referred object in video frames given anatural language query, plays a key role in linking textual descriptionsto observed objects in untrimmed egocentric recordings. However, exist-ing egocentric Video REC benchmarks primarily focus on short videoclips, where some target object appears densely within frames. Suchsettings do not reflect real-world egocentric recordings, which are long-form, untrimmed, and characterized by sparse object occurrences andcomplex activity transitions. To address this limitation, we introduceLongEgoRefer, a novel and challenging benchmark constructed fromlong-form videos in the Ego4D dataset. LongEgoRefer contains 1,498referring expressions with an average video duration of 45 minutes. Thebenchmark exhibits extreme target sparsity, detailed linguistic descrip-tions, and complex human–object interactions embedded in long, dy-namic egocentric narratives. Consequently, it defines a demanding spatio-temporal grounding problem that requires models to identify both whenan event occurs and where the referred object appears within extendedvideo sequences. We evaluate existing Video REC approaches, includ-ing training-free baselines based on vision–language models combinedwith Grounded SAM2. Extensive experiments show that even advancedbaselines and current state-of-the-art models struggle significantly onLongEgoRefer. These results highlight the intrinsic difficulty of long-form egocentric spatio-temporal grounding and emphasize the need formore robust video understanding models. Our benchmark and code areavailable at https://github.com/shunya-kato/LongEgoRefer.LongEgoRefer0s 4345sA transparent, round bowl containing a bakedvegetable dish is shown being removed from anoven. The bowl, encased in a blue protective cloth,is then placed on the Induction stove. Its contents,a creamy vegetable and mushroom mixture, aresubsequently subjected to a brief external inspection.Human-Object Interaction3067s 3111sRefEgo Long-form video Sparse appearance Linguistic complexity0s 5sThere have white colored hot box in theshelf of the room.Fig. 1: Data comparison of LongEgoRefer and RefEgo. (a) Video durations in LongE-goRefer are orders of magnitude longer than those in RefEgo. (b) The appearance rateis significantly lower and sparser in LongEgoRefer compared to RefEgo. (c) To handlethe complexity of long-form videos, captions in LongEgoRefer are substantially longerand more descriptive than those in RefEgo. Dashed lines indicate the mean value foreach distribution.

Abstract:
We introduce VidPair-Halluc, a new benchmark for evaluating video hallucination in large video models (LVMs) under rigorous and controlled conditions. Unlike previous benchmarks that primarily rely on text-based perturbations or adversarial questions while neglecting the consistency of visual backgrounds, VidPair-Halluc features video pairs with highly similar backgrounds but distinctly different foreground semantics, enabling precise attribution of model errors to genuine hallucination rather than background variation. The benchmark is constructed through PairFlow, a pipeline that leverages recent advances in text-to-image and video generation to systematically compose stories, generate coherent video clips, and assemble them into adversarial pairs. Covering both spatial and temporal reasoning across ten semantic aspects, VidPair-Halluc comprises 1K high-quality adversarial video pairs and 11K spatio-temporal QA pairs with control over background and foreground variations. Evaluations on mainstream LVMs show persistent difficulty with robust fine-grained video understanding in adversarial settings, and code and data are available at the project page.

Abstract:
Dynamic Hand Gesture Recognition is essential for naturalhuman-computer interaction but faces challenges in continuous local ex-ecution on consumer-grade hardware due to strict computational andlatency constraints. Existing solutions fail to reconcile the high MemoryAccess Cost (MAC) and dynamic footprint of Video Transformers with thelimited temporal modeling capacity of lightweight CNNs. To address theselimitations, we propose the Split-Scan Gesture Network (S2Gest), whichrethinks temporal modeling by leveraging continuous state-space evolutionto represent discrete sequential inputs. We introduce the Split-Scan Block(S2-Block), employing a novel tube-wise scanning strategy that partitionsfeature channels to process opposing temporal directions simultaneously.By structurally partitioning feature channels to capture complementaryforward and backward dynamics, this design achieves global bidirectionalmodeling with linear-time complexity and without introducing additionalparameters, effectively overcoming the causal unidirectionality inherent instandard State Space Models (SSMs). Evaluated across three benchmarks,the scalable S2Gest model family establishes a new state-of-the-art forlightweight architectures, matching the performance of large-scale base-lines with a sub-3M parameter footprint while consistently exceeding 100clips/s on consumer-grade GPUs. Code and weights are publicly availableat https://github.com/Chen-Ke-Fan/S2Gest.

Abstract:
Test Time Training (TTT) is a mechanism in which a modeladapts to an incoming test-sample by performing some self-supervised(SSL) task and updating its weights even during inference. This proce-dure does not require labels at test-time. This paper focuses on TTT forlong-videos. A major concern with existing approaches is: 1) they per-form TTT updates using a sliding window containing frames in the past,whose compute increases linearly with the size of window. This becomescomputationally intractable when the videos are hours long. 2) TTT isperformed even when temporally close frames look similar, thereby con-suming a lot of compute.We present the Frame Forgetting Network (FFN) that: 1) operates ononly three frames within the sliding window, namely the frame that ex-its, the current frame and the frame after that. The model still managesto retain temporal context and work for hours long-videos; 2) mathemat-ically define a ‘surprise’ metric: how much ‘new information’ the incom-ing frame contains with respect to the past seen frame. This facilitatesdetermining how to modify the effective window size during TTT andconstitutes the core mechanism of an adaptive windowing algorithm. Ad-ditionally, we curate a dataset EpicTours containing up to 3 hour longvideos of walking city-tours, whereas earlier datasets on this problemwere only 5 min long. We demonstrate FFN’s empirical effectiveness ondense-segmentation, video classification tasks, generalization to depth-estimation, and multi-hour long videos. The project page can be foundat https://github.com/rajatmodi62/ffn.

Abstract:
Video understanding is often plagued by severe temporal re-dundancy, where processing dense frame sequences is both semanticallyinefficient and computationally expensive. This challenge is further am-plified when only a small subset of frames is truly relevant to the givenquery. In this paper, we propose a Query- and Content-Aware (QCA)keyframe selection framework that can select a compact yet information-rich set of frames from long videos. QCA first partitions the video intotemporal segments and estimates the information contribution of eachsegment by jointly modeling query relevance and content deviation, anddynamically allocates keyframe budget to each segment. Within eachsegment, QCA anchors on the most query-relevant frame and iterativelyincorporates additional frames to maximize diversity while maintaininghigh semantic relevance to the query. Crucially, our method requires noadditional training and can be seamlessly integrated into existing Video-LLMs. Extensive experiments across multiple long video understandingbenchmarks demonstrate that our proposed approach achieves state-of-the-art performance and has strong generalization ability. For instance,QCA achieves 67.8% on LongVideoBench using 128 frames, while GPT-4o achieves 66.7% using 256 frames. Our codes are available in GitHub.

Abstract:
Existing safety mechanisms for multimodal large languagemodels (MLLMs) face a fundamental trade-off between safety and util-ity. Model fine-tuning achieves robust safety but compromises generalutility. Input-side safety guardrails offer a lightweight alternative, yetthey suffer from severe over-refusal, indiscriminately blocking benignqueries or those the model could have safely answered through refusalor advisory responses. We identify that the root cause of over-refusallies in the input-aware paradigm: safety guardrails make safety decisionswithout considering whether the model itself is capable of generatingsafe responses. Usually, MLLMs already possess intrinsic safety mech-anisms that can transform harmful inputs into harmless outputs, butinput-side safety guardrails override this capability, degrading user ex-perience. Motivated by this insight, we propose a paradigm shift towardoutput-aware safety guardrails. Our method operates within the model’shidden state space to predict whether the forthcoming generation willbe unsafe before it is fully produced. By training a lightweight classifiervia multi-instance contrastive learning on hidden state representations,our approach distinguishes between inputs that will lead to unsafe out-puts and those that will not, even when the inputs themselves containrisky elements. This enables precise intervention only when the model’sactual response would be harmful. Extensive experiments demonstratethat our output-aware safety guardrail matches the safety performance ofexisting methods while drastically reducing over-refusal, preserving themodel’s utility and built-in safety capabilities. Our code is available athttps://github.com/kunzhan/OutGuard.

Abstract:
Computer-use agents that combine GUI interaction withstructured API calls via the Model Context Protocol (MCP) show promisefor automating software tasks. However, existing approaches lack a prin-cipled understanding of how agents should balance these two modalitiesand how to enable iterative self-improvement across diverse applica-tions. We formulate MCP-GUI interplay as a unified hybrid policylearning problem where the agent learns when each modality providescomplementary advantages, and show that distillation and experienceaugmentation target fundamentally different failure modes—requiringapplication-aware mechanism selection. Built on this formulation, wepropose a self-evolving framework with a fully automatic pipelinethat orchestrates automatic environment generation and validation, tra-jectory collection, gap-driven task synthesis, and quality-filtered training—all without manual intervention. A key innovation is our experiencebank, which accumulates LLM-learned rules from trajectory comparison,enabling inference-time improvement without fine-tuning. Systematiccross-application analysis across three desktop applications revealsthat the optimal strategy depends on MCP–GUI composition and tool-chain complexity: distillation achieves 77.8% pass rate on MCP-dominanttasks (+17.8pp), while the experience bank excels on GUI-intensive tasks(+10.0pp). Code available at: https://github.com/Tiantian-H/EE-MCP

Abstract:
Adapting Multimodal Large Language Models (MLLMs) forhour-long videos is bottlenecked by context window limits. Existing effi-ciency heuristics blindly sacrifice fidelity. They frequently discard tran-sient decisive moments, blur fine-grained evidence, and waste represen-tational bandwidth on irrelevant backgrounds. We propose Tempo, anefficient, query-aware framework that compresses long videos for down-stream understanding. Tempo unifies a Small Vision-Language Model(SVLM) to act as a local temporal compressor. It casts visual token re-duction as an early cross-modal distillation process, generating compact,intent-aligned video representations in a single forward pass. To enforcestrict inference budgets without breaking temporal causality, we intro-duce Adaptive Token Allocation (ATA). Exploiting the SVLM’s inher-ent zero-shot relevance prior and empirical semantic front-loading, ATAacts as a training-free, O(1) dynamic router. It allocates dense band-width to query-critical segments while compressing redundancies downto minimal temporal anchors to maintain the global storyline. Exten-sive experiments demonstrate that our compact 6B architecture achieveshighly competitive performance with aggressive dynamic compression(0.5–16 tokens/frame). On the extreme-long LVBench (4101s), Temposcores 52.3 under a strict 8K visual budget, outperforming proprietarymodels like GPT-4o and Gemini 1.5 Pro. Crucially, empirical profiling re-veals that Tempo frequently compresses hour-long videos to token countsbelow theoretical limits, proving that true long-form understanding relieson intent-driven efficiency rather than greedily padded context windows.

Abstract:
Despite rapid progress in Multi-modal Large Language Mod-els (MLLMs), understanding long-form videos is still bottlenecked bylimited context windows. While recent keyframe sampling methods at-tempt to mitigate this by distilling video inputs into a compact set ofquery-relevant frames, navigating the vast spatio-temporal search spaceremains challenging, as spatial detail and temporal coverage often conflict.To address this, we introduce LENS, a training-free keyframe samplingframework that dynamically decides when to zoom in for fine-graineddetails and when to zoom out for broader context based on the textquery. Concretely, LENS adaptively allocates a limited frame budgetbetween spatial zoom-ins, which highlight query-relevant regions withinindividual frames, and temporal zoom-outs, which expand the temporalscope through multi-frame aggregation, enabling the model to reasonacross multiple granularities while capturing both high-fidelity details andlong-range context. Across diverse long-form video benchmarks, LENSconsistently outperforms prior state-of-the-art keyframe sampling meth-ods and delivers substantial gains over uniform sampling, improvingVideo-MME accuracy from 53.3% to 60.7% with Qwen2.5-VL. Code isavailable at https://github.com/zhangce01/LENS.

Abstract:
Latent diffusion models (LDMs) have recently significantlyadvanced embodied learning in constructing powerful embodied manipu-lation world models. However, despite the remarkable performance, exist-ing LDMs predominantly rely on Variational Autoencoders (VAEs) opti-mized for natural scenes while failing to account for the unique character-istics of embodied manipulation scenarios, yielding latent representationsthat are neither compact nor controllable, thereby hindering efficienttraining of LDMs and precise robotic control. To solve this problem, wepresent EmbodiedVAE, a novel video VAE that provides compact yetcontrollable latent representations tailored for the robotic manipulationworld models. Specifically, EmbodiedVAE adopts a dual-encoder, single-decoder architecture with an asymmetric spatio-temporal compressionmodule, which automatically disentangles the robot arm’s motion frombackground environment, resulting in overall compactness while provid-ing explicit embodied latent to support fine-grained action control. Tofurther preserve the temporal consistency of learned robotic motion la-tent, we introduce an optimal-transport-based consistency module thatexplicitly enforces motion fidelity and inter-frame coherence. Extensiveexperiments demonstrate that our proposed EmbodiedVAE achieves su-perior reconstruction quality with high compression rate, while enablingmore precise action control in robotic manipulation scenarios with anaverage of 2dB PSNR improvement over state-of-the-art video VAEs.Code is available at: https://github.com/Mutual-Luo/EmbodiedVAE

Abstract:
In this paper, we present JoVA, a streamlined frameworkthat unifies joint video-audio generation and editing. While existingmethods often rely on fragmented, task-specific architectures or complexfusion mechanisms, JoVA employs native joint representation learningfor direct video, audio, and text interaction in a dual-branch architec-ture. This design eliminates redundant alignment modules and effectivelyunifies diverse multimodal tasks within a single model. Furthermore, weutilize channel-wise conditioning for flexible image and video reference toavoid massive token expansion, alongside a mouth-area loss to enhancelip alignment. To fully empower and systematically evaluate this frame-work, we construct a comprehensive training corpus encompassing video-audio generation and editing datasets, and introduce unified benchmarkstailored for these multimodal tasks. Extensive experiments demonstratethat JoVA achieves state-of-the-art performance across benchmarks, es-tablishing it as an extensible framework for versatile content creation.Project page: https://visual-ai.github.io/jova

Abstract:
Recent advances in Chain-of-Thought reasoning have beenreported to enhance video reasoning capabilities of multimodal large lan-guage models (MLLMs). This progress naturally raises a question: Canthese models perform complex video reasoning in a manner comparable tohuman experts? However, existing video benchmarks primarily evaluatevisual perception and grounding, with questions that can be answeredbased on explicit prompts or isolated visual cues (e.g., “What is thewoman wearing?”). Such benchmarks do not fully capture the intricaciesof real-world reasoning, where humans must actively search for, integrate,and analyze multiple clues before reaching a conclusion. To address this,we introduce Video-Holmes, a benchmark inspired by the reasoningprocess of Sherlock Holmes, designed to evaluate the complex video rea-soning capabilities of MLLMs. Video-Holmes consists of 1,837 questionsderived from 270 manually annotated suspense short films, spanningseven carefully designed tasks. Each task is constructed by first identify-ing key events and causal relationships within films, and then designingquestions that require models to actively locate and connect multiplerelevant visual clues scattered across video clips. Evaluation of state-of-the-art MLLMs reveals that while these models generally excel at vi-sual perception, they encounter difficulties with integrating informationand often miss critical clues. For example, the best-performing model,Gemini-3.0-Pro, achieves an accuracy of only 49.6%, with most modelsscoring below 40%. We aim for Video-Holmes to serve as a “Holmes-test”for multimodal reasoning, motivating models to reason more like humansand emphasizing the ongoing challenges in this field.

Abstract:
The quadratic time and memory complexity of the attention mechanism in modern Transformer-based video generators makes endto-end training for ultra-high-resolution videos prohibitively expensive. Motivated by this limitation, we introduce a training-free approach that leverages video Diffusion Transformers pre-trained at their native scale to synthesize higher-resolution videos without any additional training or adaptation. At the core of our method lies an inward sliding-window attention mechanism, which originates from a key observation: maintaining each query token’s training-scale receptive field is crucial for preserving visual fidelity and detail. However, naive local window attention, unfortunately, often leads to repetitive content and exhibits a lack of global coherence in the generated results. To overcome this challenge, we devise a dual-path pipeline that backs up window attention with a novel cross-attention override strategy, enabling the semantic content produced by local attention to be guided by another branch with a full receptive field and, therefore, ensuring holistic consistency. Furthermore, to improve efficiency, we incorporate a cross-attention caching strategy for this branch to avoid the frequent computation of full 3D attention. Extensive experiments demonstrate that our method delivers ultra-highresolution videos with fine-grained visual details and high efficiency in a training-free paradigm. Meanwhile, it achieves superior performance on VBench, even compared to training-based alternatives, with competitive or improved efficiency.

Abstract:
Temporal Action Segmentation (TAS) assigns action labelsto each frame by modeling the temporal structure of actions. While of-fline TAS leverages full temporal context, online TAS cannot directlyaccess future information during inference. Existing online TAS methodsmainly focus on modeling historical dependencies, but temporal ambigu-ity still arises due to the incomplete temporal dependencies, leading tofragmented predictions. To tackle this challenge, we propose Dual-PathTemporal Modeling (DPTM) for online TAS, a two-stage framework thatexplicitly models and transfers the future dependencies that are inacces-sible during online inference. In the first stage, we adopt an offline-styletraining scheme where bidirectional dependencies are decomposed intounidirectional historical and future components via a temporal decom-position strategy. In the second stage, we employ a predictive distillationstrategy that uses recent historical frames to predict the separated futuredependencies without violating causality. These designs enable DPTMto leverage more complete temporal context while remaining suitable foronline inference. Extensive experiments demonstrate that DPTM signifi-cantly improves temporal coherence in segmentation results and achievesstate-of-the-art performance, demonstrating the effectiveness of modelingdependencies beyond historical frames for reducing the temporal ambi-guity. The code is available at: https://github.com/zczheng777/DPTM/.

Abstract:
The next frontier for video generation lies in developing models capable of zero-shot reasoning, where understanding real-world scientific laws is crucial for accurate modeling of physical outcomes under diverse conditions. However, existing video benchmarks are physical commonsense-based, offering limited insight into video models’ scientific reasoning capability. We introduce VideoScience-Bench1, a benchmark for evaluating undergraduate-level scientific understanding in video models. Each prompt encodes a composite scientific scenario that requires understanding and reasoning across multiple scientific concepts to generate the correct phenomenon. The benchmark comprises 200 evaluation items built from 160 expert-curated test cases (160 text-to-video prompts; 40 also include image conditioning) spanning 14 topics and 103 concepts. We conduct expert-annotated evaluations across seven state-of-the-art video models in T2V and I2V settings, along five dimensions: Prompt Consistency, Phenomenon Congruency, Dynamism, Immutability, and Spatio-Temporal Coherence. Using a VLM-as-a-Judge to assess video generations, we observe strong correlation with human assessments. To the best of our knowledge, VideoScience-Bench is the first benchmark to evaluate video models not only as generators but also as reasoners, requiring their generations to demonstrate scientific understanding consistent with expected physical and chemical phenomena.

Abstract:
While multimodal Large Language Models (MLLMs) excel at offline video understanding, an interesting question of how far they are from serving as a real-time procedural coach remains unknown. Such a role typically requires an MLLM to continuously monitor the execution, detect mistakes, and provide corrective guidance in a closed-loop interaction. In this paper, we construct GuideMe, the first multi-domain benchmark for streaming video that supports training and evaluation of MLLMs for closed-loop interactive task guidance. It comprises 2,458 videos spanning 223.7 hours across diverse domains (e.g., cooking, object manipulation, daily-life guidance, and fitness), with 47,775 interaction samples covering next-step instructions, completion feedback, error detection, and corrective guidance. To evaluate existing models on GuideMe, we design a three-component assessment framework to measure the capabilities of representative MLLMs, which consists of temporal-semantic bipartite matching for sequence-level alignment, behavioral classification for intervention timing, and LLM-as-a-Judge for content quality. Extensive experiments highlight a critical performance asymmetry: despite excelling at providing instructions, existing MLLMs consistently fail to identify execution errors and respond with corrective feedback. Code and data are released at (cid:140)homepage.

Abstract:
Visual grounding (VG) aims to precisely localize the objectin input images based on its natural language descriptions. Most recentlyproposed methods address this task using Transformer-based architec-tures that can inject textual information into visual features. However,according to the image tokenization procedure, a large number of vi-sual tokens will be located in text-irrelevant background areas. Thesetokens can introduce noise into the attention calculation, thus reduc-ing the significance of foreground object tokens and ultimately affect-ing the effectiveness of these methods. To this end, we propose a novelToken Blurring (ToB) module, which dynamically merges image tokensbased on the pair-wise visual similarity between them and their tex-tual relevance with input expressions. By reducing the number of text-irrelevant background tokens and preserving the density of text-referredones, ToB can improve both model effectiveness and efficiency in solvingVG tasks. Extensive experiments on RefCOCO, RefCOCO+, and Re-fCOCOg datasets show that Transformer-based models equipped withour ToB module yield better results while reducing computational over-head compared to various existing VG methods. Our code is available athttps://github.com/Mr-Bigworth/ToB.

Abstract:
Learning efficient and expressive visual representation haslong been the pursuit of computer vision research. While Vision Trans-formers (ViTs) gradually replace traditional Convolutional Neural Net-works (CNNs) as more scalable vision learners, their applications areplagued by the quadratic complexity of the self-attention mechanism. Toaddress the challenge, we introduce a new linear-time sequence modelingmethod Test-Time Training (TTT) into vision and propose Vision-TTT,which treats visual sequences as datasets and compresses the visual to-ken sequences in a novel self-supervised learning manner. By incorporat-ing the dual-dataset strategy and Conv2d-based dataset preprocessing,Vision-TTT effectively extends vanilla TTT to model 2D visual cor-relations with global receptive fields. Extensive experiments show thatVittt-T/S/B achieve 77.7%, 81.8%, 82.7% Top-1 accuracy on ImageNetclassification and also greatly outperform their counterparts on down-stream tasks. At 1280 × 1280 resolution, Vittt-T reduces FLOPs by79.4% and runs 4.72× faster with 88.9% less memory than DeiT-T.These results demonstrate the expressiveness and efficiency of Vision-TTT as an alternative for the next-generation visual backbone. Codesare available at https://github.com/imKQv/Vision-TTT.

Abstract:
Through pre-training on extensive text and image datasets, current multi-modal large language models (MLLMs) achieve strong performance on general tasks. However, circuit schematics present a unique challenge for MLLMs due to their dense component layouts and distinct topological logic, demanding fine-grained structural parsing to extract the electrical semantics. To address this, we propose Circuit-MLLM, a multimodal reasoning framework that reformulates circuit topology analysis as a process of device localization, path tracing, and sequential reasoning within the latent space. We introduce a circuit knowledge mining mechanism that deeply aligns the model’s latent representations with structurally rich features derived from multi-granularity circuit vision experts, enabling the model to effectively internalize topological semantics. Building upon these internalized semantics, we devise a topology-guided sequencing strategy that decouples reasoning from the rigid raster-scan order, enforcing stepwise inference along the circuit’s topological logic in latent space. Across diverse circuit analysis tasks, Circuit-MLLM consistently outperforms strong baselines, notably achieving a 25% higher average score than GPT-5.1, which demonstrates the effectiveness of our framework in circuit schematic topology analysis. Code is publicly available at https://github.com/IC-Yuan/Circuit-MLLM.

Abstract:
Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve the performance of the policy, but its scaling mechanism has seldom been studied; (2) historical information is essential, as embodied tasks are inherently longhorizon and sequential, making sole reliance on current observations for action scaling inadequate due to the lack of historical context utilization. To address these challenges, we introduce E-TTS, a modular and plugand-play Embodied Test-Time Scaling framework that unifies reasoning and action scaling for robotic manipulation via history-aware iterative refinement with vision-language verifiers. To support joint reasoning-action scaling, E-TTS performs reasoning-action joint sampling and scoring in a pairwise manner. To better utilize historical information, E-TTS uses a history buffer to store historical context, which is then used by reasoning and action verifiers to evaluate the sampled candidates. Unlike conventional open-loop TTS methods, E-TTS introduces feedback generation into the sampling process to form a closed-loop iterative refinement mechanism, enhancing both inference efficiency and environmental adaptability. Each component functions as an independent and composable module, allowing flexible and adaptive configuration depending on task requirements. To evaluate the advantages of our framework, we conduct experiments across 4 different benchmarks, 6 environments, 3 embodiments, and 4 base vision-language-action models. The experimental results demonstrate that, without requiring additional expert data collection or retraining, E-TTS consistently improves performance, achieving up to a 33.14% increase in simulation and 26.62% in real-world scenarios. Our project page is https://27yw.github.io/E-TTS-Web/. Equal contribution.

Abstract:
Recent 3D vision-language models (3D VLMs) construct ge-ometry aware tokens by projecting 2D visual features into world coordi-nates, enabling spatial reasoning for tasks such as 3D question answering.However, this design generates thousands of tokens per scene, resultingin substantial computational and memory overhead. While token com-pression has been extensively studied in 2D VLMs, existing approachesrely on semantic relevance or attention-based selection that overlook thestructured spatial nature of 3D tokens. Moreover, redundancy in 3D rep-resentations cannot be resolved by spatial proximity alone, as object-leveltoken imbalance persists even after spatial aggregation. To address this,we propose 3DZip, a three-stage token compression framework that firstapplies coarse voxelization to remove point-level redundancy, then selectsanchor tokens based on feature-space diversity via a Determinantal PointProcess, and finally merges remaining tokens under spatial constraintsto preserve geometric coherence. Experiments on three 3D question an-swering benchmarks demonstrate that 3DZip consistently outperformsexisting compression methods, retaining 94.7% of the original perfor-mance with only 128 tokens, achieving a 1.92× faster inference speed.

Abstract:
The computational cost of training a vision-language model(VLM) can be reduced by sampling the training data. Previous work onefficient VLM pre-training has pointed to the importance of semanticdata balance, adjusting the distribution of topics in the data to improveVLM accuracy. However, existing efficient pre-training approaches maydisproportionately remove rare concepts from the training corpus. As aresult, long-tail concepts remain insufficiently represented in the trainingdata and are not effectively captured during training. In this work, weintroduce a dynamic cluster-based sampling approach (DynamiCS) thatdownsamples large clusters of data and upsamples small ones. We firstdemonstrate the advantage of our cluster-scaling approach, which main-tains the relative order of semantic clusters in the data and emphasizesthe long-tail. This approach contrasts with current work, which focusesonly on flattening the semantic distribution of the data. Then, we showthe importance of dynamic sampling, which applies sampling at eachepoch to improve cross-epoch data diversity and make upsampling prac-tical. Our experiments show that DynamiCS reduces the computationalcost of VLM training and provides a performance advantage for long-tail concepts. Code available at https://github.com/MingliangLiang3/DynamiCS.

Abstract:
Understanding visual scenes requires not only recognizing objects but also reasoning about their spatial relationships. Unlike general vision-language tasks, spatial reasoning requires integrating multiple inductive biases, such as 2D appearance cues, depth signals, and geometric constraints, whose reliability varies across contexts. This motivates spatial adaptability: the ability to flexibly coordinate reasoning strategies depending on the input. However, most existing approaches rely on a single reasoning pipeline that implicitly learns a fixed spatial prior, limiting their ability to adapt under distribution changes. Multi-agent systems offer a promising alternative by aggregating diverse reasoning trajectories, but prior spatial reasoning attempts primarily employ homogeneous agents, restricting the inductive-bias diversity they can leverage. In this work, we introduce SpatiO, a heterogeneous multi-agent framework for spatial reasoning that coordinates multiple vision-language specialists with complementary inductive biases. To enable effective collaboration, we propose Test-Time Orchestration (TTO), a calibration mechanism that dynamically evaluates and reweights agents based on their observed reliability during inference, without modifying model parameters. Extensive experiments on diverse spatial reasoning benchmarks, including 3DSRBench, STVQA-7k, CV-Bench, and Omni3D-Bench, demonstrate that SpatiO consistently improves spatial reasoning performance over both closed-source and open-source baselines. The project page is available at https://cy-h1329.github.io/spatio/.

Abstract:
Despite the remarkable progress of large vision languagemodels (LVLMs), object hallucination remains a fundamental challengethat hinders their trustworthy deployment. A key finding motivates ourwork: real and hallucinated object tokens are clearly separable in hid-den representations, yet this separability is largely lost at the language-modeling (LM) head. We propose TruthLens, a self-evaluation frame-work that teaches the LM head to expose a per-object truthfulness sig-nal without any auxiliary model or additional inference cost. Concretely,a rarely-used special token is repurposed as a reference token. For eachobject-token position, we extract the log-probability assigned to this spe-cial token by the LM head, and define its difference from a predefinedconstant as the truthfulness score. The model is then fine-tuned withan MSE objective that drives scores toward 1 for real objects and 0for hallucinated ones, while a divergence constraint preserves the origi-nal generation capability. Despite being trained on only a limited set ofobject categories, TruthLens generalizes effectively to benchmarks withsubstantially larger label spaces. Extensive experiments across multipleLVLMs demonstrate state-of-the-art performance; notably, on Qwen2.5-VL-7B, TruthLens outperforms the previous best method on MS-COCOby over 17% in AUROC. Our code is available at https://github.com/wyqstan/TruthLens.

Abstract:
Medical Large Vision-Language Models (Med-LVLMs) haveemerged as powerful tools for medical image understanding and au-tomated reporting. However, existing models often suffer from modal-ity bias, where correlations between background context and diagnos-tic labels cause over-reliance on spurious priors rather than lesion evi-dence, leading to clinically irrelevant or hallucinated outputs. To addressthis, we propose a Counterfactual Medical Preference Optimization(CoMedPO) framework that learns an unbiased policy from a biasedreference model. Unlike conventional direct preference optimization (DPO)methods that inherit dataset biases, CoMedPO mitigates spurious cor-relations while preserving the valuable indirect effect between lesionand background dependencies in medical images. This causal formula-tion yields a new preference-based loss with theoretical guarantees forbias mitigation and robust policy learning. Our framework is model-agnostic and seamlessly integrates with existing Med-LVLM alignmentpipelines. Extensive experiments on Med-VQA and medical report gen-eration demonstrate that CoMedPO consistently improves factual accu-racy and clinical relevance, outperforming state-of-the-art DPO variants.These results suggest that CoMedPO provides a principled route towardtrustworthy and clinically grounded multimodal medical reasoning. Ourcode is available at https://github.com/zxgapollo/CoMedPO.

Abstract:
Model editing promises a fast, targeted way to correct postdeployment mistakes in medical vision–language models (VLMs) without costly retraining. However, existing multimodal model editing benchmarks focus on general-purpose tasks and do not reflect realistic clinical domain requirements and variability. To address this, we introduce M3Bench, a clinically grounded benchmark for multimodal model editing that evaluates whether an edit remains reliable, precise and generalizable under the challenges of image and text variation, modality and protocol shifts, clinical knowledge composition, and temporal progression. M3Bench contains 16,276 questions spanning diverse anatomy, modalities, and specialties, and supports both single and sequential edits: Evaluating 4 representative editors across 6 medical and general VLMs, we indicate that no method excels across all criteria. Gradient-based editors achieve strong transfer but suffer from catastrophic locality violations, whereas memory-based methods preserve locality but lack compositional generality and exhibit high backbone-dependent hyperparameter sensitivity. We further attribute these failures to the latent space geometry of VLMs and how different editing methods shift its landscape. Overall, M3Bench establishes a rigorous clinical stress test for multimodal model editing and offers actionable guidance for safer post-deployment adaptation. The benchmark is publicly available at https://github.com/BioMed-AI-Lab-U-Michgan/M3Bench.

Abstract:
Recent Vision-Language-Action (VLA) models report im-pressive success rates on robotic benchmarks, but whether these scoresreflect genuine embodied reasoning remains questionable. To addressthis gap, we introduce BeTTER, a diagnostic Benchmark for TestingTrue Embodied Reasoning. By applying targeted causal interventions(e.g., spatial layout shifts and temporal extrapolation) under strict kine-matic isolation, BeTTER decouples high-level cognitive failures fromlow-level execution errors. Systematic evaluations reveal that state-of-the-art VLAs break down under these interventions, exhibiting lexical-kinematic shortcuts, semantic feature collapse, behavioral inertia, andcausal state-tracking failures. Our analysis traces these failures to twocoupled sources: real-time deployment constraints, such as capacity com-pression and myopic perception, which weaken semantic representations,and behavioral cloning, which can amplify predictive but non-causal cor-relations into shortcut policies under static training distributions. Wefurther show that highly static evaluations mask these defects by per-mitting overfitting to sensorimotor priors. Real-world robotic validationconfirms that these failures are not simulation artifacts, highlighting theneed for future VLA paradigms to preserve semantic and causal reason-ing while maintaining efficient continuous control. Code and benchmark:https://github.com/BeingBeyond/BeTTER.

Abstract:
Multimodal Large Language Models (MLLMs) have demon-strated remarkable abilities when analyzing images, yet translating thesecapabilities to few-shot image classification remains challenging. To bridgethis gap, we present DeCoDe, a simple yet effective technique that en-ables off-the-shelf MLLMs to act as strong few-shot classifiers withoutany additional training. Our approach builds on the idea of few-shotclassification as a set of pairwise image comparisons, decomposing thetask into a set of binary decisions. Given a query image and a supportimage from a candidate class, the MLLM is prompted to decide whetherthe two images depict the same class. The logit corresponding to an af-firmative response is then used as a similarity score to assign the queryimage to the most likely class. While this already yields good results,we show that providing additional high-level information, such as thedata domain, to the model further improves performance. Our evalu-ation provides an extensive analysis of various inference variants on asuite of twelve datasets, six established and six newly curated few-shotbenchmarks spanning across diverse domains. The results show that theproposed simple decomposition technique can turn off-the-shelf MLLMsinto powerful few-shot learners, significantly outperforming current state-of-the-art few-shot methods on both standard and novel domains. Codeis available at https://github.com/yunhanwang1105/DeCoDe.

Abstract:
Vision-language models (VLMs) often underperform on evi-dence intensive tasks because decisive visual evidence are small, localized,and easy to overlook, leading to failures in evidence readout even whenhigh-level reasoning is intact. Prior inference-time visual interventionscan improve grounding without retraining, but they are largely open-loop and lack a mechanism to verify whether highlighted evidence is ac-tually used. We study answer-span prediction entropy as a model-internalfeedback signal and show that naive entropy minimization is ambiguous,since low entropy may arise from evidence-grounded confidence or short-cut collapse. To resolve this ambiguity, we introduce low-entropy anchorsand an entropy-shaping objective that reduces answer uncertainty whilepreserving baseline high-confidence tokens. We instantiate this principlein SPOT-E, a plug-and-play test-time method that produces question-conditioned spotlights, optimized per instance via light-weight tuningbased on Group Relative Policy Optimization (GRPO). Across all bench-marks and different VLM families, SPOT-E yields consistent gains andimproved robustness under visual corruptions. Code is publicly availableat: https://github.com/YinBo0927/SPOT-E

Abstract:
Vision-Language-Action (VLA) models have emerged as apromising paradigm for end-to-end autonomous driving. However, exist-ing VLAs’ traffic relies heavily on text-centric visual question answeringand chain-of-thought reasoning data, which emphasizes linguistic rea-soning rather than action-grounded planning. As a result, the learnedrepresentations capture semantic knowledge but lack spatial dependenciescrucial for reliable trajectory prediction. We propose DriveTeach-VLA,a framework that explicitly teaches VLAs what to see and where tolook. Driving-aware Vision Distillation (DVD) injects driving-specificperceptual priors into the vision encoder, while 2D Trajectory-GuidedPrompts (2D-TGP) provide spatial conditioning aligned with feasibledriving trajectories. Together they form a vision-guided learning pipeline:what to see (DVD pretraining) → where to look (TGP-guided SFT) →how to act (TGP-guided GRPO). DriveTeach-VLA achieves the state-of-the-art performance on NAVSIM and nuScenes. Our code is available at:https://github.com/ShivaTeam/DriveTeach-VLA.

Abstract:
Recent advancements in vision-language models (VLMs) haveleveraged large language models (LLMs) to achieve performance on parwith closed-source systems like GPT-4V. However, deploying these mod-els in real-world scenarios, particularly on resource-constrained devices,remains challenging due to their substantial computational demands.This has spurred interest in distilling knowledge from large VLMs intosmaller, more efficient counterparts. A key challenge arises here from thediversity of VLM architectures, which are built on different LLMs andemploy varying token types—differing in vocabulary size, token splits,Changing Teacher VLMs for Distillation MMMU70GenRecalMMB GPT-4oInternVL2.5-78B → InternVL2.5-8B60 Qwen2-VLAccuracy (%)MM-Vet InternVL2.550 MolmoMMMU40NVLM-72B → InternVL2.5-8B LLaVA-OneVisionInternVL2-76B → InternVL2.5-8BMMMU-Pro Qwen2-VL-72B → InternVL2.5-8BInternVL2.5-78B → InternVL2.5-8B300 20 40 60 80 1 2 4 7 8 … 72 78Accuracy (%) Model Size (B)Fig. 2: (Left) Comparison of the challenging benchmark performances, MMB [36], MM-Vet [63], MMMU [65], and MMMU-Pro [66] by changing large VLMs. The more pow-erful large VLMs we select, the greater the performance improvement we can achieve.(Right) Comparing the performance of the challenging benchmark: MMMU [65], withGenRecal and various VLMs across model sizes. Note that all the experiments in Fig. 1and Fig. 2 are conducted on the equal training dataset.and token index ordering. To address this challenge of limitation to aspecific VLM type, we present Generation after Recalibration (Gen-Recal), a general-purpose distillation framework for VLMs. GenRecalincorporates a Recalibrator that aligns and adapts feature representa-tions between heterogeneous VLMs, enabling effective knowledge trans-fer across different types of VLMs. Through extensive experiments onmultiple challenging benchmarks, we demonstrate that GenRecal signif-icantly improves baseline performances, eventually outperforming large-scale open- and closed-source VLMs.

Abstract:
Large Vision-Language Models (LVLMs) excel at multimodaltasks but remain prone to object hallucinations. Prior training-free reme-dies often uniformly strengthen visual signals, which may also amplifyirrelevant regions and introduce spurious evidence, harming fluency. Wepropose Context-aware Attention Intervention (CAI), a training-free inference-time mechanism that enforces a see only when needed prin-ciple via two-axis selectivity: where to look and when to intervene. Ateach decoding step, CAI derives token-specific visual relevance fromearly-layer representations to localize semantically aligned regions, andapplies a conservative, entropy- and depth-gated attention tilt only foruncertainty-spiking tokens in deeper layers where visual grounding de-grades, leaving confident tokens and irrelevant regions largely unchanged.This targeted intervention strengthens visual grounding while preserv-ing linguistic fluency, and it yields consistent improvements even with-out contrastive decoding, which remains optional as an auxiliary bias-suppression module. Extensive experiments across multiple LVLM back-bones and benchmarks show that CAI achieves state-of-the-art halluci-nation mitigation, and our analysis characterizes CAI as a KL-minimalattention reweighting with bounded interference under inactive gates orsmall tilts. Code is available at https://github.com/Iris1946/CAI.

Abstract:
Vision-Language Models (VLMs) have demonstrated excep-tional performance in various multi-modal tasks. Recently, there hasbeen an increasing interest in improving the personalization capabilitiesof VLMs. To better integrate user-provided concepts into VLMs, manymethods use positive and negative samples to fine-tune these models.However, the scarcity of user-provided positive samples and the low qual-ity of retrieved negative samples pose challenges for existing techniques.To reveal the relationship between sample and model performance, wesystematically investigate the amount and diversity impact of positiveand negative samples (easy and hard) on VLM personalization tasks.Based on the detailed analysis, we introduce Concept-as-Tree (CaT),which represents a concept as a tree structure, thereby enabling the datageneration of positive and negative samples with varying difficulty anddiversity, and can be easily extended to multi-concept scenarios. Witha well-designed data filtering strategy, our CaT framework can ensurethe quality of generated data, constituting a powerful pipeline. We per-form thorough experiments with various VLM personalization baselinesto assess the effectiveness of the pipeline, alleviating the lack of positivesamples and the low quality of negative samples. Our results demon-strate that CaT equipped with the proposed data filter significantlyenhances the capabilities of VLMs across personalization benchmarks.To the best of our knowledge, this work is the first controllable syn-thetic data pipeline for VLM personalization. The code is released athttps://github.com/zengkaiya/CaT.

Abstract:
Large Vision-Language Models (LVLMs) rely extensively onVisual Instruction Tuning (VIT) to elicit their multimodal reasoning ca-pabilities. However, we find a discrepancy: VIT often packs multiple lan-guage tasks about the same image for conversational, multi-turn training,whereas existing benchmarks evaluate LVLMs in isolated, single-turn sce-narios. The models can suffer from visual attention decay and contextualoverfitting during multi-turn training, making it hard for them to realizetheir full potential in the mismatched test phase. To close the gap, wepropose learning with Stochastic Turn Depth (StochasT), which stochas-tically groups language tasks for the same image into clusters of vary-ing sizes (turn depth) while preserving their organic order. Hence, whileStochasT draws on Dropout and stochastic depth for ResNets, it doesnot actually drop anything to maximize the utility of the training data.Furthermore, we introduce a challenging, benchmark-agnostic evaluationmechanism based on the Balanced Latin Square to measure LVLMs’ ro-bustness under varying contextual dependencies. Extensive experimentsdemonstrate that StochasT effectively grants LVLMs strong, harmonizedcapabilities for both single-turn and multi-turn use cases. Code is avail-able at: https://yuanqing-ai.github.io/StochasT.

Abstract:
Adverse weather image restoration aims to recover clear vis-ibility from degraded images in complex weather conditions. Existingworks attempt to address this problem by modeling relationships betweenpixels, however, this paradigm defies the spatially non-uniformity factof degradations and learns non-discriminative features from semantic-conflict regions. In this paper, we propose SSR, a Semantic-center guildedState space model for image Restoration. The key idea of SSR is to shiftthe conventional scanning strategy of pixel-serial to semantic-guildedone. Specifically, we introduce a Superpixel-guided Selective Scan Mech-anism (S3 M), which first partitions the image into perceptually coher-ent regions via superpixel clustering and then performs relations mod-eling within the semantic-related regions. Moreover, a Region-level Gat-ing Mechanism (RGM) is developed to perform intra-region calibra-tion by modulating degradation outliers within each semantic super-pixel unit along the channel dimension. Extensive experiments on 6well-established benchmarks demonstrate that SSR performs favorablyagainst state-of-the-art models with competitive computational cost. Thesource code is publicly available at https://github.com/LIDAYU-DayuLi/SSR.

Abstract:
Lookup table (LUT)-based image denoising methods haveattracted increasing attention due to their high efficiency and hardware-friendly properties. However, existing RGB-LUT approaches require threeidentical LUTs to process RGB channels in parallel, resulting in largeon-chip SRAM consumption. A simple alternative is to apply LUT pro-cessing only to the luminance (Y) channel in the YUV color space toreduce memory usage. However, this naive strategy leads to degradedrestoration quality, since ignoring the chrominance (UV) channels in-troduces color distortion and residual artifacts. In this work, we pro-pose Hybrid-LUT, a YUV-based asymmetric channel-processing frame-work that combines LUT and filtering in a unified design. Specifically, amulti-band LUT branch with pixel-level weight fusion is applied to theY channel to recover fine textures, while lightweight filtering is used forthe UV channels to maintain color consistency. This design reduces LUTstorage by two-thirds compared with RGB-LUT methods while main-taining the same runtime throughput. Extensive experiments show thatHybrid-LUT achieves state-of-the-art (SOTA) performance across mul-tiple benchmarks with only 421 KB of storage. In particular, our methodsurpasses existing LUT-based denoising approaches by at least 0.63 dBCPSNR on real-world datasets, demonstrating its effectiveness for imagedenoising on resource-constrained edge devices.

Abstract:
Creating 3D visual illusions, a single 3D mesh that revealsentirely different semantics from various viewing angles, is a fascinat-ing but tough challenge. Existing optimization-based methods are slowand can produce oversaturated colors. In contrast, naive stitching ap-proaches fail to produce geometrically coherent objects. This results invisible unnatural seams and semantic leaks. In this paper, we presenta fast and training-free framework for generating text-driven 3D visualillusions. Our approach decouples the generation into two stages. First,we propose a cross-space dual-branch denoising process. This processdynamically decodes 3D latents into voxel space for CLIP-guided orien-tation alignment and Signed Distance Field (SDF) blending, which en-sures seamless geometric fusion. Second, we introduce a view-conditionedtexture synthesis module that projects and aggregates view-specific 2Ddiffusion priors onto the fused geometry. Extensive experiments demon-strate that our method generates highly realistic, dual-semantic 3D illu-sions in just 3–5 minutes. It significantly outperforms existing methodsin geometric integrity, semantic recognizability, and efficiency. Projectpage: https://siang1105.github.io/JanusMesh.github.io/Target Junction view Target Junction view Target Junction viewview view view“A parrot”𝜃" 𝜃" 𝜃"Target Target Targetview view view𝜃! 𝜃! 𝜃!“A boat”❌ Severe over-saturation ❌ Visible backside leakage ✅ Dual-semantic coherency❌ Slow optimization (~40 min) ❌ Unnatural geometric seams ✅ Fast & Training-free (~3-5 min)(a) SDS-Based Method (b) Direct Concatenation (c) OursFig. 2: Comparison of 3D visual illusion generation methods. (a) SDS-BasedMethods suffer from severe over-saturation and slow optimization. (b) Direct Con-catenation exposes unnatural geometric seams and semantic leakage at target views(red arrows). (c) Our method creates a seamless, dual-semantic coherent 3D mesh.Unlike previous approaches, our method does not require training. It generates high-quality 3D visual illusions in just 3–5 minutes while completely preventing geometricinterference between the two semantics.

Abstract:
While the field of co-speech gesture generation has seen sig-nificant advances, producing holistic, semantically grounded gestures re-mains a challenge. Existing approaches rely on external semantic re-trieval methods, which limit their generalisation capability due to de-pendency on predefined linguistic rules. Flow-matching-based methodsproduce promising results; however, the network is optimised usingonly semantically congruent samples without exposure to negative ex-amples, leading to learning rhythmic gestures rather than sparse mo-tion, such as iconic and metaphoric gestures. Furthermore, by modellingbody parts in isolation, the majority of methods fail to maintain cross-modal consistency. We introduce a Contrastive Flow Matching-basedco-speech gesture generation model that uses mismatched audio–textconditions as negatives, training the velocity field to follow the cor-rect motion trajectory while repelling semantically incongruent trajec-tories. Our model ensures cross-modal coherence by embedding text,audio, and holistic motion into a composite latent space via cosine andcontrastive objectives. Extensive experiments and a user study demon-strate that our proposed approach outperforms state-of-the-art methodson two datasets, BEAT2 and SHOW. Our project page is available at:https://marcos452.github.io/HoliticSemGes/

Abstract:
Reconstructing high-fidelity, relightable 3D avatars from asingle in-the-wild image is a challenging ill-posed problem, primarilyhindered by the scarcity of high-quality PBR data and the complex-ity of disentangling illumination from intrinsic materials. In this paper,we present a data-efficient framework that leverages the robust priorsof a unified pre-trained diffusion backbone to sequentially address tex-ture completion, delighting, and material decomposition. Unlike exist-ing methods that rely on fragmented pipelines or extensive proprietarydatasets, we utilize cascaded Low-Rank Adaptations (LoRAs) to adaptthe strong generative prior of the diffusion model for each sub-task inUV space. Specifically, we first employ an Inpainting LoRA to completemissing UV textures caused by occlusion, leveraging the model’s seman-tic understanding to generate semantically and photometrically coher-ent details. Subsequently, a Light-Homogenization LoRA and a novelCross-Intrinsic Attention mechanism are introduced to remove baked-inlighting and collaboratively synthesize pixel-aligned PBR maps (Albedo,Normal, Roughness, Specular, and Displacement). To ensure physicalplausibility, we impose a UV-space differentiable BRDF shading lossduring the decomposition stage, forcing the generative process to adhereto the rendering equation without the artifacts typical of rasterization-based supervision. Extensive experiments demonstrate that our method,trained on fewer than 100 real 3D scans, generates comprehensive, 4K-resolution PBR assets with superior realism and generalization comparedto state-of-the-art methods, and all training code and model weights willbe released upon acceptance.

Abstract:
There are two prevalent ways for automatic 3D scene con-struction: procedural generation and 2D lifting. Among these, panorama-based 2D lifting has emerged as a promising technique, leveraging pow-erful 2D generative priors to produce immersive, realistic, and diverse3D environments. In this work, we advance this technique to generategraphics-ready 3D scenes suitable for physically based rendering (PBR),relighting, and simulation. Our key insight is to repurpose 2D generativemodels for panorama perception of geometry, textures, and PBR mate-rials. Unlike existing 2D lifting approaches that emphasize appearancegeneration and neglect the perception of intrinsic properties, we presentOmniX, a versatile and unified framework for panorama generation, per-ception, and completion. Built upon cross-modal adapter structure andcyclic spatial operators, OmniX effectively repurposes pre-trained 2Dflow matching priors for joint modeling of multimodal, seamless equirect-angular representations. Furthermore, we construct a large-scale syn-thetic panorama dataset comprising high-quality multimodal panoramasfrom diverse indoor and outdoor scenes. Extensive experiments demon-strate the effectiveness and generality of OmniX as a unified frameworkfor panorama generation and perception across geometry, lighting, andsemantics, enabling graphics-ready 3D scene generation and opening newpossibilities for immersive and physically realistic virtual world creation.Project page is available at https://yukun-huang.github.io/OmniX/.

Abstract:
Latent diffusion models (LDMs) achieve state-of-the-art im-age synthesis, yet their reconstruction-style denoising objective providesonly indirect semantic supervision: high-level semantics emerge slowly,requiring longer training and limiting sample quality. Recent works injectsemantics from Vision Foundation Models (VFMs) either externally viarepresentation alignment or internally by jointly modeling only a nar-row slice of VFM features inside the diffusion process, under-utilizingthe rich, nonlinear, multi-layer spatial semantics available. We intro-duce REGLUE (Representation Entanglement with Global-Local UnifiedEncoding), a unified latent diffusion framework that jointly models (i)VAE image latents, (ii) compact local (patch-level) VFM semantics, and(iii) a global (image-level) [CLS] token within a single SiT backbone.A lightweight convolutional semantic compressor nonlinearly aggregatesmulti-layer VFM features into a low-dimensional, spatially structuredrepresentation, which is entangled with the VAE latents in the diffusionprocess. An external alignment loss further regularizes internal repre-sentations toward frozen VFM targets. On ImageNet 256×256, REGLUEconsistently improves FID and accelerates convergence over SiT-B/2 andSiT-XL/2 baselines, as well as over REPA, ReDi, and REG. Extensive exper-iments show that (a) spatial VFM semantics are crucial, (b) non-linearcompression is key to unlocking their full benefit, and (c) global tokensand external alignment act as complementary, lightweight enhancementswithin our global–local–latent joint modeling framework. The code isavailable at https://github.com/giorgospets/reglue.

Abstract:
Training Latent Diffusion Models (LDMs) within FederatedLearning (FL) has attracted increasing attention due to its ability tocombine the powerful generative capacity of LDMs with the privacy-preserving properties of FL. However, FL requires sharing the globalmodel with multiple participants, which risks unauthorized model dis-tribution or resale by malicious clients. While an intuitive approach isto adopt existing VAE-based watermarking techniques for LDMs in FL,this strategy falls short in addressing such threats due to two fundamen-tal challenges: (1) Existing methods support ownership verification butlack the ability to trace model leakage to a specific malicious client; (2)VAE-based watermarks are vulnerable, as they can be removed simplyby replacing the decoder with a clean counterpart. In this paper, we pro-pose FedOT, the first framework for ownership verification and leakagetracing in federated LDMs. Specifically, to address the first challenge, wedesign a chunked watermark, where the first part is for ownership verifi-cation, and the second part is used for client identification. Furthermore,to overcome the second challenge and secure the model against VAEreplacement attack, we introduce Latent Vector Transformation (LVT),which strengthens the connection between the VAE and U-Net latentspaces by modifying the original latent distribution of the VAE. Conse-quently, any attempt to replace the VAE for watermark removal leads tosignificant image quality degradation, making the LDM model unusable.Extensive experiments demonstrate that FedOT achieves superior per-formance in both ownership verification and traceability. Project page:https://spyzixuan.github.io/FedOT/.

Abstract:
3D Gaussian Splatting (3DGS) achieves impressive rendering quality but degrades drastically under sparse views. Through optimization manifold analysis, we reveal that sparse supervision produces anisotropic loss surfaces where rendering quality collapses sharply along three geometric fragility axes: position shifts, rotation/scale ill-conditioning, and opacity instability. Guided by this analysis, we propose Stable 3DGS (StableGS), which steers optimization toward isotropic stability via geometric probing. Unlike the generic, uniform smoothing of methods like SAM, which cannot fit 3DGS, StableGS uses domain-specific geometric probes to target the splatting operator’s unique instabilities. Our approach comprises: (1) Attribute-Space Probing that varies each Gaussian’s attributes based on Hessian-informed sensitivity analysis to measure rendering fragility, then minimizes loss at probe points; (2) Viewpoint-Space Probing that samples geometrically critical poses and enforces consistency between base and probed configurations. StableGS significantly advances state-of-the-art (SOTA) performance across multiple benchmarks, achieving substantial improvements such as +0.64dB PSNR on DTU [14] with 74% anisotropy reduction in optimization manifold. Code is available at https://github.com/zyl123456aB/StableGS.

Abstract:
Multi-view 3D surface reconstruction is a longstanding challenge in computer vision. Although recent large-scale reconstruction methods based on 3D Gaussian Splatting (3DGS) achieve impressive novelview synthesis, producing high-quality surfaces over large scenes remains difficult, due to complex geometry, long optimization, and limited memory. In this paper, we propose a novel yet simple partitioning method to efficiently and faithfully reconstruct large-scale scene surfaces. Our key insight lies in a scene partitioning method based on viewpoint orientation. This partitioning approach ensures that views with similar orientations are jointly involved for more accurate depth estimations, leading to precise surface reconstructions and balanced computation on multiple GPUs in parallel. In addition, we propose a strategy to detect and repair missing regions in the initial point cloud caused by sparse viewpoints or insufficient textures, thereby further improving the geometric quality. Extensive experiments on the GauU-Scene, MatrixCity, and UrbanScene3D datasets demonstrate that our method outperforms the state-of-the-art approaches in surface reconstruction for large-scale scenes. Project page: https://hanl2010.github.io/VOP-GS. 2 L. Han et al.

Abstract:
We propose a feed-forward method for dense Signed DistanceField (SDF) regression from unstructured image collections in less thanthree seconds, without camera calibration or post-hoc fusion. Our keyinsight is that the intermediate feature space of pretrained multi-viewfeed-forward geometry transformers (FFGT) already encodes a powerfuljoint world representation; yet, existing pipelines discard it, routing fea-tures through per-view prediction heads before assembling 3D geometrypost-hoc, which discards valuable completeness information and accu-mulates inaccuracies. We instead perform 3D extraction directly fromFFGT features via learned volumetric extraction: voxelized canonicalembeddings that progressively absorb multi-view geometry informationthrough interleaved cross- and self-attention into a structured volumet-ric latent grid. A simple convolutional decoder then maps this grid to adense SDF. We additionally propose a scalable, validity-aware supervi-sion scheme directly using SDFs derived from depth maps or 3D assets,tackling practical issues like non-watertight meshes. Our approach yieldscomplete and well-defined distance values across sparse- and dense-viewsettings and demonstrates geometrically plausible completions.

Abstract:
High-fidelity reconstruction of large-scale aerial scenes using3DGS confronts dual challenges: restricted nadir views result in facade"blind spots" and geometric collapse in textureless regions, while themassive scale of high-resolution data induces severe computational loadimbalance during parallel training. In this paper, we propose DPGS, aholistic framework that orchestrates visual foundation model priors andgenerative diffusion priors to achieve robust reconstruction. First, we in-troduce a foundation model-driven pixel-wise dense initialization to mit-igate geometric incompleteness from sparse SfM points, effectively pre-venting floating artifacts in weak-texture areas. Second, to address com-putational bottlenecks, we propose an observation density balanced par-titioning method. By formulating a graph partitioning problem weightedby feature track lengths and observation frequency, we dynamically bal-ance the rendering load across sub-blocks. Finally, we devise a diffusion-prior guided geometry completion mechanism. Leveraging a 3D-awarediffusion model with spiraling side-view sampling, we recover texturesfor unobserved facade blind spots. DPGS achieves state-of-the-art ren-dering quality, geometric completeness, and efficiency across standardbenchmarks and a self-collected dataset. Project page: https://zsddd.github.io/dpgs.

Abstract:
Advances in radiance fields have enabled photorealistic novelview synthesis. In several domains, large-scale real-world datasets havebeen developed to support comprehensive benchmarking and to facilitateprogress beyond scene-specific reconstruction. However, for distractor-free radiance fields, a large-scale dataset with clean and cluttered imagesper scene remains lacking, limiting the development. To address thisgap, we introduce DF3DV-1K, a large-scale real-world dataset compris-ing 1,048 scenes, each providing clean and cluttered image sets for bench-marking. In total, the dataset contains 89,924 images captured using con-sumer cameras to mimic casual capture, spanning 17 scenario types, 128distractor types, and 161 scene themes across indoor and outdoor envi-ronments. A curated subset of 41 scenes, DF3DV-41, is systematicallydesigned to evaluate the robustness of distractor-free radiance field meth-ods under challenging scenarios. Using DF3DV-1K, we benchmark ninerecent distractor-free radiance field methods and 3D Gaussian Splatting,identifying the most robust methods and the most challenging scenarios.Beyond benchmarking, we demonstrate an application of DF3DV-1K byfine-tuning a diffusion-based 2D enhancer to improve radiance field meth-ods, achieving average improvements of 0.96 dB PSNR and 0.057 LPIPSon the held-out set (e.g., DF3DV-41) and the On-the-go dataset. We hopeDF3DV-1K facilitates the development of distractor-free vision and pro-motes progress beyond scene-specific approaches. The dataset and leader-board are available at https://johnnylu305.github.io/df3dv1k_web/.

Abstract:
Instant reconstruction of dynamic humans and 3D human-object interactions from uncalibrated sparse-view videos is critical fornumerous downstream applications. Existing methods, however, are ei-ther limited by the slow reconstruction speeds or incapable of generat-ing novel-time representations. To address these challenges, we proposeForge4D, a feed-forward 4D human-object reconstruction and interpo-lation model that efficiently reconstructs temporally aligned represen-tations from uncalibrated sparse-view videos, enabling both novel-viewand novel-time synthesis. Our model simplifies the 4D reconstructionand interpolation problem as a joint task of streaming 3D Gaussian re-construction and dense motion prediction. For the task of streaming 3DGaussian reconstruction, we introduce learnable state tokens to enforcetemporal consistency in a memory-friendly manner. For novel-time syn-thesis, we design a novel motion prediction module to predict dense mo-tions for each 3D Gaussian between two adjacent frames. To overcomethe lack of the ground truth for dense motion supervision, we formulatedense motion prediction as a dense point matching task and introducea self-supervised retargeting loss to optimize this module. An additionalocclusion-aware optical flow loss is introduced to ensure motion consis-tency with plausible human and object movement, providing strongerregularization. Extensive experiments demonstrate the effectiveness ofour model on both in-domain and out-of-domain datasets. Code andmodels will be made publicly available.

Abstract:
Existing Multi-view Anomaly Detection (MAD) methods as-sume that all views are completely available and model each view sep-arately. However, in real industrial scenarios, information in the viewmay be missing due to faults such as occlusion, which leads to the per-formance degradation of existing methods due to the lack of a multi-viewconsistency prior. To address this, we explored a more challenging task:Incomplete Multi-View Anomaly Detection (IMVAD), in which someareas of each view were masked. We proposed a pipeline for automati-cally generating the IMVAD dataset and generated the RIMAD datasetbased on the Real-IAD dataset through this pipeline. In addition, in or-der to effectively utilize the information of multiple views in the absenceof view information, we propose IMMoE, which consists of two keymodules: (1) Multi-View Expert Fusion (MVEF) effectively fuses multi-view information through a multi-view expert network and guides thereconstruction of a single view; (2) Local Anomaly Enhancement En-coder (LAEE) effectively prevents the model from overfitting the maskregion by applying dropout to local features. Our method achieves state-of-the-art performance on both the RIMAD and Real-IAD datasets, es-pecially on RIMAD, we have increased the pixel-level and image-levelmetrics by 11.8% and 2.8%, respectively. Our source code is available athttps://github.com/HULEI7/IMMoE.

Abstract:
3D Gaussian Splatting (3DGS) has emerged as a promis-ing paradigm for reconstructing streamable free-viewpoint video (FVV)from multi-view videos. However, 3DGS-based FVVs typically lack userinteraction and editing capabilities, which diminishes the immersive ex-perience. Recent research has integrated language features from CLIPinto 3DGS via distillation, enabling open-vocabulary queries and sup-porting many downstream applications. Nevertheless, the stringent re-quirements of FVV, low frame size and high FPS, make current languageGaussian representations unsuitable for language-embedded FVV. In thispaper, we propose DLGStream, a novel language-embedded FVV repre-sentation that streams time-varying language features alongside Gaus-sian attributes to support 4D environment interaction, scene editing, andspatial intelligence. Specifically, we propose a dual-opacity dynamic lan-guage Gaussian representation, which maintains two opacity attributesfor color and language features to deal with performance degradationthat occurs when colors and features are jointly optimized. Further-more, we introduce an interpolation-based deformation field to reducetemporal redundancy. This deformation field can also be used for 4Dframe interpolation, boosting FVV sequences from low to high FPS. Ex-perimental results demonstrate that DLGStream achieves superior per-formance in both on open-vocabulary segmentation and reconstructionquality with an average frame size of merely 43 KB. The code is availableon https://github.com/kkkzh/DLGStream.

Abstract:
Few-shot 3D Point Cloud Semantic Segmentation (FS-PCS)aims to predict per-point labels for an unlabeled point cloud, given onlya few labeled examples. To extract representations from the limited la-beled set, existing methods have constructed prototypes with FarthestPoint Sampling (FPS). However, we found that this convention resultsin performance instability due to its sensitivity to FPS-induced varia-tions, while the prototype generation process remains underexplored inthe field. This motivates us to investigate deterministic prototype gen-eration method based on attention mechanism. Despite its potential, wefound that vanilla attention module suffers from the distributional gapbetween prototypical tokens and support features. To overcome this, weprovide a simple approach, White Aggregation and Restoration Module(WARM), which resolves the misalignment by wrapping cross-attentionwith whitening and coloring transformations. Specifically, whitening alig-ns the features to tokens before the attention process, and coloring subse-quently restores the original distribution to the attended tokens. This de-sign enables robust attention, thereby generating prototypes that capturethe semantic relationships in support features. WARM achieves state-of-the-art performance with a significant margin on the S3DIS dataset, andcompetitive performance on the ScanNet dataset. Further experimentsdemonstrate its effectiveness in deterministic prototype generation. Codeis publicly available at: https://github.com/JiyunIm00/WARM.git

Abstract:
Online 3D reconstruction models perform poorly on longvideos. This happens because regressing poses relative to a fixed first-frameanchor forces extrapolation far beyond the training distribution. Smalldrifts accumulate and amplify into significant geometric collapse. However,we observe that per-frame depth remains stable throughout this failure.The backbone’s local geometry remains intact; only the global pose headbreaks down. Motivated by this decoupling, we introduce Scal3R. Thisapproach reformulates online reconstruction as multi-reference relativepose querying. We use lightweight learnable tokens, which make upabout ∼1% of the parameters, and inject them into a completely frozenbackbone via asymmetric attention. This setup queries poses relative tomultiple past keyframes. An online pose-graph optimization system withloop closure suppresses long-range drift. Scal3R reaches convergence in8 hours on a single GPU. It reduces the average ATE by over 60% onKITTI compared to the online baseline. It also achieves state-of-the-artperformance across Virtual KITTI, Sintel, TUM-Dynamic, ScanNet, and7-Scenes. Project page: https://linjohnss.github.io/scal3r/

Abstract:
Semantic segmentation for uncrewed aerial vehicles (UAVs) isfundamental for aerial scene understanding, yet existing RGB and RGB-Tdatasets remain limited in scale, diversity, and annotation efficiencydue to the high cost of manual labeling and the difficulties of accurateRGB-T alignment on off-the-shelf UAVs. To address these challenges, wepropose a scalable geometry-driven 2D-3D-2D paradigm that leveragesmulti-view redundancy in high-overlap aerial imagery to automaticallypropagate labels from a small subset of manually annotated RGB imagesto both RGB and thermal modalities within a unified framework. Bylifting less than 3% of RGB images into a semantic 3D point cloud andrendering it into all views, our approach enables dense pseudo ground-truth generation across large image collections, automatically producing97% of RGB labels and 100% of thermal labels while achieving 91% and88% annotation accuracy without any 2D manual refinement. We furtherextend this 2D–3D–2D paradigm to cross-modal image registration, using3D geometry as an intermediate alignment space to obtain fully automatic,strong pixel-level RGB-T alignment with 87% registration accuracy and nohardware-level synchronization. Applying our framework to existing geo-referenced aerial imagery, we construct SegFly, a large-scale benchmarkwith over 20,000 high-resolution RGB images and more than 15,000geometrically aligned RGB-T pairs spanning diverse urban, industrial, andrural environments across multiple altitudes and seasons. On SegFly, weestablish the Firefly baseline for RGB and thermal semantic segmentationand show that both conventional architectures and vision foundationmodels benefit substantially from SegFly supervision, highlighting thepotential of geometry-driven 2D-3D-2D pipelines for scalable multi-modalaerial scene understanding. The SegFly dataset and our Firefly baselineare available at https://github.com/markus-42/SegFly.

Abstract:
Masked autoencoding has emerged as a prominent paradigmfor self-supervised learning on 3D point clouds, achieving competitiveperformance across downstream tasks. Unlike its 2D counterpart, 3Dmasked autoencoding directly reconstructs spatial coordinates, makingit inherently susceptible to positional leakage. In this work, we identifythat the decoder in existing 3D MAE frameworks tends to over-rely onpositional information, which weakens semantic representation learningand leads to suboptimal feature quality. To address this issue, we proposeMPL-MAE, a masked point learning framework that mitigates positionalover-reliance while enhancing the utilization of encoder features. Specifi-cally, we introduce a recalibrated positional embedding module that sup-presses metric-dominant coordinate signals while preserving geometrictopology, together with a gated positional interface module that dynam-ically regulates positional injection during reconstruction. These designspromote a more balanced interaction between spatial priors and seman-tic features, yielding robust and informative representations. Extensiveexperiments across downstream tasks demonstrate that MPL-MAE con-sistently achieves competitive performance, validating its effectiveness.Code is available at https://github.com/yanx57/MPL-MAE.

Abstract:
While Multimodal Large Language Models demonstrate im-pressive semantic capabilities, they often suffer from spatial blindness,struggling with fine-grained geometric reasoning and physical dynamics.Existing solutions typically rely on explicit 3D modalities or complexgeometric scaffolding, which are limited by data scarcity and generaliza-tion challenges. In this work, we propose a paradigm shift by leveragingthe implicit spatial prior within large-scale video generation models. Weposit that to synthesize temporally coherent videos, these models inher-ently learn robust 3D structural priors and physical laws. We introduceVEGA-3D (Video Extracted Generative Awareness), a plug-and-playframework that repurposes a pre-trained video diffusion model as a La-tent World Simulator. By extracting spatiotemporal features from inter-mediate noise levels and integrating them with semantic representationsvia a token-level adaptive gated fusion mechanism, we enrich MLLMswith dense geometric cues without explicit 3D supervision. Extensiveexperiments across 3D scene understanding, spatial reasoning, and em-bodied manipulation benchmarks demonstrate that our method outper-forms state-of-the-art baselines, validating that generative priors providea scalable foundation for physical-world understanding. Code is publiclyavailable at https://github.com/H-EmbodVis/VEGA-3D.

Abstract:
Functional 3D scene graphs offer a versatile and flexible rep-resentation for 3D scene understanding and robotic manipulation, de-fined by object nodes, interactive elements, and functional relationshipedges. However, their potential remains underexplored due to the limitedcoverage of existing benchmarks and the overly straightforward designof previous pipelines, which primarily focus on large-scale furniture butlack of hierarchical structures. Therefore, in this work, we extend thebenchmark coverage by introducing dense tabletop objects and explicitmulti-level functional relationships. This expansion introduces criticalchallenges involving small-scale, dense, and similar instances, with lack ofvisual anchoring in relational reasoning, instance confusion during cross-frame fusion, and attribution uncertainty under dynamic viewpoints. Toaddress these issues, we propose an open-vocabulary pipeline based on 2Dvisual grounding and 3D graph optimization. Specifically, we anchor fine-grained functional edges from 2D visual evidence, and associate nodesacross frames in 3D using multiple cues. Furthermore, edge associationis formulated as temporal graph optimization, integrating evidence ac-cumulation, entropy regularization, and temporal smoothing to robustlydetermine the functional connections of each node. Finally, global hierar-chy shaping is performed to recover the hierarchical graph structure. Ex-tensive experiments demonstrate that the proposed method can reliablyinfer functional 3D scene graphs in challenging real-world scenes, therebyfurther unlocking their potential for practical applications. Code is avail-able at https://github.com/Hbelief1998/HHOpenFunGraph-ECCV26.

Abstract:
Long-range 3D object detection is critical for safe autonomous driving at highway speeds, yet existing radar-camera fusion methods face notable limitations at extended ranges. BEV-based approaches effectively encode scene-level context but incur rapidly growing computational cost and struggle to preserve fine-grained object-level detail, while querybased methods provide efficient object-centric encoding but lack sufficient scene-level context. Temporal fusion introduces additional challenges: distant objects produce only a few radar returns and occupy only a few image pixels, requiring scene-level accumulation, while high-speed motion causes large inter-frame displacements that require object-level motion modeling. BEV-based aggregation alleviates sparsity through multi-frame accumulation but is less suited to individual object motion, whereas query-based modeling captures object-level motion but provides limited scene-level temporal context. In this paper, we propose Horizon3D, a sparse radar-camera fusion framework for long-range 3D object detection that jointly captures object-level detail and scene-level context in both spatial and temporal dimensions through a hybrid representation that combines Gaussian primitives with sparse BEV features. Horizon3D first employs Keypoint-Guided Gaussian Initialization (KGGI) to initialize Gaussian primitives at object keypoints estimated from radar and camera features. Object-Centric Sparse Fusion (OCSF) aggregates cross-modal features around these primitives and splats the refined Gaussians onto the BEV plane, where they are fused with sparse radar BEV features to combine object-level detail with scene-level context. Finally, Dual-Path Temporal Fusion (DPTF) aggregates temporal cues through a BEV path for multi-frame feature accumulation and a Gaussian path for propagating primitives across frames to encode per-object motion. Extensive evaluations on TruckScenes demonstrate that Horizon3D achieves stateof-the-art performance for radar-camera 3D object detection. On the validation set, our approach outperforms the previous best method by +3.0 NDS and +1.6 mAP while maintaining a sparse representation with competitive inference speed.

Abstract:
3D learning systems implicitly assume that objects occupy a coherent reference frame. Nonetheless, in practice, every asset arrives with an arbitrary global rotation, and models are left to resolve directional ambiguity on their own. This persistent misalignment suppresses pose-consistent generation, and blocks the emergence of stable directional semantics. To address this issue, we construct CanoVerse, a massive canonical 3D dataset of 320K objects over 1,156 categories – an order-ofmagnitude increase over prior work. At this scale, directional semantics become statistically learnable: CanoVerse improves 3D generation stability, enables precise cross-modal 3D shape retrieval, and strengthens zero-shot point-cloud orientation estimation even for out-of-distribution data. This is achieved by a new canonicalization framework that reduces alignment from minutes to seconds per object via compact hypothesis generation and lightweight human discrimination, transforming canonicalization from manual curation into a high-throughput data generation pipeline. Project page: https://github.com/123321456-gif/Canoverse

Abstract:
Extracting routable road networks from satellite imagery requires accurate topology recovery beyond pixel-level segmentation. Recent methods decompose the task into keypoint detection and connectivity prediction, but reliably inferring road connectivity under structural ambiguity remains challenging. Overpasses introduce non-planar crossings that can create false shortcuts, and occlusions break visual continuity, requiring long-range structural reasoning. We formulate road extraction as fully unobserved connectivity inference and construct a global but sparse Euclidean graph from detected keypoints to better leverage the long-term context. To improve link prediction, we transform this graph into its line graph and perform reasoning using a Graph Transformer. We propose an end-to-end pipeline that integrates vision-based segmentation, sparse graph construction, and structured inference. Our method explicitly models overpasses and uses topology-preserving vertex extraction to mitigate routing-critical errors. This approach achieves state-of-the-art performance on City-scale, SpaceNet, and Global-scale benchmarks in topology metrics including TOPO-F1 and APLS. In addition to quantitative gains, our method improves reconstruction of multi-level road structures crucial for real-world routing reliability. The code is available at: https://github.com/wzzzzzzy/LineGraph2Road.

Abstract:
Existing ViT-based weather forecasting models apply uni-form computation across all spatial tokens, even though nearby atmo-spheric grid points often contain similar values and large regions evolvesmoothly over time. This makes much of the intermediate per-token com-putation redundant. Standard token-efficiency methods, such as pruningor merging, reduce cost by removing or fusing tokens. However, weatherforecasting is a spatiotemporal dense prediction problem in which a his-tory of atmospheric states must be mapped to future values on the orig-inal latitude-longitude grid. Thus, every grid cell must retain a physi-cally meaningful representation, especially under autoregressive rollout.We introduce Sparse-Reslim, a parameter-free plug-in routing modulethat makes sparse token processing compatible with this fixed-grid re-quirement. Sparse-Reslim routes only 25% of spatial tokens through theexpensive middle transformer blocks and treats those blocks as residualupdates: it computes the change produced for the routed tokens and scat-ters only this delta back to the full sequence. Unselected tokens keep theirpre-routing representations exactly, so no grid cell is dropped or replacedby a mask token, and no fusion layer or additional parameters are intro-duced. Across ERA5 resolutions up to the operational 0.25° standard andtwo model families, a deterministic Transformer and a diffusion model,Sparse-Reslim improves forecast accuracy on every evaluated variablewhile substantially reducing cost: training is about 2.5× faster in themain settings and reaches 3.18× speedup at 0.25°, with over 2.2× lowerpeak memory. A controlled decomposition shows that the accuracy gaincomes primarily from sparse routing itself, while random token selectionprovides an additional regularization benefit without selector overhead.Code is available at https://github.com/janet-sw/Sparse-Reslim.

Abstract:
Arbitrary slice super-resolution reconstructs isotropic vol-umes from anisotropic clinical acquisitions by synthesizing intermedi-ate slices at arbitrary scales. However, treating this ill-posed inverseproblem as unconstrained residual-based regression risks hallucinatinganatomically implausible structures or altering the originally observeddata. To address both concerns, this paper presents the Dual-PriorNull-Space Learning (DP-NSL) framework, which reformulates the taskas a constrained recovery process guided by two complementary priors.A Measurement-Consistent Projection (MCP) enforces a DeterministicObservation Prior : the reconstruction undergoes an exact orthogonalprojection that reproduces every acquired slice with zero error, confin-ing all learned details to the unobservable null space. Within this nullspace, a Mixture-of-Splines (MoS) module imposes a Geometric Conti-nuity Prior by dynamically mixing B-spline experts of different analyticorders, allowing each anatomical region to be modeled with a content-aware level of continuity. To promote spatial coherence, a Local SpatialConsistency Decoder (LSCD) further injects local inductive bias. Ex-periments on three CT and one MRI benchmark show that DP-NSLoutperforms existing approaches while strictly preserving measurementconsistency. Code is available at https://github.com/DeepMed-Lab-ECNU/Medical-Image-Reconstruction.

Abstract:
In-camera JPEG previews are ubiquitous in raw image formats and provide an sRGB reference at negligible storage cost. Although existing metadata-based reconstruction frameworks can exploit this side information when recovering raw images, their context models often become computationally expensive especially at high resolution, e.g., 4K raw image, given that attention mechanisms scale quadratically with feature maps, hindering its practical application. To address these limitations, we propose MambaRaw, a JPEG-conditioned metadatabased raw image reconstruction framework that uses State Space Models (SSMs) to estimate entropy parameters efficiently. Our key contribution comprises a Spatial-Energy Coupled Context Modeling mechanism with two lightweight modules: (1) TileMambaBlock, which performs Mambastyle selective scanning only on information-dense tiles to improve the efficiency; and (2) Energy-Aware Refinement (EAR), an identity-initialized residual module that enhance feature representation to match the longtail energy distribution of raw signals. Extensive experiments on three camera datasets (Sony, Olympus, Samsung) show consistent improvements over strong metadata-based baselines and set a new state of the art for JPEG-guided raw reconstruction with great efficiency. Notably, at low metadata bitrates, MambaRaw increases PSNR by 1.2–1.4 dB and reduces end-to-end coding latency by about 9%. Code is released at https://github.com/Peizeli1/MambaRaw.

Abstract:
Clinical cardiac MRI spans diverse contrasts, sampling tra-jectories, accelerations, scanners, and patient populations, yet many de-terministic deep-learning reconstruction methods remain protocol spe-cific. We present the Scalable Deep Unrolled Model (SDUM), which in-tegrates a Restormer-based unrolled reconstructor, per-cascade coil sen-sitivity estimation, sampling aware weighted data consistency, and uni-versal conditioning on cascade index and acquisition metadata. A sin-gle SDUM model achieves state-of-the-art performance across all CM-RxRecon2025 tracks without task-specific fine-tuning and outperformsPromptMR+ on CMRxRecon2024 by +0.55 dB. Scaling experimentsshow near-logarithmic gains with depth up to 18 cascades (r=0.986,R2 =0.973) and continued but diminishing gains from data scaling (32.72 dBat 40% to 33.18 dB at 100%). SDUM also generalizes in a zero-shot set-ting to unseen in-house chemical exchange saturation transfer (CEST)MRI (43.57 dB PSNR, 0.9769 SSIM). When trained separately on fastMRIbrain, SDUM surpasses PC-RNN by +1.8 dB. These results supportSDUM as a scalable framework for robust MRI reconstruction acrossheterogeneous acquisition settings beyond cardiac MRI.Code: https://github.com/NVIDIA-Medtech/NV-Raw2insights-MRIModel: https://huggingface.co/nvidia/NV-Raw2insights-MRI

Abstract:
High-precision medical diagnosis relies not only on staticimaging features but also on the implicit diagnostic memory experts in-stantly invoke during image interpretation. We pinpoint a fundamentalcognitive misalignment in medical VLMs caused by discrete tokeniza-tion, leading to quantization loss, long-range information dissipation,and missing case-adaptive expertise. To bridge this gap, we proposeMedSynapse-V, a framework for latent diagnostic memory evolution thatsimulates the experiential invocation of clinicians by dynamically synthe-sizing implicit diagnostic memories within the model’s hidden stream.Specifically, it begins with a Meta Query for Prior Memorizationmechanism, where learnable probes retrieve structured priors from ananatomical prior encoder to generate condensed implicit memories. Toensure clinical fidelity, we introduce Causal Counterfactual Refine-ment (CCR) which leverages reinforcement learning and counterfactualrewards derived from region-level feature masking to quantify the causalcontribution of each memory, thereby pruning redundancies and aligninglatent representations with diagnostic logic. This evolutionary processculminates in Intrinsic Memory Transition (IMT), a privileged-autonomous dual-branch paradigm that internalizes teacher-branch di-agnostic patterns into the student-branch via full-vocabulary divergencealignment. Comprehensive empirical evaluations across multiple datasetsdemonstrate that MedSynapse-V, by transferring external expertise intoendogenous parameters, significantly outperforms existing state-of-the-art methods, particularly Chain-of-Thought (CoT) paradigms, in diag-nostic accuracy and multi-dataset generalization without compromisingthe inference efficiency of standard VLMs.

Abstract:
Decades of orbital missions have produced multi-modal re-mote sensing data for the Moon, spanning optical imagery, spectroscopy,thermal emission, radar, gravity, and elemental composition. Yet thesedatasets remain fragmented across archives, and no benchmark exists forevaluating machine learning on lunar data. We introduce Moonstone, thefirst multi-modal foundation model benchmark for lunar remote sensing.Our contributions are: (1) a 28-channel, 128 pixels-per-degree (∼237 m)global lunar pretraining dataset from seven instrument families acrossfive missions, (2) MG-MAE, a modality-grouped masked autoencoderwith per-group convolutional tokenizers, a shared Vision Transformerencoder, attention masking for missing modalities, coverage-adaptivemasking for heterogeneous spatial coverage, and spectral continuity reg-ularization for physically plausible reconstructions, and (3) a benchmarkof six downstream tasks covering classification, regression, and segmen-tation. MG-MAE pretrained features outperform scratch baselines onall tasks and surpass both ImageNet-pretrained and vanilla MAE base-lines by large margins. We release the pretraining dataset, code, and thebenchmark suite.3

Abstract:
All-in-One medical image restoration (MedIR) aims to ad-dress diverse tasks across modalities and degradation types using a singleuniversal model. Existing methods typically prioritize modeling inter-task heterogeneity (e.g., distinct data distributions and degradation types).However, they largely neglect the inherent homogeneity present in med-ical images, such as widely shared anatomical structures within andacross modalities, which can be leveraged to ease model training andimprove generalization. To this end, we propose UniH3 , a novel frame-work that Unifies Hierarchical Homogeneity and Heterogeneity for all-in-one medical image restoration. Specifically, to comprehensively exploithomogeneity, we introduce a Hierarchical Homogeneity Memory (H2 M)module that progressively distills intra- and inter-task homogeneity pri-ors from high-quality images during training, and adaptively retrieves themost relevant priors tailored to the input for guided restoration. Theseretrieved priors are then injected into the restoration pipeline via an ef-ficient Homogeneity-Guided Attention (HGA) mechanism. Furthermore,to comprehensively address heterogeneity, we design a Hierarchical Het-erogeneity Balancer (H2 B) that mitigates both inter- and intra-task con-flicts during optimization, facilitating balanced and effective multi-tasklearning. Extensive experiments on two large-scale benchmarks—MedIR-2D-500K and MedIR-3D-3K—demonstrate that UniH3 achieves state-of-the-art performance on both all-in-one and single-task medical imagerestoration. We hope this work establishes a strong benchmark and ad-vances the development of general-purpose medical image restorationmodels. Code is available at https://github.com/Yaziwel/UniH3.

Abstract:
In this paper, we reveal an important yet overlooked problem in image denoising: under signal-dependent camera noise models, dark regions suffer from inherently low Signal-to-Noise Ratio (SNR), as signal intensity decays far faster than noise variance diminishes, making detail recovery in dark areas fundamentally challenging. Yet rather than compensating for this difficulty, MSE-trained denoisers exacerbate it—reconstructing dark pixels up to 6× worse relative to their perband noise floor. This bias stems from two compounding factors: signaldependent noise inflates bright-pixel residuals, and the network’s Jacobian norm increases monotonically with brightness. Together, these cause bright regions to chronically dominate gradient updates at the expense of dark ones. To this end, we propose Brightness Bias-Robust Denoising (BBRD), a drop-in replacement for MSE loss that partitions pixels into brightness bands, normalizes per-band error by empirical noise variance, and applies Group Distributionally Robust Optimization (Group-DRO) to dynamically upweight whichever band is currently worst, with zero additional parameters or inference cost. Across 8 architectures and 2 datasets in our experiments, BBRD is the only method among 13 tested alternatives that improves each brightness band simultaneously, achieving up to +0.45 dB on dark bands, +0.32 dB on bright bands, and +0.65 dB aggregate Peak Signal-to-Noise Ratio (PSNR) on SIDD, with the largest per-band gains in the darkest regions where detail recovery matters most. Code is available at https://github.com/xmedlab/BBRD.

Abstract:
The adoption of powerful diffusion models is hindered bytheir significant inference latency. Recent “cache-then-forecast” schemesalleviate this issue by accelerating DiTs using derivative-based polynomi-als, but they suffer from severe quality degradation at high accelerationratios. Our analysis reveals its root cause: the discrete extrapolation per-formed on representations that are misaligned with the continuous diffu-sion trajectory and are numerically unstable. Thus, accelerated DiTs suf-fer from accumulated spatial errors, noisy derivative amplification, andhigh-order instability. We therefore reformulate accelerated inference asstable macro-trajectory extrapolation in ordinary differential equation(ODE) space. Instead of predicting intermediate features, we align fore-casting with the model’s Global Drift (GD), i.e., the end-to-end stateevolution, thereby eliminating feature inconsistency and memory over-head. However, even this smooth macro-trajectory remains vulnerableto the derivative fallacy: its higher-order temporal derivatives are intrin-sically noisy. Thus, we introduce a derivative-free barycentric Lagrangeextrapolator to effectively bypass derivative instability and approxima-tion error. We further propose a bounded Phase Mapping that regu-larizes the extrapolation domain, suppressing oscillatory error growth.These elements collectively constitute ResilPhase, a noise-resilient ac-celeration framework. Experiments on FLUX.1-dev and HunyuanVideodemonstrate state-of-the-art fidelity under aggressive acceleration ratios.Code is publicly available at https://github.com/zqc214/ResilPhase.

Abstract:
Low-light image enhancement is an essential computer visiontask to improve image contrast and to decrease the effects of colorbias and noise. Many existing interpretable deep-learning algorithmsexploit the Retinex theory as the basis of model design. However,previous Retinex-based algorithms, that consider reflected objects asideal Lambertian ignore specular reflection in the modeling process andconstruct the physical constraints in image space, limiting generalizationof the model. To address this issue, we preserve the specular reflectioncoefficient and reformulate the original physical constraints in theimaging process based on the Kubelka-Munk theory, thereby constructingconstraint relationship between illumination, reflection, and detection, theso-called triple physical constraints (TPCs) theory. Based on this theory,the physical constraints are constructed in the feature space of the modelto obtain the TPC network (TPCNet). Comprehensive quantitativeand qualitative benchmark and ablation experiments confirm that theseconstraints effectively improve the performance metrics and visual qualitywithout introducing new parameters, and demonstrate that our TPCNetoutperforms other state-of-the-art methods on 10 datasets. The code isavailable at https://github.com/2020shijingyi/TPCNet

Abstract:
Multimodal large language models (MLLMs) are promisingfor forgery detection, but most methods treat them as fixed black boxes.Preliminary studies have shown that this passive use overlooks instabili-ties in the internal data flow, leading to representation drift. In this work,we introduce the Asymmetric Anchoring Paradigm (AAP), an open-boxapproach that reshapes the MLLM’s internal data flow (rather than ap-pending external components) by re-purposing its pre-trained visual en-coder as an active truth anchor. AAP has two key steps: (1) for realimages, we impose an anchor-alignment constraint that pulls represen-tations toward the truth anchor, yielding a highly stable, low-variancemanifold; (2) for tampered images, we measure their deviation from theanchor and use the resulting error map, which spatially quantifies depar-tures from real-world priors, as a precise cue for the localization decoder.Comprehensive evaluations on SID-Set and OpenSDID demonstrate thatAAP substantially improves detection accuracy and the localization IoUwithin manipulated regions. By opening the black box and anchoring tothe encoder’s real-world priors, AAP turns MLLMs from passive compo-nents into actively regularized detectors for practical forgery detection,offering a new insight for the field. The code is open-sourced and publiclyavailable at https://github.com/rstao-bjtu/AAP.

Abstract:
Astronomical imaging remains noise-limited under practicalobserving conditions. Standard calibration pipelines remove structuredartifacts but largely leave stochastic noise unresolved. Although learning-based denoising has shown strong potential, progress is constrained byscarce paired training data and the requirement for physically inter-pretable models in scientific workflows. We propose a physics-based noisesynthesis framework tailored to CCD noise formation in the telescope.The pipeline models photon shot noise, photo-response non-uniformity,dark-current noise, readout effects, and localized outliers arising fromcosmic-ray hits and hot pixels. To obtain low-noise inputs for synthesis,we stack multiple unregistered exposures to produce high-SNR bases. Re-alistic noisy counterparts synthesized from these bases using our noisemodel enable the construction of abundant paired datasets for supervisedlearning. Extensive experiments on our real-world multi-band datasetcurated from two ground-based telescopes demonstrate the effectivenessof our framework in both photometric and scientific accuracy. Code isavailable at https://github.com/ShuhongLL/Denoising-Deep-Sky.

Abstract:
Deep representations across modalities are inherently inter-twined. In this paper, we systematically analyze the spectral character-istics of various semantic and pixel encoders. Interestingly, our studyuncovers a highly inspiring and rarely explored correspondence betweenan encoder’s feature spectrum and its functional role: semantic encodersprimarily capture low-frequency components that encode abstract mean-ing, whereas pixel encoders additionally retain high-frequency informa-tion that conveys fine-grained detail. This heuristic finding offers a uni-fying perspective that ties encoder behavior to its underlying spectralstructure. We define it as the Prism Hypothesis, where each datamodality can be viewed as a projection of the natural world onto ashared feature spectrum, just like the prism. Building on this insight,we propose Unified Autoencoding (UAE), a model that harmonizessemantic structure and pixel details via an innovative frequency-bandmodulator, enabling their seamless coexistence. Extensive experimentsdemonstrate that UAE effectively unifies semantic abstraction and pixel-level fidelity within a single latent space, achieving state-of-the-art per-formance. Moreover, we show that UAE can be directly applied to pixel-space modeling, significantly improving both FID and IS over the vanillaJIT baseline. Code is avaliable at: https://github.com/WeichenFan/UAE.

Abstract:
Shadows encode rich information about scene geometry andillumination, yet existing methods either predict a unified shadow maskor overlook attached shadows entirely. We address this gap by propos-ing a framework for jointly detecting cast and attached shadows throughexplicit physical modeling of light direction and surface geometry un-der a dominant directional-light setting. Our approach is grounded in asimple observation: surfaces facing away from the light source tend tofall into shadow. We exploit the reciprocal relationship between shadowformation and light estimation to construct a closed feedback loop, adual-module architecture in which a shadow detection module and alight estimation module iteratively refine each other. At each pass, up-dated light estimates, together with surface normals, produce partial at-tached shadow maps that guide detection, while improved shadow pre-dictions sharpen light estimation. To support training and evaluation,we introduce a dataset of 1,458 images with manually annotated castand attached shadow masks sourced from three existing benchmarks.Experiments demonstrate that our proposed method outperforms priormethods, with at least a 33% reduction in attached-shadow BER, whilemaintaining strong full-shadow and cast-shadow performance. Projectpage: https://shilin21.github.io/attached_detection/

Abstract:
Vision-Language Navigation (VLN) for Unmanned AerialVehicles (UAVs) demands complex visual interpretation and continuouscontrol in dynamic 3D environments. Existing hierarchical approachesrely on dense oracle guidance or auxiliary object detectors, creating se-mantic gaps and limiting genuine autonomy. We propose AeroVLA, aminimalist end-to-end Vision-Language-Action framework mapping rawvisual observations and fuzzy linguistic instructions directly to contin-uous physical control signals. First, we introduce a streamlined dual-view perception strategy that reduces visual redundancy while preserv-ing essential cues for forward navigation and precise grounding, whichadditionally facilitates future simulation-to-reality transfer. To reclaimgenuine autonomy, we deploy a fuzzy directional prompting mechanismderived solely from onboard sensors, completely eliminating the depen-dency on dense oracle guidance. Ultimately, we formulate a unified con-trol space that integrates continuous 3-Degree-of-Freedom (3-DoF) kine-matic commands with an intrinsic landing signal, freeing the agent fromexternal object detectors for precision landing. Extensive experiments onthe TravelUAV benchmark demonstrate that AeroVLA achieves state-of-the-art performance in seen environments. Furthermore, it exhibitssuperior generalization in unseen scenarios by achieving nearly threetimes the success rate of leading baselines, validating that a minimal-ist, autonomy-centric paradigm captures more robust visual-motor rep-resentations than complex modular systems. Code is available at: https://github.com/XuPeng23/AeroVLA

Abstract:
Current Vision-Language-Action (VLA) paradigms in autonomous driving primarily rely on Imitation Learning (IL), which introduces inherent challenges such as distribution shift and causal confusion. Online Reinforcement Learning offers a promising pathway to address these issues through trial-and-error learning. However, applying online reinforcement learning to VLA models in autonomous driving is hindered by inefficient exploration in continuous action spaces. To overcome this limitation, we propose MindDrive, a VLA framework comprising a large language model (LLM) with two distinct sets of LoRA parameters. The one LLM serves as a Decision Expert for scenario reasoning and driving decision-making, while the other acts as an Action Expert that dynamically maps linguistic decisions into feasible trajectories. By feeding trajectory-level rewards back into the reasoning space, MindDrive enables trial-and-error learning over a finite set of discrete linguistic driving decisions, instead of operating directly in a continuous action space. This approach effectively balances optimal decision-making in complex scenarios, human-like driving behavior, and efficient exploration in online reinforcement learning. Extensive experiments validate the efficacy of our online reinforcement learning framework, which outperforms state-of-the-art IL and offline Reinforcement Learning methods on the challenging Bench2Drive benchmark. To the best of our knowledge, this is the first work to demonstrate the effective application of online reinforcement learning to VLA models in autonomous driving.

Abstract:
Visual navigation policies are widely regarded as a critical re-search direction, as they emulate human navigation behavior by leverag-ing egocentric visual observations. However, unlike LiDAR point cloudsor depth maps, visual observations do not provide explicit geometricinformation for navigation, especially in cluttered or dynamic environ-ments, which motivates the need for learning-based models and large-scale data. To this end, we propose to leverage Vision-Language-Action(VLA) models to learn diverse navigation capabilities from synthetic ex-pert data and to alleviate the sim-to-real gap by co-training on large-scalereal-world Visual Question Answering (VQA) data. Specifically, we de-velop MM-Nav, a 7B multi-view VLA model featuring custom-designedarchitectures and enabling a 7 Hz inference speed with 360◦ observation.For large-scale navigation data, we collect a total of 1.5 million expertdemonstrations from three reinforcement learning (RL) experts, eachtrained with privileged information in a challenging, tailor-made envi-ronment and specialized in one of three navigation capabilities: reaching,squeezing, and avoiding. We then iteratively train MM-Nav on these data,dynamically balancing the training data ratio across the three capabil-ities based on their respective performance. Through extensive exper-iments in synthetic and real-world environments, we demonstrate thatour model achieves strong performance and generalization on differentbenchmarks. MM-Nav obtains a success rate of 88.1% on the InternVLA-N1 System-1 point-goal navigation benchmark. Moreover, we find thatour student VLA model outperforms the RL teachers, demonstrating thesynergistic effect of integrating multiple capabilities. Extensive real-worldexperiments further confirm the effectiveness of our method.

Abstract:
Synthesizing high-fidelity and controllable 4D LiDAR datais crucial for creating scalable simulation environments for autonomousdriving. This task is inherently challenging due to the sensor’s uniquespherical geometry, the temporal sparsity of point clouds, and the com-plexity of dynamic scenes. To address these challenges, we present LiS-TAR, a novel generative world model that operates directly on the sen-sor’s native geometry. LiSTAR introduces a Hybrid-Cylindrical-Spherical(HCS) representation to preserve data fidelity by mitigating quanti-zation artifacts common in Cartesian grids. To capture complex dy-namics from sparse temporal data, it utilizes a Spatio-Temporal At-tention with Ray-Centric Transformer (START) that explicitly modelsfeature evolution along individual sensor rays for robust temporal co-herence. Furthermore, for controllable synthesis, we propose a novel 4Dpoint cloud-aligned voxel layout for conditioning and a correspondingdiscrete Masked Generative START (MaskSTART) framework, whichlearns a compact, tokenized representation of the scene, enabling effi-cient, high-resolution, and layout-guided compositional generation. Com-prehensive experiments validate LiSTAR’s state-of-the-art performanceacross 4D LiDAR reconstruction, prediction, and conditional genera-tion. Our method achieves substantial quantitative gains: improving re-construction IoU by up to 85%, lowering prediction L1 Med by up to64%, and reducing generation MMD by 61%. This level of performanceprovides a powerful new foundation for creating realistic and control-lable simulations for autonomous driving systems. Project Page: https://ocean-luna.github.io/LiSTAR.github.io/.

Abstract:
To enable personalized, real-time coaching using AugmentedReality glasses or fixed camera setups in domains such as sports, cooking,or music, a system must understand not just what a person does, buthow well they execute an activity. In an ego-exo video setting, this re-quires simultaneously detecting individual skilled actions and classifyingeach as correct or needing improvement, which Ego-Exo4D’s proficiencydemonstration benchmark formalized. We first adapt seven state-of-the-art temporal action detection architectures to this task, extend the eval-uation protocol to disentangle detection from grading, and show that ex-isting methods grade near-randomly. We then introduce SkillSpotter , apose-aware multi-view architecture that jointly detects and grades skilledactions through three task-specific modules: (1) adaptive temporal sup-pression to handle the varying density of skilled actions across diverseactivities, (2) gated 3D body pose fusion to leverage body kinematics as acomplementary signal to visual features, and (3) bidirectional cross-viewattention to combine ego and exo views e!ectively. SkillSpotter improvesclass-specific mAP from 12.40 to 21.82 (+76%) and balanced accuracyfrom 55.99% to 60.40% over the best baseline. SkillSpotter ’s modulestransfer to other temporal action detection models with consistent gainsand our method generalizes beyond Ego-Exo4D to HoloAssist.Code: https://github.com/eth-siplab/SkillSpotter

Abstract:
As embodied AI transitions to real-world deployment, thesuccess of the Vision-and-Language Navigation (VLN) task tends to evolvefrom mere reachability to social compliance. However, current agentssuffer from a “Goal-driven trap”, prioritizing physical geometry (“can Igo?”) over semantic rules (“may I go?”), frequently overlooking subtleregulatory constraints. To bridge this gap, we establish Rule-VLN, the firstlarge-scale urban benchmark for rule-compliant navigation. Spanning amassive 29k-node environment, it injects 177 diverse regulatory categoriesinto 8k constrained nodes across four curriculum levels, challenging agentswith fine-grained visual and behavioral constraints. We further proposethe Semantic Navigation Rectification Module (SNRM), a universal, zero-shot module designed to equip pre-trained agents with safety awareness.SNRM integrates a coarse-to-fine visual perception VLM framework withan epistemic mental map for dynamic detour planning. Experimentsdemonstrate that while Rule-VLN challenges state-of-the-art models,SNRM significantly restores navigation capabilities, reducing CVR by19.26% and boosting TC by 5.97%. The project page is available athttps://jeffry-wen.github.io/Rule-VLN/.

Abstract:
Real-world deployment of embodied agents requires activeexploration, visual grounding, and interactive intent disambiguation. How-ever, existing frameworks often rely on privileged simulator states or as-sume complete instructions, bypassing realistic deployment challenges.To bridge this gap, we present REAL, an agentic framework for open-world mobile manipulation. REAL establishes sim-to-real-consistent en-vironment APIs without oracle perception and integrates a simulateduser to enable human-in-the-loop interaction. Within this environment,we design diverse task compositions to drive data collection, supervisedfine-tuning, and online reinforcement learning, systematically optimizingagent performance. To comprehensively evaluate this approach, we in-troduce REAL-Bench, a benchmark spanning 241 tasks across activeexploration, visual distraction, articulated manipulation, and interactivedisambiguation. Experimental results demonstrate that our trained agentoutperforms leading commercial closed-source VLMs on interactive taskswith a 56.9% success rate. Further empirical analysis reveals that ourhierarchical training pipeline successfully aligns the model’s tool-use ca-pabilities while maintaining robust open-vocabulary reasoning under ex-tended exploration horizons. Finally, we deploy and evaluate our frame-work on a physical dual-arm mobile robot, where it achieves a 78.3%end-to-end success rate over 60 real-world episodes. These physical trialsdemonstrate robust zero-shot transferability to unseen household scenar-ios, validating that our sim-to-real-consistent design successfully bridgesthe reality gap for long-horizon mobile manipulation. Code is availableat https://github.com/InternRobotics/REAL.

Abstract:
Remote sensing underpins crucial applications such as disas-ter relief and ecological field surveys, where systems must understandcomplex scenes and constraints and make reliable decisions. Currentremote-sensing benchmarks mainly focus on evaluating perception andreasoning capabilities of multimodal large language models (MLLMs).They fail to assess planning capability, stemming either from the diffi-culty of curating and validating planning tasks at scale or from evaluationprotocols that are inaccurate and inadequate. To address these limita-tions, we introduce NeSy-Route, a large-scale neuro-symbolic benchmarkfor constrained route planning in remote sensing. Within this benchmark,we introduce an automated data-generation framework that integrateshigh-fidelity semantic masks with heuristic search to produce diverseroute-planning tasks with provably optimal solutions. This allows NeSy-Route to comprehensively evaluate planning across 10,821 route-planningsamples, nearly 10 times larger than the largest prior benchmark. Fur-thermore, a three-level hierarchical neuro-symbolic evaluation protocol isdeveloped to enable accurate assessment and support fine-grained analy-sis on perception, reasoning, and planning simultaneously. Our compre-hensive evaluation of various state-of-the-art MLLMs demonstrates thatexisting MLLMs show significant deficiencies in perception and planningcapabilities. We hope NeSy-Route can support further research and de-velopment of more powerful MLLMs for remote sensing.The dataset andcode are available at https://mingyang1010.github.io/NeSy-Route/.

Abstract:
World models have become central to autonomous driving,where accurate scene understanding and future prediction are crucialfor safe control. Recent work has explored using vision–language models(VLMs) for planning, yet existing approaches typically treat perception,prediction, and planning as separate modules. We propose UniDrive-WM, a unified VLM-based world model that jointly performs driving-scene understanding, trajectory planning, and trajectory-conditioned fu-ture image generation within a single architecture. UniDrive-WM’s tra-jectory planner predicts a future trajectory, which conditions a VLM-based image generator to produce plausible future frames. These predic-tions provide additional supervisory signals that enhance scene under-standing and iteratively refine trajectory generation. We further comparediscrete and continuous output representations for future image predic-tion, analyzing their influence on downstream driving performance. Ex-periments on the challenging Bench2Drive benchmark show that UniDrive-WM produces high-fidelity future images and improves planning perfor-mance by 7.3% in L2 trajectory error and 10.4% in collision rate overthe previous best method. These results demonstrate the advantagesof tightly integrating VLM-driven reasoning, planning, and generativeworld modeling for autonomous driving. The project page is available athttps://unidrive-wm.github.io/UniDrive-WM.

Abstract:
An efficient and accurate system for detecting errors in pro-cedural tasks is crucial for supporting human needs in daily life, asit can provide instant notifications and guide people to correct mis-takes. In this work, we study real-time online error detection in pro-cedural videos from a simple but overlooked perspective: the predictionbehavior of action detectors themselves. Instead of designing complexarchitectures or specialized supervision, we observe that action detec-tors naturally exhibit different prediction characteristics depending ontheir sensitivity to input dynamics and temporal context. We thereforepropose ESTANet (Error-Sensitive and Temporally-vArying Network),a lightweight framework that detects errors by exploiting inconsistenciesamong action predictions produced by a small set of action detectors.We construct standard and error-sensitive action detectors that behavesimilarly on correct executions but respond differently when errors occur.Meanwhile, detectors operating with different temporal contexts furtheramplify prediction inconsistencies when the procedure deviates from theintended sequence. During inference, we detect errors by aggregating mis-matches between standard and error-sensitive predictions through ma-jority voting to flag frames that contain errors. Extensive experimentson EgoPER, Assembly-101-O, and EPIC-Tent-O demonstrate that ES-TANet achieves state-of-the-art performance in online error detectionwhile maintaining real-time efficiency with a lightweight architecture.Our results highlight that leveraging the intrinsic properties of actiondetectors can yield a powerful and practical solution for online error de-tection without increasing architectural design complexity. Our code isavailable at: https://github.com/robert80203/ESTANet

Abstract:
Large vision-language models with CLIP as a core compo-nent have achieved remarkable progress across a wide range of tasks,yet they remain highly vulnerable to adversarial attacks. Existing ad-versarial fine-tuning methods typically optimize CLIP under a singlefixed perturbation strength, resulting in weak robustness generalizationand semantic instability. To address this limitation, we propose Path-Consistent Fine-Tuning (PACO), a new framework for unsupervised ad-versarial fine-tuning. Rather than optimizing solely on adversarial exam-ples, PACO adopts a two-stage procedure that regularizes representa-tions on continuous local paths along adversarial directions. Specifically,it first anchors adversarial embeddings to a clean reference to preventsevere semantic drift. Building upon this, it constructs a local path be-tween the clean sample and the adversarial anchor, regularizing inter-mediate representations to encourage a linear transition, which yields amore stable visual embedding. Extensive experiments show that PACOachieves a superior robustness-accuracy trade-off: it not only mitigatesthe clean-performance degradation common to prior methods, but alsodelivers superior robustness across diverse attack settings and perturba-tion strengths. Our code is available at https://github.com/Trusted-LLM/PACO.

Abstract:
Diffusion models (DMs), despite their impressive capabilitiesacross a wide range of generative tasks, have been shown to be vulnera-ble to backdoor attacks. However, existing backdoor methods face criticaltrade-offs among key factors: attack performance, stealthiness, time com-plexity, and required poison rates. For example, achieving high attackperformance typically demands a high poison rate and prolonged train-ing, which undermines stealthiness, making the attack more detectableby backdoor defenses. This paper proposes TooBad (trigger optimizationfor backdoor diffusion models), a backdoor framework which introducesa novel DM-tailored trigger optimization technique to dramatically en-hance the performance of backdoor attacks on DMs. Experiments on rep-resentative benchmarks such as CIFAR-10 show that TooBad can achievehigh ASRs (> 85%) at only 0.5% poison rate, significantly lower thanthe 10% typically required by prior work on the same datasets. At 5%poison rate, TooBad reaches nearly 100% ASR within just 3-5 backdoorinjection epochs1 , whereas existing methods need at least 30-50 epochsat double the poison rate for comparable results. Despite its potency,TooBad easily evades SOTA defenses and maintains high utility. Theseresults reveal a critical threat on DMs and highlight the need for morerobust defenses against such stealthy yet efficient attacks. Our code isavailable at https://github.com/tuanvu171/TooBad.

Abstract:
Foundation model pseudo-labeling—labeling data strictly viazero-shot inference—enables massive scale, but performance is under-mined by hallucinations that evade standard thresholds. To eliminatethese errors, we introduce the Turing-inspired Label Imitation Game(LIG), a framework that formalizes pseudo-label pruning as an adversar-ial interrogation. Rather than filtering labels via isolated thresholds, weuse the LIG to train a Turing Test Network (TTN), a task-agnostic“judge” that evaluates candidate pseudo-labels within a dataset-wide con-text. Experiments across four diverse datasets demonstrate the TTN’srobustness, consistently enhancing label accuracy for three state-of-the-art vision-language models without costly supervision or retraining. Cru-cially, we demonstrate that learned semantic-contextual logic is a robustalternative to spatial-geometric verification, enabling a unique zero-shottask transfer capability—a TTN trained strictly on image classificationdatasets can effectively prune complex object detection pseudo-labels.This pruning yields F1 -score gains of 28% for the worst-performing base-line categories and 44% with task-specific fine-tuning. Significantly, wealso observe Category Revival, where the TTN pruning “detoxifies” thetraining signal for downstream models and enables them to recover fromzero recall on transfer-vulnerable classes. The pre-trained TTN modelsand code are available at https://github.com/voxel51/ttn.

Abstract:
Continual Test-Time Adaptation (CTTA) aims to maintainmodel performance under evolving target domains by adapting onlinewithout labeled data. However, practical deployments often cannot re-tain the source dataset due to privacy or licensing constraints, and purelysource-free CTTA methods tend to become unstable under long-termdistribution shift, suffering from compounding self-training errors andcatastrophic forgetting. We introduce DO-ALL (Distill Once, AdaptLife-Long), a plug-and-play framework that revisits source informationin a compact and privacy-conscious form via Dataset Distillation (DD).Before deployment, DO-ALL performs DD to produce a small set of syn-thetic distilled anchors that summarize the source distribution. Duringadaptation, each target sample is matched with its most semanticallyaligned anchor, which provides a stable reference for various CTTA viasource replay, representation alignment, and manifold-smoothing regu-larization. DO-ALL can be seamlessly integrated into existing CTTA al-gorithms, consistently improving long-term robustness across CIFAR100-C, ImageNet-C, and the CCC benchmark. This demonstrates the po-tential of leveraging DD to enable stable and continuous adaptationwithout retaining raw source data. The code is available at https://github.com/blue-531/DOALL.

Abstract:
In real-world deployments, scene text detectors inevitablyface distribution shifts beyond the training distribution. Prior work of-ten depends on large-scale scene-text pretraining, yet evaluation undercross-domain changes and real-world imaging degradations remains lim-ited. We propose TextDS, an efficient framework for scene text detec-tion under distribution shifts. First, we propose a data-efficient dual-encoder design with visual foundation models, eliminating the reliance onlarge-scale scene-text pretraining. Second, we introduce Step-wise LoRAadaptation (SWLoRA), which performs progressive low-rank refinementwith a dynamic early-exit mechanism for effective feature adaptation.Third, we propose Common Subspace Fusion (CSF) to align and fuse thetwo branches in a shared subspace while retaining complementary, shift-robust information. Finally, we construct adverse-condition scene text de-tection datasets to address the gap in evaluating under imaging degrada-tion. Experiments show that TextDS achieves competitive performancein scene text detection, demonstrating robustness across domains andadverse imaging conditions with only 4.9M trainable parameters. Thecode is publicly available at https://github.com/ZChenDang/TextDS

Abstract:
Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language modelsto downstream tasks. However, when only partial labels are available, itsperformance is often limited by label ambiguity and insufficient supervi-sory information. To address this issue, we propose Holistic Optimal La-bel Selection (HopS), leveraging the generalization ability of pre-trainedfeature encoders through two complementary strategies. First, we designa local density-based filter that selects the top frequent labels from thenearest neighbors’ candidate sets and uses the softmax scores to identifythe most plausible label, capturing structural regularities in the featurespace. Second, we introduce a global selection objective based on optimaltransport that maps the uniform sampling distribution to the candidatelabel distributions across a batch. By minimizing the expected trans-port cost, it can determine the most likely label assignments. These twostrategies work together to provide robust label selection from both lo-cal and global perspectives. Extensive experiments on eight benchmarkdatasets show that HopS consistently improves performance under par-tial supervision and outperforms all baselines. Those results highlightthe merit of holistic label selection and offer a practical solution forprompt learning in weakly supervised settings. The code is available athttps://github.com/Qizhoay/HopS.

Abstract:
Fine-tuning pre-trained point-cloud backbones typically up-dates all parameters, resulting in substantial computation and memoryoverhead. More importantly, modern point backbones rely on aggressivetokenization and downsampling, which yields compact global tokens butirreversibly discards fine-grained local geometry, an inherent bottleneckfor parameter-efficient adaptation. Consequently, existing PEFT meth-ods that operate only on these coarsened tokens can modulate globalsemantics but struggle to recover the missing multi-scale locality. Wepresent Point Ladder Tuning (PLT), a locality-aware PEFT frameworkthat performs hierarchical, instance-conditioned adaptation while keep-ing the backbone frozen. PLT forms a lightweight closed loop: (i) a Hier-archical Ladder Network (HLN) constructs a multi-resolution local fea-ture pyramid directly from raw points; (ii) a Local–Global Fusion (LGF)aligns and fuses local pyramids with intermediate backbone semantics;and (iii) a Dynamic Prompt Generator produces instance-aware multi-scale prompts to modulate the frozen backbone effectively. For denseprediction, we further introduce a lightweight segmentation head thatprogressively upsamples fused features and leverages backbone priors torefine fine structures. Extensive experiments on classification and denseprediction show that PLT consistently surpasses prior PEFT baselineswith minimal tunable parameters. PLT achieves state-of-the-art perfor-mance using only 2.71% trainable parameters for classification and 7.69%for dense prediction, and scales favorably to larger backbones, requiringmerely 0.36% parameters on PointGPT-L.

Abstract:
Incremental Vision-Language Object Detection (IVLOD) em-powers pre-trained open-vocabulary detectors to continuously learn newvisual concepts from sequential tasks without forgetting foundationalknowledge. However, existing methods predominantly rely on oracle taskidentities during inference, severely limiting their open-world practical-ity. In this paper, we formalize Task-Agnostic IVLOD (TA-IVLOD), amore realistic setting where such priors are unavailable, requiring themodel to simultaneously recognize classes from all learned tasks. Thisunconstrained setting exposes severe performance degradation due tocross-task semantic interference and parameter conflicts. To tackle thesechallenges, we propose TADA, a novel modular framework. Specifically,TADA introduces Stochastic Prompt Augmentation to mitigate semanticinterference via training-time noise injection, and Test-Time Distribution-Aware Fusion to dynamically weight class-specific LoRA experts, effec-tively resolving parameter conflicts. Extensive evaluations demonstratethat our method significantly outperforms baselines in standard IVLODand yields substantial improvements in the rigorous TA-IVLOD set-ting on the ODinW-13 benchmark, while effectively preserving zero-shot generalization on the MS COCO dataset. The code is available athttps://github.com/yonghanjiang/TADA.

Abstract:
Machine unlearning in Vertical Federated Learning (VFL)has attracted growing interest, yet existing methods certify forgettingsolely using output-level metrics. We challenge these works by intro-ducing Mirage, a representation-level auditing framework built fromfour complementary diagnostics. Mirage combines linear probe recovery(LPR), centered kernel alignment (CKA), feature separability scoring,and layer-wise recovery analysis to assess what a representation actuallyretains. Extensive experiments across seven datasets and seven baselinemethods following recent VFL unlearning protocols reveal three key find-ings. (1) Forgetting gap: methods that pass output-level certification stillretain substantial class structure in their representations, with LPR ex-ceeding the retrained baseline by up to 15.4 points. CKA shows thatthese models remain structurally closer to the original than to the re-trained reference, and separability scores indicate persistent geometricdiscrimination. (2) Unlearning trilemma: no existing method simultane-ously achieves high utility, output-level forgetting, and representation-level forgetting. (3) Class-sample asymmetry: class-level forgetting leavesstrong representational traces (LPR exceeding 96% on several datasets),whereas sample-level forgetting is indistinguishable from chance (LPR≈ 50%). Layer-wise analysis further shows that residual class informationpersists across network depths. These findings call for representation-aware evaluation standards in federated unlearning research. Code ispublicly available at https://github.com/YuZhenyuLindy/Mirage.

Abstract:
Category-level 6D pose estimation of articulated objects froma single-view input is a fundamental yet challenging task. Its core dif-ficulty lies in occlusion-induced pose and shape ambiguities, which arefurther exacerbated by highly entangled pose-shape representations andreal-world depth noise. Existing generative methods often neglect theinherent correlations between pose and shape, resulting in geometricallyinconsistent or physically implausible predictions. To address this limi-tation, we propose a novel generation-and-verification framework for thistask: First, a Pose-Shape Hypothesis Generator jointly models the dis-tributions of pose and shape to generate intrinsically coupled hypothesispairs, providing explicitly paired candidates for subsequent verification.Second, a Part-Level Point Cloud Denoising module recovers clean pointclouds from noisy and partial depth observations, ensuring robust inputsfor the verification stage. Finally, a Cross-Space Consistency Verifica-tion module explicitly verifies the geometric consistency between the ob-served point clouds and canonical shapes using a continuous SDF-basedscoring mechanism, which effectively prunes geometrically implausiblehypotheses. Extensive experiments on challenging benchmarks demon-strate that our method significantly improves the robustness and accu-racy of pose estimation in complex, heavily occluded scenarios. Projectpage: https://github.com/KaifengT/ArtPS.

Abstract:
We introduce 4DGS360, a di!usion-free framework for 360→dynamic object reconstruction from casual monocular video. Existingmethods often fail to reconstruct consistent 360→ geometry, as their heavyreliance on 2D-native priors causes initial points to overfit to visible sur-face in each training view. 4DGS360 addresses this challenge through anadvanced 3D-native initialization that mitigates the geometric ambigu-ity of occluded regions. Our proposed 3D tracker, AnchorTAP3D, pro-duces reinforced 3D point trajectories by leveraging confident 2D trackpoints as anchors, suppressing drift and providing reliable initializationthat preserves geometry in occluded regions. This initialization, com-bined with optimization, yields coherent 360→ 4D reconstructions. Wefurther present iPhone360, a new benchmark where test cameras areplaced up to 135→ apart from training views, enabling 360→ evaluationthat existing datasets cannot provide. Experiments show that 4DGS360achieves state-of-the-art performance on the iPhone360, iPhone, andDAVIS datasets, both qualitatively and quantitatively. Project websiteat https://jaewon040.github.io/4dgs360/

Abstract:
Existing stylized motion generation models have shown theirremarkable ability to understand specific style information from the stylemotion, and insert it into the content motion. However, capturing intra-style diversity, where a single style should correspond to diverse motionvariations, remains a significant challenge. In this paper, we propose aclustering-based framework, ClusterStyle, to address this limitation.Instead of learning an unstructured embedding from each style motion,we leverage a set of prototypes to effectively model diverse style pat-terns across motions belonging to the same style category. We considertwo types of style diversity: global-level diversity among style motionsof the same category, and local-level diversity within the temporal dy-namics of motion sequences. These components jointly shape two struc-tured style embedding spaces, i.e., global and local, optimized via align-ment with non-learnable prototype anchors. Furthermore, we augmentthe pretrained text-to-motion generation model with the Stylistic Mod-ulation Adapter (SMA) to integrate the style features. Extensive ex-periments demonstrate that our approach outperforms existing state-of-the-art models in stylized motion generation and motion style transfer.Project page: https://1233chen.github.io/ClusterStyle/.

Abstract:
Non-rigid 3D shape matching is a fundamental task in com-puter vision and graphics. In this paper, we propose a hybrid self-supervisedmethod based on a coarse-to-fine strategy, which ensures consistencybetween the coarse mapping and the refined correspondence producedby our refinement module. The architecture features a dual-branch de-sign, consisting of two symmetric functional map learning streams: onebased on the Laplacian basis and the other utilizing the elastic ba-sis. Extensive experiments show that our approach not only maintainscomputational efficiency, but also achieves state-of-the-art performanceacross a variety of challenging scenarios, including non-isometric defor-mations and topological noise. Finally, we rigorously demonstrate thatcontrastive energies promote feature discrimination. Furthermore, inte-grating these energies with existing methods yields consistent improve-ments, validating the overall efficacy of our approach. Our code is avail-able at https://github.com/LuoFeifan77/Coarse-to-Fine-Hybrid-Self-Supervised-Matching.

Abstract:
Visual Place Recognition (VPR) has been traditionally for-mulated as a single-image retrieval task. Using multiple views offers clearadvantages, yet this setting remains relatively underexplored and existingmethods often struggle to generalize across diverse environments. In thiswork, we introduce UniPR-3D, the first VPR architecture that effectivelyintegrates geometry-aware information from multiple views. UniPR-3Dbuilds on a VGGT backbone capable of encoding multi-view 3D represen-tations, which we adapt by designing feature aggregators and fine-tuningthem for the place recognition task. To construct our descriptor, wejointly leverage VGGT’s 3D tokens, but also intermediate 2D ones. Basedon their distinct characteristics, we design dedicated aggregation modulesfor 2D and 3D features, allowing our descriptor to capture fine-grainedtexture patterns while also reasoning across viewpoints. To further en-hance generalization, we incorporate both single- and multi-frame aggre-gation schemes, along with a variable-length sequence retrieval strategy.Our experiments show that UniPR-3D sets a new state of the art, out-performing both single- and multi-view baselines and highlighting theeffectiveness of geometry-grounded tokens for VPR. Our code and mod-els will be made publicly available on Github. We will release the codeand datasets publicly on https://github.com/dtc111111/UniPR-3D.

Abstract:
Event cameras have recently shown promising capabilities ininstantaneous motion estimation due to their robustness to low light andfast motions. However, computing wide-baseline correspondence betweentwo arbitrary views remains a significant challenge, since event appear-ance changes substantially with motion, and learning-based approachesare constrained by both scalability and limited wide-baseline supervi-sion. We therefore introduce the first event matching model that achievescross-dataset wide-baseline correspondence in a zero-shot manner:a single model trained once is deployed on unseen datasets withoutany target-domain fine-tuning or adaptation. To enable this capabil-ity, we introduce a motion-robust and computationally efficient atten-tion backbone that learns multi-timescale features from event streams,augmented with sparsity-aware event token selection, making large-scaletraining on diverse wide-baseline supervision computationally feasible.To provide the supervision needed for wide-baseline generalization, wedevelop a robust event motion synthesis framework to generate large-scale event-matching datasets with augmented viewpoints, modalities,and motions. Extensive experiments across multiple benchmarks showthat our framework achieves a 37.7% improvement over the previousbest event feature matching methods. Code and data are available at:https: // github. com/ spikelab-jhu/ Match-Any-Events .

Abstract:
Recent advances in text-to-video (T2V) diffusion models have demonstrated remarkable generative capabilities, yet their reliance on loosely curated training data raises pressing safety and copyright concerns. Concept erasure offers a principled remedy by removing unwanted semantics from pretrained models while preserving remaining concepts. However, existing approaches typically operate at a coarse granularity misaligned with the fine-grained, distributed nature of concept representations, leading to incomplete removal or degraded generation quality. We argue that surgical erasure fundamentally requires intervention at the level of monosemantic features, where each unit encodes a single interpretable concept. To this end, we propose EraseSAE, a novel framework that leverages sparse autoencoders to achieve surgical concept erasure in DiT-based T2V diffusion models via a principled decompose-attributeerase pipeline. We first introduce the Partitioned Convolutional Sparse Autoencoder, which decomposes dense spatiotemporal activations into disentangled, interpretable sparse features while preserving spatiotemporal coherence. A contrastive attribution mechanism then contrasts activations from paired prompts to isolate concept-specific feature kernels. At inference, timestep-resolved spatiotemporal masks derived from the identified kernels confine erasure to regions where the target concept is active, leaving unrelated content intact. Extensive experiments across diverse diffusion models and concept erasure tasks demonstrate that EraseSAE achieves precise and robust concept removal with minimal quality degradation, substantially outperforming state-of-the-art methods. The code is available at https://github.com/HiDream-ai/EraseSAE.

Abstract:
Diffusion models have made significant progress in both text-to-image (T2I) generation and text-guided image editing. However, thesemodels are typically built with billions of parameters, leading to high la-tency and increased deployment challenges. While on-device diffusionmodels improve efficiency, they largely focus on T2I generation and lacksupport for image editing. In this paper, we propose DreamLite, a com-pact unified on-device diffusion model (0.39B) that supports bothT2I generation and text-guided image editing within a single network.DreamLite is built on a pruned mobile U-Net backbone and unifies con-ditioning through in-context spatial concatenation in the latent space.It concatenates images horizontally as input, using a (target | blank)configuration for generation tasks and (target | source) for editing tasks.To stabilize the training of this compact model, we introduce a task-progressive joint pretraining strategy that sequentially targets T2I, edit-ing, and joint tasks. After high-quality SFT and reinforcement learning,DreamLite achieves GenEval (0.72) for image generation and ImgEdit(4.11) for image editing, outperforming existing on-device models and re-maining competitive with several server-side models. By employing stepdistillation, we further reduce denoising processing to just 4 steps, en-abling our DreamLite could generate or edit a 1024 × 1024 image in lessthan 3s on iPhone 17 pro. To the best of our knowledge, DreamLiteis the first unified on-device diffusion model that supports both imagegeneration and image editing. Our code and model are available at here.

Abstract:
Recent advancements in Unified Multimodal Models (UMMs)have significantly advanced text-to-image (T2I) generation, particularlythrough the integration of Chain-of-Thought (CoT) reasoning. How-ever, existing CoT-based T2I methods largely rely on abstract natural-language planning, which lacks the precision required for complex spa-tial layouts, structured visual elements, and dense textual content. In thiswork, we propose CoCo (Code-as-CoT), a code-driven reasoning frame-work that represents the reasoning process as executable code, enablingexplicit and verifiable intermediate planning for image generation. Givena text prompt, CoCo first generates executable code that specifies thestructural layout of the scene, which is then executed in a sandboxed en-vironment to render a deterministic draft image. The model subsequentlyrefines this draft through fine-grained image editing to produce the fi-nal high-fidelity result. To support this training paradigm, we constructCoCo-10K, a curated dataset containing structured draft–final imagepairs designed to teach both structured draft construction and correc-tive visual refinement. Empirical evaluations on StructT2IBench, OneIG-Bench, and LongText-Bench show that CoCo achieves improvements of+68.83%, +54.8%, and +41.23% over direct generation, while also out-performing other generation methods empowered by CoT. These resultsdemonstrate that executable code is an effective and reliable reasoningparadigm for precise, controllable, and structured text-to-image genera-tion. The code is available at: https://github.com/micky-li-hd/CoCo

Abstract:
Training-free guidance (TFG) steers a pretrained diffusionmodel toward a desired attribute at inference. To be effective, this guid-ance must be applied from the earliest, high-noise steps of sampling.Because its objective (a classifier or energy) is defined on clean images,ϵ- and v-prediction models must first estimate the clean image x̂ fromthe noisy state at each step, and the accuracy of that estimate deter-mines how easily guidance drifts off the data manifold. x-prediction,a recent alternative, outputs the clean image directly, removing thissource of error even at high noise. This is our motivation. We providea theoretical analysis of how each prediction target shapes this accu-racy, and introduce guided-class FID (Child FID), a metric that exposesthe manifold damage standard evaluation misses. Experiments on anew fine-grained bird benchmark and on style transfer confirm that x-prediction keeps guided samples on the manifold most reliably, making itthe strongest foundation for training-free guidance. Code is available athttps://github.com/ManLuML/on-manifold-tfg.

Abstract:
Facial expression image editing requires fine-grained control to strictly preserve human identity and background while precisely manipulating expression. However, existing editing benchmarks primarily focus on general scenarios, lacking high-quality facial images and corresponding editing instructions. Furthermore, current evaluation metrics exhibit systemic biases in this task, often favoring lazy editing or overfit editing. To bridge these gaps, we propose FED-Bench, a comprehensive benchmark featuring rigorous testing and an accurate evaluation suite. First, we carefully construct a benchmark of 747 triplets through a cascaded and scalable pipeline, each comprising an original image, an editing instruction, and a ground-truth image for precise evaluation. Second, we introduce FED-Score, a cross-granularity evaluation protocol that disentangles assessment into three dimensions: Alignment for verifying instruction following, Fidelity for testing image quality and identity preservation, and Relative Expression Gain for quantifying the magnitude of expression changes, effectively mitigating the aforementioned evaluation biases. Third, we benchmark 18 image editing models, revealing that current approaches struggle to simultaneously achieve high fidelity and accurate expression manipulation, with fine-grained instruction following identified as the primary bottleneck. Finally, leveraging the scalable characteristic of introduced benchmark engine, we provide a 20k+ in-the-wild facial training set and demonstrate its effectiveness by fine-tuning a baseline model that achieves significant performance gains. Our benchmark and code are available at https://github.com/hiixfj/FED-Bench.

Abstract:
We propose A2-Edit, a unified inpainting framework for arbitrary object categories, which allows users to replace any target region with a reference object using only a coarse mask. To address the issues of severe homogenization and limited category coverage in existing datasets, we construct a large-scale, multi-category dataset UniEdit-500K, which includes 8 major categories, 209 fine-grained subcategories, and a total of 500,104 image pairs. Such rich category diversity poses new challenges for the model, requiring it to automatically learn semantic relationships and distinctions across categories. To this end, we introduce the Mixture of Transformer module, which performs differentiated modeling of various object categories through dynamic expert selection, and further enhances cross-category semantic transfer and generalization through collaboration among experts. In addition, we propose a Mask Annealing Training Strategy (MATS) that progressively relaxes mask precision during training, reducing the model’s reliance on accurate masks and improving robustness across diverse editing tasks. Extensive experiments on benchmarks such as VITON-HD and AnyInsertion demonstrate that A2-Edit consistently outperforms existing approaches across all metrics, providing a new and efficient solution for arbitrary object editing. The code is released at https://github.com/huayu-zheng/A2Edit.

Abstract:
Video super-resolution (VSR) using large-scale Diffusion Transformer (DiT) priors achieves exceptional perceptual quality but is often impractical due to the quadratic computational cost of processing dense spatio-temporal token sequences. Existing efficiency-oriented methods risk irreversible detail loss and temporal flickering, a vulnerability especially pronounced in one-step diffusion models. To address this, we propose TRaM-VSR, a Token Routing and Merging framework for adaptive token allocation, leveraging both context-aware video priors and network-level priors. First, token importance is estimated by fusing motion-sensitive temporal cues with semantic text similarity, isolating dynamic objects and structural boundaries. Next, this importance is further calibrated and adjusted by an offline planner to guide routing across optimally grouped network blocks. Technically, within each routed group, structurally critical tokens are processed in a highfidelity local stream, while less informative tokens are aggregated into a compact global stream, both modulated by network depth and aligned with the multigranular nature of diffusion models. Extensive experiments show that TRaM-VSR accelerates inference significantly while preserving state-of-the-art reconstruction quality and robust temporal consistency. The code is available at https://github.com/Ree1s/TRaM-VSR.

Abstract:
Single-image dehazing aims to recover a clear scene from a hazy image and is generally formulated as an image-to-image translation task; however, it faces two limitations. Its performance depends heavily on the haze-formation priors embedded in the model. Prevailing methods adopt the Atmospheric Scattering Model (ASM), whose assumptions of single scattering and homogeneous media are often violated, leading to residual haze and color drift. Moreover, large-scale real hazy/clear pairs are impractical to collect, and existing synthesis approaches fail to reproduce the full complexity of natural haze. To address these issues, we present RTE-FM-Dehazer, a novel dehazing approach, together with a scalable data pipeline. Unlike the ASM, the Radiative Transfer Equation (RTE) jointly accounts for both scattering and absorption, naturally accommodating the non-homogeneous, multiple-scattering media that characterize real hazy scenes. Motivated by the structural similarity between the RTE diffusion-absorption term and the ODE in flow matching, we introduce a diffusion-absorption regularizer derived from a reduced RTE, to steer the flow matching trajectory at each step. Next, leveraging modern vision–language models, we build an automated pipeline and release P-HAZE, a dataset of 50000 realistic hazy/clear pairs. Extensive evaluations demonstrate that RTE-FM-Dehazer, trained solely on P-HAZE, effectively eliminates artifacts like residual haze and color drift, exhibits strong cross-domain generalization, and achieves leading results on five real-world dehazing benchmarks. Code and data are available at https://github.com/vincentweikey/RTE-FM-Dehazer.

Abstract:
Leveraging representation encoders for generative modelingoffers a path for efficient, high-fidelity synthesis. However, standard diffu-sion transformers fail to converge on these representations directly. Whilerecent work attributes this to a capacity bottleneck—proposing computa-tionally expensive “width scaling” of diffusion transformers—we demon-strate that the failure is fundamentally geometric. We identify GeometricInterference as the root cause: standard Euclidean flow matching forcesprobability paths through the low-density interior of the hypersphericalfeature space of representation encoders, rather than following the mani-fold surface. To resolve this, we propose Riemannian Flow Matchingwith Jacobi Regularization (RJF). By constraining the generativeprocess to the manifold geodesics and correcting for curvature-inducederror propagation, RJF enables standard Diffusion Transformer architec-tures to converge without width scaling. Our method RJF enables thestandard DiT-B architecture (131M parameters) to converge effectively,achieving an FID of 3.37 where prior methods fail to converge.

Abstract:
The recent success of inference-time scaling in large languagemodels has inspired similar explorations in video diffusion. In particular,motivated by the existence of “golden noise” that enhances video quality,prior work has attempted to improve inference by optimising or searchingfor better initial noise. However, these approaches have notable limita-tions: they either rely on priors imposed at the beginning of noise sam-pling or on rewards evaluated only on the denoised and decoded videos.This leads to error accumulation, delayed and sparse reward signals, andprohibitive computational cost, which prevents the use of stronger searchalgorithms. Crucially, stronger search algorithms are precisely what couldunlock substantial gains in controllability, sample efficiency and genera-tion quality for video diffusion, provided their computational cost can bereduced. To fill in this gap, we enable efficient inference-time scaling forvideo diffusion through latent reward guidance, which provides interme-diate, informative and efficient feedback along the denoising trajectory.We introduce a latent reward model that scores partially denoised la-tents at arbitrary timesteps with respect to visual quality, motion qual-ity, and text alignment. Building on this model, we propose LatSearch,a novel inference-time search mechanism that performs Reward-GuidedResampling and Pruning (RGRP). In the resampling stage, candidatesare sampled according to reward-normalised probabilities to reduce over-reliance on the reward model. In the pruning stage, applied at the finalscheduled step, only the candidate with the highest cumulative reward isretained, improving both quality and efficiency. We evaluate LatSearchon the VBench-2.0 benchmark and demonstrate that it consistently im-proves video generation across multiple evaluation dimensions comparedto the baseline Wan2.1 model. Compared with the state-of-the-art, ourapproach achieves comparable or better quality while reducing runtimeby up to 79%.

Abstract:
Recent works such as REPA have shown that guiding dif-fusion models with external semantic features (e.g., DINO) can signifi-cantly accelerate the training of diffusion transformers (DiTs). However,the use of pretrained external features as guidance signals introducesadditional dependencies. We argue that DiTs actually have the powerto guide the training of themselves, and propose Self-Transcendence,an effective method that achieves fast convergence using internal featuresupervision only. The desired internal guidance features should meet tworequirements: structurally clean to help shallow blocks separate noisefrom signal, and semantically discriminative to help shallow layers learneffective representations. With this consideration, we first align the DiTfeatures with the clean VAE latent features, a native component of la-tent diffusion, for a short training phase (e.g., 40 epochs) to improvetheir structural representations, then apply the classifier-free guidanceto the intermediate features, enhancing their discriminative capabilityand semantic expressiveness. These enriched internal features, learnedentirely within the model, are used as supervision signals to guide a newDiT training from scratch. Compared to existing self-contained meth-ods, our approach achieves a significant performance boost. It can evensurpass REPA, which uses the external DINO features as guidance, inboth generation quality and convergence speed for both class-to-imageand text-to-image generation tasks. Codes and models can be found athttps://github.com/csslc/Self-Transcendence.

Abstract:
Zero-shot composed image retrieval (ZS-CIR) aims to re-trieve a target image by editing a reference image with a natural-languageinstruction, without relying on domain-specific annotated triplets. Mostexisting ZS-CIR methods rely on textual inversion to translate the ref-erence image into pseudo-text tokens and then compose them with theinstruction via simple concatenation in the text space, which can belossy and brittle for fine-grained semantics. In this work, we propose anew paradigm, namely FlowCIR, that casts ZS-CIR as conditionalsemantic transport between reference and target embeddings. Leverag-ing conditional flow matching, our model learns a lightweight transportfield that maps the instruction representation toward a target-alignedquery embedding conditioned on the reference image. Since FlowCIRoperates on pre-extracted VLM embeddings and trains only a smalltransport module without updating the image or text encoder, it of-fers a computationally efficient training protocol compared with priortextual-inversion-based approaches. We further identify negation and re-moval as a major failure mode of VLM-based composition. To addressthis, we propose an inference-only Multi-Negative Steering strategy thatsteers a negation-containing relative instruction away from its negatedsemantics, mitigating the limited negation handling of VLMs and im-proving robustness on negation-heavy queries. Extensive experiments onstandard CIR benchmarks demonstrate that FlowCIR achieves strongand competitive performance compared with recent ZS-CIR methods.Project page: https://hkust-longgroup.github.io/FlowCIR

Abstract:
Vision-Language Models (VLMs) empower embodied agentsto execute complex instructions, yet they remain vulnerable to contex-tual safety risks where benign commands become hazardous due to subtleenvironmental states. Existing safeguards often prove inadequate. Rule-based methods lack scalability in object-dense scenes, whereas model-based approaches relying on prompt engineering suffer from unfocusedperception, resulting in missed risks or hallucinations. To address this,we propose an architecture-agnostic safeguard featuring Context-GuidedChain-of-Thought (CG-CoT). This mechanism decomposes risk assess-ment into active perception that sequentially anchors attention to inter-action targets and relevant spatial neighborhoods, followed by semanticjudgment based on this visual evidence. We support this approach witha curated grounding dataset and a two-stage training strategy utilizingReinforcement Fine-Tuning (RFT) with process rewards to enforce pre-cise intermediate grounding. Experiments demonstrate that our modelsignificantly enhances safety, improving risk match rates by over 30%compared to base models while reducing oversafety. Beyond hazard de-tection, the generated visual anchors serve as actionable spatial con-straints for downstream planners, facilitating explicit collision avoidanceand safety trajectory generation.

Abstract:
While Multi-modal Large Language Models (MLLMs) havedemonstrated superior performance on isolated tasks such as visual ques-tion answering, their reliability remains limited in real-world scenariosthat require highly compositional visual-language reasoning. A primeexample is automated exam grading, a real-world practical task thatdemands the integration of three core capabilities: detection and local-ization, text recognition, and reasoning for correctness judgment. Cur-rent benchmarks largely focus on isolated tasks and cannot fully eval-uate MLLMs’ end-to-end ability in such complex settings. To bridgethis gap, we present GradingBench, a comprehensive benchmark basedon automated exam grading in Chinese K–12 education, which system-atically evaluates MLLMs across the entire grading pipeline. Grading-Bench comprises full-page exam papers from real educational settings,containing 3,284 sub-questions annotated with reference answers. Weevaluate MLLMs across three levels: single-question, specified-question,and full-page grading. Our experiments show that localization failureis the main bottleneck and reveal limitations in integrated multi-taskexecution. Although multi-round interaction and supervised fine-tuningbring slight improvements, the limited gains reflect fundamental weak-nesses in compositional ability. GradingBench serves as a challenging,application-driven benchmark for the community, underscoring the needfor seamless perception-cognition fusion in MLLMs to unlock their po-tential in practical domains. All code and data are available at https://github.com/ERRORSEMI/GradingBench.

Abstract:
Multimodal large language models (MLLMs) deliver impressive vision-language reasoning, but suffer steep inference latency because self-attention scales quadratically with sequence length and thousands of visual tokens contributed by high-resolution images. Naively pruning less-informative visual tokens reduces this burden, yet indiscriminate removal can erase contextual cues essential for background or fine-grained questions, undermining accuracy. In this paper, we present ParVTS (Parallel Vision Token Scheduling), a training-free scheduling framework that partitions visual tokens into subject and non-subject groups, processes them in parallel to transfer their semantics into question tokens, and discards the non-subject path mid-inference to reduce computation. This scheduling reduces computational complexity, requires no heuristics or additional modules, and is compatible with diverse existing MLLM architectures. Experiments across multiple MLLM backbones show that ParVTS prunes up to 88.9% of visual tokens with minimal performance drop, achieving 1.77× speedup and 70% FLOPs reduction. Our code is released at https://github.com/CrispyFeSo4/ParVTS.

Abstract:
Visual document retrieval requires rapidly locating relevantpages from large multi-modal corpora in response to user queries. Whilerecent methods powered by Multi-modal Large Language Models (MLLMs)show competitive accuracy, they suffer from prohibitive computationalcosts by applying intensive MLLM encoding to every single page. Mean-while, we observe that user queries are typically keyword-anchored, con-taining semantically rich words that are expected to appear directly inthe visible text of relevant pages, offering an efficient cue for quickly nar-rowing down candidate pages. Building on this insight, we propose Light-STAR, an efficient framework that decomposes visual document retrievalinto: 1) LLM-free Visual Selection, which utilizes content-grounded queryencoding to focus on informative words and employs LLM-free visual em-beddings to produce a high-recall candidate set; and 2) Vision-adaptiveSemantic Refinement, which further performs fine-grained semantic match-ing exclusively on these top candidates via adaptive region-wise featurefusion to effectively combine textual and layout cues, optimized through ahardness-aware contrastive objective. Experimental results demonstratethat LightSTAR achieves state-of-the-art retrieval accuracy while reduc-ing end-to-end latency by several-fold, offering a highly practical solutionto the accuracy-efficiency trade-off in visual document retrieval. Code isavailable at https://github.com/bokufa/LightSTAR.

Abstract:
Answering questions accurately and efficiently in embodiedscenarios presents significant challenges due to limited computationaland memory resources for Vision Language Model (VLM) inference. Ex-isting methods adopt visual search key frame retrieval method to se-lect critical question-related key frames for VLM input. However, visualsearch methods are inefficient because they require visual search amongthousands of video frames for each individual user query. In this work, wepropose a memory tree guided key frame selection paradigm for efficient3D question answering in embodied scenarios. Our method leveragesa compact and reusable 3D scene representation, termed MemTree3D,which supports real-time online construction leveraging camera 6-DoFposes. MemTree3D captures multi-level 3D scene information, enabling aLarge Language Model to efficiently query and retrieve question-relevantkey frames through our scoring-based frame selection without repro-cessing the entire video stream. On OpenEQA, our method improvesthe LLM-Match of GPT-4o by 17.4%, LLaVA-OneVision-7B by 5.8%,outperforms existing visual search methods. Our code is available athttps://github.com/hsiangwei0903/MemTree3D.

Abstract:
Post-training via Supervised Fine-Tuning (SFT) and Rein-forcement Learning (RL) is crucial for enhancing reasoning in Multi-modal Large Language Models (MLLMs), yet existing paradigms of-ten reach a performance bottleneck due to the limitations of staticdata. While current methods leverage self-reflection or self-evolution topush these boundaries, they still suffer from cognitive drift and hallu-cinated reasoning paths caused by low-quality synthetic data. To ad-dress these challenges, we propose Anchor E volution (AnE), a newparadigm that integrates truth-anchored data curation and model evo-lution, achieving faithful and steady performance gains at the reason-ing frontier. Specifically, we propose Truth Anchor Expansion, whichpinpoints the model failing frontier via trajectory rollouts and lever-ages ground-truth databases to retrieve high-fidelity anchors for faithfuldata curation. Subsequently, we introduce the Scaffold-Stripping Mech-anism to internalize reasoning capabilities. This mechanism first an-chors reasoning paths via scaffold-augmented supervision to mitigatethe learning complexity and distribution drift of direct SFT on rawdata, then leverages RL to strip the scaffold template, thereby effec-tively transitioning the reasoning paths into intrinsic model capabilities.Experimental results on multimodal reasoning benchmarks show thatour method substantially advances the model performance frontier, im-proving the base model by 10.3% across eight multimodal benchmarksand achieving state-of-the-art results. The code will be publicly availableat https://github.com/wangzehao0704/AnE.

Abstract:
Multimodal survival analysis aims to improve cancer prognosis using heterogeneous biomedical data, such as histopathology images and genomic profiles. A common strategy is to align representations across modalities so that shared signals can be captured. However, strong cross-modal alignment can also remove modality-specific evidence that is critical for survival prediction. In this paper, we revisit multimodal survival learning from a simple observation: effective models should first discover shared patterns across modalities, and then preserve modalityspecific signals. This motivates a representation learning principle that we refer to as Together Then Apart. Based on this idea, we propose TTA, a framework that balances cross-modal alignment and representation distinctiveness. TTA first performs prototype-based alignment to capture shared survival-related structures between modalities. It then encourages modality-specific distinctiveness through an anchor-guided contrastive objective. To further account for modality imbalance and noisy correspondences, we model cross-modal interactions using unbalanced optimal transport. We evaluate the proposed approach on multiple TCGA cancer cohorts with paired histopathology and genomic data. TTA consistently improves survival prediction over recent multimodal survival models. Moreover, the learned prototype structures reveal interpretable cross-modal patterns associated with clinical outcomes. Code is available at https://github.com/Y-Research-SBU/TTA.

Abstract:
Recently, self-evolving large multimodal models (LMMs) havereceived attention for improving visual reasoning in a purely unsuper-vised setting. However, multi-role self-play and self-consistency rewardschemes in existing self-evolving LMMs optimize answer agreement with-out ensuring the decoder attends to visual content, relying instead onstatistical language priors to produce self-consistent outputs. This leadsto a persistent failure mode we term visual under-conditioning, wherethe decoder relies on language priors rather than the image during gen-eration, manifesting as insufficient attention to visual tokens. As a result,current self-evolving LMMs struggle on vision–language understandingtasks such as image captioning and visual question answering. To addressthis, we propose VISE (Visual Invariance Self-Evolution), a purely un-supervised self-evolving framework that directly regularizes the model’svisual conditioning policy through two complementary invariance-basedrewards: a geometric invariance reward that enforces spatial consistencyunder known transformations, and a semantic invariance reward thatpenalizes evidence-agnostic generation by requiring the model to recog-nize the absence of evidence when predicted regions are perturbed. VISEoperates within a single model without specialist roles, external rewardmodels, or annotations, and is trained on raw unlabeled images. Ex-periments on 18 benchmarks demonstrate the efficacy of our approach.Using Qwen3-VL-2B as the base model, VISE achieves gains of +16.85CIDEr on COCO and +19.66 CIDEr on TextCaps, reduces object hal-lucination by 5.0 Chair-I points, and generalizes across four model fam-ilies and scales. Our code and models are available at https://mbzuai-oryx.github.io/VISE/.

Abstract:
While Vision-Language Models (VLMs) have significantly advanced remote sensing interpretation, enabling them to perform complex, step-by-step reasoning remains highly challenging. Recent efforts to introduce Chain-of-Thought (CoT) reasoning to this domain have shown promise, yet ensuring the visual faithfulness of these intermediate steps remains a critical bottleneck. To address this, we introduce GeoSolver, a novel framework that transitions remote sensing reasoning toward verifiable, process-supervised reinforcement learning. We first construct GeoPRM-2M, a large-scale, token-level process supervision dataset synthesized via entropy-guided Monte Carlo Tree Search (MCTS) and targeted visual hallucination injection. Building upon this dataset, we train GeoPRM, a token-level process reward model (PRM) that provides granular faithfulness feedback. To effectively leverage these verification signals, we propose Process-Aware Tree-GRPO, a reinforcement learning algorithm that integrates tree-structured exploration with a faithfulness-weighted reward mechanism to precisely assign credit to intermediate steps. Extensive experiments demonstrate that our resulting model, GeoSolver- 9B, achieves state-of-the-art performance across diverse remote sensing benchmarks. Crucially, GeoPRM unlocks robust Test-Time Scaling (TTS). Serving as a universal geospatial verifier, it seamlessly scales the performance of GeoSolver-9B and directly enhances general-purpose VLMs, highlighting its remarkable cross-model generalization. The code will be available at https://github.com/minglangL/GeoSolver.

Abstract:
State space models, especially Visual State Space Duality (VSSD), have emerged as efficient linear-time alternatives to Transformers for dense visual tasks. However, we observe that VSSD compresses spatial context into a global aggregation that suppresses high-frequency responses, causing excessive boundary smoothing in remote sensing semantic segmentation. To address this, we propose CRISP, a calibration framework with two components. Its core, the Duality Calibration Operator (DCO), restores local contrast and boundary responses through residual injection and frequency calibration within the VSSD backbone, without altering its linear complexity. To retain the recovered detail, an Orthogonal Multi-Prototype (OMP) head assigns multiple orthogonally constrained prototypes per class to model large intra-class variance. Extensive experiments on Potsdam, Vaihingen, and LoveDA show that, with only ∼30M parameters, CRISP achieves consistent gains in mean F1 (mF) and mIoU while remaining competitive with state-of-the-art methods. Code is available at https://github.com/crazylifeha/crisp.

Abstract:
Current generative models, including GANs and diffusionmodels, have reached an outstanding level of photorealism, posing sig-nificant risks to privacy and security. To ensure real-world applicability,deepfake detectors must generalise effectively to unseen generators. How-ever, most existing approaches rely on supervised training with both realand fake images, which limits their generalisation especially across gener-ators categories (e.g. GANs vs DMs). In this work, we introduce µFlow ,a one-class deepfake detector trained only on real images without relyingon pseudo-deepfakes or synthetic artifacts. Our approach builds on theobservation that averaging multiple images amplifies consistent genera-tive traces, producing highly discriminative feature representations. Weleverage this property by modelling the distribution of features extractedfrom averaged images and training a normalizing flow to align the featurespace of individual images with this distribution. This alignment yieldsa likelihood-based criterion that separates real and fake samples whilepromoting strong generalisation. We evaluate µFlow on a fully out-of-distribution setting, where both real and fake datasets are unseen duringtraining. Experimental results show that our method significantly out-performs SOTA detectors. Project page: opontorno.github.io/MuFlow.

Abstract:
Referring Remote Sensing Image Segmentation (RRSIS) hasachieved significant progress through the integration of VLMs and theSegment Anything Model (SAM). However, this progress largely relieson strong pre-trained capabilities, while leaving two fundamental limi-tations insufficiently addressed: (1) Architectural Weak-Coupling, wherethe unidirectional flow forces reliance on coarse VLM prompts and wastesSAM’s pixel-level structural guidance, causing localization drift; and (2)Object-Centric Semantic Bias, where models overemphasize dominantobject semantics while remaining insensitive to spatial reasoning cru-cial for RRSIS. Motivated by these observations, we propose CROSS, atightly integrated paradigm for RRSIS. First, we introduce Linguistic-Guided Cascaded Distillation (LGCD) to bridge the architectural gap,which distills SAM’s geometric affinities as soft regularizers into VLM in-termediate layers, injecting dense structural priors to refine localization.Second, Perspective-Spatial Contrastive Learning (PSCL) imposes cross-anchored constraints by mining mask-filtered deceptive distractors andspatial-linguistic counterfactuals as hard negatives, explicitly shatter-ing semantic shortcuts to enforce genuine logical consistency. Extensiveexperiments on RRSIS benchmarks demonstrate that CROSS achievesstate-of-the-art performance and maintains precise localization even un-der severe spatial description perturbations, standing as a robust newparadigm for RRSIS. https://clarence-cv.github.io/CROSS/.

Abstract:
Multi-modality image fusion (MMIF) enhances scene representation by exploiting complementary cues from different modalities. Adverse weather, however, causes significant image degradation, disrupting feature representation and requiring simultaneous feature restoration and cross-modal complementarity. Existing methods often struggle with effective representation learning under such conditions, limiting their practical performance. To address these challenges, we propose a mask-guided MMIF method that integrates feature restoration and interaction. We first introduce "Pseudo Ground Truth" to simplify training, promoting faster and more effective feature learning. Then, we design a mask generation mechanism based on the mapping relationship between the fused result and the source images, quantifying the relative contribution of each modality during the fusion process. By incorporating the proposed mask-guided cross-modal cross-attention mechanism, the network is encouraged to selectively attend to informative features during modality interaction, mitigating the risk of overfitting to the static distribution of the "Pseudo Ground Truth". Additionally, we propose a mask-guided learning strategy and a task-coupled degradation-aware learning strategy to balance feature restoration and interaction. Extensive experiments on synthetic and real-world datasets demonstrate that our method surpasses state-of-the-art approaches in visual quality, quantitative metrics, and downstream tasks. The source code is available at https://github.com/ixilai/AMG-Fuse.

Abstract:
Multispectral object detection enhances perceptual robust-ness in challenging environments by fusing complementary informationfrom RGB and infrared modalities. However, existing methods predomi-nantly follow a parameter-expanding paradigm, introducing customizedfusion modules that inevitably increase computational overhead and dis-rupt the topological consistency with unimodal pre-trained weights. Inthis paper, we challenge this convention and propose ZPA-MDETR,a zero-parameter adaptive multispectral framework without additionallearnable fusion parameters. By re-examining the DETR architecture,we reveal that its inherent query-memory interaction mechanism pro-vides a natural foundation for cross-modal fusion. Specifically, ZPA-MDETR strictly preserves the architecture of the unimodal DETR andachieves flexible and effective fusion via an asymmetric input organiza-tion strategy, where one modality serves as the memory, and the otherinitializes object queries. To bridge the cross-modal distribution dis-crepancy, we introduce parameter-free semantic alignment constraintsduring training. Extensive experiments on four public datasets acrossdifferent scenes demonstrate that ZPA-MDETR outperforms state-of-the-art methods while maintaining the same parameter scale as theunimodal framework, establishing a favorable trade-off between detec-tion accuracy and deployment efficiency. The source code is available athttps://github.com/UserXiangYang/ZPA-MDETR.

Abstract:
Despite recent progress, vision-language encoders strugglewith two core limitations: (1) weak alignment between language anddense vision features, which hurts tasks like open-vocabulary semanticsegmentation; and (2) high token counts for fine-grained visual repre-sentations, which limits scalability to long videos. This work addressesboth limitations. We propose T-REN (Text-aligned Region Encoder Net-work), an efficient encoder that maps visual data to a compact setof text-aligned region-level representations (or region tokens). T-RENachieves this through a lightweight network added on top of a frozenvision backbone, trained to pool patch-level representations within eachsemantic region into region tokens and align them with region-level textannotations. With only 3.7% additional parameters compared to thevision-language backbone, this design yields substantially stronger densecross-modal understanding while reducing the token count by orders ofmagnitude. Specifically, T-REN delivers +5.9 mIoU on ADE20K open-vocabulary segmentation, +18.4% recall on COCO object-level text-image retrieval, +15.6% recall on Ego4D video object localization, and+17.6% mIoU on VSPW video scene parsing, all while reducing tokencounts by more than 24× for images and 187× for videos compared to thepatch-based vision-language backbone. The code and model are availableat https://github.com/savya08/T-REN.

Abstract:
Infrared and visible video fusion combines the object saliencyfrom infrared images with the texture details from visible images toproduce semantically rich fusion results. However, most existing meth-ods are designed for static image fusion and cannot effectively han-dle frame-to-frame motion in videos. Current video fusion methods im-prove temporal consistency by introducing interactions across frames,but they often require high computational cost. To mitigate these chal-lenges, we propose MAVFusion, an end-to-end video fusion frameworkfeaturing a motion-aware sparse interaction mechanism that enhancesefficiency while maintaining superior fusion quality. Specifically, we lever-age optical flow to identify dynamic regions in multi-modal sequences,adaptively allocating computationally intensive cross-modal attentionto these sparse areas to capture salient transitions and facilitate inter-modal information exchange. For static background regions, a lightweightweak interaction module is employed to maintain structural and appear-ance integrity. By decoupling the processing of dynamic and static re-gions, MAVFusion simultaneously preserves temporal consistency andfine-grained details while significantly accelerating inference. Extensiveexperiments demonstrate that MAVFusion achieves state-of-the-art per-formance on multiple infrared and visible video benchmarks, achieving aspeed of 14.16 FPS at 640 × 480 resolution. The source code is availableat https://github.com/ixilai/MAVFusion.

Abstract:
Most existing knowledge distillation methods focus on ho-mogeneous models (e.g., CNN→CNN), thereby overlooking the flexi-bility and potential of knowledge transfer across heterogeneous mod-els. Due to intrinsic inductive bias discrepancies between heterogeneousmodels that cause spatial distribution inconsistencies, prior heteroge-neous distillation methods often weaken or discard spatial informationin heterogeneous representations. However, the spatial information inrepresentations often encodes transferable global structural semantics aswell as architecture-specific local details, and therefore should not bedirectly ignored. To better leverage the spatial information encoded inheterogeneous representations, we propose a Spatial–Frequency Joint-Aware Heterogeneous Knowledge Distillation framework (SFKD). Byleveraging the complementary properties of wavelet transform spatiallocality and Fourier representations in characterizing global energy dis-tributions, we first apply multi-level discrete wavelet transform to ex-plicitly decouple spatial information. The resulting wavelet sub-bandsare further refined by a dual-stream dual-stage refinement module, andfinally combined with a Gaussian-filtered frequency loss to selectivelycapture informative global information. Extensive experiments on mul-tiple benchmark datasets under both homogeneous and heterogeneousmodels demonstrate the superiority of our method. Code is available athttps://github.com/cpcpWang/SFKD.

Abstract:
Open-Vocabulary Change Detection (OVCD) faces a funda-mental granularity gap: foundation models prioritize high-level semanticabstraction while change detection requires pixel-level spatial fidelity.Traditional OVCD methods rely on instance extraction models, intro-ducing spatial semantic ambiguities and leading to over-segmentationor under-segmentation in remote sensing scenarios. To bridge this gap,we propose Free-CD, a training-free OVCD framework that reformu-lates the task by predicting a change probability distribution rather thanenforcing binary change masks via instance boundaries. We introduceRIFI-Up, an upsampler that enforces a resolution-invariant feature in-version constraint, enabling high-fidelity detail recovery without seman-tic artifacts. Additionally, we propose a Bayesian Probability Correctionmechanism that separates change priors from semantic categorization,introducing a correction term to rectify change area discrimination andenforce bi-temporal semantic consistency. Extensive experiments on fourremote sensing datasets demonstrate that Free-CD consistently outper-forms current methods, establishing new standards in accuracy and gen-eralization for open-vocabulary change detection. Code is available athttps://github.com/20374230/FreeCD.

Abstract:
Stereo-based 3D obstacle perception for autonomous driv-ing is currently constrained by an imbalanced triplet: deployment cost,detection accuracy, and open-set adaptability. While existing methodsstruggle to balance these three competing objectives, there is an urgentdemand for high-precision, real-time algorithms capable of detecting ar-bitrary obstacles in the wild. In this paper, we present DDStereo, a novelDual-Decoder Stereo Transformer that achieves a synergistic integrationof 3D object detection and Out-of-Distribution (OoD) road anomaly de-tection. Leveraging the geometric priors of stereo disparity, our approacheffectively couples 3D attribute regression with open-set foreground de-tection within a streamlined dual-branch decoder architecture. Conven-tional methods rely on complex feature-level fusion; DDStereo main-tains execution efficiency by employing a decoupled decoding strategyand shared object-level queries to ensure cross-modal target alignment.Extensive evaluations of public benchmarks demonstrate that DDStereonot only achieves state-of-the-art accuracy under open-set and closed-set protocols. Our method delivers real-time performance comparable tomonocular 3D detection baselines, providing a cost-effective solution forthe perception of obstacles of the normal and OoD category. Code andmodels are available at https://github.com/shiyi-mu/DDStereo.

Abstract:
Feature tracking plays a fundamental role in understanding scene motion and supports various downstream tasks. Event cameras, with their high temporal resolution and asynchronous sensing, enable low-latency and motion-robust perception, making them well-suited for feature tracking under fast and non-linear motion. However, existing event-based feature tracking methods rely on fixed heuristic rules based on hand-tuning for event accumulation. Such strategies fail to adapt to diverse motion dynamics, leading to degraded performance under abrupt motion changes or low-motion scenarios. In this paper, we model event accumulation as a sequential decision-making problem and introduce reinforcement learning (RL) framework to adaptively control the accumulation process for online event-based feature tracking. Our approach trains a RL agent that decides whether to continue accumulating events or to perform tracking inference based on motion cues. The proposed adaptive temporal agent enables dynamic adaptation to varying motion patterns without relying on hand-crafted rules. Furthermore, we introduce a Dynamic Event-based Tracking (DEFT) dataset with dynamic motion distributions to evaluate the robustness of the feature tracking. Extensive experiments demonstrate that integrating our plug-and-play framework to existing feature tracking methods consistently outperforms heuristic-based approaches, improving robustness under dynamic motion while offering a better balance between tracking accuracy and efficiency. Our project codes and datasets are available at https://github.com/kmax2001/GoSTOP.

Abstract:
Implicit Neural Representations (INRs) achieve state-of-theart results across image representation, 3D modeling, and novel-view synthesis, yet their performance hinges on tightly coupled design decisions— activation family, initialization scheme, and per-layer hyperparameters— whose interdependencies render manual tuning unreliable and greedy search provably suboptimal. We introduce OptiINR, the first framework that recasts INR configuration as a global optimization problem over a mixed-variable space of discrete activation choices (e.g., SIREN, WIRE, FINER) and their continuous parameters. OptiINR consists of a structured layer-wise INR configuration space and an adaptive Bayesian optimization procedure for navigating it. The search space spans activation families, activation-specific continuous parameters, initialization/scaling choices, per-layer learning rates, and positional-encoding usage. Across five tasks—1D audio, 2D image, 3D occupancy, spatiotemporal PDE, and neural radiance field synthesis—OptiINR consistently outperforms handtuned baselines and prior automated methods, revealing task-dependent activation patterns that challenge prevailing one-size-fits-all design conventions. Code is available at https://github.com/Sippengg/OptiINR.

Abstract:
The structural vulnerabilities of point cloud–based 3D objectdetectors remain poorly understood. Prior work has studied adversarialrobustness primarily on isolated 3D object models, while recent LiDARspoofing attacks target richer and more realistic driving scenes but focusmainly on physical realizability rather than understanding detector be-havior or attack efficiency. In this work, we investigate how LiDAR-baseddetectors rely on spatial evidence in complex scenes and whether these re-liance patterns can be exploited to induce failures more efficiently. To thisend, we propose an explainability-guided adversarial analysis methodology.We introduce the Saliency-LiDAR (SALL) method, which aggregates In-tegrated Gradient attributions across scenes to produce universal saliencymaps for LiDAR-based 3D object detectors. Guided by these maps, wedesign the Explainability-aware Frustum Attack (EFA), which selectivelyperturbs only the most influential frustums rather than uniformly attack-ing entire object regions. Experiments on KITTI and nuScenes, acrossdetectors such as PointPillars and SECOND, show that EFA reducesdetection recall by more than 15 percentage points while requiring 25–50%fewer perturbed frustums than the SOTA non–saliency-aware baseline.These findings reveal that modern 3D detectors concentrate discrimina-tive evidence in a small subset of spatial regions, exposing a structuralrobustness vulnerability in current LiDAR perception systems. Our codeis released at https://github.com/SecMindLab/Saliency_LiDAR.

Abstract:
Visual object tracking relies on modeling cross-frame dynam-ics, and recent approaches employ learnable temporal tokens to encodehistorical information. However, our analysis reveals that these tokensare often marginalized by dominant spatial features during joint opti-mization. This imbalance leads to shortcut learning where the networkover-relies on current-frame appearance, causing temporal representationcollapse. To address this, we propose DASTrack, a framework featur-ing Decoupled Auxiliary Supervision (DAS). By masking the templatesduring an auxiliary forward pass, DAS compels the network to localizetargets using only temporal tokens and thus encode robust cross-framepriors. This mechanism is applied strictly during training and discardedat inference to preserve the original architecture and computational ef-ficiency. DASTrack yields consistent and interpretable average perfor-mance gains across multiple backbones. Our findings demonstrate thatthe primary bottleneck in temporal tracking stems from biased learn-ing dynamics rather than architectural design, offering a novel trainingparadigm for eliciting latent temporal capabilities. The code and modelswill be released at: https://github.com/ZhangDailing8/DASTrack.

Abstract:
Change captioning is the task of generating natural languagedescriptions that explain the changes between a pair of images. Althoughdifferent change types (e.g., color shifts, object additions) exhibit distinctvisual cues and require specialized reasoning processes, existing methodsoften overlook these distinctions. To address this limitation, we proposeMulti-Expert Diagnosis for Image Change (MEDIC), a novel frameworkthat introduces change-type awareness by explicitly modeling changecategories. We build our MEDIC as a memory network to dynamicallyretrieve type-relevant visual patterns conditioned on the input. This de-sign allows each expert to flexibly capture diverse variations within eachchange type and focus on the most informative cues for its designatedchange type. By routing inputs through type-specialized experts andlearning dedicated representations for each change category, MEDICgenerates more precise and type-aware change descriptions. Extensiveexperiments demonstrate that proposed MEDIC consistently outper-forms across diverse and challenging datasets. The code is available athttps://github.com/VisualAIKHU/MEDIC.

Abstract:
Language-guided prompt-based continual learning emergesas a promising approach to tackle catastrophic forgetting in a dynamicenvironment under rehearsal-free constraint. However, existing state-of-the-art (SOTA) requires additional resources, such as LLM-generateddescriptors and an LLM decoder that may not be available in a real ap-plication. Meanwhile, the more straightforward method experiences sub-optimality due to the high similarity between language text embeddings.To address this problem, we propose a resource-efficient novel language-guided approach and algorithm that incorporates two novel ideas: (1)mixed vision and language modality for prompt generation, and (2) Dif-ferential prefix tuning for the model training process. Our experimentalanalysis shows that our method outperforms existing language-guidedprompt-based methods, i.e., up to 30%, 26%, and 10% for final aver-age accuracy, cumulative average accuracy, and final forgetting mea-sure, respectively. The historical analysis confirms our method’s stability-plasticity balance in every task. Our extended analysis shows that ourmethod consistently achieves better performance in various prompt lengthsand ViT layers. For further study and reproducibility, we also providerigorous analysis, details, and source code of our method in the sup-plementary document. The implementation of the proposed method isavailable at https://github.com/anwarmaxsum/D-VLAM.

Abstract:
Predictive Coding (PC) offers a biologically motivated al-ternative to backpropagation via local weight updates, yet routing er-ror between layers still relies on an autograd Jacobian-transpose (J⊤ )product—the last non-local operation in PC. We show that this depen-dency is largely avoidable. For any layer f (x)=Act(Norm(L(x))) withfrozen normalization statistics, the exact J⊤ factors into three locallyavailable terms, J⊤ v = L⊤ (s ⊙ σ ′ (pre-act) ⊙ v), where σ ′ is the activationderivative and s=γ/σrun is the normalization gain. Prior weight-feedbackmethods omitted both corrections; restoring them closes the transportgap for this layer class. Note that locality here holds up to three as-sumptions, which we state upfront—weight symmetry (L⊤ mirrors theforward operator, as assumed by all PC), a soft spectral-norm controlthat is not synapse-local, and a nearest-neighbour approximation forMaxPool. Substituting the identity into PC yields WF-Act-PC, whichremoves the autograd backward pass from error transport. On CIFAR-10/100 (50 epochs, 5 seeds), WF-Act-PC is the only PC method whoseaccuracy improves with depth, surpassing iPC—the strongest classicalPC baseline—by 2.7–22.3 pp on CIFAR-10. With both methods tunedper architecture, it matches or exceeds a comparably-tuned backpropa-gation baseline on the deeper CIFAR-10 architectures (VGG-9: 93.57%vs. 92.43%; ResNet-18: 92.76% vs. 91.54%) and on the harder Tiny-ImageNet benchmark, while trailing tuned BP on the deeper CIFAR-100VGG cells. Our WF-Act-PC implementation is publicly available athttps://github.com/jlshen025/pcax.

Abstract:
Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully followphysical laws remains an open challenge. Existing methods mainly basedon graphics or prompt extension struggle to generalize beyond simplesimulated environments or learn implicit physics reasoning. The scarcityof training data with rich physics interactions and phenomena is alsoa problem. In this paper, we first introduce a Physics-Augmented videodata construction Pipeline, PhyAugPipe, that leverages a vision–languagemodel (VLM) with chain-of-thought reasoning to collect a training dataset,PhyVidGen-135K. Then we formulate a principled Physics-aware Group-wise Direct Preference Optimization, PhyGDPO, framework that usesreal-world video as winning case to guarantee correct physics learningand builds upon the groupwise Plackett–Luce probabilistic model tocapture holistic preferences beyond pairwise comparisons. We design aPhysics-Guided Rewarding (PGR) scheme that uses VLM-based physicalrewards to direct the optimization to focus on challenging physics. Plus,we propose a LoRA-Switch Reference (LoRA-SR) scheme that avoidsfull-model duplication as reference for efficient training. Experimentsshow that our method outperforms state-of-the-art methods. Our codeand data is at https://github.com/caiyuanhao1998/Open-PhyGDPO

Abstract:
We present HairWeaver, a diffusion-based pipeline that ani-mates a single human image with realistic and expressive hair dynamics.While existing methods successfully control body pose, they lack specificcontrol over hair, and as a result, fail to capture the intricate hair mo-tions, resulting in stiff and unrealistic animations. HairWeaver overcomesthis limitation using two specialized modules: a Motion-Context-LoRAto integrate motion conditions and a Style-Alignment-LoRA to pre-serve the subject’s photoreal appearance across different data domains.These lightweight components are designed to guide a video diffusionbackbone while maintaining its core generative capabilities. By trainingon a specialized dataset of dynamic human motion generated from aCG simulator, HairWeaver affords fine control over hair motion and ulti-mately learns to produce highly realistic hair that responds naturally tomovement. Comprehensive evaluations demonstrate that our approachsets a new state of the art, producing lifelike human hair animationswith dynamic details.

Abstract:
How can we tell whether a video has been sped up or sloweddown? How can we generate videos at different speeds? Although videoshave been central to modern computer vision research, little attentionhas been paid to perceiving and controlling the passage of time. In thispaper, we study time as a learnable visual concept and develop modelsfor reasoning about and manipulating the flow of time in videos.We first exploit the multimodal cues and temporal structure naturallypresent in videos to learn, in a self-supervised manner, to detect speedchanges and estimate playback speed. We then show that these learnedtemporal reasoning models enable us to curate the largest slow-motionvideo dataset to date from noisy in-the-wild sources. Such slow-motionfootage, typically filmed by high-speed cameras, contains substantiallyricher temporal detail than standard videos. Using this data, we furtherdevelop models capable of temporal control, including speed-conditionedvideo generation, which produces motion at specified playback speed,and temporal super-resolution, which transforms low-FPS, blurry videosinto high-FPS sequences with fine-grained temporal details. Our findingshighlight time as a manipulable, perceptual dimension in video learning,opening doors to temporally controllable video generation, temporalforensics detection, and potentially richer world-models that understandhow events unfold over time. Project page: https://seeing-fast-and-slow.github.io/.

Abstract:
Recent advances in world models have greatly enhanced in-teractive environment simulation. Existing methods mainly fall into two⋆ Equal contribution. † Corresponding author.categories: (1) static world generation models, which construct 3D en-vironments without active agents, and (2) controllable-entity models,which allow a single entity to perform limited actions in an otherwise un-controllable environment. In this work, we introduce CustomX, leverag-ing the realism and structural grounding of static world generation whileextending controllable-entity models to support user-specified characterscapable of performing open-ended actions. Users can provide a 3DGSscene and a character, then use natural language to direct the characterto perform diverse behaviors, ranging from basic locomotion to object-centric interactions, while freely exploring the environment. CustomXsynthesizes temporally coherent video clips that preserve visual fidelitywith the provided scene and character, formulated as a conditional au-toregressive video generation problem. Built upon a pre-trained videogenerator, our training strategy significantly enhances motion dynamicswhile maintaining generalization across actions and characters. Our eval-uation covers a broad range of aspects, including visual quality, characterconsistency, action controllability, and long-horizon coherence.

Abstract:
Extracting dynamic 4D object interactions from massive, inthe-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs. However, existing monocular 4D reconstruction methods primarily focus on isolated objects, often failing under the severe occlusions and complex dynamics inherent in multi-object interactions. To bridge this gap, we propose HAT- 4D, the first agentic framework designed to reconstruct the 3D geometry, temporal dynamics, and physical interactions of multiple objects from a single video. By integrating VLMs with a multi-level human-in-the-loop feedback mechanism, HAT-4D efficiently resolves depth ambiguities and interaction-induced occlusions during 3D generation and 4D propagation, yielding physically plausible assets without relying on expensive multicamera rigs. As a scalable data engine, HAT-4D facilitates the creation of MVOIK-4D, an open-world benchmark for monocular 4D interaction reconstruction, accompanied by a novel multi-dimensional evaluation protocol focused on physical plausibility and temporal consistency. Extensive experiments demonstrate that HAT-4D achieves SOTA performance on most evaluation metrics, while maintaining competitive semantic alignment. Ablation studies show that introducing a small amount of human feedback improves interaction reconstruction. Moreover, the data produced by HAT-4D effectively improves baseline performance when used for finetuning. Our data and code are available at project webpage.

Abstract:
Autoregressive video diffusion models have emerged as apromising approach for long video generation, achieving strong perfor-mance in streaming settings. However, existing methods are restrictedto forward temporal generation, whereas practical video creation oftenrequires flexible generation order, e.g., conditioning on future context toextend backward, or on both past and future context for inbetween gener-ation. We bridge this gap by training a single autoregressive model thatsupports generation in arbitrary temporal directions. A key technicalchallenge arises from the Causal 3D VAE widely used in video diffusionmodels, where latents are encoded strictly conditioned on past context.While suited for forward generation, this causal structure causes inter-block discontinuities when generation proceeds backward. To addressthis, we introduce blockwise anchor latents, a set of auxiliary latentsthat aim to restore the missing past context at block boundaries dur-ing backward generation. Built on this design, we propose UniTemp, abidirectional distillation framework that trains an autoregressive studentmodel for any-direction video generation. At inference time, UniTempconditions on arbitrary past and/or future frames, improving control-lability for both bidirectional and inbetween generation. Through ex-tensive experiments, we demonstrate that UniTemp maintains compet-itive performance on short and long video generation in comparison toforward-only methods, while enabling diverse creation workflows such asbidirectional video extension, inbetween generation, looping video gen-eration, scene transition, and visual story generation. Project website:https://lzhangbj.github.io/projects/unitemp/.

Abstract:
Chain-of-thought (CoT) reasoning has enabled multi-modallarge language models (MLLMs) to tackle complex visual reasoning tasksby generating explicit intermediate reasoning steps in natural language.However, this text-based reasoning paradigm is inherently slow at infer-ence time with even thousands of tokens and fundamentally constrainedby the expressiveness of natural language. In this paper, we proposeCoLT (Chain of Latent Thoughts), a novel framework that teachesmulti-modal models to reason through a chain of latent thought repre-sentations instead of verbose text tokens, which can perform thinkingwith as few as 3 steps. Naively forcing the model to think with latentstates easily produces meaningless semantics and makes training unsta-ble. To effectively regulate the latent reasoning process, we introduce alightweight external decoder that provides step-level supervision for eachlatent reasoning step in two complementary directions: a forward modethat decodes latent thoughts into the textual reasoning of the next step,and a backward mode that aligns decoder hidden states with the model’slatent thoughts given preceding textual context. We further incorporateinternal supervision that encourages coherent step-by-step latent transi-tions. The decoder and internal supervision are removed during inferenceto maintain high efficiency of latent reasoning. Extensive experiments oneight benchmarks demonstrate that CoLT not only outperforms existinglatent reasoning methods such as CODI and SIM-CoT, but also surpasseslatent visual reasoning approaches that rely on auxiliary images withcostly annotation requirements. Compared to text CoT methods, CoLTcan notably reduce the inference time by 10.1× and text decoding timeby 22.6×. Code is released at https://github.com/hulianyuyy/CoLT.

Abstract:
Video foundation models now reach human-level accuracy onphysical-reasoning benchmarks, yet such tasks require predicting unob-served physical outcomes. Do these models perform human-like forwardsimulation, or do they exploit statistical regularities in visible scenes?Accuracy alone cannot distinguish these strategies. We introduce a distri-butional evaluation framework that treats model seeds and human ratersas populations, enabling comparison of consensus, uncertainty, and strat-egy. On the Physion benchmark, we evaluate three ViT-L architectures(V-JEPA2, VideoMAEv2, DINOv2). V-JEPA2 narrows the accuracy gapto ∼1 percentage point (73.2% vs. 74.2%), yet model–human disagree-ment reaches 26.4%, far exceeding human–human disagreement (4.8%),with substantially lower agreement (κ ≈ 0.48 vs. 0.91). The divergenceis scenario-structured: models outperform humans on geometric reason-ing (linking, +11.8 pp), while human advantages appear on causal-chainand other future-dynamics scenarios in the released split (e.g., dominoes,−10.5 pp). Strategy fingerprinting confirms all three architectures sharenon-human strategies while none aligns with humans. Attribution analy-sis suggests that unobservable outcome features, rather than visible sceneproperties, predict this divergence, consistent with models relying moreon scene-level statistical regularities than on explicit forward simulation,a systematic divergence that accuracy alone cannot reveal. Code is avail-able at https://github.com/fanhong-li/model-human-divergence.

Abstract:
Distilling video generation models to extremely low infer-ence budgets (e.g., 2–4 NFEs) is crucial for real-time deployment, yetremains challenging. Trajectory-style consistency distillation often be-comes conservative under complex video dynamics, yielding an over-smoothed appearance and weak motion. Distribution matching distilla-tion (DMD) can recover sharp, mode-seeking samples, but its local train-ing signals do not explicitly regularize how denoising updates composeacross timesteps, making composed rollouts prone to drift. To overcomethis challenge, we propose Self-Consistent Distribution Matching Distil-lation (SC-DMD), which explicitly regularizes the endpoint-consistentcomposition of consecutive denoising updates. For real-time autoregres-sive video generation, we further treat the KV cache as a quality param-eterized condition and propose Cache-Distribution-Aware training. Thistraining scheme applies SC-DMD over multi-step rollouts and introducesa cache-conditioned feature alignment objective that steers low-qualityoutputs toward high-quality references. Across extensive experiments onboth non-autoregressive backbones (e.g., Wan 2.1) and autoregressivereal-time paradigms (e.g., Self Forcing), our method, dubbed Salt, con-sistently improves low-NFE video generation quality while remainingcompatible with diverse KV-cache memory mechanisms. Project page:https://xingtongge.github.io/Salt.

Abstract:
Aligning screenplay scenes to video shots is a foundationaltask for narrative video understanding. Unlike subtitles or captions,screenplays combine dialogue, visual direction, and narrative cues in asingle document, making them ill-suited for standard video-text retrievaland leaving prior methods to exploit only subtitle overlaps. Methodsranging from alignment based on textual subtitle to contrastive encodersand temporal grounding networks can potentially bridge this gap, yetno benchmark systematically compares these paradigms or isolates thecontributions of dialogue and visual information to alignment quality.We introduce “From Script to Shot”, a benchmark of 50 feature filmsspanning nine decades with over 55K shot-level scene alignment an-notations verified by human annotators. To disentangle text matchingfrom visual grounding, we evaluate fourteen approaches—from sparsetext retrieval to recent vision-language models such as FG-CLIP 2 andQwen3-VL-Embedding, on an identical pipeline. Dialogue-based retrievalperforms poorly on non-dialogue scenes, and contrastive encoders aloneunderperform direct text matching. Adaptive multimodal fusion achievesthe strongest overall results, reaching 0.599 mSIoU when BM25 is fusedwith a Qwen3-VL-Embedding, yet even the strongest encoder leaves aconsistent gap between dialogue and non-dialogue scenes, exposing thelimits of current visual encoding for narrative grounding. By quantify-ing this gap, the benchmark provides a controlled testbed for studyingscreenplay-conditioned alignment. We demonstrate downstream utilitythrough zero-shot movie scene segmentation and screenplay-guided videogeneration, confirming that the alignment signal generalizes beyond thebenchmark itself. We release the full dataset, parser and parser outputs,toolkit, and baselines at https://github.com/jungucho92/script2shotto support research on long-form multimodal video understanding.2 J. Cho et al. 사용 폰트: 노토산스한국:https://fonts.google.com/noto/specimen/Noto+Sans+KRScreenplay -Video Alignment Prior works(dialogue -based)Screenplay SCENE 69 dialogue D D D D D Dthe script of a movie,H INT HOTEL ROOM-HOURS LATERincluding actinginstructions and D HAZEL Good morning.subtitle#69 scene directions. D FRANNIE Actually, it'sfive o'clock.Good morning. … it's five o’ … How was the park? Good morning. … it's five o’ … How was the park?D HAZEL How was the park? D D D D D D. D FRANNIE Never made it.D HAZEL Mom, what do you mean? Never made it. Mom, what … … do you mean? Never made it. Mom, what … … do you mean?#70 H headingSCENE 70 mixedfectly tailored BLACK H INT HOTEL ROOM– LATERN D D D D DN Frannie opens the door to find GusGus is here. (TO GUS) in a perfectly tailored BLACK SUIT.partial Looking sharp. Thank you ma’am . Looking sharp. Thank you ma’am . Hazel! Gus is here.D FRANNIE Hazel! Gus is here. (TOD dialogue GUS) Looking sharp.D N D N Dbathroom. She wears a D GUS Thank you ma’am.Not predictedhe looks... N A few beats later, Hazel appears in aHazel! Gus is here. Wow. Wow.pale blue sundress.D GUS Wow.N narrativeSCENE 86 non-dialogueey're readyE CLAIM to go.– DAY no subtitle ✘ N N NShot 1465 H INT AIRPORT– DAYs Michael standing Not predicted Not predicted Not predictedN Michael with sign: “My Beautiful Family.”that says - instead of #86 He kisses his wife, and hugs Hazel.y (and Gus)." Uponcourse. He kisses his Notation SCREENPLAY VIDEO SHOTS (KEYFRAMES)ake his hand butFig. 1: Screenplay-video alignment maps each shot to its corresponding screenplayscene. Each scene combines a scene heading (H), narrative direction (N), and dialogue(D). Shots carry subtitle text only when spoken dialogue is present. Prior dialogue-based methods align subtitle-matched shots correctly but fail entirely on non-dialoguescenes, whereas our benchmark evaluates all scene types under a unified protocol.

Abstract:
Multimodal large language models (MLLMs) can inadver-tently memorize privacy-sensitive information during training. Whileexisting unlearning methods can remove such content, they often severelydegrade the model’s foundational capabilities, such as general imageunderstanding. This critical shortfall motivates our investigation into be-nign memory forgetting, the precise removal of targeted, privacy-sensitiveknowledge while rigorously preserving unrelated capabilities. To pioneerand evaluate progress toward this objective, we introduce S-MLLMUnBench, the first benchmark designed to jointly and quantitatively assessan unlearning method’s efficacy in knowledge erasure and the preservationof image understanding. Furthermore, we propose the Sculpted MemoryForgetting Adapter (SMFA), a new framework that enables benign mem-ory forgetting. SMFA confines forgetting to designated memory regions,maintaining overall model performance. By initially fine-tuning the modelto replace sensitive outputs with refusals, SMFA generates a memoryforgetting adapter, followed by a retaining anchor-guided masking mech-anism that safeguards unrelated knowledge. Extensive experiments onS-MLLMUn Bench demonstrate that existing methods fail to achievebenign forgetting, whereas our proposed SMFA serves as an effectivebaseline, successfully achieving targeted knowledge erasure without com-promising the model’s foundational visual capabilities. Code and dataare available at https://github.com/zeng-zhen/S-MLLMUn.

Abstract:
Image-conditioned video diffusion models achieve impressivevisual realism but often suffer from weakened motion fidelity, e.g., re-duced motion dynamics or degraded long-term temporal coherence, es-pecially after fine-tuning. We study motion alignment in video diffusionmodels post-training. To address this, we introduce pixel-motion rewardsbased on pixel flux dynamics, capturing both instantaneous and long-term motion consistency. We further propose Smooth Hybrid Fine-tuning(SHIFT), a scalable reward-driven framework that unifies supervisedfine-tuning and advantage-weighted fine-tuning. Benefiting from noveladversarial advantages, SHIFT improves convergence speed and miti-gates reward hacking. Experiments show that our approach efficientlyresolves dynamic-degree collapse in modern video diffusion models su-pervised fine-tuning. Project page: https://xiye20.github.io/projects/SHIFT/.

Abstract:
Egocentric vision systems capture human behavior from visi-ble cues, but overlook physiological indicators of autonomic states suchas stress, engagement, and attention. Heart rate variability (HRV) is awidely used noninvasive marker of autonomic regulation under stress.HRV reflects small timing differences between successive heartbeats andhas so far been out of reach for egocentric platforms, where motion andnoise in gaze video mask exactly this fine-grained timing. We proposeEgoHRV, a method that estimates HRV as well as heart rate (HR) fromthe gaze cameras that are already integrated into egocentric headsets. Ourpipeline combines a 3D backbone with a novel low–high decompositionmodule that extracts the blood volume pulse (BVP) signal from gazevideo. Our cross-domain pretraining aligns the frequency-domain repre-sentations of contact-based and camera-derived signals. This alignmentgives EgoHRV the temporal precision to recover HRV from the subtlefluctuations in gaze video. EgoHRV achieves state-of-the-art accuracy forHR and HRV estimation from egocentric video, and its uncertainty-awaredesign improves downstream behavioral modeling. Integrating our HRVestimates and confidence measures into EgoExo4D’s proficiency estimatorraises accuracy by 17.8%. Beyond skill, continuous HRV estimation alsoopens egocentric systems to stress- and arousal-aware estimation tasks.Code: https://github.com/eth-siplab/EgoHRV

Abstract:
Recent progress in video large language models (Video-LLMs) has enabled strong offline reasoning over long and complex videos. However, real-world deployments increasingly require streaming perception and proactive interaction, where video frames arrive online and the system must decide not only what to respond, but also when to respond. In this work, we revisit proactive activation in streaming video as a structured sequence modeling problem, motivated by the observation that temporal transitions in streaming video naturally form span-structured activation patterns. To capture this span-level structure, we model activation signals jointly over a sliding temporal window and update them iteratively as new frames arrive. We propose STRIDE (Structured Temporal Refinement with Iterative DEnoising), which employs a lightweight masked diffusion module at the activation interface to jointly predict and progressively refine activation signals across the window. Extensive experiments on diverse streaming benchmarks and downstream models demonstrate that STRIDE shows more reliable and temporally coherent proactive responses, significantly improving “when-to-speak” decision quality in online streaming scenarios. The STRIDE codebase and demonstrations are available on our project webpage.

Abstract:
Self-supervised pretraining has transformed computer visionby enabling data-efficient fine-tuning, yet high-resolution pretraining typ-ically requires server-scale infrastructure, limiting custom in-domain train-ing for many research laboratories. Masked Autoencoders (MAE) reducecomputation by encoding only visible tokens, but combining MAE withhierarchical downsampling architectures has remained structurally chal-lenging due to dense grid priors and mask-aware design compromises. Weintroduce AFFMAE, a masking-friendly hierarchical pretraining frame-work built on adaptive, off-grid token merging. AFFMAE removes dense-grid assumptions while preserving hierarchical scalability during pre-training and fine-tuning. To support this architecture, we developed nu-merically stable mixed-precision Triton kernels and a lightweight, point-based decoder that can be directly repurposed as a segmentation head.On high-resolution microscopy segmentation, AFFMAE matches MAEfinetuning performance on foot process width estimation with ViT back-bone at equal parameter counts while being 2x faster during pre-trainingand halving peak memory usage. Furthermore, AFFMAE achieves up to5x throughput speedups fine-tuning at the 1024px resolution, providinghigh-resolution model training on desktop hardware. Code available athttps://github.com/najafian-lab/affmae.

Abstract:
Vision-Language Models (VLMs) create a severe visual fea-ture bottleneck by using a crude, asymmetric connection that links onlythe output of the vision encoder to the input of the large language model(LLM). This static architecture fundamentally limits the ability of LLMsto achieve comprehensive alignment with hierarchical visual knowledge,compromising their capacity to accurately integrate local details withglobal semantics into coherent reasoning. 5 To resolve this, we introduceCross-Layer Injection (CLI), a novel framework that forges a dy-namic “many-to-many” bridge between the two modalities. CLI con-sists of two synergistic, parameter-efficient components: an AdaptiveMulti-Projection (AMP) module that harmonizes features from di-verse vision layers, and an Adaptive Gating Fusion (AGF) mech-anism that empowers the LLM to selectively inject the most relevantvisual information based on its real-time decoding context. We validatethe effectiveness and versatility of CLI by integrating it into LLaVA-OneVision and LLaVA-1.5. Extensive experiments on 28 diverse bench-marks demonstrate significant performance improvements, establishingCLI as a scalable paradigm that unlocks deeper multimodal understand-ing by granting LLMs on-demand access to the full visual hierarchy. Codeis available at https://github.com/codefuse-ai/CLI.

Abstract:
Large Vision-Language Models (VLMs) now act as agents in interactive environments, where success requires coherent reasoning and decision-making across turns. Although end-to-end training in agentic environments can improve such multi-turn decision-making abilities, current methods mainly rely on either token-wise optimization over concatenated token trajectories or turn-wise optimization with uniform withinturn credit. In this work, we establish theoretical formulations for the two levels of optimization and derive a hybrid advantage that serves both objectives. Furthermore, with an appropriate choice of discount factor and learning target, we prove that a unified critic model can estimate values for both turn-wise and token-wise. As such, we propose HyGAE, an actor-critic framework that jointly optimizes tokenand turn-level objectives with the hybrid advantage and unified critic. We conduct extensive evaluations of HyGAE across five multi-turn decisionmaking environments, where it achieves an average success rate of 91% and a significant improvement of 10% over other methods. Furthermore, we provide an in-depth analysis showing that the exact analytic form of the hybrid advantage and return is crucial for optimization. Project Page: https://wx-zhang.github.io/hygae-web/.

Abstract:
Recent advancements in Multimodal Large Language Models(MLLMs) have demonstrated impressive fine-grained perception capabil-ities. However, existing benchmarks predominantly rely on explicit tex-tual cues or low-resolution inputs, failing to evaluate a model’s ability toautonomously perceive implicit visual cues in high-resolution. To bridgethis gap, we introduce DiCoBench, a comprehensive, multi-image high-resolution benchmark designed for cross-image fine-grained perception.DiCoBench consists of 765 meticulously curated samples categorizedinto two progressive tracks: Differential Visual Cues and Commonal-ity Visual Cues, covering 8 distinct perception tasks. By formulatingthe benchmark as a multiple-choice question task and utilizing high-resolution imagery (approaching 2K), we eliminate evaluation metricbias and pose a substantial challenge to current state-of-the-art MLLMs.Our extensive evaluation of 18 diverse MLLMs reveals a striking perfor-mance gap compared to human accuracy (98.3%), with top-performingmodels struggling significantly with micro-scale detail capture. We be-lieve DiCoBench will serve as a challenging testbed to drive future re-search in autonomous, high-resolution multi-image perception. Dataset:https://huggingface.co/datasets/oking0197/DiCoBench

Abstract:
Traditional Shot Boundary Detection (SBD) inherently strug-gles with complex transitions by formulating the task around isolated cutpoints, frequently yielding corrupted video shots. We address this fun-damental limitation by formalizing the Shot Transition Detection (STD)task. Rather than searching for ambiguous points, STD explicitly detectsthe continuous temporal segments of transitions. To tackle this, we pro-pose TransVLM, a Vision-Language Model (VLM) framework for STD.Unlike regular VLMs that predominantly rely on spatial semantics andstruggle with fine-grained inter-shot dynamics, our method explicitly in-jects optical flow as a critical motion prior at the input stage. Through asimple yet effective feature-fusion strategy, TransVLM directly processesconcatenated color and motion representations, significantly enhancingits temporal awareness without incurring any additional visual tokenoverhead on the language backbone. To overcome the severe class im-balance in public data, we design a scalable data engine to synthesizediverse transition videos for robust training, alongside a comprehensivebenchmark for STD. Extensive experiments demonstrate that TransVLMachieves superior overall performance, outperforming traditional heuris-tic methods, specialized spatiotemporal networks, and top-tier VLMs.

Abstract:
Large Vision-Language Models (LVLMs) have shown remark-able potential across a wide array of vision-language tasks, leading totheir adoption in critical domains such as finance and healthcare. How-ever, their growing deployment also introduces significant security andprivacy risks. Malicious actors could potentially exploit these models toextract sensitive information, highlighting a critical vulnerability. Recentstudies show that LVLMs often fail to consistently refuse instructionsdesigned to compromise user privacy. While existing work on privacyprotection has made meaningful progress in preventing the leakage ofsensitive data, they are constrained by limitations in both generalizationand non-destructiveness. They often struggle to robustly handle unseenprivacy-related queries and may inadvertently degrade a model’s per-formance on standard tasks. To address these challenges, we introduceNeural Gate, a novel method for mitigating privacy risks through neuron-level model editing. Our method improves a model’s privacy safeguardsby increasing its rate of refusal for privacy-related questions, cruciallyextending this protective behavior to novel sensitive queries not encoun-tered during the editing process. Neural Gate operates by learning a fea-ture vector to identify neurons associated with privacy-related conceptswithin the model’s representation of a subject. This localization then pre-cisely guides the update of model parameters. Through comprehensiveexperiments on MiniGPT and LLaVA, we demonstrate that our methodsignificantly boosts the model’s privacy protection while preserving itsoriginal utility. The code is available at https://github.com/Xiangkui-Cao/Neural-Gate.

Abstract:
Test-time adaptation (TTA) has emerged as a prominentstrategy for adapting vision-language models to distribution shifts dur-ing inference. We conduct a per-sample analysis of model predictionsbefore and after adaptation, and observe two failure modes in existingTTA methods that echo previous work. Adaptations are frequently neg-ligible, yielding no change in the model’s predictions, and more severely,they can be detrimental by flipping previously correct predictions to in-correct ones. This naturally raises a question: Can we identify and skipsuch negligible or harmful adaptations? In this work, we introduce a newproblem of selective adaptation, which aims to determine whether agiven test sample should undergo adaptation or be skipped. To this end,we propose Cross-Augmentation Similarity (CAS), a simple baseline thatperforms adaptation only when predictions across augmented views ex-hibit low similarity. Notably, CAS not only preserves but in some casesimproves overall accuracy, even when skipping nearly 85% of the adap-tation process. We hope other researchers will explore this new directionand surpass the performance of our baseline. Our code is available athttps://github.com/sirujiang/selective-adaptation.

Abstract:
Traditional Visual Grounding (VG) predominantly relies ontextual descriptions to localize objects, a paradigm that inherently strug-gles with linguistic ambiguity and often ignores non-verbal deictic cuesprevalent in real-world interactions. In natural egocentric engagements,hand-pointing combined with speech forms the most intuitive referringmechanism. To bridge this gap, we introduce EgoPoint-Ground, thefirst large-scale multimodal dataset dedicated to egocentric deictic visualgrounding. Comprising over 15k interactive samples in complex scenes,the dataset provides rich, multi-grained annotations including hand-targetbounding box pairs and dense semantic captions. We establish a com-prehensive benchmark for hand-pointing referring expression resolution,evaluating a wide spectrum of mainstream Multimodal Large LanguageModels (MLLMs) and state-of-the-art VG architectures. Furthermore, wepropose SV-CoT, a novel baseline framework that reformulates groundingas a structured inference process, synergizing gestural and linguistic cuesthrough a Visual Chain-of-Thought paradigm. Extensive experimentsdemonstrate that SV-CoT achieves an 11.7% absolute improvement overexisting methods, effectively mitigating semantic ambiguity and advanc-ing the capability of agents to comprehend multimodal physical intents.Code: https://github.com/lingli1724/Egopoint

Abstract:
We present URHead, a unified representation for high-fidelityand animatable head avatars that fundamentally redefines mesh-Gaussianintegration. While mesh-based methods offer precise geometric controlbut lack photorealistic detail, and Gaussian-based approaches achievephotorealism but suffer from poor structural consistency, existing hybridsolutions fail to fully leverage their complementary strengths. Our keycontribution is a UV-space unification where both representations sharea common UV parameterization. Through joint optimization with adap-tive gaussian sampling, our method automatically learns to disentangleand allocate appropriate roles to each component. URHead maintainsfull parametric controllability while preserving subject-specific details,outperforms existing state-of-the-art methods in reconstruction qualityand animation consistency.Our code is available at https://github.com/Lseonghak/URHead

Abstract:
Accurate visible-to-thermal image generation holds signifi-cant application value in fields such as vehicle detection and autonomousdriving. However, existing methods often suffer from the problem thatthe generated results are inconsistent with environmental conditions andvehicle states (e.g., moving/static). Moreover, the scarcity of synchro-nized and calibrated visible-thermal image pairs has further hinderedthe development of these fields. We propose AV2T-Gen, an innovativeenvironment and vehicle guided visible-to-thermal generation frameworkbuilt upon the Instruct-Pix2Pix Diffusion Models for aerial imagery,which distinguishes itself from existing methods by explicitly incorpo-rating environment parameters and vehicle state information for thefirst time. The framework first maps images to the latent space usinga VAE to process image features efficiently, and then generates direc-tional features for vehicles in different environmental conditions usingmultimodal feature information. Meanwhile, we also proposed a visible-thermal paired dataset with environmental conditions and vehicle states.Experimental results have demonstrated that the generated thermal im-ages outperform existing approaches in terms of physical realism andvehicle detail discrimination significantly, with more accurate reflectionof environmental and vehicle characteristics. The code is available athttps://github.com/NPU-CVPG/AV2T.

Abstract:
While garments are essential for realistic digital humans,their topological variety makes them much harder to model than para-metric bodies. Traditional tailoring relies on 2D sewing patterns, yetbridging these patterns to 3D geometry currently requires physical simu-lations. We present Stitched Embeddings, the first simulation-free frame-work to unify 3D garment reconstruction and sewing pattern inferencewithin a single bidirectional latent space. By leveraging the geometricpriors of a pretrained 3D foundation model, our approach overcomesthe data scarcity typically associated with high-quality garment mod-eling. We propose to use the BoxMesh as a critical intermediate repre-sentation to align 2D panels into 3D configurations without the com-putational overhead of a simulator. This architecture achieves state-of-the-art accuracy in pattern reconstruction while significantly improvingefficiency. Furthermore, our differentiable pipeline enables novel applica-tions, including pattern recovery from meshes and 3D editing from 2Dpatterns. Finally, this work provides a scalable link between neural 3Dvision and the physical garment manufacturing pipeline. Project page:

Abstract:
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time LiDAR and camera synthesis in autonomous driving simulation. However, simulating LiDAR with 3DGS remains challenging for extrapolated views beyond the training trajectory, as existing methods are typically trained on single-traversal sensor scans, suffer from severe overfitting and poor generalization to novel ego-vehicle paths. To enable reliable simulation of LiDAR along unseen driving trajectories without external multi-pass data, we present LiDAR-EVS, a lightweight framework for robust extrapolated-view LiDAR simulation in autonomous driving. Designed to be plug-and-play, LiDAR-EVS readily extends to diverse LiDAR sensors and neural rendering baselines with minimal modification. Our framework comprises two key components: (1) pseudo extrapolated-view point cloud supervision with multi-frame LiDAR fusion, view transformation, occlusion curling, and intensity adjustment; (2) spatially-constrained dropout regularization that promotes robustness to diverse trajectory variations in real-world driving. Extensive experiments demonstrate that LiDAR-EVS achieves SOTA performance on extrapolated-view LiDAR synthesis across three datasets, making it a promising tool for data-driven simulation, closed-loop evaluation, and synthetic data generation in autonomous driving. Code and demos are available at: https://lastbasket.github.io/LiDAR-EVS_page/.

Abstract:
Omnidirectional stereo matching (OSM) estimates 360◦ depthfrom multi-view fisheye images, where inherent fisheye distortion causessparse matching cues in seam regions. However, existing methods rely onspatially uniform update policies, degrading performance in these chal-lenging regions. To address this limitation, we propose PDF-Omni, ageometry-aware OSM framework built on a Poincaré Dual Disk distor-tion field that models spatially varying geometric uncertainty across seamregions. We introduce a distortion-informed GRU with distortion-awareattention to selectively expand the receptive field in high-uncertaintyregions while preserving efficiency in low-uncertainty ones. We furtherpresent a local entropy cumulative loss to supervise high-entropy costdistributions in ambiguous regions. Experimental results demonstratestate-of-the-art performance with consistent gains especially in seam re-gions. The code is available at https://github.com/ynskyang/PDF-Omni

Abstract:
High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and for CT, radiation dose. Medical 3D Super-Resolution (Med3DSR) offers a computational alternative, but existing methods commonly rely on per-subject optimization, pretrained priors, or coordinatebased implicit representations, which compromise anatomical fidelity and limit efficiency. To address these limitations, we present MedGSSR, a fully end-to-end feed-forward framework that represents volumes as an explicit 3D Gaussian field for Med3DSR. Unlike coordinate-based implicit functions, our explicit 3D Gaussian representation naturally enhances signal continuity and local high-frequency fidelity. Specifically, MedGSSR explicitly decouples the reconstruction process into coarsegrained structural preservation and fine-grained textural refinement through the proposed Pyramid Anatomical Encoder and a Hierarchical Gaussian Projector. To support arbitrary-scale super-resolution, we introduce sub-voxel Gaussian decomposition and a Differentiable Gaussian Voxelizer that directly queries the continuous 3D intensity field, reducing discretization artifacts. Extensive experiments on MRI and CT benchmarks demonstrate that MedGSSR significantly outperforms state-ofthe-art methods. Notably, our framework exhibits robust generalizability across unseen datasets without requiring per-subject optimization, enabling fast inference and high-fidelity volumetric super-resolution in practical clinical settings. Our project webpage, including code, is at https://william2ai.github.io/medgssr

Abstract:
Novel View Synthesis (NVS) enables the generation of un-seen views of a scene from a single or multiple images, allowing users tofreely explore an object from any viewpoint. Despite the recent impres-sive qualitative improvements of generative models for this task, existingmethods struggle to provide global and intuitive control of target view-points because they either use input-relative camera poses or are limitedto generating sparse global views. This lack of global pose control severelylimits the number of downstream tasks potentially enabled by NVS. Toaddress this limitation, we propose a novel approach for precise cameracontrol in a customizable Normalized Object Coordinate Space (NOCS),requiring single or few unposed images. Our method operates solely onthe absolute camera pose of the target view in NOCS, eliminating theneed for a relative world frame or camera poses of the input images. Un-like previous methods that treat NVS as a standalone generation task, weformulate it as an image editing problem and build upon state-of-the-artediting models to leverage their superior generalization capability. Cam-era information is injected as dedicated camera tokens via an in-contextmulti-modal conditioning strategy. To alleviate the inherent ambiguityof NOCS, we incorporate text descriptions that explicitly define the ob-ject’s canonical coordinate frame, which also enhances generalization tounseen object categories. Furthermore, we curate a high-quality datasetwith consistently aligned orientations and corresponding NOCS text def-initions. Extensive experiments demonstrate that our method robustlygenerates novel views with accurate and consistent orientations fromarbitrary unposed images across diverse categories, achieving state-of-the-art image quality and fidelity.

Abstract:
The rise of 3D Gaussian Splatting has revolutionized pho-torealistic 3D asset creation, yet a critical gap remains for their inter-active appearance refinement and editing. Existing approaches based ondiffusion or optimization are ill-suited for this task, as they are oftenprohibitively slow, lack the precision for fine-grained control, or—mostimportantly—are fundamentally destructive to the original asset’s iden-tity. To address this, we introduce SplatPainter, a state-aware feedfor-ward model that enables continuous, high-fidelity appearance editing of3D Gaussian assets from user-provided 2D views. Our method directlypredicts updates to the attributes of a compact, feature-rich Gaussianrepresentation and leverages Test-Time Training to create a state-aware,iterative workflow. The versatility of our approach allows a single ar-chitecture to perform diverse tasks, including high-frequency local detailrefinement, local paint-over, and consistent global recoloring, all at inter-active speeds while strictly preserving the asset’s original structure. Ourproject website is at: https://y-zheng18.github.io/SplatPainter/.

Abstract:
3D Gaussian Splatting (3DGS) enables photorealistic real-time novel view synthesis, yet placing a virtual camera to capture a de-sired frame remains largely manual. Existing language-guided approachesin 3D scenes mainly focus on object-centric grounding, determining whatto observe but rarely controlling how it should appear in a single frame,such as subject orientation or frame layout. To address this limitation,we introduce a new task, Text-Instructed Viewpoint Grounding (TIVG),which aims to identify a 6-DoF camera pose in a 3D Gaussian scenewhose rendered frame aligns with a text instruction. To solve this task,we propose CapFrame, a partially differentiable framework that con-verts language into geometric pseudo labels for camera pose optimization.CapFrame follows a Retrieve–Translate–Refine pipeline: it retrieves rele-vant views and ranks them through a Question-Evaluation process withMLLMs, translates the instruction into orientation and layout pseudo la-bels, and refines the camera pose via differentiable optimization with lay-out and orientation losses in 3DGS. Experiments on 38 real-world sceneswith 135 instructions indicate that CapFrame produces viewpoints betteraligned with texts than heuristic viewpoint search and adapted trajectorygeneration baselines, validated by VLM metrics, MLLM judges, and userstudies. Code is available at: https://github.com/jirongli/CapFrame

Abstract:
Recent progress in 3D Gaussian Splatting (3DGS) has en-abled dense visual SLAM with pinhole cameras, yet most pipelines arenot designed for panoramic imagery. We present Cube-Splat, the firstpanoramic GS-SLAM framework that factorizes each 360◦ frame into acubemap of four fixed-orientation virtual pinhole views sharing a singleoptical center. By designating the front face as the primary pose state, weaccumulate gradients from all faces via an adjoint mapping, thereby en-abling multi-face observations to coherently update a single state whilestrictly preserving cross-view geometric consistency. Concurrently, ourmapping module densifies and optimizes anisotropic Gaussians using ag-gregated cubemap rays for high-fidelity, dense reconstruction. Further-more, to rigorously evaluate panoramic SLAM under diverse and chal-lenging conditions, we introduce SynPano, a highly scalable, photoreal-istic synthetic dataset featuring parameterized complex trajectories andmulti-modal ground truth. Extensive evaluations on two public bench-marks (PALVIO and OmniBlender) and our SynPano dataset, collec-tively encompassing both indoor and outdoor scenes, demonstrate thatCube-Splat achieves state-of-the-art (SOTA) performance in tracking ac-curacy and reconstruction fidelity. Both the source code and the SynPanodataset are available at https://github.com/guoxf304/CubeSplat.

Abstract:
3D Gaussian Splatting (3DGS) enables high-quality real-time 3D rendering but faces challenges in efficiently scaling to ultra-dense scenes and high resolutions due to computational bottlenecks thatlimit its use in latency-sensitive applications. Instead of optimizing thesplatting pipeline itself, we propose 3DGS3 , a unified post-renderingframework that jointly performs super sampling and frame interpolationthrough differentiable processing of low-resolution outputs to achieveboth high-resolution and high-frame-rate rendering. Our Gradient -Aware Super Sampling (GASS) module leverages the continuousdifferentiability of 3DGS to extract image gradients that guide a GRU-based refinement network to enable high-fidelity super sampling. Fur-thermore, a Lightweight Temporal Frame Interpolation (LTFI)module based on a compact U-Net-like backbone fuses temporal anddifferentiable spatial cues from consecutive frames to synthesize tem-porally coherent intermediate frames. Experiments on public datasetsdemonstrate that 3DGS3 achieves superior rendering efficiency and vi-sual quality when compared with state-of-the-art methods and remainscompatible with existing 3DGS acceleration techniques. The project codeis available at: https://github.com/Eabo20001/3DGS3.

Abstract:
Implicit neural representation (INR) has achieved remark-able progress in novel view synthesis and image/video coding in recentyears. Compared to conventional end-to-end image codecs, INR-basedcompressors demonstrate significant advantages in decoding complexity.However, their practical application has been hindered by the inferiorencoding speed and underutilized decoding efficiency. In this work, wepropose a feedforward INR image coding architecture, Practical INRImage Codec (PIC), that computes all the necessary information forINR network in a single forward pass, achieving an encoding speed of20 FPS. Additionally, we implement a highly optimized decoder thatreaches 2000 FPS decoding speed, significantly surpassing JPEG’s per-formance at comparable rate-distortion (RD) performance. To the best ofour knowledge, this work presents the first learning-based image codecthat simultaneously outperforms or is comparable with JPEG in bothRD performance and decoding speed while maintaining practical encod-ing speed. Code is available at https://github.com/actcwlf/PIC.

Abstract:
Multi-view 3D Visual Question Answering (MV3D-VQA) re-quires integrating partial observations into a coherent 3D scene repre-sentation and selecting informative viewpoints for multi-step spatial rea-soning. However, current multimodal LLMs are typically trained withsparse, answer-level supervision, which often yields inconsistent cross-view reasoning and brittle view selection. We present DR-MV3D (DenseReward for MV3D-VQA), a map-grounded learning framework that pro-vides dense, verifiable rewards to supervise the reasoning process. Our ap-proach decomposes MV3D-VQA into (i) allocentric global map construc-tion, (ii) question-conditioned view-trajectory planning, and (iii) egocen-tric grounding for answer prediction. To make intermediate steps learn-able without manual annotations, we introduce two rewards: a global con-sistency reward that aligns the predicted map with geometry-consistentpseudo targets from frozen 3D vision foundation models (e.g., VGGT+ SAM3), and a local trajectory reward that supervises ordered view-point selection. We optimize the full pipeline with trajectory-level pol-icy optimization (GRPO). Experiments on MindCube, VSI-Bench, andBLINK (MV) show that DR-MV3D consistently improves over strongmulti-image baselines, supporting the effectiveness of process-level densesupervision for multi-view 3D reasoning.Code is available at: https://github.com/kaist-cvml/dr-mv3d

Abstract:
We study the problem of physically plausible shadow castingwhen animating 3D Gaussian Splatting (3DGS) avatars, either individu-ally or in multi-avatar and object-interaction scenarios, within existing3DGS scenes. In contrast to prior methods that rely on binary hit tests andmesh-based shadow casters, our method performs shadow computationentirely in Gaussian space, without requiring any mesh reconstruction. Weintroduce RAGA, a Ray-Traced Gaussian Shadow Casting formulationbased on exact ray–Gaussian line integrals. For each occluding Gaussian,we integrate the opacity profile along the shadow ray and normalize bythe theoretical maximum integral, producing a weight that captures howthe ray traverses the occluder rather than merely whether an intersectionoccurred. To reduce temporal variance from clothing deformations in ani-mated avatars, we further introduce an avatar proxy representation thatstabilizes shadow casting while preserving visual fidelity. We implementWork partly done as intern at Snap Inc.RAGA using custom CUDA kernels integrated with the NVIDIA OptiXframework; as such, our shadow tracer runs at rates of about 50 FPS.We evaluate on single-avatar, multi-avatar, and avatar–object interactionscenarios across multiple datasets, demonstrating substantially improvedshadow realism, temporal stability, and scene coherence. Our project pageis available at https://miraymen.github.io/raga/.

Abstract:
Open-vocabulary 3D scene graph methods typically operatein two stages: first reconstruct, then enrich with vision-language mod-els, leaving the graph unqueryable during exploration. We argue thatthis sequential coupling is unnecessary and propose an asynchronous ar-chitecture in which lightweight online mapping runs concurrently withheavyweight semantic refinement. A probabilistic voxel-based backbonemaintains stable object identities incrementally, while background VLMagents progressively enrich the graph. This framework resolves duplicateobject tracks through semantic loop closure, attaches fine-grained visualattributes and derives spatial relations between objects. A multi-targetframe scheduler amortizes VLM cost by selecting a small set of infor-mative frames that jointly cover multiple targets. The resulting scenegraph is queryable during exploration and grows in semantic richnessover time. Our method matches or outperforms existing open-vocabulary3D scene graph methods on semantic segmentation (ScanNet, Replica)and surpasses the prior state-of-the-art across three visual groundingbenchmarks (Sr3D+, Nr3D, ScanRefer) by 15.3 to 18.8 A@0.25. Projectpage: https://denizbickici.github.io/thinkgraphs/

Abstract:
Probabilistic point cloud registration has attracted increasing attention due to its robustness to noise, outliers and occlusions. However, existing methods often suffer from high computational cost and neglect the role of informative priors. In this paper, we propose a new probabilistic registration method based on graph signal processing (GSP), called graph coherent point drift (GraphCPD). Specifically, we design a high-pass graph filter to extract high-frequency components, which are theoretically proven to be invariant under rigid transformations. These components are combined with point coordinates and normals to form a high-dimensional graph signal. We construct a local graph based on the graph signal and use the graph Laplacian model for registration. Compared with the classical Gaussian mixture models (GMMs), graph Laplacian models provide more discriminative geometric representations and enhance the model’s ability to capture graph structure. Furthermore, we exploit the invariance of high-frequency components to define prior probabilities, significantly reducing the corresponding search space and improving the speed of registration. Experimental results demonstrate that our method improves runtime efficiency over most existing probabilistic methods, while maintaining competitive registration accuracy, especially on large-scale point clouds. The source code is available at https://github.com/YCLai1997/GraphCPD.

Abstract:
Three-dimensional ultrasound (US) is a safe, radiation-freecomplementary modality to CT and X-rays for longitudinal monitoring,yet its segmentation-derived partial point clouds are extremely artifact-laden. Consequently, it is challenging to recover a clean and completeanatomical structure from such US point clouds. In this paper, we presentUBone3D, a novel framework based on physics-rectified conditional flowmatching (CFM) that performs point cloud completion directly from par-tial US observations. UBone3D models deterministic physics artifacts(e.g., surface thickening, streaking, dropouts) via a simulated physicsproxy, and introduces test-time physics rectification to steer the shapecompletion. At inference, the completion is jointly steered by two decou-pled forces: (1) anatomical plausibility enforced by a CT-trained gen-erative shape prior, BoneFM, and (2) physics consistency enforced byUSimNet in the ultrasound formation space. Extensive experimentson simulated and in-vivo data demonstrate significant improvements inreconstruction accuracy and anatomical fidelity over existing baselines.Project page: https://answerrtx.github.io/UBone3D-Proj/.

Abstract:
We introduce LaMP, a dual-expert Vision-Language-Actionframework that embeds dense 3D scene flow as a latent motion prior forrobotic manipulation. Existing VLA models regress actions directly from2D semantic visual features, forcing them to learn complex 3D physicalinteractions implicitly. This implicit learning strategy degrades underunfamiliar spatial dynamics. LaMP addresses this limitation by aligninga flow-matching Motion Expert with a policy-predicting Action Expertthrough gated cross-attention. Specifically, the Motion Expert generatesa one-step partially denoised 3D scene flow, and its hidden states condi-tion the Action Expert without full multi-step reconstruction. We eval-uate LaMP on the LIBERO, LIBERO-Plus, and SimplerEnv-WidowXsimulation benchmarks as well as real-world experiments. LaMP consis-tently outperforms evaluated VLA baselines across LIBERO, LIBERO-Plus, and SimplerEnv-WidowX benchmarks, achieving the highest re-ported average success rates under the same training budgets. On LIBERO-Plus OOD perturbations, LaMP shows improved robustness with an av-erage 9.7% gain over the strongest prior baseline. Our project page isavailable at https://summerwxk.github.io/lamp-project-page/.

Abstract:
Diffusion MRI of the fetal brain has potential to provide important insights into early development of white matter fibers. However, in vivo fetal acquisitions are severely corrupted by unpredictable motion, making the data unusable for downstream analysis. To address this challenge, we propose an implicit neural representation framework for fetal diffusion MRI reconstruction. The proposed method models the diffusion signal as a spatially and angularly continuous field by predicting direction-independent spherical harmonics (SH) coefficients from continuous spatial coordinates. Based on this representation, we formulate a physics-based forward slice acquisition model that links the underlying clean diffusion volume with the observed motion-corrupted slices, enabling joint optimization of the diffusion signal, fetal motion and acquisition artefacts. Furthermore, the single slice orientation acquisition typically used for each diffusion gradient introduces an ill-posed reconstruction problem along the slice-select direction. To mitigate this issue, we introduce a novel Through-Plane Continuity regularization loss that enforces smoothness in through-plane direction to suppress stripe artifacts, while preserving genuine anatomical details. Experiments on one in vivo fetal dataset and two simulated neonatal datasets demonstrate that the proposed framework achieves improved reconstruction fidelity, sharper anatomical features, and stronger robustness to acquisition artifacts. The code is available at: https://github.com/baby-MedIA/INR-dMRI-Recon

Abstract:
Multimodal large language models (LLMs) are increasinglyused to generate dermatology diagnostic narratives directly from images.However, reliable evaluation remains the primary bottleneck for respon-sible clinical deployment. We introduce a novel evaluation frameworkthat combines DermBench, a meticulously curated benchmark, withDermEval, a robust automatic evaluator, to enable clinically meaning-ful, reproducible, and scalable assessment. We build DermBench, whichpairs 4000 real-world dermatology images with expert-certified diagnos-tic narratives and uses an LLM-based judge to score candidate narrativesacross clinically grounded dimensions, enabling consistent and compre-hensive evaluation of multimodal models. For individual case assessment,we train DermEval, a reference-free multimodal evaluator. Given an im-age and a generated narrative, DermEval produces a structured critiquealong with an overall score and per-dimension ratings. This capabil-ity enables fine-grained, per-case analysis, which is critical for identi-fying model limitations and biases. Experiments on a diverse dataset of4500 cases demonstrate that DermBench and DermEval achieve closealignment with expert ratings, with mean deviations of 0.251 and 0.117(out of 5) respectively, providing reliable measurement of diagnostic abil-ity and trustworthiness across different multimodal LLMs. The bench-mark resources are publicly available at https://github.com/yuhos16/DermBench.

Abstract:
Chemical reaction diagram parsing aims to automaticallyconvert reactions from images into machine-readable formats. Currentleading approaches employ end-to-end generative models to directly de-code reaction diagrams into structured coordinate sequences, performingobject localization and relationship modeling in a unified process. How-ever, two critical bottlenecks remain: severe data scarcity due to thehigh cost of expert annotation, and output instability caused by thecomplex sequential dependencies between chemical reactions, where mi-nor prefix errors can lead to cascading parsing failures. In this paper, wepropose RobustRDP, a Robust Reaction Diagram Parser built on amultimodal large language model (MLLM). Its superior performance pri-marily stems from two advancements: Synthetic-to-Real Data Scal-ing: We develop a layout-driven synthesizer and an efficient annotationplatform to create a large-scale training set, along with a more compre-hensive evaluation benchmark. Robustness-Oriented Training: Wepropose a three-stage progressive training strategy (Pretraining, Multi-Task SFT, DPO). The SFT stage employs two auxiliary tasks includ-ing region-guided reaction parsing and prefix-perturbed reaction parsingto mitigate sequential dependencies between reactions and enhance themodel’s robustness against prior errors. The DPO stage further stabilizesoutputs in failure-prone scenarios. Extensive experiments demonstratethe superior performance of RobustRDP, establishing a solid foundationfor automated reaction diagram parsing. Code and model weights areavailable at https://github.com/jaydetang/RobustRDP.

Abstract:
With the advancement of imaging technology, ultra-highdefinition images have become increasingly essential in modern visual applications. However, existing multi-focus image fusion remains largely confined to low-resolution images and faces three major barriers in UHD scenarios, namely data availability, model adaptability, and deployment feasibility, which severely hinder its practical application. To shatter these barriers, first, we propose the UHD-MFF dataset, the first largescale ultra-high-resolution multi-focus fusion dataset. Second, we propose a scale-specialized lookup-table framework tailored for ultra-highresolution images, termed as UMF-LUT. It consists of Coarse-Region Lookup Table (C-LUT) and Detail-Edge Lookup Table (D-LUT). Specifically, C-LUT performs joint queries of multiple gradient cues and semantic cues at low-resolution scales to enable region-level decision-making. Also, D-LUT operates at high-resolution scales, leveraging efficient Laplacian cues to provide complementary edge-level decision information. Such a design makes the model particularly well-suited for ultra-high-resolution multi-focus image fusion. Finally, it offers strong deployability with minimal computational overhead, enabling real-time 4K multi-focus fusion and showing promising potential for smartphone. Extensive experiments demonstrate that it outperforms SOTA methods in both visual fidelity and quantitative metrics. It effectively advances the development of multifocus image fusion toward ultra-high-resolution imaging scenarios. The code is available at https://github.com/zyb5/UHD-MFF.

Abstract:
Image retouching is commonly formulated as enhancing over-all visual quality through color adjustment, but in practice, it also servesto emphasize visual focus by guiding viewers’ attention toward a specificsubject or region. Achieving such focus-oriented retouching is inherentlychallenging, as it requires well-coordinated global and local adjustmentsto manipulate perceptual saliency while maintaining visual naturalness.This intricate process typically demands substantial professional exper-tise. In this study, we propose EyeControl, a multi-modal large lan-guage model (MLLM)-driven agent with a diffusion-based retouchingexecutor that enables visual focus enhancement under weak user intent.With only a few clicks or coarse strokes, EyeControl directs visual at-tention to the intended region, effectively “dotting the eye” of the image.The core idea is to explicitly link the weak user intention with the tar-get editing region and the corresponding tonal adjustment operationsduring retouching. To achieve this, the system first interprets the in-tent and image content to infer the visual focus and generate structuredintent guidance for the retouching executor. Second, the retouching ex-ecutor is encouraged to respond more strongly to the target region, ex-plicitly aligning its attention map with a designed pseudo-intent map.We also introduce an operation-consistency constraint to improve coor-dination between global and local adjustments, achieving more naturaland coherent retouching. Additionally, we contribute ControlArt-Bench,a high-quality evaluation dataset for visual focus enhancement. Exten-sive evaluations demonstrate that EyeControl yields perceptually ap-pealing results with stronger intent alignment. Code will be released athttps://github.com/DragonisCV/EyeControl.

Abstract:
Accurate, dense depth estimation is crucial for robotic per-ception, but commodity sensors often yield sparse or incomplete measure-ments due to hardware limitations. Existing RGBD-fused depth comple-tion methods learn priors jointly conditioned on training RGB distri-bution and specific depth patterns, limiting domain generalization androbustness to various depth patterns. Recent efforts leverage monoculardepth estimation (MDE) models to introduce domain-general geomet-ric priors, but current two-stage integration strategies relying on explicitrelative-to-metric alignment incur additional computation and introducestructured distortions. To this end, we present Any2Full, a one-stage,domain-general, and pattern-agnostic framework that reformulates com-pletion as a scale-prompting adaptation of a pretrained MDE model.To address varying depth sparsity levels and irregular spatial distribu-tions, we design a Scale-Aware Prompt Encoder. It distills scale cuesfrom sparse inputs into unified scale prompts, guiding the MDE modeltoward globally scale-consistent predictions while preserving its geomet-ric priors. Extensive experiments demonstrate that Any2Full achievessuperior robustness and efficiency. It outperforms OMNI-DC by 32.2%in average AbsREL and delivers a 1.4× speedup over PriorDA with thesame MDE backbone, establishing a new paradigm for universal depthcompletion. Codes and checkpoints are available at https://github.com/zhiyuandaily/Any2Full.

Abstract:
Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as falls, particularly for mobility-challenged individuals. Existing datasets are often clip-based, lacking the frame-level detail needed to recognize actions online, as they unfold. To address this, we introduce SAFER-Activities, a dataset for fall detection and physical activity monitoring, with a dedicated subset for wheelchair use scenarios. It comprises over 66 hours of video data captured by multiple cameras, with 85,310 action instances and frame-level annotations for 30 action classes. We benchmark action recognition on SAFER-Activities with 2D and 3D skeleton models, RGB models with frozen backbones, and multimodal fusion strategies, and evaluate on in-lab, out-of-distribution, and crossdataset test sets. Skeleton-based models generalize best under domain shift; fusing frozen RGB features with the skeleton stream improves in-domain recognition over the baseline CNN1D, most clearly on the wheelchair subset, but degrades out of distribution. Cross-dataset and qualitative evaluations confirm that models trained on SAFER-Activities transfer well to unseen environments and external fall data. To support research on robust fall detection and activity monitoring, we release the dataset and code at https://safer-activities.github.io/.

Abstract:
Large language models (LLMs) can improve autonomous driving planning but are costly to query online, and existing fast–slow planners often rely on hand-designed triggering rules that either over-call the slow system or call it at the wrong times. We formulate slow-system invocation as a resource-aware sequential decision problem and propose the Adaptive Slow-System Control Gate (ASSCG), which makes framelevel Query/Cache/Drop decisions to refresh, reuse, or suppress slow guidance. ASSCG uses an RWKV backbone for efficient long-horizon gating and is trained with supervised fine-tuning followed by GRPOstyle compute-aware reinforcement fine-tuning. We apply ASSCG to two different fast–slow architectures: (i) AsyncDriver on nuPlan Hard20 closed-loop evaluation, where ASSCG improves score to 67.28 (+2.28) while reducing average end-to-end inference latency by ∼60%; and (ii) a RecogDrive-based dual system that we build by replacing its original VLM-2B module with a lightweight ViT-based fast planner and adding an LLM slow planner, evaluated on NAVSIM, where ASSCG achieves 91.4 PDMS (+0.6) and increases average speed by ∼25%. The project page, including video visualizations and additional results, is available at https://williamxuanyu.github.io/asscg/.

Abstract:
Visual search in 3D environments requires embodied agentsto actively explore their surroundings and acquire task-relevant evidence.However, existing visual search and embodied AI benchmarks, includ-ing EQA, typically rely on static observations or constrained egocen-tric motion, and thus do not explicitly evaluate fine-grained viewpoint-dependent phenomena that arise under unrestricted 5-DoF viewpointcontrol, such as disambiguating object attributes observable only fromspecific angles. To address this limitation, we introduce E3VS-Bench,a benchmark for embodied 3D visual search where agents must controltheir viewpoints in 5-DoF to gather viewpoint-dependent evidence forquestion answering. E3VS-Bench consists of 99 high-fidelity 3D scenesreconstructed using 3D Gaussian Splatting and 2,014 question-drivenepisodes. 3D Gaussian Splatting enables photorealistic free-viewpointrendering that preserves fine-grained visual details (e.g., small text andsubtle attributes) often degraded in mesh-based simulators, thereby al-lowing the construction of questions that cannot be answered from asingle view and instead require active inspection across viewpoints in5-DoF. We evaluate multiple state-of-the-art VLMs and compare theirperformance with humans. Despite strong 2D reasoning ability, all mod-els exhibit a substantial gap from humans, highlighting limitations inactive perception and coherent viewpoint planning specifically under full5-DoF viewpoint changes. The benchmark, code, and dataset are publiclyavailable at https://k0uya.github.io/e3vs-proj/.

Abstract:
Enabling embodied agents to imagine future states is essen-tial for robust and generalizable visual navigation. Yet, state-of-the-artsystems typically rely on modular designs that decouple navigation plan-ning from visual world modeling, which often induces state–action mis-alignment and weak adaptability in novel or dynamic scenarios. We pro-pose UniWM, a unified, memory-augmented world model that integratesegocentric visual foresight and planning within a single multimodal au-toregressive backbone. UniWM explicitly grounds action selection in vi-sually imagined outcomes, tightly aligning prediction with control. Mean-while, a hierarchical memory mechanism fuses short-term perceptual cueswith longer-term trajectory context, supporting stable and coherent rea-soning over extended horizons. Extensive experiments on four challeng-ing benchmarks (Go Stanford, ReCon, SCAND, HuRoN) and the 1XHumanoid Dataset show that UniWM improves navigation success ratesby up to 30%, substantially reduces trajectory errors against strong base-lines, generalizes zero-shot to the unseen TartanDrive dataset, and scalesnaturally to high-dimensional humanoid navigation. These results po-sition UniWM as a principled step toward unified, imagination-drivenembodied navigation. All materials are committed to be open-sourced.

Abstract:
Monocular 3D lane detection offers a cost-effective solutionfor autonomous driving perception but is inherently challenged by theill-posed problem of inferring 3D geometry from a 2D image. Standard-Definition (SD) maps offer a lightweight prior for road topology, promis-ing enhanced accuracy for distant or occluded lanes. However, their in-herent inaccuracies and representation as road-level centerlines, whichmisalign with the actual lanes to be perceived, pose significant challengesfor direct integration. To tackle this, we propose SGC-Lane, a novelframework that advances monocular 3D lane detection through SD mapguidance and lane completion. Specifically, we propose an FV-GuidedMap Encoder that generates a lane probability map and structured mapfeatures, where the feature channels are aligned with potential lane in-stances. This probability map directs attention toward lane-specific re-gions in the front view (FV), refining image features and initializing lanequeries by the Local Spatial Cross-Attention module for precise geome-try decoding. In addition, to mitigate short-range truncation, we designa Step Consistency Completion (SCC) Head that is used to predict andcluster lane endpoints, effectively recovering missing segments to producecomplete 3D lane representations. On the large-scale OpenLane dataset,our method achieves a 65.6% F1-score, showing improved performanceagainst existing approaches with the assistance of only coarse road-levelSD map priors. The improvements are especially evident in challengingconditions, such as roads with sharp curves and complex intersections.Code is released at https://github.com/FuqingJIang/SGC-Lane.

Abstract:
A pivotal step in autonomous driving simulation involves in-serting foreground vehicles with predefined trajectories into simulatedscenes. This process enhances scene diversity and facilitates the creationof various corner cases for testing and improving autonomous drivingmodels. However, existing methods often rely on pre-reconstructed 3Dassets, which frequently lead to lighting inconsistencies between the in-serted foreground and the background. Moreover, the reliance on limited,manually-curated 3D assets hinders large-scale deployment. To addressthese challenges, we propose DriveWeaver, a novel framework for con-trollable vehicle insertion in autonomous driving simulation. Specifically,for a masked target insertion area, DriveWeaver performs video inpaint-ing conditioned on vehicle point clouds to generate high-quality, tempo-rally consistent vehicles. This video-inpainting-based approach ensuresseamless blending between the foreground and background, while thereadily available point cloud conditions enable superior generalization.To support long-term generation, we further design a global-to-local hier-archical inpainting strategy, ensuring the consistent identity and appear-ance of the inserted vehicles. Meanwhile, we extract explicit 3D Gaussianrepresentations of the inserted vehicles through an urban reconstructionpipeline to enable real-time rendering for autonomous driving simula-tion. Extensive experiments across diverse datasets demonstrate thatour method outperforms existing baselines in visual realism and geomet-ric consistency, providing a robust tool for scalable autonomous drivingscene augmentation.

Abstract:
Vision encoders are a critical component of vision-languagemodels, and scaling their capacity effectively improves performance. How-ever, dense scaling increases compute cost and inference latency. Mixture-of-Experts (MoE) architectures offer a compelling alternative, having en-abled efficient scaling in LLMs, yet the MoE design space for CLIP-stylevision encoders remains underexplored at State-of-the-Art (SOTA) lev-els. In this work, we systematically study MoE designs for vision encoderscaling and find that fine-grained MoE topologies yield substantial gainsover both dense and standard MoE counterparts. We further proposean auxiliary-loss-free balancing variant for better expert utilization, anddesign a specialized MoE kernel to mitigate inference latency overhead.To enhance video capabilities while preserving image knowledge, we in-troduce frame-level distillation paired with a novel freezing mechanism.We pretrain a series of Mixture-of-Experts Vision Encoders (MoE-ViE)across a range of sizes, all consistently outperforming their dense counter-parts. Our largest model matches the zero-shot performance of a SOTAencoder 1.7× its size at 76% of its latency. When aligned with an LLM,MoE-ViE surpasses all compared encoders on image and video bench-marks, including those with up to 5× more activated parameters.

Abstract:
Recovering 3D human body motion from video is impor-tant for applications such as rehabilitation assessment and sports per-formance evaluation. For prosthesis users, this requires capturing bothnatural body joints and the geometry of the prosthetic device, a challengethat existing methods are not designed to address. Model-based estima-tors rely on body models trained on non-amputee individuals and cannotrepresent prosthesis geometry, while model-free methods lack body kine-matic priors and are unreliable under occlusion. This challenge is partic-ularly prominent for users of running-specific prostheses (RSPs), wherethe RSP has a complex curved geometry and moves dynamically duringexercise. To fill this gap, we collect RSP3D, the first 3D dataset of RSPusers, covering essential daily-life and exercise actions from participantswith varied amputation conditions, using a multi-camera marker-basedmotion capture setup. We formally define the task of prosthesis-aware 3Dpose estimation, evaluate representative methods in a zero-shot setting,and confirm their individual limitations. We further propose a hybridbaseline combining model-based body joint estimation with model-freeRSP shape recovery, establishing a starting point for future research.Our project page is available at https://ut-vision.github.io/RSP3D/

Abstract:
Virtual immunohistochemistry (IHC) multiplex staining hasemerged as a highly promising non-destructive solution in digital pathol-ogy. However, existing universal IHC staining models typically guide thegeneration of specific biomarkers by injecting task-specific text prompts,which often struggle to bridge the semantic gap with complex patholog-ical structures, leading to the "manifold drift" problem. Moreover, thesemodels lack specificity in modeling heterogeneous tissue components.In this paper, we propose HUSE, a Histocomponent-driven Universalmodel for virtual IHC multiplex Staining via joint manifold Evolution.HUSE introduces Joint Manifold Anchoring (JMA) as a global solution totreat H&E images as persistent structural scaffolds, effectively anchoringthe generation trajectory to the intrinsic image manifold. Complementingthis, a localized refinement scheme named Histocomponent-driven Mix-ture of Experts (Hi-MoE) and Representation Conflict Gating (RCG)is introduced to precisely capture local topological features of complexmanifolds. Furthermore, we pioneer an mIF-driven multi-marker IHCgeneration paradigm to construct large-scale 1(H&E) : N (IHC) train-ing matrices; evaluations by senior pathologists verify the feasibility ofthis paradigm as well as its exceptional clinical realism and structuralfidelity. Extensive experiments on two distinct types of datasets demon-strate that HUSE achieves state-of-the-art performance. Code is releasedat https://github.com/DeepMed-Lab-ECNU/HUSE.

Abstract:
While mistake understanding in procedural video is advanc-ing toward increasingly complex multi-step tasks and diverse mistaketypes, it remains unclear whether current MLLMs can even recognize andexplain mistakes at individual step levels. This study proposes CookingMistake Recognition and Explanation (Cook-MRE), a dataset for assess-ing MLLMs in-depth performance in understanding cooking mistakeswithin each single-step. The evaluation uses an LLM-based approachalong two aspects: recognition (mistake presence and type classification)and explanation (error correction). By focusing on mistakes in the formof deviations from instructions, Cook-MRE reuses an existing cookingvideo dataset and synthesizes mistake samples through modification oftext instructions. It covers four basic operation elements and includes dif-ficulty labels based on visual distinguishability. Evaluation of 13 MLLMsshowed that our comprehensive metric, Full Pass Accuracy, was sub-stantially lower than mistake presence accuracy alone, indicating thedifficulty of explaining mistakes in detail even for single steps. Analysis byutilizing our type and difficulty labels showed notably low performanceon visually challenging problems, and scaling model size yields limitedimprovement on them. The dataset and code are publicly available athttps://github.com/Shun-Takashige/cook-mre-benchmark.

Abstract:
Video diffusion models achieve strong frame-level fidelity butstill struggle with motion coherence, dynamics and realism, often pro-ducing jitter, ghosting, or implausible dynamics. A key limitation is thatthe standard denoising MSE objective provides no direct supervision ontemporal consistency, allowing models to achieve low loss while still gen-erating poor motion. We propose MoGAN, a motion-centric post-trainingframework that improves motion realism without reward models or hu-man preference data. Built atop a 3-step distilled video diffusion model,we train a DiT-based optical-flow discriminator to differentiate real fromgenerated motion, combined with a distribution-matching regularizer topreserve visual fidelity. With experiments on Wan2.1-T2V-1.3B, MoGANsubstantially improves motion quality across benchmarks. On VBench,MoGAN boosts motion score by +7.3% over the 50-step teacher and+13.3% over the 3-step DMD model. On VideoJAM-Bench, MoGAN im-proves motion score by +7.4% over the teacher and +8.8% over DMD,while maintaining comparable or even better aesthetic and image-qualityscores. A human study further confirms that MoGAN is preferred formotion quality (52% vs. 38% for the teacher; 56% vs. 29% for DMD).Overall, MoGAN delivers significantly more realistic motion without sac-rificing visual fidelity or efficiency, offering a practical path toward fast,high-quality video generation.

Abstract:
Generative models have achieved remarkable progress, yetapplying them to satellite imagery remains challenging. Unlike natu-ral imagery, satellite scenes are structured by spatially complex andsemantically distinct geometries. Prior work addresses this complexityby adapting natural image frameworks using dense rasters or sparseprompts, trading off annotation cost and fidelity while breaking com-patibility with vector primitives commonly used to represent geographicinformation. We introduce TerraDiT-Ω, a unified spatial control frame-work that generates satellite imagery directly from any native geospatialprimitive. By jointly leveraging precise annotations (polygons, polylines)and coarser ones (bounding boxes, points), the model supports control-lable layouts across varying annotation budgets, broadening applicabilityto design tasks such as urban planning while remaining naturally com-patible with end-to-end GeoAI workflows. To effectively leverage theseprimitives during generation, we propose Geometry-Aware Local Atten-tion, a conditioning mechanism that injects explicit geometric cues intothe attention space. Across all conditioning formats, our approach con-sistently outperforms both dense-control and sparse-control baselines.Furthermore, this flexibility enables controllable synthetic data augmen-tation using a single generative model, improving downstream perfor-mance on land-cover segmentation, object detection, road graph extrac-tion, and scene classification. Code, data, and weights are available at

Abstract:
Despite the recent success of multimodal discrete diffusionlanguage models (multimodal dLLMs) in generating text through iter-ative demasking with bidirectional attention, object hallucination re-mains a critical challenge. Existing hallucination mitigation methods aredesigned for autoregressive generation and encounter fundamental mis-matches when applied to multimodal dLLMs, either doubling inferencecost through contrastive decoding or suffering from ambiguous step andposition alignment under bidirectional context. To address this, we pro-pose Unmasking-Time Visual Calibration (UVC), a lightweight, training-free framework that mitigates hallucinations natively within the demask-ing process. UVC extracts multi-granularity contrastive activation shiftvectors offline by comparing model responses to clean and visually de-graded inputs, identifies the most visually informative attention headsvia per-head AUC scoring, and injects the pre-computed calibration sig-nals exclusively at still-masked positions during inference, requiring noadditional forward passes. Extensive experiments on POPE and MMEwith two representative multimodal dLLMs demonstrate that UVC con-sistently reduces hallucinations by a substantial margin while introducingless than one percent inference overhead. Additional evaluation on open-ended captioning with the CHAIR metric further confirms that UVC sub-stantially lowers hallucination rates without affecting generation length,and generalizes effectively across benchmarks without retraining. Codeis available at https://github.com/THU-BPM/UVC.

Abstract:
Recent advances in Large Language Models (LLMs) and Text-to-Image (T2I) models have led to the emergence of Unified Multimodal Models (UMMs), where multimodal understanding and image generation are tightly integrated within a shared architecture. Prior studies suggest that such reciprocity enhances cross-functionality performance through shared representations and joint optimization. However, the safety implications of this tight coupling remain largely unexplored, as existing safety research predominantly analyzes understanding and generation functionality in isolation. In this work, we investigate whether cross-functionality reciprocity itself constitutes a structural source of vulnerability in UMMs. We propose RICE: Reciprocal Interaction–based Cross-functionality Exploitation, a novel attack paradigm that explicitly exploits bidirectional interactions between understanding and generation. Using this framework, we systematically evaluate Generation-toUnderstanding (G→U) and Understanding-to-Generation (U→G) attack pathways, demonstrating that unsafe intermediate signals can propagate across modalities and amplify safety risks. Extensive experiments show high Attack Success Rates (ASR) in both directions, revealing previously overlooked safety weaknesses inherent to UMMs. The code is available at https://github.com/tunantu/UMM-Safety.

Abstract:
Recent advancements in unified multimodal understandingand visual generation (or multimodal generation) models have been hin-dered by their quadratic computational complexity and dependence onlarge-scale training data. We present OmniMamba, the first linear-architecture-based multimodal generation model that generates both text and imagesthrough a unified next-token prediction paradigm. The model fully lever-ages Mamba-2’s high computational and memory efficiency, extending itscapabilities from text generation to multimodal generation. To addressthe data inefficiency of existing unified models, we propose two key inno-vations: (1) decoupled vocabularies to guide modality-specific generation,and (2) task-specific LoRA for parameter-efficient adaptation. Further-more, we introduce a decoupled two-stage training strategy to mitigatedata imbalance between two tasks. Equipped with these techniques, Om-niMamba achieves competitive performance with JanusFlow while sur-passing Show-o across benchmarks, despite being trained on merely 2Mimage-text pairs, which is 1,000 times fewer than Show-o. Notably, Om-niMamba stands out with outstanding inference efficiency, achieving upto a 119.2× speedup and 63% GPU memory reduction for long-sequencegeneration compared to Transformer-based counterparts. Code and mod-els are released at https://github.com/hustvl/OmniMamba.

Abstract:
Ensuring safe content generation in text-to-image diffusionmodels remains a critical challenge. Existing safety mechanisms focuson model editing or trajectory steering, yet the design of the blacklist,the set of unsafe concepts these methods steer away from, is an equallypivotal but largely under-explored dimension. Through controlled exper-iments on blacklist-guided concept erasure, we uncover three findingsthat overturn common assumptions: longer keyword phrases do not nec-essarily improve safety, enlarging the blacklist yields diminishing returns,and mixing in irrelevant concepts degrades performance. Together theypoint to a single principle: blacklist effectiveness is governed by how wellits keywords cover unsafe regions of the embedding space, not by individ-ual keyword quality or list size. Building on this principle, we introduceMAPS, a framework for automated blacklist optimization. MAPS usesreinforcement learning to discover a prompt token that, when insertedinto an LLM prompt, elicits semantically diverse safety keywords, andthen assembles category-specific blacklists for distinct unsafe concepts.At inference, it selects the most relevant blacklist for each prompt viaembedding similarity, maximizing coverage of the threat while avoidinginterference from unrelated concepts. MAPS acts as a drop-in enhance-ment for existing trajectory-steering methods, reducing attack successrates by 25–52% (relative) across five red-teaming benchmarks on thenudity concept while preserving generation quality, all without modelediting or modifications to the steering procedure. Our code is availableat https://github.com/juyangbai/MAPS.

Abstract:
Autoregressive text-to-image generation has recently achieved remarkable progress, offering high-fidelity synthesis via a unified generative framework. However, fine-grained semantic control remains challenging due to the attribute entanglement and the misalignment between textual and fine-grained visual representations. In this paper, we introduce Attribute Token Arithmetic (ATA), a method that enables disentangled and continuous attribute control in visual autoregressive modelling. Inspired by the vector arithmetic property observed in word embeddings, ATA identifies semantic directions corresponding to visual attributes (e.g., aging, fatness, emotion) directly within the pretrained autoregressive latent space. These directions are learned from a single reference image, without model retraining or large-scale supervision. During generation, attributes can be continuously adjusted and compositionally combined through simple arithmetic operations with other attribute tokens. Extensive experiments demonstrate that ATA achieves identitypreserving, fine-grained, and multi-attribute adjustment, outperforming existing autoregressive editing baselines in controllability, generality, and computational efficiency. Our code will be released here.

Abstract:
Training-free motion customization imposes motion patterns from reference videos onto video generators through test-time computation. Most existing methods target full diffusion models, requiring many denoising steps and high computational cost. With the rise of efficient distilled models, a natural question arises: can test-time motion customization be applied directly to distilled generators with their accelerated sampling and efficiency gains? However, our analysis reveals that existing training-free techniques fail on distilled models. Distillation fundamentally alters the denoising dynamics that prior test-time guidance relies on, and the large denoising steps of distilled generators discard the dense intermediate states that score guidance requires, rendering existing motion control strategies incompatible with fast generation. To address this limitation, we propose MotionEcho, a novel training-free test-time distillation framework that enables motion customization for distilled video generators. The key idea is to correct the student model’s sampling trajectory with restricted usage of a high-quality diffusion teacher at inference time. Teacher supervises the student’s denoising by re-noising the student’s endpoint onto its dense trajectory to form a motion-aligned clean endpoint, then interpolating it with the student’s, while an adaptive scheduling mechanism determines when and how much teacher guidance is needed. As a result, MotionEcho restores generative trajectories for distilled video generators via lightweight, adaptive test-time teacher guidance, enabling accurate motion control without compromising generation efficiency. Extensive experiments on multiple distilled video generation models demonstrate that our method significantly improves motion fidelity and visual quality while retaining the efficiency advantages of distilled generation. Project page: MotionEcho.

Abstract:
Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations. Recent approaches attempt to balance this tradeoff via posterior sampling or multi-stage generative pipelines, yet remain computationally expensive and architecturally complex. To overcome these limitations, we propose PCFlow (Perceptually Consistent Flow Matching), a unified framework that directly parameterizes a continuous transport from degraded observations to clean targets, jointly optimizing distortion and perceptual quality. While its latent consistency flow objective drives stable and efficient few-step inference, a Latent Consistency Perceptual Loss (LCPL) imposes semantic constraints directly on the guiding velocity field, steering the dynamics toward visually sharp data manifolds. Furthermore, recognizing the inherent conflict between structural and perceptual consistencies, we integrate a conflict-free gradient projection strategy to stabilize the multi-objective optimization landscape. Combined with lightweight, convolution-only backbone, PCFlow achieves competitive performance across diverse restoration tasks at a fraction of traditional computational costs.

Abstract:
Representation Alignment (REPA) has emerged as a simpleway to accelerate Diffusion Transformers training in latent space. At thesame time, pixel-space diffusion transformers such as Just image Trans-formers (JiT) have attracted growing attention because they remove adependency on a pretrained tokenizer, and then avoid the reconstruc-tion bottleneck of latent diffusion. This paper shows that the REPA canfail for JiT. REPA yields worse FID for JiT as training proceeds andcollapses diversity on image subsets that are tightly clustered in the rep-resentation space of pretrained semantic encoder on ImageNet. We tracethe failure to an information asymmetry: denoising occurs in the high di-mensional image space, while the semantic target is strongly compressed,making direct regression a shortcut objective. We propose PixelREPA,which transforms the alignment target and constrains alignment witha Masked Transformer Adapter that combines a shallow transformeradapter with partial token masking. PixelREPA improves both train-ing convergence and final quality. PixelREPA reduces FID from 3.66 to3.17 for JiT-B/16 and improves Inception Score (IS) from 275.1 to 284.6on ImageNet 256×256, while achieving > 2× faster convergence. Finally,PixelREPA-H/16 achieves FID= 1.81 and IS= 317.2.

Abstract:
Video Temporal Grounding (VTG) aims to localize momentsin untrimmed videos based on language queries. A fundamental chal-lenge lies in learning cross-modal representations that capture bothsemantic correspondence and temporal specificity. Existing approachespredominantly rely on token-level visual-textual cross-attention, whichmay not fully capture two essential properties: generalizability acrossvideos and distinguishability within videos. In practice, semanticallyrelated vocabularies may correspond to similar visual concepts, whiletemporally sparse yet crucial semantics can be easily diluted by fre-quently occurring visual patterns. To address these challenges, we pro-pose Semantic-Explicit Detection Transformer (SE-DETR), a unifiedframework that explicitly models semantic structures for video-languagegrounding. We introduce a Semantics-Proxied Alignment (SPA) mod-ule that learns semantic proxies dynamically optimized during training,and enables concept-level alignment between multimodal representations.Furthermore, we design a Temporal Sparsity Modulation (TSM) mod-ule that estimates the temporal sparsity of semantic components anddynamically reweights word-guided visual features to highlight informa-tive concepts for accurate moment localization. Extensive experimentson five benchmarks demonstrate that SE-DETR consistently achievescompetitive performance across Video Moment Retrieval, Highlight De-tection, and Video Summarization tasks. The source code is avaliable at:https://github.com/hu-cheng-yang/ECCV26-SE-DETR.

Abstract:
Current video generation models produce highly realistic vi-suals but frequently fail to maintain physical and causal consistencywhen facing complex dynamic events, such as collisions or collapses.We attribute this limitation to the fact that existing models primar-ily fit visual data distributions, entangling dynamics with appearancewithout explicit physical priors to govern motion evolution. To addressthis, we introduce PhysAlign, a framework for Dynamical Event-DrivenPhysically Consistent Video Generation. PhysAlign decouples physicalpriors from visual appearance via a Physical Dynamics Extractor anda discrete Universal Physical Codebook. Recognizing the temporal lo-cality of dynamic events, an Event-Aware Temporal Gating module dy-namically controls the injection of physical priors, while Physics-GuidedAlignment distills continuous physical topologies from a video founda-tion model. Furthermore, we construct EventPhy, a 26K-video datasetwith structured, VLM-generated causal annotations to benchmark dy-namical event-driven video generation. Extensive evaluations show thatPhysAlign achieves state-of-the-art physical reasoning, obtaining leadingSemantic Adherence and Physical Commonsense scores on challengingbenchmarks like PhysicsIQ and VideoPhy, while preserving high visualfidelity. Project Page: https://github.com/FanQi-AI/PhysAlign

Abstract:
Graphical user interface (GUI) grounding is a key capa-bility for computer-use agents, mapping natural-language instructionsto actionable regions on the screen. Existing Multimodal Large Lan-guage Model (MLLM) approaches typically formulate GUI groundingas a text-based coordinate generation task. However, directly gener-ating precise coordinates from visual inputs is challenging and oftendata-intensive. A more intuitive strategy is to first identify instruction-relevant visual patches and then determine the exact click location withinthem. Motivated by recent observations that general MLLMs exhibitnative grounding ability embedded in their attention maps, we pro-pose GUI-AIMA, an attention-based and coordinate-free supervised fine-tuning framework for efficient GUI grounding. GUI-AIMA aligns the in-trinsic multimodal attention of MLLMs with patch-wise grounding sig-nals. These signals are calculated adaptively for diverse user instructionsby multi-head aggregation on simplified query-visual attention matri-ces. Besides, its coordinate-free manner can easily integrate a plug-and-play zoom-in stage. GUI-AIMA-3B was trained with only 509k sam-ples (∼101k screenshots), demonstrating exceptional data efficiency andverifying that light training can trigger the native grounding capabil-ity of MLLMs. It achieves state-of-the-art performance among 3B mod-els, attaining an average accuracy of 61.5% on ScreenSpot-Pro, 92.1%on ScreenSpot-v2, 68.1% on OSWorld-G, 79.1% on MMBench-GUI-L2and 60.0% on UI-Vision. Project page: https://github.com/sjz5202/GUI-AIMA.

Abstract:
Safety alignment of text-to-image (T2I) diffusion models aimsto suppress harmful generations while preserving utility on benign prompts.Recent methods often appear to deliver high safety with high utility, butthis conclusion rests largely on coarse global utility metrics (e.g., FID,CLIPScore) that are insensitive to fine-grained semantic correctness, cre-ating an illusion of high utility. We show that when utility is measuredwith structured evaluation, this illusion breaks: on TIFA (Text-to-ImageFaithfulness evaluation with Question Answering), safety-aligned modelssuffer substantial drops in semantic fidelity, including failures in objectcounts, attributes, and relationships. To diagnose the source of this gap,we analyze the text-encoder prompt embedding space and uncover se-mantic collapse, a contraction of embedding spread coupled with distor-tion of inter-prompt similarity structure, which strongly correlates withstructured utility loss. Guided by this insight, we propose Structure-Aware Geometric Regularization (SAGE)1 , a safety alignment objec-tive that explicitly preserves embedding spread and inter-prompt rela-tional structure during adaptation. Our method restores structured util-ity (TIFA +5.0% over prior state-of-the-art) while maintaining strongsafety performance and competitive coarse-grained utility scores.

Abstract:
Despite the rapid progress of Multimodal Large LanguageModels (MLLMs) in 2D vision-language tasks, robust multi-view spatialreasoning remains a fundamental bottleneck due to the lack of struc-tured 3D cognitive pathways in existing datasets. To address this, weintroduce MV-STRIDE, a Multi-View hierarchical SpaTial Reasoningdataset with Interdependent and Decomposed capabilitiEs. Moving be-yond x001D at data structures, MV-STRIDE explicitly models the dependencyrelationships between foundational perception, scene understanding, andcomplex contextual reasoning, providing a coherent learning pathwayaligned with human spatial cognition. We develop a systematic QA gen-eration pipeline leveraging diverse 3D scene sources that enforces cross-view dependency constraints to prevent single-view solvability, generat-ing multi-level spatial reasoning tasks supported by cognitively groundedchain-of-thought supervision for complex inference. Extensive evalua-tions demonstrate that our multi-stage training framework based on ourhierarchical dataset achieves state-of-the-art performance across multiplespatial reasoning benchmarks, notably the multi-view oriented MMSI-Bench. Our approach enables MLLMs to maintain robust, 3D-consistentspatial reasoning across diverse viewpoints. The code and dataset areavailable at https://co1dspring.github.io/MV-STRIDE/.

Abstract:
Multimodal Large Language Models (MLLMs) have shownimpressive abilities in understanding and reasoning over conventionalimages. However, their perception of 360° images remains largely un-derexplored. Unlike conventional images, 360° images capture the en-tire surrounding environment, enabling holistic spatial reasoning butintroducing challenges such as geometric distortion and complex spa-tial relations. To comprehensively assess MLLMs’ capabilities to per-ceive 360° images, we introduce 360Bench, a Visual Question Answering(VQA) benchmark featuring 7K-resolution 360° images, seven represen-tative (sub)tasks with annotations carefully curated by human annota-tors. Using 360Bench, we systematically evaluate seven MLLMs and sixenhancement methods, revealing their shortcomings in 360° image per-ception. To address these challenges, we propose Free360, a training-free scene-graph-based framework for high-resolution 360◦ VQA. Free360decomposes the reasoning process into modular steps, applies adaptivespherical image transformations to 360° images tailored to each step,and seamlessly integrates the resulting information into a unified graphrepresentation for answer generation. Experiments show that Free360consistently improves its base MLLM and provides a strong training-free solution for 360◦ VQA tasks. The source code and dataset will beavailable at https://tranhuyen1191.github.io/360Bench-Free360/.

Abstract:
Knowledge-Based Visual Question Answering (KB-VQA) aimsto evaluate whether Visual Language Models (VLMs) can retrieve, ground,and reason over external structured knowledge beyond visual evidence.In practice, answer accuracy is widely adopted as the primary evalu-ation metric, implicitly treating correctness as a proxy for knowledge-grounded reasoning. However, for existing KB-VQA benchmarks, thisproxy relies on critical assumptions that are often overlooked and ren-dered unreliable by benchmark issues: annotated answer must be deriv-able from the associated knowledge base, question must be well-posedwith su!cient constraints, and visual setting must meaningfully requiregrounded disambiguation. In this work, we show that these assumptionsare systematically violated in existing KB-VQA benchmarks. Our au-dit reveals substantial instances with missing or contradicted answersand underspecified questions that render accuracy a misleading metric.Furthermore, we find that existing datasets rely on visually trivial, single-entity scenes that bypass the need for sophisticated visual-to-knowledgemapping. We demonstrate that even with controlled architectures, theseflaws lead to distorted model rankings and overestimations of reasoningcapabilities. To address this, we introduce (1) a principled audit-and-repair protocol that restores answer derivability and question clarity,and (2) a controlled multi-entity augmentation protocol that introducesvisual ambiguity to challenge initial retrieval and grounded reasoning.Re-evaluation under corrected and augmented settings yields markedlydi"erent performance trends. Our findings call for rethinking evaluationprotocols and designing more interaction-aware KB-VQA benchmarksthat prioritize verifiable reasoning over simple matching. 1

Abstract:
Document parsing is a fundamental task in multimodal understanding, supporting a wide range of downstream applications such as information extraction and intelligent document analysis. Benefiting from strong semantic modeling and robust generalization, VLM-based end-to-end approaches have emerged as the mainstream paradigm in recent years. However, these models often suffer from substantial inference latency, as they must autoregressively generate long, full-page sequences when processing long-form documents. While recent hybrid methods mitigate this issue via region-level parallel decoding with VLMs, independent region decoding loses full-page context and might weaken global coherence. To address this issue, we propose Hierarchical Speculative Decoding (HSD), a two-stage local-to-global framework for document parsing. HSD first employs a lightweight pipeline drafter to predict region partitions and generate coarse drafts for each region. The first stage verifies the generated region-level drafts in parallel for efficiency, while the second stage further performs page-level verification on these refined outputs to preserve full-page coherence. Experimental results show that our HSD achieves a near-lossless 2.78× speedup with HunyuanOCR on OmniDocBench v1.5 and up to 7.04× speedup on long-document parsing tasks, demonstrating the effectiveness of our proposed method. The code is available at https://github.com/whlscut/HSD.

Abstract:
While Multimodal Large Language Models (MLLMs) ex-cel in visual understanding, the quadratic complexity imposed by densehigh-resolution visual tokens poses significant challenges for edge-devicedeployment. Current training-free pruning strategies predominantly de-pend on attention weights. However, viewed from the frequency domain,the self-attention mechanism functions as a low-pass filter, causing thediscard of essential high-frequency signals (e.g., textures and edges) andresulting in feature over-smoothing. In this paper, we present Spatial-Window Integrated Frequency-aware Token Pruning (SWIFT), a seam-less plug-and-play framework designed for efficient token compression.SWIFT leverages two novel components: a Frequency-Aware Indicator(FAI), which identifies fine-grained details by estimating high-frequencyresiduals through matrix factorization, and a Spatial-Window Integra-tion (SWI) module, which prevents spatial structure collapse and posi-tional bias via localized retention. Extensive evaluations on Qwen2.5VL-3B and LLaVA benchmarks show that under a practical 50% compressionratio, SWIFT achieves a 2.10× speedup with near-lossless performance.Under an extreme 75% compression stress test, SWIFT yields an averageperformance drop of 5.1% across 8 benchmarks, while delivering a 2.66×speedup and 69.07% KV cache reduction. SWIFT exhibits superior ro-bustness in detail-intensive tasks compared to SOTA methods, providinga potent lightweight solution for on-device MLLM applications. Our codeis available at https://github.com/damo-lgl/SWIFT.

Abstract:
Vision-Language Models (VLMs) deployed as situated agentsin high-resolution visual environments require active perception — theability to dynamically decide where to look through operations like zoom-ing, cropping, and panning. However, current training paradigms pro-duce models that mimic the surface form of such operations withoutfunctionally depending on their outputs, a phenomenon we term lazyperception. We trace this to a fundamental learning asymmetry: whencoarse global views combined with language priors suffice for moderateaccuracy, the model has no incentive to learn harder multi-step visualsearch. If a model can succeed without actively looking, it will neverlearn to look. This motivates Starve to Perceive, a training paradigm thatconstrains visual bandwidth — restricting each observation to a tight to-ken budget so that no single view suffices for task completion, therebystrongly incentivizing strategic and efficient active perception. Despiterequiring no auxiliary losses, reward shaping, or architectural changes— serving as a minimal, plug-in modification to standard post-trainingpipelines — models trained under perceptual starvation achieve substan-tial gains of 5% average relative improvement across diverse benchmarks.Our codes and data will be publicly available at https://github.com/WhuanY/Starve2Perceive.

Abstract:
Learned Image Signal Processing (ISP) pipelines offer powerful end-to-end performance but are critically dependent on large-scale paired raw-to-sRGB datasets. This reliance on costly-to-acquire paired data remains a significant bottleneck. To address this challenge, we introduce a novel, unsupervised training framework based on Optimal Transport capable of training arbitrary ISP architectures in both unpaired and paired modes. We are the first to successfully apply Unbalanced Optimal Transport (UOT) for this complex, cross-domain translation task. Our UOT-based framework provides robustness to outliers in the target sRGB data, allowing it to discount atypical samples that would be prohibitively costly to map. A key component of our framework is a novel “committee of expert discriminators,” a hybrid adversarial regularizer. This committee guides the optimal transport mapping by providing specialized, targeted gradients to correct specific ISP failure modes, including color fidelity, structural artifacts, and frequency-domain realism. To demonstrate the effectiveness of our approach, we retrained existing state-of-the-art ISP architectures using our paired and unpaired setups. Our experiments show that while our framework, when trained in paired mode, improves upon the original paired methods across all metrics, our unpaired mode concurrently achieves quantitative and qualitative performance that provides a competitive alternative to the original pairedtrained counterparts. The code and pre-trained models are available at: https://github.com/gosha20777/EGUOT-ISP.git.

Abstract:
Recently, open-vocabulary 3D object detection (3D-OVD)has gained increasing attention for its ability to detect unseen objects in3D scenes. Existing approaches typically adopt a two-stage pipeline thatfirst discovers novel objects using foundation models and then trains a3D-OVD model based on these discovered objects. Although effective,this pipeline often suffers from inaccurate localization and mismatchedclassification during the discovery stage, which subsequently limits theperformance of the model training stage. To address these limitations,we advocate for improving both the reliability of novel object discov-ery and the robustness of model training, and propose an innovativeframework. Specifically, for reliable discovery, our co-distillation strategydistills high-quality novel objects by applying Hungarian matching overa comprehensive score that incorporates geometric consistency, struc-tural objectness, and semantic certainty. To enhance robust model train-ing, we further propose a dual-guidance learning scheme, incorporatinga scene-awareness-guided uncertainty regularization for the regressionhead and an LLM-guided hierarchical alignment for the classificationhead, effectively mitigating the negative effects of imprecise 3D boundingboxes and semantic ambiguity. Extensive experiments on SUN RGB-Dand ScanNetV2 demonstrate that our method achieves significant per-formance gains over state-of-the-art approaches. Code is available athttps://github.com/shangboyuan/Co-3DGT.

Abstract:
Cross-Domain Few-Shot Object Detection (CDFSOD) aimsto transfer knowledge from data-rich upstream generic domains to down-stream expert domains using scarce training data, where the significantdomain gap and data scarcity make it an unsolved challenge. To ad-dress this problem, we revisit a natural yet underexplored approach inCDFSOD: data augmentation, by directly synthesizing data through dif-fusion models to supplement limited training samples. However, due tolarge domain gaps, we find that current diffusion methods cannot pro-duce good results, leading to performance even lower than using theoriginal images. To address these limitations, we divide the domain gapsinto visual gaps and semantic gaps for separate analysis. For the visualgap, we find that the diffusion model cannot distinguish noise from use-ful information on expert domains, which can be mitigated by addingweakened noise. For the semantic gap, we find that the background se-mantics shows much smaller gaps between domains than foreground se-mantics, and we can bridge this gap by background inpainting. Basedon the above analysis, we propose a method (Selective Inpainting withTailored Noise, SITN) to dynamically take different strategies for down-stream data synthesis based on their different gaps from the generaldomain, including a Generation Module for adding tailored noise and aSelection Module to dynamically select the inpainting regions. Extensiveexperiments on 6 datasets of CDFSOD and 4 datasets of cross-domainfew-shot segmentation (CDFSS) validate that we can synthesize helpfuldata, achieving new state-of-the-art performance. Our codes is availableat https://github.com/zzzzj311-droid/Free-Lunch-SITN.

Abstract:
Noisy labels pose a significant challenge in training deep neu-ral networks, as corrupted annotations inevitably induce overfitting andsignificantly degrade generalization. However, existing methods typicallyrely on static metrics and overlook training dynamics, struggling to effec-tively separate clean samples from noisy ones under complex and highnoise scenarios. In this paper, we propose a temporal attention-basedframework for learning with noisy labels. Specifically, we introduce an at-tention temporal consistency module for reliable sample selection, whichseparates samples by quantifying the evolutionary stability of visual at-tention during training. Then, to address the scarcity of clean samples inhigh-noise scenarios, we design an adaptive attention masks module tosynthesize images from clean and noisy samples, which employs differen-tial masking strategies to generate high-quality training samples. Finally,we present a mask-guided feature alignment module, which introducesfeature consistency regularization to facilitate the learning of robustrepresentations for noisy data. Extensive experiments demonstrate thatour method outperforms state-of-the-art approaches, achieving accuracygains of 1.6%/2.8% on CIFAR-10/100 under 90% symmetric noise, re-spectively. Code is available at https://github.com/iCAN-SZU/MSA

Abstract:
Weakly supervised semantic segmentation aims to achievepixel-level predictions using image-level labels. Existing methods typi-cally entangle semantic recognition and object localization, which oftenleads models to focus exclusively on sparse discriminative regions. Al-though foundation models show immense potential, many approachesstill follow the tightly coupled optimization paradigm, struggling to ef-fectively alleviate pseudo-label noise and often relying on time-consumingmulti-stage retraining or unstable end-to-end joint optimization. To ad-dress the above challenges, we present ModuSeg, a training-free weaklysupervised semantic segmentation framework centered on explicitly de-coupling object discovery and semantic assignment. Specifically, we in-tegrate a general mask proposer to extract geometric proposals withreliable boundaries, while leveraging semantic foundation models to con-struct an offline feature bank, transforming segmentation into a non-parametric feature retrieval process. Furthermore, we propose seman-tic boundary purification and soft-masked feature aggregation strate-gies to effectively mitigate boundary ambiguity and quantization errors,thereby extracting high-quality category prototypes. Extensive experi-ments demonstrate that the proposed decoupled architecture better pre-serves fine boundaries without parameter fine-tuning and achieves highlycompetitive performance on standard benchmark datasets. Code is avail-able at https://github.com/Autumnair007/ModuSeg.

Abstract:
Continual learning enables vision systems to adapt to ever-changing data distributions. Despite significant advances, existing ap-proaches fail to capture continuous and concurrent shifts in classes anddomains, a critical capability for real-world deployment. This work in-troduces Online VIL (Online Versatile Incremental Learning),a novel scenario where class concepts and visual domains evolve simul-taneously online without explicit boundaries. To better adapt to thechallenges of such dynamic environments that more closely resemblereal-world conditions, we propose a novel framework TopFlow, Topologypreservation with Flow matching representation that contains two com-plementary mechanisms: Domain-agnostic Flow Matching (DFM)and Global Topology Preservation (GTP). DFM guides the modelto have domain-agnostic representations by integrating the geodesic flowkernel into contrastive learning. In contrast, GTP maintains the globalstructure of the feature space without explicitly storing past examples. Ourextensive experiments demonstrate that TopFlow e!ectively addresses thelimitations of existing methods within the Online VIL scenario, achievingstate-of-the-art performance in challenging Online VIL. The proposedmethods suggest potential directions for building continual learning sys-tems in realistic dynamic environments. Our implementation code isavailable at https://github.com/KU-VGI/Online-VIL.

Abstract:
Personalization in emotion recognition (ER) is essential foran accurate interpretation of subtle and subject-specific expressive pat-terns. Recent advances in vision–language models (VLMs) such as CLIPdemonstrate strong potential for leveraging joint image–text represen-tations in ER. However, CLIP-based methods either depend on CLIP’scontrastive pretraining or on LLMs to generate descriptive text prompts,which are noisy, computationally expensive, and fail to capture fine-grained expressions, leading to degraded performance. In this work, weleverage Action Units (AUs) as structured textual prompts within CLIPto model fine-grained facial expressions. AUs encode the subtle muscleactivations underlying expressions, providing localized and interpretablesemantic cues for more robust ER. We introduce CLIP-AU, a lightweightAU–guided temporal learning method that integrates interpretable AUsemantics into CLIP. It learns generic, subject-agnostic representationsby aligning AU prompts with facial dynamics, enabling fine-grained ERwithout CLIP fine-tuning or LLM-generated text supervision. AlthoughCLIP-AU models fine-grained AU semantics, it does not adapt to subject-specific variability in subtle expressions. To address this limitation, wepropose CLIP-AUTT, a video-based test-time personalization method thatdynamically adapts AU prompts to videos from unseen subjects. By com-bining entropy-guided temporal window selection with prompt tuning,CLIP-AUTT enables subject-specific adaptation while preserving tempo-ral consistency. Our experiments on three challenging video-based subtleER datasets — BioVid, StressID, and BAH — indicate that CLIP-AUand CLIP-AUTT outperform state-of-the-art CLIP-based FER and TTAmethods. Code: https://github.com/osamazeeshan/CLIP-AUTT.

Abstract:
Existing label-deficient concealed object segmentation (LDCOS) methods either rely on consistency constraints within a meanteacher framework or employ the Segment Anything Model (SAM) as a fixed pseudo-label generator. Interestingly, task-specific segmenters and foundation models exhibit complementary failure modes: segmenters lack generalization under scarce labels, while SAM suffers from domain gaps in concealed scenes. A natural solution is to employ co-training to mutually promote both models. However, we observe that standard homogeneous co-training cannot exploit this complementarity because architecturally identical networks tend to share similar error patterns, especially when targets are heavily concealed. To address this, we present SCALER (Segmenter-Coordinated Asymmetric LEaRning), a framework that jointly optimizes a mean-teacher segmenter and a learnable SAM through two alternating phases with model-specific optimization strategies. In Phase I, the segmenter is updated under fixed SAM supervision using entropy-based image-level and uncertainty-based pixellevel weighting to suppress unreliable pseudo-label regions. In Phase II, SAM is updated via an augmentation invariance loss and a noise resistance loss, which exploit SAM’s inherent perturbation robustness rather than treating it identically to the segmenter. This asymmetric design is the key distinction from prior co-training and one-way distillation methods: each model’s learning strategy is tailored to its own inductive bias, enabling mutual enhancement. Experiments across eight LDCOS tasks demonstrate consistent gains, and SCALER improves both the lightweight segmenter and the foundation model, serving as a general plug-and-play paradigm for label-scarce conditions. The code is available at https://github.com/ChunmingHe/SCALER.

Abstract:
Weakly Supervised Video Anomaly Detection aims to detect abnor-mal events in long, untrimmed videos using only video-level supervision, provid-ing a practical solution to the high cost of temporally localized annotations. How-ever, abnormal events in real-world surveillance are often determined by complexcontextual interactions among actions, objects, and environments, making it dif-ficult for existing approaches to identify which factors actually cause an anomaly.Most prior methods rely on holistic representations or explanations that lack ex-plicit attribution to the semantic factors driving abnormal events. In this paper,we present CLUE-VAD, a structured semantic decomposition framework for ex-plainable weakly supervised video anomaly detection. Rather than relying solelyon holistic video representations or free-form explanations, CLUE-VAD decom-poses each video segment into structured semantic clues—Action, Environment,and Object—and estimates their category-aware contributions to anomaly predic-tion. By linking anomaly scores to clue-specific weights and keyword-level evi-dence, we provides factor-level attribution and supports transparent explanationgeneration. Extensive experiments on VAD benchmarks demonstrate that CLUE-VAD achieves strong detection performance among X-VAD methods while pro-viding fine-grained, clue-grounded explanations for anomaly predictions. Theproject page is available at https://visualsciencelab-khu.github.io/CLUE-VAD_project/.

Abstract:
Although fully supervised methods have substantially ad-vanced segmentation in boundary areas of point clouds, effective weaklysupervised approaches remain scarce. This is primarily because limitedsupervision rarely reaches boundary regions, leaving them lacking reli-able supervision. We propose a novel 2D-assisted pseudo-label propaga-tion paradigm that does not rely on the model’s own predictions or anyexternal foundation models, yet is able to generate high-purity pseudo-labels. Compared with SAM-based 2D-3D projection, our pseudo-labelsare purer and more uniformly distributed. Even under 1 pt/obj settingon S3DIS, our initial offline propagation achieves >94.4% accuracy (≈93k pts per scene). We decomposed the pseudo-labels generation pro-cess from the main network, and applied a divide-and-conquer strategy:supervision from interior pseudo-labels serves to stabilize the representa-tion of core class regions, while boundary pseudo-labels are leveraged toenhance boundary robustness. This design reduces the confirmation biasinherent in classic online labeling and alleviates the lack of boundary su-pervision in existing weakly supervised models. Experiments show thatour method outperforms existing state-of-the-art methods. Our codeswill be released at: https://github.com/paul-swu/Bound3D.

Abstract:
Zero-Shot Industrial Anomaly Detection (IAD) is criticalfor rapid deployment in agile manufacturing. We investigate a highlypractical Batch Zero-Shot IAD setting, strictly constrained by smallintra-batch references and common pose variations. Directly applyingexisting mutual-scoring methods fails because training-free features lackgeneralized anomaly semantics and spatial robustness, resulting in am-biguous scoring distances and an “anomaly-to-anomaly shortcut” duringmatching, which can lead to missed detections. To address this, we pro-pose DeMuS (Decoupled Mutual Scoring), a novel two-stage learningframework. By training two lightweight adapters via a two-stage aux-iliary process, our method extracts highly discriminative anomaly se-mantics for accurate scoring (how-to-score), while explicitly decouplingrobust part-level correspondence learning (where-to-match) to guaranteereliable physical alignment. This synergistic mechanism effectively pre-vents semantic confusion under unaligned inputs. Extensive experimentson MVTec AD, VisA, and Real-IAD demonstrate DeMuS achieves state-of-the-art performance and superior robustness against pose shifts, main-taining efficient inference for online inspection. Our code is available athttps://github.com/evoLonation/DeMuS.

Abstract:
Modern stereo-capable smartphones enable immersive XRcontent capture. However, hardware heterogeneity across camera mod-ules often causes severe asymmetric blur artifacts. Existing methods andbenchmarks largely assume homogeneous stereo setups and therefore donot explicitly address such asymmetric degradation. To bridge this gap,we present a dedicated framework for heterogeneous stereo deblurring.First, we introduce the heterogeneous stereo deblurring (HSD) dataset,constructed from real smartphone stereo captures via multi-frame inte-gration. Second, we propose physically- and epipolar-constrained crossattention (PECA), a lightweight module that restricts cross-view match-ing to an epipolar search window bounded by an optics-derived dis-parity upper bound. By enforcing physically valid disparity constraints,PECA enables efficient and reliable cross-view feature fusion. Moreover,our confidence-weighted attention with residual fusion emphasizes cross-guided deblurring when correspondences are reliable, while naturallyfalling back to self-deblurring in occluded or unreliable regions. PECAis architecture-agnostic and consistently improves CNN-, Transformer-,and NAFNet-based baselines. Extensive experiments on HSD show thatPECA-enhanced models achieve improved restoration performance withfavorable efficiency. The dataset and source code are publicly released athttps://github.com/shinhoju/PECA.

Abstract:
Event-based vision, characterized by low redundancy, focuson dynamic motion, and inherent privacy-preserving properties, natu-rally fits the demands of video anomaly detection (VAD). However, theabsence of dedicated event-stream anomaly detection datasets and effec-tive modeling strategies has significantly hindered progress in this field.In this work, we take the first major step toward establishing event-based VAD as a unified research direction. We first construct multi-ple simulated event-stream based benchmarks for video anomaly de-tection, featuring synchronized event and RGB recordings. Leveragingthe unique properties of events, we then propose an EVent-centric spa-tiotemporal Video Anomaly Detection framework, namely EWAD, withthree key innovations: an event density aware dynamic sampling strategyto select temporally informative segments; a density-modulated tempo-ral modeling approach that captures contextual relations from sparseevent streams; and an RGB-to-event knowledge distillation mechanismto enhance event-based representations under weak supervision. Exten-sive experiments on three benchmarks demonstrate that our EWADachieves significant improvements over existing approaches, highlight-ing the potential and effectiveness of event-driven modeling for videoanomaly detection. The benchmark datasets and code are available athttps://github.com/kanyutingfeng/EWAD.

Abstract:
Aerial-Ground Re-Identi(cid:28)cation (AG-ReID) is constrained by the viewpoint-domain gap, as drastic viewpoint disparities occlude or distort discriminative features, making cross-viewpoint image retrieval challenging. While existing methods rely on paired cross-view annotations, real-world deployments, such as wilderness search-and-rescue (SAR), often lack target-domain data, requiring retrieval from groundlevel references alone. To our knowledge, we are the (cid:28)rst to address this challenge by formalizing the Single-View AG-ReID (SV AGReID) setting, where models trained on a single real viewpoint must generalize to an unseen viewpoint. We propose 3D Lifting-based Elevated Novel-view Synthesis (3D-LENS), a uni(cid:28)ed framework combining geometrically-consistent novel view synthesis that leverages large-scale 3D mesh reconstruction, with a robust representation learning scheme to mitigate synthetic-to-real bias. Unlike 2D generative baselines that su(cid:27)er from geometric inconsistencies or prior 3D methods that are restricted to class-speci(cid:28)c templates, our approach ensures view-consistent synthesis across diverse categories without prede- (cid:28)ned templates that fail to capture (cid:28)ne-grained details, such as carried objects. Extensive experiments demonstrate that our method achieves state-of-the-art performance on SV AG-ReID scenarios. Code and data will be released at https://github.com/TurtleSmoke/3D-LENS. • •

Abstract:
Accurate document layout analysis remains a critical bottleneck for document parsing systems, due to the intricate coupling among heterogeneous document layout elements, geometric distortions (e.g., paper warping and bending, perspective variations), and reading order within diverse layout structures. Existing approaches typically rely on fragmented multi-stage pipelines or computationally heavy generative Transformer architectures, leading to error propagation and limited efficiency. In this paper, we present RT-DocLayout, a highly efficient end-to-end framework for document layout analysis, designed as a front-end for document parsing tasks. The proposed model unifies classification, detection, pixel-level segmentation, and reading order prediction for layout elements within a single 33M-parameter architecture. Built upon the RT-DETR, our key contribution is a unified multi-task formulation within a single query-based decoder that simultaneously classifies, regresses bounding box, generates masks, and constructs relationship to reason reading order. By jointly learning geometric and structural representations, RT-DocLayout introduces multi-task optimization that substantially improves robustness under real-world document distortions. Extensive experiments on OmniDocBench v1.5 and Real5-OmniDocBench demonstrate state-ofthe-art performance in document layout analysis while maintaining realtime inference speed(132.1 FPS). When coupled with downstream OCR engines, RT-DocLayout significantly improves full-document reconstruction quality, providing a scalable and practical foundation for real-world document intelligence systems. The code and models are available at https://github.com/PaddlePaddle/ PaddleOCR.

Abstract:
Video understanding requires models to continuously track and update world state during playback. Although existing benchmarks have advanced video understanding evaluation across multiple dimensions, they provide limited visibility into how models maintain world state over time. We propose SVCBench, a Streaming Video Counting Benchmark that repositions counting as a minimal, controlled probe for diagnosing models’ world-state maintenance capability. We decompose this capability into object counting and event counting, forming 8 fine-grained subcategories. Object counting covers tracking currently visible objects and cumulative unique identities, while event counting covers detecting instantaneous actions and tracking complete activity cycles. SVCBench contains 406 videos with frame-by-frame annotations of 10,071 event occurrences and object state changes, yielding 1,000 streaming QA pairs with 4,576 query points distributed along video timelines. By observing state maintenance trajectories through streaming multipoint queries, we design three complementary metrics to diagnose numerical precision, trajectory consistency, and temporal awareness. Evaluations of mainstream video-language models show that current models still exhibit significant deficiencies in spatial-temporal state maintenance, with especially poor performance on periodic event counting. SVCBench provides a diagnostic framework for measuring and improving state maintenance in video understanding systems. Our code and data are available at https://buaa-colalab.github.io/SVCBench.

Abstract:
Self-supervised stereo matching holds great promise by eliminating the reliance on expensive ground-truth data. Its dominant paradigm, based on photometric consistency, is however fundamentally hindered by the occlusion challenge—an issue that persists regardless of network architecture. The essential insight is that for any occluders, valid feedback signals can only be derived from the unoccluded areas on one side of the occluder. Existing methods attempt to address this by focusing on the erroneous feedback from the other side, either by identifying and removing it, or by introducing additional regularities for correction on that basis. Nevertheless, these approaches have failed to provide a complete solution. This work proposes a more fundamental solution. The core idea is to transform the fixed state of one-sided valid and one-sided erroneous signals into a probabilistic acquisition of valid feedback from both sides of an occluder. This is achieved through a complete framework, centered on a pseudo-stereo inputs strategy that decouples the input and feedback, without introducing any additional constraints. Qualitative results visually demonstrate that the occlusion problem is resolved, manifested by fully symmetrical and identical performance on both flanks of occluding objects. Quantitative experiments thoroughly validate the significant performance improvements resulting from solving the occlusion challenge. The code is available at https://github.com/qrzyang/PseudoStereo.

Abstract:
Existing red-teaming studies on GUI agents face two funda-mental limitations: adversarial perturbations require white-box accessunavailable in commercial deployments, while prompt injection is increas-ingly neutralized by stronger safety alignment. To study robustness undera more practical threat model, we propose Semantic-level UI ElementInjection, a black-box red-teaming paradigm that overlays safety-alignedand harmless UI elements onto screenshots to misdirect the agent’s visualgrounding. Our method couples a modular Editor–Overlapper–Victimpipeline with iterative search that samples multiple candidate edits, keepsthe best cumulative overlay, and adapts future prompt strategies basedon previous failures. Experiments across 19 victim models spanning 8model families show that strategic optimization substantially outperformsrandom injection (3.5–6.9× on the most robust victims) and transfersnear-perfectly across architectures, confirming model-agnostic visual-semantic vulnerabilities. After the first successful attack, the victim stillclicks the attacker-controlled icon in over 15% of subsequent independenttrials versus below 1% for random injection, establishing that strategicallyplaced icons act as persistent attractors that causally redirect groundingrather than introducing incidental clutter. Public code is available athttps://github.com/HashTAG00002/UI-Injection.

Abstract:
Adversarial Robustness Distillation (ARD) is the most viableway for transferring defensive capabilities from over-parameterized teach-ers to lightweight student models. However, existing ARD techniques relyon the assumption that the teacher’s decision boundary is a perfect geo-metric oracle. In this paper, we identify a critical gap from the stabilityperspective that such exact matching forces blind inheritance of teacher’ssensitivity noise, punishing the student’s stability even if they are morerobust. To address this challenge, we propose the AEGIS (AdversarialError Gating and Instability Suppression) framework that replaces sim-ple logit matching with gated supervision. It leverages a pair of logit andsensitivity gates to rectify potential errors/instability on the categoricaland gradient-level, respectively. Extensive evaluations on CIFAR-10/100,and Tiny-ImageNet show that AEGIS significantly outperforms state-of-the-art methods, achieving a 3.12% gain under the strong AutoAttackand preserving nearly 50% accuracy under large perturbation intensities(ϵ = 16/255) where traditional baselines collapse. Code is available athttps://github.com/JingqiJi03/AEGIS.

Abstract:
Cervical cytology is practiced as a sequence of roles: screen-ing decides where to look, careful reading determines what is present,and reporting accounts for the decision. We translate this workflow intoa compact and structurally transparent model. Screening is cast as afree–energy allocation over four explicit, cytology–native concepts (nu-clear–envelope roughness, orientation disorder, local N/C ratio, chro-matin heterogeneity), yielding a closed-form spatial attention as a soft-max of negative energy. Reading is handled by an analytic backbonebuilt from fixed geometric responses, nonnegative near–diagonal chan-nel mixing, and convex residual updates, producing stable and auditablefeatures. A probabilistic readout with a linear head then provides anintrinsic evidence–to–logit accounting without post–hoc explainers. De-spite only 2.2M parameters, the model achieves state-of-the-art accuracyon DSCC (93.2% ACC), competitive results on SIPaKMeD (98.5% ACC)and Herlev (76.8% ACC), and favorable latency. Source code is publiclyavailable at https://github.com/Dichao-Liu/GeoCEAN.

Abstract:
Sparse autoencoders (SAEs) have recently been proposed asinterpretable tools for concept-level manipulation, under the assumptionthat isolated features can serve as controllable intervention points. In thiswork, we systematically evaluate this assumption in the context of ob-ject erasure and steering in diffusion models. We show that while SAEsreliably detect and localize semantic concepts within diffusion modelactivations, direct intervention in their latent space frequently inducesout-of-distribution activations, resulting in severe visual artifacts. To dis-entangle detection from intervention, we use SAE activations purely assemantic detectors to identify image regions containing the target object,and replace those patch embeddings with the ones that do not containit. This detection-based replacement preserves the diffusion model’s ac-tivation statistics and produces significantly cleaner erasure results thanlatent steering. Our findings reveal a fundamental gap between conceptdetection and concept intervention in diffusion models: monosemanticor sparse features are not inherently suitable as control knobs for steer-ing. These results position SAEs as powerful interpretability tools foranalyzing generative models, but highlight important limitations whenused for direct manipulation, such as unlearning. We release the code athttps://eidoslab.github.io/PER/.

Abstract:
Human body fitting, which aligns parametric body models,such as SMPL, to raw 3D point clouds of clothed humans, serves as acrucial first step for downstream tasks like animation and texturing. Aneffective fitting method should be both locally expressive – capturingfine details such as hands and facial features – and globally robust tohandle real-world challenges, including clothing dynamics, pose variations,and noisy or partial inputs. Existing approaches typically excel in onlyone aspect, lacking an all-in-one solution. We upgrade ETCH to ETCH-X,which leverages a tightness-aware fitting paradigm to filter out clothingdynamics (“undress”), extends expressiveness with SMPL-X, and replacesexplicit sparse markers (which are highly sensitive to partial data) with im-plicit dense correspondences (“dense fit”) for more robust and fine-grainedbody fitting. Our disentangled “undress” and “dense fit” modular stages en-able separate and scalable training on composable data sources, includingdiverse simulated garments (CLOTH3D), large-scale full-body motions(AMASS), and fine-grained hand gestures (InterHand2.6M), improvingoutfit generalization and pose robustness of both bodies and hands. Ourapproach achieves robust and expressive fitting across diverse clothing,poses, and levels of input completeness, delivering a substantial perfor-mance improvement over ETCH on both 1) seen data, such as 4D-Dress(MPJPE-All, 33.0% ↓) and CAPE (V2V-Hands, 35.8% ↓), and 2) unseendata, such as BEDLAM2.0 (MPJPE-All, 80.8% ↓; V2V-All, 80.5% ↓).Code and models will be released at xiaobenli00.github.io/ETCH-X.

Abstract:
Hair simulation and rendering are challenging due to complex strand dynamics, diverse material properties, and intricate light–hair interactions. Recent video diffusion models can generate high-quality videos, but they lack fine-grained control over hair dynamics. We present ControlHair, a hybrid framework that integrates a physics simulator with conditional video diffusion to enable precise and controllable dynamic hair rendering. ControlHair adopts a three-stage pipeline: it first encodes physics conditions into per-frame geometry using a simulator, then extracts per-frame control signals, and finally feeds control signals into a video diffusion model to generate videos with desired hair dynamics. This cascaded design decouples physics reasoning from video generation, supports diverse physics, and makes training the video diffusion model easy. Trained on a curated 10K video dataset, ControlHair outperforms textand pose-conditioned baselines, delivering precisely controlled hair dynamics. We also demonstrate three use cases of ControlHair, including dynamic hairstyle try-on, bullet-time effects, and cinemagraphic. Project page: https://linwk20.github.io/controlhair-web.

Abstract:
Stylized video generation requires synthesizing dynamic con-tent that reflects the artistic characteristics of a reference image whilemaintaining semantic control and temporal coherence. Existing fine-tuning-based approaches suffer from two recurring limitations: motion degrada-tion caused by training on static image stylization datasets, which lacktemporal supervision and erode the temporal priors of pre-trained back-bones; and a representation bottleneck from encoder-based style injec-tion, which discards fine-grained stylistic details and bounds stylizationquality by the encoder’s representational capacity. We address both limi-tations through a unified pipeline that covers dataset construction, modelarchitecture, and training strategy. Specifically, we construct MoStyle-5K, a stylized video dataset of 5,000 videos spanning 27 artistic stylesand 4,000 preference pairs, built via a decoupled content-motion syn-thesis pipeline that explicitly preserves temporal priors in the supervi-sion signal. Building on this, we propose Implicit Gated Style To-ken Injection (IGST), which directly concatenates style patches as to-kens to bypass the encoder bottleneck, disables RoPE for style tokensto remove spatial alignment bias, and applies head-wise gated atten-tion to suppress residual structural correlations. The framework is opti-mized with a hybrid image-video objective and refined via Diffusion-DPO post-training using the structured preference pairs in MoStyle-5K. Experiments show that our method achieves superior style fidelityand motion dynamics compared with existing baselines. Project page:https://sixi111.github.io/MoStyle/.

Abstract:
Multi-Exposure Fusion (MEF) effectively extends dynamicrange, but practical deployment is hindered by motion-induced ghostingand the scarcity of high-quality dynamic benchmarks. Current bench-marks largely neglect dynamic scenes and lack reliable ground truth,making it difficult to handle the complexity of real-world motions. In re-sponse, we introduce ExpoMotion, a large-scale benchmark designed toevaluate deghosting capabilities. Comprising 1,738 sequences and 10,909images across diverse environments, it covers a wide range of motionsand provides high-fidelity GTs constructed through an expert-guided ac-quisition pipeline. To tackle the complex dynamics and extreme con-ditions captured in this benchmark, we propose the Householder Or-thogonal Projection network (HOP), which revisits MEF deghostingfrom a mathematical perspective via Householder transformation, de-coupling multi-frame alignment into exposure pre-alignment and ghostfiltering. Specifically, the Global Priors Illumination Alignment (GPIA)module first rectifies drastic dynamic range discrepancies by utilizingglobal statistics for exposure harmonization. Regarding ghost removal,our Householder Orthogonal Attention (HOA) models artifacts as or-thogonal perturbations. By employing a dynamic Householder reflector,HOA effectively projects ghosts out of the feature manifold while pre-serving high-frequency details. Experiments demonstrate that our Ex-poMotion dataset enables superior generalization and artifact-free detailrestoration, while also validating the effectiveness and efficiency of theHOP method. The dataset and code are available at https://github.com/Leo-LiuYao/ExpoMotion.

Abstract:
Monocular video depth estimation requires temporal con-sistency, geometric accuracy, and generalization across diverse scenar-ios—yet existing methods struggle to achieve all three simultaneously.Discriminative models excel at per-frame accuracy but suffer from tem-poral drift due to limited context windows, while generative methodsimprove consistency and generalization at the cost of extensive train-ing data (10M+ samples) and lack of geometric precision. In responseto these issues, we introduce ICDepth, a framework that adapts pre-trained text-to-video diffusion transformers for video depth estimationvia In-Context Conditioning (ICC), leveraging their rich spatial-temporalpriors. To address key challenges in transferring ICC from generationto dense prediction, we propose: (1) SAND-Attention, which ensuresprecise spatial-temporal alignment via shared RoPE and enforces uni-directional attention to prevent noise contamination; (2) SRFM, whichinjects DINOv2 semantic and resolution priors to enhance geometric pre-cision. ICDepth achieves state-of-the-art results on multiple benchmarkswith remarkable data efficiency, trained on only 0.8M frames (6–13×less than competing generative methods), while demonstrating strongzero-shot generalization to diverse domains.

Abstract:
Object removal must prevent the masked target from reap-pearing and reconstruct the occluded background with structural andcontextual fidelity, rather than merely filling a hole plausibly. Recentdataset-free approaches manipulate the diffusion model’s internal self-attention to prevent it from referencing the masked region, yet they failin two critical ways: (i) they treat the masked region as the sole fore-ground, misinterpreting non-target objects as background and regener-ating them, and (ii) they apply uniform attention constraints withoutdistinguishing diverse background subtypes, leading to textural blurringand structural misalignment. Both failures stem from the absence of ex-plicit background-aware reasoning. We propose EraseLoRA, a dataset-free framework that replaces attention surgery with background-awarereasoning and test-time adaptation. The first stage, Background-awareForeground Exclusion (BFE), leverages a multimodal large-language modelto separate target foreground, non-target foregrounds, and clean back-ground from a single image-mask pair. The second stage, Background-aware Reconstruction with Subtype Aggregation (BRSA), performs test-time optimization that treats inferred background subtypes as comple-mentary pieces, enforcing their consistent integration through recon-struction and alignment objectives without explicit attention interven-tion. As a model-agnostic plug-in applicable to diverse diffusion back-bones, EraseLoRA reconstructs backgrounds at least 23% more faithfulto the original scene than previous dataset-free methods while nearlyhalving unwanted foreground re-generation, and surpasses all dataset-driven approaches in both aspects despite requiring no training data.Code is available at https://shjo-april.github.io/EraseLoRA.

Abstract:
Video generation models are increasingly capable of producing realistic videos, but they still struggle to generate videos that follow basic physical laws. Compounding this is a lack of reliable granular evaluation methods for localizing and specifying physical law violations in videos. We address this by introducing Physics Question Scene Graph (PQSG), a hierarchical question-based evaluation pipeline. PQSG evaluates generated videos by checking their faithfulness to a prompt across objects, actions, and adherence to physical laws using a graph-based hierarchy of questions generated by a vision-language model (VLM), guided by high-quality in-context examples. By representing questions as a graph, PQSG introduces logical dependencies within questions, ensuring that each query is contextually valid. Moreover, PQSG provides granular assessments of which qualities of the video violate physical plausibility constraints. We validate PQSG by creating FinePhyEval, a dataset with physics-based prompts and corresponding generated videos from diverse state-of-the-art video generation models (Sora 2, Veo 3, and Wan 2.1), with each video annotated across multiple categories by humans. Using FinePhyEval, we measure the correlation between PQSG’s fine-grained scores and human judgments, showing higher overall correlations than prior work. We also find that PQSG ranks closedsource models higher than Wan 2.1 on physical realism. Lastly, we show that the annotations we provide in FinePhyEval can also be used for subtask evaluation: we benchmark two strong VLMs on generating and answering questions, finding that while models can create human-like questions, they still fall short of human performance in answering them.

Abstract:
Face Video Restoration (FVR) aims to recover high-fidelity facial videos from degraded input while preserving identity and semantic consistency across frames. Existing methods often struggle to simultaneously address three key challenges: identity shift, viewpoint-entangled guidance, and perceptual realism. To tackle these issues, we propose TIGER, a structured tri-prior fusion framework that Tames Identity, Geometry, and gEnerative pRiors for high-quality FVR. Specifically, an Identity Prior is first established by injecting subject-discriminative embeddings into the latent space, effectively anchoring the subject’s identity against severe degradations. Then, to provide temporally consistent structural guidance for dynamic videos, TIGER constructs a Geometry Prior by lifting 2D reference cues into a disentangled 3D parameter space, creating a geometric anchor through cross-source parameter fusion. Moreover, to achieve better efficiency without compromising realism, we harness the video generation model’s Generative Prior through a one-step rectified flow. We further design a progressive three-stage training optimazition strategy that refines structural fidelity, textural reconstruction, and distribution-level realism to ensure robust optimization. We also construct a large-scale FVR dataset to facilitate robust training and standardized evaluation. Extensive experiments demonstrate that TIGER achieves state-of-the-art performance in both identity fidelity and temporal stability, delivering a high-quality, efficient and identityconsistent FVR. Project page: https://yzhoulv.github.io/Tiger/.

Abstract:
Real-world sRGB image denoising remains challenging due to the nonlinear characteristics of sensor noise and the difficulty of acquiring aligned clean-noisy image pairs. Supervised denoisers often overfit to limited paired datasets, while self-supervised methods still depend on sufficiently diverse noisy observations. These limitations motivate scalable noise synthesis methods that can model real-world noise without clean ground truth or camera metadata. We propose YeTI, a real-world sRGB noise generation framework that learns from only two noisy observations of the same scene. YeTI uses a Reconstruction Autoencoder to disentangle scene structure and noise characteristics, and models the latent noise distribution with a one-step Conditional Diffusion Transformer trained using consistency objectives. Given a single noisy input at inference time, YeTI generates realistic, signal-dependent noise while preserving the underlying scene content. Extensive experiments demonstrate the effectiveness of YeTI across real-world benchmarks. We evaluate noise generation on SIDD and further assess generalization on SIDD+, MAI2021, and SID, covering smartphone and diverse consumer-camera sensors. Downstream denoising results on DND further show that denoisers trained with YeTI-synthesized images achieve strong real-world performance, highlighting the practical value of clean-image-free and metadata-free noise generation. Code is available at: https://github.com/ByungWanLim/YeTI-You-Only-NeedTwo-Noisy-Images-for-Real-World-sRGB-Noise-Generation

Abstract:
Face images captured in real-world low light suffer multiple degradations—low illumination, blur, noise, and low visibility, etc. Existing cascaded solutions often suffer from severe error accumulation, while generic joint models lack explicit facial priors and struggle to resolve clear face structures. In this paper, we propose PASDiff, a Physics-Aware Semantic Diffusion in a training-free manner. To achieve a plausible illumination and color distribution, we leverage inverse intensity weighting and Retinex theory to introduce photometric constraints, thereby reliably recovering visibility and natural chromaticity. To faithfully reconstruct facial details, our Style-Agnostic Structural Injection (SASI) extracts structures from an off-the-shelf facial prior while filtering out its intrinsic photometric biases, seamlessly harmonizing identity features with physical constraints. Furthermore, we construct WildDark-Face, a real-world benchmark of 700 low-light facial images with complex degradations. Extensive experiments demonstrate that PASDiff significantly outperforms existing methods, achieving a superior balance among natural illumination, color recovery, and identity consistency. Code and dataset will be available at https://github.com/IVIPLab/PASDiff.

Abstract:
End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving. However, standard Supervised FineTuning (SFT) often suffers from reasoning hallucinations and conservative biases. While traditional tool-augmented frameworks and Chain-ofThought (CoT) approaches mitigate these issues, they incur exorbitant token consumption and unacceptable latency, rendering real-time deployment impractical. To resolve this reliability-efficiency trade-off, we propose CritiqueDriveVLM, a novel unified three-stage framework internalizing reasoning directly into the VLM. First, we introduce Critique-Driven Multi-Turn Reinforcement Learning (RL) guided by a multi-dimensional verifier. By providing granular scalar feedback and a multi-turn penalty, we force the policy to internalize logical deduction, cultivating a robust System-2 Teacher that achieves high accuracy without fragile external tools. Subsequently, we propose Latent Thought Distillation to overcome the latency bottleneck. By aligning the Student’s latent representations with the Teacher’s fully converged reasoning states, we compress deep logical capabilities into a fast, CoT-free System-1 Student. Extensive experiments on the widely-used DriveLMM-o1 benchmark demonstrate remarkable improvements. Compared to the base model, our tool-free Teacher significantly boosts Multiple Choice Quality (MCQ) from 55.54% to a state-of-the-art 76.54%. Crucially, our distilled Student preserves competitive reasoning depth while drastically minimizing generation length to an average of merely 28 tokens. This slashes inference latency by 88% (from 3482 ms to 416 ms), paving a highly robust pathway for low-latency autonomous driving. Our source code is available at https://github.com/MICLAB-BUPT/CritiqueDriveVLM.

Abstract:
Deep research agents integrate fragmented evidence throughmulti-step tool use. BrowseComp offers a text-only testbed for such agents,but existing multimodal benchmarks rarely require both weak visual cuescomposition and BrowseComp-style multi-hop verification. Geolocationis a natural testbed because answers depend on combining multipleambiguous visual cues and validating them with open-web evidence.Thus, we introduce GeoBrowse, a geolocation benchmark that combinesvisual reasoning with knowledge-intensive multi-hop queries. Level 1 testsextracting and composing fragmented visual cues, and Level 2 increasesquery difficulty by injecting long-tail knowledge and obfuscating keyentities. To support evaluation, we provide an agentic workflow GATEwith five think-with-image tools and four knowledge-intensive tools, andrelease expert-annotated stepwise traces grounded in verifiable evidencefor trajectory-level analysis. Experiments show that GATE outperformsdirect inference and open-source agents, indicating that no-tool, search-only or image-only setups are insufficient. Gains come from coherent,level-specific tool-use plans rather than more tool calls, as they morereliably reach annotated key evidence steps and make fewer errors whenintegrating into the final decision. The GeoBrowse benchmark and codesare provided in https://github.com/ornamentt/GeoBrowse.

Abstract:
Online video understanding requires models to perform continuous perception and long-range reasoning within potentially infinite visual streams. Its fundamental challenge lies in the conflict between the unbounded nature of streaming media input and the limited context window of Multimodal Large Language Models (MLLMs). Current methods primarily rely on passive processing, which often face a trade-off between maintaining long-range context and capturing the fine-grained details necessary for complex tasks. To address this, we introduce EventMemAgent, an active online video agent framework based on a hierarchical memory module. Our framework employs a dual-layer strategy for online videos: short-term memory detects event boundaries and utilizes event-granular reservoir sampling to process streaming video frames within a fixed-length buffer dynamically; long-term memory structuredly archives past observations on an event-by-event basis. Furthermore, we integrate a multi-granular perception toolkit for active, iterative evidence capture and employ Agentic Reinforcement Learning (Agentic RL) to end-to-end internalize reasoning and tool-use strategies into the agent’s intrinsic capabilities. Experiments show that EventMemAgent achieves competitive results on online video benchmarks. The code will be released here: https://github.com/lingcco/EventMemAgent.

Abstract:
Existing underwater object tracking (UOT) methods can bebroadly divided into two categories: frame-level trackers and video-leveltrackers. Frame-level trackers process each frame independently, usingenhancement or adaptation techniques to handle underwater distortions.However, this often results in inconsistent feature styles and weakenedtemporal correlations. Video-level trackers attempt to improve temporalconsistency by modeling dependencies across frames through autoregres-sive mechanisms. Despite this, they still struggle with persistent featuredegradation and tracking drift caused by challenging underwater condi-tions. To overcome these limitations, we propose FeatTracker, a feature-level tracking framework that enforces both short- and long-range tem-poral feature consistency, effectively preserving semantic integrity andensuring temporal stability for robust underwater object tracking. Theframework integrates two key innovations: the Short-Range TemporalFeature Consistency module, which combines diffusion-like feature en-hancement with a dynamic memory pool to mitigate degradation andpreserve local temporal coherence; and the Long-Range Temporal Fea-ture Consistency module, which leverages wavelet decomposition to sepa-rate stable structural components from transient details within historicaltokens, effectively preventing the loss of trajectory details. Extensive ex-periments demonstrate that FeatTracker achieves state-of-the-art perfor-mance across four UOT benchmarks. Our code and models are availableat https://github.com/fishgfish/FeatTracker.

Abstract:
Multimodal Large Language Models (MLLMs) have recentlydemonstrated strong progress in visual–linguistic understanding, yet theirperformance on text-centric video reasoning remains highly sensitive toinput quality. Real-world user-provided videos frequently contain motionblur, compression artifacts, noise, and low-resolution text, substantiallyimpairing reliable text reading and downstream reasoning. WhetherMLLMs can robustly read and reason over in-the-wild text under di-verse quality conditions remains an unanswered fundamental question.We introduce ClearText-Video (CTVid), a large-scale, scene-text-awarebenchmark for studying text-centric video understanding under controlledquality variation. CTVid contains 4,639 real-world text-rich egocentricvideos, 550K+ frames, 1.6M human-verified scene-text annotations, and220K+ spatial/temporal question–answer pairs in Chinese and English.For each high-quality video, CTVid provides content-matched Degraded-Quality and Restored-Quality variants, supporting two task families:Text-Centric Video Restoration and Multi-Video Quality VideoQA. Weevaluate 18 representative restoration methods and 16 state-of-the-artMLLMs on CTVid. The results show that visual enhancement does notguarantee textual fidelity or downstream reasoning gains: blur is moredamaging than low resolution, restored videos can alter the textual evi-dence used by MLLMs, and OCR-only pipelines remain far below directmultimodal reasoning. CTVid exposes the gap between video restorationand text-grounded understanding, providing a rigorous foundation forrestoration-aware, quality-robust text-centric video systems. Benchmark:https://github.com/jinlong17/CTVid-Bench.

Abstract:
Interactive long video generation requires prompt switching to introduce new subjects or events, while maintaining perceptual fidelity and coherent motion over extended horizons. Recent distilled streaming video diffusion models reuse a rolling KV cache for long-range generation, enabling prompt-switch interaction through re-cache at each switch. However, existing streaming methods still exhibit progressive quality degradation and weakened motion dynamics. We identify two failure modes specific to interactive streaming generation: (i) at each prompt switch, current cache maintenance cannot simultaneously retain KV-based semantic context and recent latent cues, resulting in weak boundary conditioning and reduced perceptual quality; and (ii) during distillation, unbounded time indexing induces a positional distribution shift from the pretrained backbone’s bounded RoPE regime, weakening pretrained motion priors and long-horizon motion retention. To address these issues, we propose Anchor Forcing, a cache-centric framework with two designs. First, an anchor-guided re-cache mechanism stores KV states in anchor caches and warm-starts re-cache from these anchors at each prompt switch, reducing post-switch evidence loss and stabilizing perceptual quality. Second, a tri-region RoPE with region-specific reference origins, together with RoPE re-alignment distillation, reconciles unbounded streaming indices with the pretrained RoPE regime to better retain motion priors. Experiments on long videos show that our method improves perceptual quality and motion metrics over prior streaming baselines in interactive settings.

Abstract:
Encoding videos into discrete tokens can enable concise andunified multi-modal LLMs. Previous discrete video VAEs suffer from un-stable training, long training time, and degraded reconstruction quality.We revisit the relationship between continuous and discrete VAEs andfind that bridging discrete and continuous representations improves dis-crete token learning. Based on this insight, we propose a unified progres-sive training framework that (i) jointly optimizes continuous and discretereconstructions within a single network, and (ii) progressively derives afamily of VAEs at different compression ratios, leading to faster conver-gence and better final performance. Furthermore, leveraging this unifiedtraining, a single VAE can achieve competitive performance for bothcontinuous and discrete representations. Meanwhile, we propose two ar-chitectural improvements to further boost the performance of discreteVAEs. First, inspired by the use of enlarging continuous latent dimensionto boost reconstruction, we propose multi-token quantization, improvingPSNR by nearly 1 dB at the same token compression ratio. Second, weintroduce first-frame enhancement, which uses a lower-compression firstframe as an anchor in causal VAEs, alleviates the limited-context is-sue of the first frame and significantly improves reconstruction underhigh-compression settings (e.g., 4 × 16 × 16). The code is available athttps://github.com/HVision-NKU/OneVAE.

Abstract:
This paper introduces Actor as Its Own Critic, a unifiedreinforcement learning framework, Cycle Group Relative Policy Opti-mization (CycleGRPO), that jointly optimizes region understanding andlocalization for Multimodal Large Language Models (MLLMs). Unlikeexisting separate pipelines, we leverage the inherent duality between thetwo tasks to construct a self-evaluating reinforcement learning paradigm:“region → text → region”. Specifically, a single MLLM first acts as theactor to generate region captions, then immediately transitions to a criticto ground its generated text back in the spatial domain. Therefore, Cy-cleGRPO requires only region inputs, e.g., masks or bounding boxes,entirely bypassing the need for textual ground truths. A quality-awaretoken-level cycle-consistency reward is employed to assess the seman-tic discriminability of text captions via their physical localization ac-curacy. Empirically, built upon SAMTok, our CycleGRPO frameworksuccessfully bootstraps both capabilities simultaneously. Without anytask-specific fine-tuning, the framework yields consistent performancegains across a wide range of benchmarks, including region captioning,region VQA, grounded dialogue, and referring segmentation. Overall,CycleGRPO offers a straightforward and scalable way to advance pixel-level capabilities in MLLMs. Code and models are released at https://github.com/devinxzhang/CycleGRPO.

Abstract:
While Large Vision-Language Models (LVLMs) have signifi-cantly advanced GUI agents’ capabilities in parsing textual instructions,interpreting screen content, and executing tasks, a critical challenge per-sists: the irreversibility of agent operations—where a single erroneous ac-tion can trigger catastrophic deviations. To address this, we propose theGUI Action Critic’s Data Flywheel System (GAIA), a training frame-work that enables the models to have iterative critic capabilities, whichare used to improve the Test-Time Scaling (TTS) of basic GUI agents’performance. Specifically, we train an Intuitive Critic Model (ICM)using positive and negative action examples from a base agent first. Thiscritic evaluates the immediate correctness of the agent’s intended actions,thereby selecting operations with higher success probability. Then, theinitial critic guides agent actions to collect refined positive/negative sam-ples, initiating the self-improving cycle. The augmented data then trainsa second-round critic with enhanced discernment capability. We conductexperiments on various datasets and demonstrate that the proposed ICMcan improve the test-time performance of various closed-source and open-source models, and the performance can be gradually improved as thedata is recycled. The code, dataset, and accompanying datasheet will bepublicly released at https://github.com/SeerRay-Lab/GAIA.

Abstract:
Remote Sensing Image Change Captioning (RSICC) aimsto describe changes between bi-temporal remote sensing images andholds significant research and application value. However, most exist-ing methods rely on conventional deep learning architectures, and thelimited model capacity constrains performance. Although large-modelpost-training techniques have achieved great success in general domains,their direct transfer to RSICC remains challenging due to data scarcityand the need for fine-grained change understanding. To address this, wepropose RSICCLLM, the first post-training framework for large vision-language models in RSICC. Specifically, we design a data genera-tion paradigm, release the instruction dataset RSICI, and establish atask-specific RSICC benchmark. We further introduce Difference-aware Supervised Fine-tuning to explicitly extract change repre-sentations and guide the model in perceiving and understanding tem-poral differences. In addition, we propose Dual-Negative PreferenceOptimization (DNPO), which employs two complementary negative-sample construction strategies to construct the preference dataset RSICPand further refine model performance. Extensive experiments validatethe superior capability of RSICCLLM, which achieves outstanding re-sults with only 7B parameters, surpassing models of substantially largerscales. The code and dataset will be made publicly available at https://github.com/keaill/RSICCLLM.

Abstract:
Video generative models show emerging reasoning behaviors.It is essential to ensure that generated events remain causally consistentacross frames for reliable deployment, a property we define as reasoningcoherence. To bridge the gap in literature for missing reasoning coher-ence evaluation, we propose MME-CoF-Pro, a comprehensive videoreasoning benchmark to assess reasoning coherence in video models.Specifically, MME-CoF-Pro contains 303 samples across 16 categories,ranging from visual logical to scientific reasoning. It introduces Rea-soning Score as evaluation metric for assessing process-level necessaryintermediate reasoning steps, and includes three evaluation settings, (a)no hint, (b) text hint, and (c) visual hint, enabling a controlled investi-gation into the underlying mechanisms of reasoning hint guidance. Eval-uation results in 7 open and closed-source video models reveals insightsincluding: (1) Video generative models exhibit weak reasoning coherence,decoupled from generation quality. (2) Text hints boost apparent correct-ness but often cause inconsistency and hallucinated reasoning (3) Visualhints benefit structured perceptual tasks but struggle with fine-grainedperception. We provide our project page along with data and code at:https://video-reasoning-coherence.github.io/.

Abstract:
3D reconstruction from sparse views is a challenging task in3D computer vision. Recent studies on 3D Gaussian Splatting (3DGS)have achieved remarkable results with sparse views in novel view syn-thesis, yet reconstructing high-quality geometric surfaces from sparseviews remains a challenge, due to the limited geometry clues and thediscreteness of Gaussians. In this paper, we propose a novel 3DGS-basedmethod for high-fidelity surface reconstruction from sparse views. Ourkey insight is to introduce a normal-guided depth propagation approach,which can extend depth information from high-confidence regions to con-strain the depth in low-confidence areas. Additionally, we propose an ab-normal depth edge-aware regularization to address depth discontinuitiescaused by the discreteness of Gaussians. Extensive experiments on DTUand Tanks-and-Temples datasets demonstrate that our method outper-forms the state-of-the-art methods in sparse view surface reconstruction.Project page: https://hanl2010.github.io/DP-GS.

Abstract:
Open-vocabulary scene understanding aims to recognize ar-bitrary semantic concepts in 3D beyond a fixed set of categories. Re-cent methods built upon 3D Gaussian Splatting (3DGS) enable sceneunderstanding by distilling features from vision–language models, butmost rely on per-scene optimization and dense observations, which limitscalability. More recent generalizable 3DGS methods show promise, butthey perform poorly on sparse-view satellite imagery due to the uniquepushbroom imaging mode, limited geometric constraints, and extremescale variations. To address these limitations, we propose SkySplat-OV,a feed-forward framework that integrates the rational polynomial co-efficient (RPC) model into a generalizable language 3DGS pipeline. AGeometry–Radiance–Semantics Fusion module, together with monoc-ular relative height supervision, improves Gaussian parameter predic-tion under limited geometric constraints. Moreover, SkySplat-OV em-ploys single-stage SAM3 distillation to handle extreme scale variationsin satellite imagery for accurate segmentation. Compared to per-sceneoptimization methods, SkySplat-OV achieves up to 1070 times speedupover LangSplatV2 with higher accuracy. It also outperforms generaliz-able 3DGS baselines, achieving 12.91%–25.75% mIoU improvements ontwo benchmarks, and demonstrates strong cross-dataset generalizationon the MVS3D test set. Additional qualitative results are available athttps://skysplat-ov.github.io/.

Abstract:
Novel view synthesis (NVS) through non-planar refractivesurfaces presents fundamental challenges due to severe, spatially varyingoptical distortions. While recent representations like NeRF and 3D Gaus-sian Splatting (3DGS) excel at NVS, their assumption of straight-lineray propagation fails under these conditions, leading to significant arti-facts. To overcome this limitation, we introduce RefracGS, a frameworkthat jointly reconstructs the refractive water surface and the scene be-neath the interface. Our key insight is to explicitly decouple the refractiveboundary from the target objects: the refractive surface is modeled via aneural height field, capturing wave geometry, while the underlying sceneis represented as a 3D Gaussian field. We formulate a refraction-awareGaussian ray tracing approach that accurately computes non-linear raytrajectories using Snell’s law and efficiently renders the underlying Gaus-sian field while backpropagating the loss gradients to the parameterizedrefractive surface. Through end-to-end joint optimization of both rep-resentations, our method ensures high-fidelity NVS and view-consistentsurface recovery. Experiments on both synthetic and real-world sceneswith complex waves demonstrate that RefracGS outperforms prior re-fractive methods in visual quality, while achieving ∼15× faster trainingand real-time rendering at 200 FPS.

Abstract:
We present FLEG, a feed-forward network that reconstructslanguage-embedded 3D Gaussians from multi-view images. Previous feed-forward language-embedded Gaussian reconstruction methods are re-stricted to a fixed number of input views and typically attach a language-aligned semantic embedding to each Gaussian, resulting in impracticalinput settings and semantic redundancy. In contrast, we introduce ageometric-semantic dual-branch distillation framework that supports avariable number of uncalibrated and unposed multi-view images as input.We also propose a novel-view-based distillation strategy during trainingthat mitigates overfitting to input views. In addition, we observe that se-mantic representations are significantly sparser than geometric ones, andper-Gaussian language embedding is unnecessary. To exploit this spar-sity, we design a decoupled language embedding strategy that representslanguage information with a sparse set of semantic Gaussians, ratherthan attaching embeddings to every Gaussian. Compared with densepixel-aligned per-Gaussian embedding schemes, our method uses only5% of the language embeddings while maintaining comparable semanticfidelity, effectively reducing storage costs. Extensive experiments demon-strate that FLEG outperforms state-of-the-art feed-forward reconstruc-tion and language-embedded Gaussian methods in both reconstructionquality and language-aligned semantic representation.

Abstract:
While single-view 3D reconstruction has seen significant pro-gress, extrapolating complex 3D structures from inherently ambiguous2D observations remains fundamentally ill-posed, particularly in the crit-ically underexplored data-scarce regime. To address this challenge, wepropose Point Diffusion Mamba (PDM), a method that integrates thegenerative power of diffusion models with the efficiency of state-spacemodel for single-view 3D reconstruction under data-scarce conditions.Specifically, PDM employs a lightweight reconstruction module tailoredto handle unordered point-cloud inputs effectively. By combining a Lo-cal Geometric Aggregation module with Mamba blocks, our approachjointly models global geometric structures and local details. In 3D re-construction, each point in the initial noisy input requires a precise pre-diction, yet the high-level features extracted by the Mamba module cap-ture only abstract semantic information from sparse points. To bridgethis gap, we introduce the Hierarchical Feature Integration Network,which fuses high-level semantic and local geometric features for eachpoint, overcoming the limitations of token-based point-cloud reconstruc-tion. Furthermore, we propose a Dynamic Weighted Sampling strategythat adaptively unifies 3D generation with single-view reconstruction byleveraging generative priors to enhance reconstruction quality. Exper-imental results on the ShapeNet and Pix3D benchmarks demonstratethat PDM outperforms state-of-the-art methods, providing an effectivesolution for 3D reconstruction under data-scarce settings. Code is avail-able at: https://github.com/NWUzhouwei/PDM.

Abstract:
Self-supervised learning (SSL) has significantly advanced 3Dmedical image analysis. However, existing pretraining methods typicallytreat multi-phase scans as isolated samples, neglecting the rich anatom-ical correspondence inherent in paired acquisitions. Clinically, contrast-enhanced CT (CECT) offers a structure-amplified view of the exactanatomy captured in a paired non-contrast CT (NCCT), yet the drasticintensity shifts caused by contrast agents make direct voxel-wise align-ment challenging. To harness this naturally occurring supervisory signalwhile decoupling structural topology from phase-specific appearance, wepropose CAST (Cross-phase Alignment of Structure and Topology),a novel paired pretraining framework. CAST employs a 3D CNN archi-tecture to explicitly align NCCT representations with CECT targets.Moving beyond conventional reconstruction, we introduce two feature-level constraints: (1) a Spectral Consistency module that utilizes3D wavelet decomposition to align frequency-aware boundaries whilesuppressing contrast-induced noise; (2) a Geometry-Aware Topo-logical Consistency module that preserves local relational graphsamong salient anatomical keypoints via dynamic top-hat sampling. Tosupport this, we construct a large-scale dataset comprising 13,850 pairedvolumetric CT scans. Extensive evaluations on public benchmarks andreal-world clinical cohorts demonstrate that CAST achieves state-of-the-art performance. Notably, our method delivers substantial gains inboundary-sensitive segmentation tasks and low-data regimes, proving itsefficacy in learning robust, phase-invariant anatomical representationsfor both NCCT and CECT downstream applications. Code is availableat https://github.com/alibaba-damo-academy/CAST.

Abstract:
Volume denoising is a foundational problem in computa-tional imaging, as many 3D imaging inverse problems face high levels ofmeasurement noise. Inspired by the strong 2D image denoising propertiesof Field of Junctions (ICCV 2021), we propose a novel, fully volumetric3D Field of Junctions (3D FoJ) representation that optimizes a junctionof 3D wedges that best explain each 3D patch of a full volume, while en-couraging consistency between overlapping patches. In addition to directvolume denoising, we leverage our 3D FoJ representation as a structuralprior that: (i) requires no training data, and thus precludes the risk of hal-lucination, (ii) preserves and enhances sharp edge and corner structuresin 3D, even under low signal to noise ratio (SNR), and (iii) can be used asa drop-in denoising representation via projected or proximal gradient de-scent for any volumetric inverse problem with low SNR. We demonstratesuccessful volume reconstruction and denoising with 3D FoJ across threediverse 3D imaging tasks with low-SNR measurements: low-dose X-raycomputed tomography (CT), cryogenic electron tomography (cryo-ET),and denoising point clouds such as those from lidar in adverse weather.Across these challenging low-SNR volumetric imaging problems, 3D FoJoutperforms the evaluated classical denoisers, untrained neural denois-ers, and denoisers trained only on noisy examples. Code is available athttps://github.com/voilalab/3D-Field-of-Junctions.

Abstract:
Nighttime color constancy still remains a challenging prob-lem in computational photography due to low-light noise and complexillumination conditions. We present RL-AWB, a novel framework com-bining statistical methods with deep reinforcement learning for night-time white balance. Our method begins with a statistical algorithmtailored for nighttime scenes, integrating salient gray pixel detectionwith novel illuminant estimation. Building on this foundation, we de-velop the first deep reinforcement learning approach for color constancythat leverages the statistical algorithm as its core, mimicking profes-sional AWB tuning experts by dynamically determining image-specificparameters at inference time, without requiring ground-truth illumi-nants or reference images. To further facilitate cross-sensor evaluation,we introduce the first multi-sensor nighttime dataset. Experiment re-sults demonstrate that our method achieves strong generalization capa-bility across low-light and well-illuminated images. Project page: https://ntuneillee.github.io/research/rl-awb/

Abstract:
Monocular 3D lane detection plays a critical role in autonomous driv-ing, yet recovering reliable 3D geometry from a single image remains challeng-ing due to inherent depth ambiguity. Prior methods project image features intoBird’s-Eye-View (BEV) space under a flat-ground assumption, causing geomet-ric distortion on real-world roads. Recent methods instead predict explicit heightmaps to capture non-planar surfaces, but still rely on sparse anchor-based regres-sion and exploit the recovered geometry merely for spatial transformation ratherthan semantic understanding. To overcome these limitations, we propose HSDF-Lane, which implicitly models the road surface as a Height-aligned Signed Dis-tance Field (HSDF) over a densely sampled 3D feature volume. Through dif-ferentiable rendering, the HSDF jointly produces an accurate height map andsurface-aligned features. We further introduce Lane-aware Semantic PositionalEncoding (LSPE), which injects a lane-existence prior derived from the surface-aligned features into the transformer queries, coupling geometric structure withsemantic guidance. Extensive experiments on the OpenLane benchmark showthat HSDF-Lane achieves state-of-the-art performance in both 3D lane detectionand height map estimation. The code is available at https://github.com/JiyongBoo/HSDF-Lane.

Abstract:
Despite remarkable advancements in Vision-and-LanguageNavigation (VLN), a significant sim-to-real gap remains. Most meth-ods are trained and evaluated in simplified or physics-limited simulators,which ignore the feedback loop between decision making and physical ex-ecution, resulting in unstable navigation performance. To bridge this gap,we propose Hi-Nav, a top-down hierarchical navigation framework thatdecomposes VLN into three controllable levels. The high-level moduleleverages a large language model to split long-horizon instructions intosub-tasks. At the middle level, an Interest Score Occupancy Map (ISOM)integrates instruction-relevant semantics with geometric traversability toperceive the surrounding environment, after which waypoint reasoning isperformed for route refinement. ISOM construction and waypoint reason-ing are jointly modeled within a unified multi-task learning framework,where a Q-Former fuses RGB-D observations and navigation instructionsinto shared query representations for efficient multi-task learning, aug-mented with learnable temporal embeddings to mitigate long-horizon for-getting. Finally, low-level behaviors are executed through ROS, enablingobstacle avoidance. We introduce a novel waypoint reasoning datasetfor comprehensive training and build a ROS/Gazebo VLN benchmarkto evaluate methods in a realistic simulation setting, which significantlyreduces the difficulty of sim-to-real transfer. Extensive experiments con-ducted in both Gazebo and the real world demonstrate strong perfor-mance, achieving up to 80% Success Rate (SR) with 0.65 m NavigationError (NE). Our code will be released at https://hiway-page.github.io/.

Abstract:
Camera-based perception systems for autonomous drivingare typically developed and evaluated using fixed sensor rigs, while real-world vehicle fleets exhibit substantial variation in camera placement,orientation, field of view, and camera count. This mismatch introducesa cross-rig domain gap in which only the geometric observation pro-cess changes. To study this effect under controlled conditions, we intro-duce Plentiful CARLA Camera Rigs, a benchmark that renders identicaldriving scenes under 14 systematically designed camera rigs. This setupenables direct analysis of cross-rig generalization without confoundingchanges in scene content or appearance. Using the benchmark, we ana-lyze cross-rig transfer behavior of representative multi-view perceptionarchitectures and observe substantial performance shifts induced by ge-ometric rig variation. To facilitate structured analysis, we further intro-duce two calibration-based descriptors derived from rig metadata: RigVariance, capturing internal rig diversity, and Rig Contrastive Distance,measuring geometric discrepancy between rigs. Our experiments showthat geometric rig differences strongly correlate with relative cross-rigperformance shifts and that Rig Contrastive Distance provides a reliableproxy for ranking transfer difficulty between sensor rigs.

Abstract:
Existing methods for adapting 2D foundation models such asSAM to 3D volumes either process slices independently—ignoring inter-slice context—or require substantial architectural changes and retraining.In this paper, we present SAM+D, a parameter-efficient framework thatlifts SAM-family models by one spatial dimension—enabling 3D volu-metric segmentation from 2D SAM and, for the first time via parameter-efficient fine-tuning, end-to-end 4D (3D+T) spatiotemporal segmenta-tion from video-based SAM2—while keeping the vast majority of pre-trained parameters frozen. SAM+D introduces two lightweight, model-agnostic modules into frozen transformer blocks: (1) Depth-RoutedLoRA (DRLoRA) experts with learned routing for spatially adaptivelow-rank updates, and (2) Depth Shift Modules (DSM) for cross-slice feature exchange at zero additional parameter cost. Together, theyprovide volume-level context while tuning only ∼2.8% of parameters forSAM and ∼3.7% for SAM2. We evaluate SAM+D in two distinct set-tings, each lifting the base model by one spatial dimension: 3D segmenta-tion, where SAM (2D → 3D) is evaluated on four CT benchmarks (KiTS,Pancreas, LiTS, Colon), and 4D segmentation, where SAM2 (2D+T →3D+T) is evaluated on a cell tracking challenge (CTC) dataset (Fluo-N3DH-SIM+). In both settings SAM+D achieves competitive or superiorresults under the single-point prompt setting while using fewer trainableparameters than existing methods, demonstrating that SAM+D general-izes across SAM-family architectures, target dimensionalities (3D, 4D),and domains spanning medical imaging and bio-scene understanding.Code is publicly available at https://github.com/JerrySongCST/SAM-Plus-D.

Abstract:
Recent studies demonstrate that Federated Learning (FL) is vulnerable to gradient leakage attacks (GLAs). Revisiting key insights in GLAs under FL reveals that (1) Defenses should focus on protecting semantic and fine-grained details of data; (2) GLAs are effective mainly in early rounds; and (3) To avoid semantic leakage, defenses shouldn’t infer the true labels of private images for obfuscation. Building upon these insights, we present a simple, time-adaptive defense strategy that obfuscates the private gradient by employing the gradient from a synthesized image. To this end, a client trains a diffusion model on its own private dataset to generate synthesized images that fit the distribution of private dataset but are distinct in fine-grained details. In addition, a synthesized image is generated conditioned on a non-identical label from the private image to resist semantic leakage. Defense analysis and empirical evaluations demonstrate that our time-adaptive and label agnostic method can better maintain the trade-off between privacy preservation and model utility against GLAs. Our implementation is publicly available at https://github.com/lalakitchen/Diff2Obs.

Abstract:
Diffusion Large Language Models (dLLMs) have achievedremarkable success in pure language tasks and recently shown promis-ing results in multimodal understanding. However, existing post-trainingmethods for multimodal dLLMs have not yet fully exploited the uniquestructure and inference paradigm of diffusion models, leaving room formore tailored optimization strategies. We present Bi-VRL, a reinforce-ment learning framework specifically designed for multimodal dLLMs,featuring a bidimensional optimization strategy that enables com-prehensive optimization across both reasoning and diffusion timestepdimensions. For the diffusion timestep dimension, our trajectory-leveloptimization learns a value function over the entire diffusion path, en-abling holistic credit assignment across denoising timesteps. For the rea-soning dimension, our thought-level optimization decomposes outputsinto reasoning steps and delivers targeted rewards to enhance logicalsoundness. This core strategy is augmented by a fine-grained multimodalalignment mechanism that systematically evaluates both the logical cor-rectness of reasoning steps and their grounding in visual evidence. Ourframework is architecture-agnostic, successfully applied to models withboth discrete vision tokens (MMaDA) and continuous vision embed-dings (LLaDA-V). Extensive experiments across challenging multimodalreasoning benchmarks demonstrate that Bi-VRL significantly outper-forms strong supervised fine-tuning baselines, establishing effective post-training methodology for multimodal dLLMs. We will release the codeat https://github.com/Gen-Verse/dLLM-RL.

Abstract:
Diffusion-based methods demonstrate significant potentialfor remote sensing image super-resolution at large scaling factors, partic-ularly in reference-based super-resolution (RefSR), where high-resolutionreference images provide critical fine-grained texture priors. However, ex-isting methods often suffer from a trade-off between over-reliance on ref-erence information, which leads to texture artifacts, and under-utilizationof such information, which results in insufficient detail recovery. To ad-dress these issues, we propose DS-DiT, a Decoupled Siamese DiffusionTransformer that decouples the interaction between low-resolution (LR)and reference (Ref) conditions within the attention mechanism. By al-lowing LR structural priors and Ref texture information to independentlyinteract with the noisy latent, the framework effectively mitigates com-petition between the two conditional sources. To further compensatefor the limited local modeling ability of global attention, we introduce aPatch-Level Weighting (PLW) module that adaptively modulates the fu-sion of conditional sources. In addition, the siamese architecture enablesan inference-time autoguidance strategy that exploits the prediction dis-crepancy between strong and weak Ref conditions to improve generationquality without additional training. Experimental results across multi-ple datasets and scaling factors show that DS-DiT outperforms existingmethods in both quantitative metrics and visual fidelity. The source codeis available at https://github.com/B1nary-L/DS-DiT.

Abstract:
Flow Matching (FM) has achieved remarkable generativeperformance, yet it suffers from exposure bias due to discrepancies be-tween training and inference. Existing mitigation strategies typicallyrely on static constraints or external heuristics. In this work, we pro-pose that exposure bias itself inherently contains dynamic signals thatcan guide its own rectification. To leverage this, we introduce DEFAR(DirEctional-Frequency Adaptive Rectification). This framework simu-lates the single-step inference process during training to identify exposurebias. It utilizes the directional and frequency adaptive feedback signalswithin bias itself to enhance the bias tolerance of the model. It consistsof two key components: (1) Anti-Drift Rectification (ADR). ADRtreats inference-time drift as a signal to learn the direction to steer devi-ated states back toward the target. ADR endows the model with intrin-sic active self-rectification capabilities; (2) Frequency Compensation(FC). Empirically, we observe that accumulated bias often stems from alack of low-frequency components in high-noise stages and exposure biascarries the missing frequency information. FC leverages the bias itself asa self-feedback weighting factor to reinforce the missing frequency com-ponents. Experiments on CIFAR-10, CelebA-64, and ImageNet-256/512show that DEFAR outperforms prior baselines and further demonstratesfavorable scalability, compatibility, and inference robustness. Code willbe made available in https://github.com/wuliwuliy/DEFAR.

Abstract:
Large-scale vision generative models, including diffusion andflow models, have demonstrated remarkable performance in visual gen-eration tasks. However, transferring these pre-trained models to down-stream tasks often results in significant parameter redundancy. In thispaper, we propose EntPruner, an entropy-guided automatic progressivepruning framework for diffusion and flow models. First, we introduceentropy-guided pruning, a block-level importance assessment strategyspecifically designed for generative models. Unlike discriminative models,generative models require preserving the diversity and condition-fidelityof the output distribution. As the importance of each module can varysignificantly across downstream tasks, EntPruner prioritizes pruning ofless important blocks using data-dependent Conditional Entropy Devia-tion (CED) metric. CED quantifies how much the distribution divergesfrom the learned conditional data distribution after removing a block.Second, we propose a zero-shot adaptive pruning framework to auto-matically determine when and how much to prune during training. Thisdynamic strategy avoids the pitfalls of one-shot pruning, mitigating modecollapse, and preserving model performance. Extensive experiments onDiT and SiT models demonstrate the effectiveness of EntPruner, achiev-ing up to 2.22× inference speedup while maintaining competitive gener-ation quality on ImageNet and three downstream datasets.

Abstract:
Analyzing whole-slide pathology images (WSIs) requires aniterative, evidence-driven reasoning process that parallels how pathol-ogists dynamically zoom, refocus, and self-correct while collecting ev-idence. However, existing computational pipelines often lack this rea-soning trajectory, resulting in opaque and unjustifiable predictions. Tobridge this gap, we present PathAgent, a training-free, large languagemodel (LLM)-based agent framework that emulates the reflective, step-wise analytical approach of human experts. PathAgent can autonomouslyexplore WSIs, iteratively and precisely locating significant micro-regionsusing the Navigator module, extracting morphological visual cues usingthe Perceptor, and integrating these findings into the continuously evolv-ing natural language trajectories in the Executor. The entire sequenceof observations and decisions forms a structured evidence trajectory,yielding traceable and evidence-grounded analyses. Evaluated across fivechallenging datasets spanning both whole-slide and patch-level settings,PathAgent exhibits strong zero-shot generalization and achieves perfor-mance comparable to human experts, surpassing task-specific baselinesacross diverse pathology tasks, including molecular subtype classifica-tion, histological grading and tumor type diagnosis. Human collaborativeevaluations demonstrate that PathAgent produces diagnostic decisionshighly consistent with expert-selected regions. These results demonstratethat PathAgent enables interpretable and clinically grounded WSI analy-sis. The code is available at https://github.com/G14nTDo4/PathAgent.

Abstract:
Multimodal large language models (MLLMs) have demon-strated powerful capabilities in general spatial understanding and rea-soning. However, their fine-grained spatial understanding and reasoningcapabilities in complex urban scenarios have not received significant at-tention in the fields of both research and industry. To fill this gap, wefocus primarily on road markings as a typical example of fine-grainedspatial elements under urban scenarios, given the essential role of theintegrated road traffic network they form within cities. Around roadmarkings and urban traffic systems, we propose RoadBench, a system-atic benchmark that comprehensively evaluates MLLMs’ fine-grainedspatial understanding and reasoning capabilities using Bird’s-Eye View(BEV) and First-Person View (FPV) image inputs. This benchmarkcomprises eight tasks consisting of 3,040 strictly manually verified testcases, constructed from 2,137 unique BEV images and 721 unique FPVimages collected from five Chinese cities with relatively consistent trafficconventions. These tasks form a systematic evaluation framework thatbridges understanding at local spatial scopes to global reasoning. Theynot only test MLLMs’ capabilities in recognition, joint understanding,and reasoning but also assess their ability to integrate image informationwith domain knowledge. After evaluating 20 mainstream MLLMs, weconfirm that RoadBench is a challenging benchmark for MLLMs whilerevealing significant shortcomings in existing MLLMs’ fine-grained spa-tial understanding and reasoning capabilities within urban scenarios. Incertain tasks, their performance even falls short of simple rule-basedor random selection baselines. These findings, along with RoadBenchitself, will contribute to the comprehensive advancement of spatial un-derstanding capabilities for MLLMs. The benchmark code is availableat https://github.com/tsinghua-fib-lab/RoadBench, and the supple-mentary material provides example data, prompts, evaluation scripts,and raw evaluation results.Road MarkingsBEV Image # of Lanes: 4Road MarkingsFPV Image # of Lanes: 4Fig. 1: Examples of road markings in BEV and FPV images. Road markings such aslane dividers and turning arrows provide fine-grained visual cues for lane counting andlane designation recognition.

Abstract:
Large vision–language models (LVLMs) exhibit strong reason-ing ability but suffer from visual forgetting during long-horizon decoding,where attention progressively drifts away from visual evidence. Existingmethods largely treat this issue as a late-stage attention decay prob-lem or attempt to mitigate it through heuristic reminders or post-hocattention lifting. Through systematic empirical analysis, we find thatperformance degradation under visual forgetting is largely driven bytwo overlooked factors: early-stage attention decay disrupts evidenceacquisition, and attention concentration on a subset of task-irrelevantvisual sink tokens. Motivated by these insights, we propose LASER, apost-training framework that regulates both the visual attention tra-jectory and intra-visual token attention distribution during reasoning.Technically, LASER introduces two complementary rewards: a VisualGrounding Reward, which encourages the model to maintain attentionon semantically salient visual tokens throughout decoding, and a SinkSuppression Reward, which penalizes excessive attention concentration onvisual sink tokens. Together, these rewards preserve early-stage groundingwhile preventing attention collapse onto uninformative regions. Exten-sive experiments on eight benchmark datasets demonstrate that LASERconsistently outperforms strong baselines, validating attention-awaretraining as an effective remedy for visual forgetting. The code is availableat https://github.com/KeViNYuAn0314/LASER.

Abstract:
Action Quality Assessment (AQA) aims to objectively evaluate performance quality from action videos. Most existing methods follow a “one-by-one” paradigm, training a separate model for each action type. This setting limits real-world deployment, as it requires prior actiontype knowledge to select the corresponding model and suffers from poor generalization across diverse actions. To address these limitations, we study the challenging task of all-in-one AQA, which aims to assess heterogeneous actions within a single unified model. We propose a novel Mixture of Action Knowledge Experts (MoAKE) framework, designed to mitigate negative knowledge transfer caused by large semantic discrepancies among actions. MoAKE learns complementary experts that capture diverse action patterns within a shared semantic space and dynamically aggregates their knowledge to adapt the assessment to the input action. Each expert is tailored with segment-aware prototypes to handle varying temporal lengths, together with an Adaptive Intraand Inter-Segment Relationship Modeling (AIISRM) module to model multi-granularity temporal dynamics. Furthermore, we establish comprehensive benchmarks for all-in-one as well as zero/fewshot AQA. Extensive experiments on three long-term datasets demonstrate that MoAKE significantly outperforms existing methods in the all-in-one setting, while also achieving consistent generalization on three short-term datasets under zero/few-shot evaluation. Code is available at https://github.com/XuHuangbiao/MoAKE.

Abstract:
Camouflaged Object Detection (COD) aims to identify andsegment camouflaged objects in complex environments, which are oftenconcealed because their color and texture are similar to the background.Several existing COD methods introduce depth maps to boost detec-tion performance via learning complementary RGB-D features, ignor-ing modality-specific characteristics of concealed objects in the depthdomain. To address this issue, we propose a depth collaborative net-work, called VCP-DCN, to mine distinguishable multi-modality featuresbeyond visual concealed prototype in depth domain. Specifically, VCP-DCN progressively performs multi-modality alignment, interaction, andfusion for the COD task. In the alignment stage, we propose a SeparablePrototype Embedding (SPE) module to learn modality-consistency andmodality-specific RGB/depth prototype tokens through prototype con-trastive learning. Furthermore, we develop a Multi-modality Dual Atten-tion (MDA) module to enhance the cross-modal feature representationthrough local response maps between modality-consistency RGB/depthprototype tokens and visual tokens on the interaction stage. Finally,we design a Depth Adaptive Injection (DAI) module to adaptively mea-sure contribution of RGB/depth features with a decision-making mecha-nism, which calculates similarity distance between RGB/depth modality-specific prototype tokens and modality-consistency ones on the fusionstage. Extensive experiments demonstrate the effectiveness of our VCP-DCN on three authoritative datasets. The code is available at https://github.com/duan-song/VCPDCN.

Abstract:
Detecting covariate shift is critical for building reliable vi-sion systems. While most prior work focuses on improving robustness toshift, explicitly detecting covariate shift remains underexplored. Existingapproaches typically rely on fully supervised training, requiring labeledexamples from both original and shifted distributions, which is often im-practical. In this paper, we show that covariate shift detection can beeffectively addressed with weaker supervision using Positive–Unlabeled(PU) learning. However, under covariate shift, in-distribution and shifteddata overlap significantly, making classical PU methods unstable and sen-sitive to noise. To overcome this challenge, we introduce Spectral PUNeighborhood Annotation (S-PUNA), a geometry-aware frame-work that progressively discovers shifted data by leveraging the localmanifold structure of visual features. Extensive experiments show thatS-PUNA achieves state-of-the-art performance in PU settings and re-markably matches the performance of fully supervised methods. More-over, our approach transfers robustly across different types of shifts,demonstrating strong generalization capabilities. Code is available athttps://github.com/fira7s/S-PUNA.

Abstract:
Facial landmark detectors trained on real human faces oftenfail to generalize effectively to stylized domains such as caricatures andartistic portraits, necessitating unsupervised domain adaptation (UDA).Although self-training is a widely used UDA strategy to bridge do-main gaps, it frequently breaks down under large domain shifts as it isprone to amplifying confident yet erroneous pseudo-label predictions. Tothis end, we propose PriSM (Parsing and Style-Mixed Consistency), anovel method for robust landmark pseudo-label validation. PriSM lever-ages two complementary signals: a Parsing Network enforces high-levelstructural alignment through face parsing consistency, while a StyleMixNetwork enforces fine-grained geometric constraints by reducing the re-construction error between the input face and another face synthesizedfrom the pseudo-label’s structure and the input face’s appearance. Ex-tensive experiments on the challenging CariFace and ArtiFace bench-marks under the UDA setting demonstrate that PriSM significantly out-performs existing state-of-the-art methods and exhibits strong gener-alizability to unseen domains. The source code is available at: https://github.com/jerrychild999922/PriSM-UDA.

Abstract:
Face recognition in unconstrained environments remains high-ly challenging due to diverse and extreme variations encountered inreal-world scenarios. To mitigate these effects, existing margin-basedapproaches model sample quality through feature magnitude. However,magnitude-based modeling alone is susceptible to identity-agnostic noise,which can degrade the reliability and discriminative power of learned rep-resentations. In this paper, we propose Dual Quality Margin Learning forFace Recognition (DQM-Face), a novel framework that enables refinedattraction and repulsion dynamics during representation learning. Ourapproach unifies conventional magnitude-based quality estimation witha newly introduced semantic quality learning mechanism, realized viasqueeze-and-excitation semantic attention. By jointly leveraging magni-tude and semantic cues, we construct enhanced quality-aware marginsthat adaptively strengthen intra-class compactness through improvedattraction during learning. To further enhance inter-class discrimina-tion, we introduce a repulsion margin formulation that explicitly en-larges inter-class separation. The unified integration of semantic qualitymodeling with dual attraction–repulsion margin optimization results ina more structured and discriminative feature geometry. Extensive ex-periments on multiple challenging benchmarks demonstrate that DQM-Face consistently outperforms state-of-the-art face recognition methods.Moreover, we show that the quality learned for margin optimization ishighly effective for face image quality assessment within the proposedframework, demonstrating that the learned quality signal is intrinsicallyaligned with the recognition objective. The code is publicly available:https://github.com/RAIB-group/DQM-Face

Abstract:
Multimodal dataset distillation (MDD) aims to synthesize acompact image-text dataset on which a visual-language model can betrained to achieve performance comparable to that trained on the orig-inal, full-scale dataset. While previous methods have achieved promis-ing results by using trajectory matching, they fail to consider the gapbetween single-semantic texts and multi-semantic images. Additionally,they usually use contrastive losses to pull together paired samples andpenalize non-paired samples, ignoring the textual information from un-paired samples that are semantically related to the corresponding images.To reduce the semantic gap, we introduce a multi-text fusion module thatstrengthens the cross-modal interaction between the image and text. Inaddition, we leverage uncertainty to adaptively guide the contribution ofnon-paired samples in the synthetic dataset, thereby improving the ef-fective utilization of information from these samples. We then provide aninformation theoretic analysis of the semantic gap and the limitations ofcontrastive supervision. Experiments on Flickr-30K and MS-COCO showthat our method consistently outperforms previous state-of-the-art MDDmethods, achieving significant improvements in retrieval performance(e.g., +6.5% in IR@10 and +4.6% in TR@10 in a 500-pair setting).Code is available at https://github.com/yiqiqiandefanhua/P2DE.

Abstract:
Sign Language Translation (SLT) is a core task in the field of AI-assisted disability. Traditional SLT methods are typically based on visible lightvideos, which are easily affected by factors such as lighting variations, rapidhand movements, and privacy concerns. This paper proposes the use of bio-inspired event cameras to alleviate the aforementioned issues. Specifically, weintroduce a new high-definition event-based sign language dataset, termed Event-CSL, which effectively addresses the data scarcity in this research area. Thedataset comprises 14,827 videos, 6,097 glosses, and 2,544 Chinese words in thetext vocabulary. These samples are collected across diverse indoor and outdoorscenes, covering multiple viewpoints, lighting conditions, and camera motions.We have also benchmarked existing mainstream SLT methods on this dataset tofacilitate fair comparisons in future research. Furthermore, we propose a novelevent-based, gloss-free sign language translation framework, termed EvSLT. Theframework first segments continuous video features into clips and employs aMamba-based memory aggregation module to compress and aggregate spatialdetail features at the clip level. Subsequently, these spatial features, along withtemporal representations obtained from temporal convolution, are then fused bya graph-guided spatiotemporal fusion module. Extensive experiments on Event-CSL, as well as other publicly available datasets, demonstrate the superior per-formance of our method. The dataset and source code are publicly available athttps://github.com/Event-AHU/OpenESL/tree/main/EvSLT.

Abstract:
Large-vocabulary instance segmentation is constrained bylong-tailed category distributions and fine-grained inter-class ambiguity.While data synthesis offers a promising alternative, current paradigmshave complementary limitations: text-to-image (T2I) methods inheritnoisy pseudo-labels and struggle on rare classes, whereas copy-pastemethods compromise contextual realism. To address these issues, we pro-pose a hybrid pipeline coupling T2I generation with context-aware image-to-image (I2I) editing. The T2I branch provides broad category and scenediversity, while a teacher-student scheme ensures label reliability by se-lectively retaining only prompt-specified categories. To strengthen su-pervision for rare classes, we introduce VRAIN (Verified Rare-classAugmentation via INstructed editing), a novel I2I editor. VRAIN insertshigh-confidence instances at semantically appropriate locations withinin-the-wild scenes, yielding semantically coherent and visually naturaledits that reduce domain gaps and enable targeted augmentation. Onthe LVIS benchmark, our method surpasses existing baselines, improv-ing overall AP by up to +4.0 points and rare-class AP by up to +9.5points, while scaling effectively with backbone capacity.

Abstract:
Single-source remote sensing object detection using opticalor SAR images struggles in complex environments. Optical images offerrich textural details but are often affected by low-light, cloud-obscured,or low-resolution conditions, reducing the detection performance. SARimages are robust to weather, but suffer from speckle noise and limitedsemantic expressiveness. Optical and SAR images provide complemen-tary advantages, and fusing them can significantly improve the detectionaccuracy. However, progress in this field is hindered by the lack of large-scale, standardized datasets. To address these challenges, we proposea new comprehensive dataset for optical-SAR fusion object detection,named Multi-resolution, Multi-polarization, Multi-scene, Multi-sourceSAR dataset (M4-SAR). It contains 112,174 instance-level aligned im-age pairs and nearly one million labeled instances with arbitrary orienta-tions, spanning six key categories. To enable standardized evaluation, wedevelop a unified benchmarking toolkit that integrates six state-of-the-art multi-source fusion methods. Additionally, we propose E2E-OSDet, anovel end-to-end multi-source fusion detection framework that mitigatescross-domain discrepancies and establishes a robust baseline for futurestudies. Extensive experiments on M4-SAR demonstrate that fusing op-tical and SAR data can improve mAP by 5.7% over single-source inputs,with particularly significant gains in complex environments.

Abstract:
Building high-fidelity digital twins of articulated objects fromvisual data remains a central challenge. Existing approaches depend onmulti-view captures of the object in discrete, static states, which severelyconstrains their real-world scalability. In this paper, we introduce Articu-lat3D, a novel framework that constructs such digital twins from casuallycaptured monocular videos by jointly enforcing explicit 3D geometric andmotion constraints. We first propose Motion Prior–Driven Initialization,which leverages 3D point tracks to exploit the low-dimensional structureof articulated motion. By modeling scene dynamics with a compact set ofmotion bases, we facilitate soft decomposition of the scene into multiplerigidly-moving groups. Building on this initialization, we introduce Ge-ometric and Motion Constraints Refinement, which enforces physicallyplausible articulation through learnable kinematic primitives parameter-ized by a joint axis, a pivot point, and per-frame motion scalars, yieldingreconstructions that are both geometrically accurate and temporally co-herent. Extensive experiments demonstrate that Articulat3D achievesstate-of-the-art performance on synthetic benchmarks and real-worldcasually captured monocular videos, significantly advancing the feasibilityof digital twin creation under uncontrolled real-world conditions. Here isour project page.

Abstract:
3D affordance grounding aims to understand how diverseobjects can be manipulated, making it a cornerstone of embodied inter-action. However, prior works struggle to generalize to out-of-distribution,open-world scenarios, leaving a critical gap between limited dataset per-formance and real-world application needs. Inspired by the saying: “WhatI can not create, I do not understand”, we find generative modelscan generate semantically valid HOI images, which indicates inherent en-coding of affordance concepts. Building on this insight, we propose DAG,the first innovative diffusion-based 3D affordance grounding frameworkthat extracts general affordance knowledge from text-to-image diffusionmodels for 3D affordance prediction. Specifically, we extract the affor-dance priors from a diffusion model to encode HOI priors, and designan affordance block with a multi-source affordance decoder for dense3D affordance prediction. Extensive experiments show that DAG con-sistently outperforms state-of-the-art methods and exhibits strong open-world generalization, even in the challenging one-shot setting. The codeof our method is released on https://github.com/hq-King/DAG.

Abstract:
Evolutions in the world, such as water pouring or ice melt-ing, happen regardless of being observed. Video world models generate“worlds” via 2D frame observations. Can these generated “worlds” evolveregardless of observation? To probe this question, we design a benchmarkto evaluate whether video world models can decouple state evolutionfrom observation. Our benchmark, StEvo-Bench, applies observationcontrol to evolving processes via instructions of occluder insertion, turn-ing off the light, or specifying camera “lookaway” trajectories. By evalu-ating video models with and without camera control for a diverse set ofnaturally-occurring evolutions, we expose their limitations in decouplingstate evolution from observation. StEvo-Bench proposes an evaluationprotocol to automatically detect and disentangle failure modes of videoworld models across key aspects of natural state evolution. Analysis ofStEvo-Bench results provide new insight into potential data and ar-chitecture bias of present-day video world models. Benchmark website:https://glab-caltech.github.io/STEVOBench/.

Abstract:
Egocentric devices, such as wearable front-facing cameras,provide a unique perspective for capturing the continuous interactionbetween a human viewer and the surrounding environment. A holisticand efficient multimodal model capable of reconstructing this 4D rep-resentation is therefore highly desirable. However, existing approachesoften rely on auxiliary inputs such as pre-computed camera trajecto-ries, treat scene perception and human ego-motion modeling as separateproblems despite their strong interdependency, and suffer from slow in-ference time. To address these limitations, we present ReViV, the firstunified framework for holistic egocentric 4D reconstruction that extractsboth viewer and view dynamics from a single monocular RGB video.We formulate the task as learning the full joint probability distribu-tion over multimodal signals, including RGB video, camera trajectory,gaze direction, full-body motion, hand motion, and depth. Powered bya Masked Generative Egocentric Transformer, ReViV operates within asingle feed-forward architecture to simultaneously reconstruct the tem-porally consistent 4D reconstruction across the viewer and the view withfast inference speed. Extensive experiments on diverse benchmarks, in-cluding HoloAssist, HOT3D, ARCTIC, Aria Digital Twin, and TACO,demonstrate that ReViV achieves state-of-the-art accuracy and efficiencyacross holistic ego-body, hand, and gaze reconstruction, camera track-ing, while maintaining highly competitive egocentric depth estimation,without relying on heavy task-specific priors. Code and models are fullyopen-sourced: https://reviv4d.github.io/.

Abstract:
Self-Supervised Monocular Depth Estimation (MDE) has garnered attention in recent years due to its independence from ground truth. However, most existing models are limited to a single scale and exhibit considerable performance degradation in complex driving environments. Networks specifically designed to handle dynamic traffic participants tend to be overly complex, hindering their deployment on resource-constrained automotive edge devices. To address these limitations and move towards robust driving perception, we propose FlexDepth, a scale-driven and flexible family of self-supervised MDE models tailored for challenging road scenarios. FlexDepth employs a two-stage static-dynamic decoupled training strategy, enabling the independent assessment of confidence for both static backgrounds and dynamic road objects. Furthermore, it introduces a meticulously designed Scale-Driven Decoder (SDD) to dynamically select components based on scale size, facilitating efficient feature fusion and the output of high-precision depth maps. Extensive experiments on standard driving benchmarks demonstrate that without any auxiliary information, our model achieves state-of-the-art performance across arbitrary scales with minimal computational overhead. Our smallest model, Flex-Nano, requires only 0.7 GFLOPs and achieves 37.6 FPS on mobile platforms, ensuring reliable real-time perception while maintaining excellent zero-shot generalization. Our source code is avalible: https://github.com/startnew/flexdepth

Abstract:
This paper addresses the problem of monocular metric depth estimation in aerial UAV imagery. Although recent data-driven methods have achieved remarkable progress in ground-level scenarios, models trained primarily on street-view and indoor datasets exhibit significant domain gaps when applied to aerial viewpoints. To tackle these challenges, we introduce AerialMetric, a benchmark dataset designed to evaluate and facilitate the adaptation of monocular metric depth estimation under UAV aerial viewpoints. The dataset consists of four complementary subsets collected from different sources, jointly covering real-world photogrammetry data, controlled aerial acquisition settings, photorealistic synthetic scenes, and in-the-wild Internet imagery. In total, AerialMetric provides 52K real-world and 16K synthetic image–depth pairs with reliable metric ground truth. Based on this dataset, we conduct systematic evaluations of existing state-of-the-art models under aerial settings and investigate the impact of viewpoint, altitude, and camera parameters on metric depth prediction. In addition, by fine-tuning representative metric depth model on our dataset, we establish a comprehensive aerial benchmark and achieve state-of-the-art performance across diverse aerial imagery. Our dataset, code, and model weight are publicly available at https://kuieless.github.io/AerialMetric-ECCV2026-page/.

Abstract:
Long-context egocentric video understanding has recently attracted significant research attention, with augmented reality (AR) highlighted as one of its most important application domains. Nevertheless, the task remains highly challenging due to the need for reasoning over extended temporal contexts and diverse, unstructured activities. Although several benchmarks exist, most egocentric datasets rely on human-worn cameras and focus mainly on visual content, with limited consideration of underlying user behavior when forming video-related queries. EgoEverything is a benchmark that uses real gaze traces as a weak attention prior, rather than as a direct proxy for user intention, when generating questions. It comprises over 5,000 multiple-choice question-answer pairs, spanning more than 100 hours of video. By integrating measured gaze traces with a lightweight spatial sampling prior, it more faithfully captures natural AR-style querying behavior and offers a realistic evaluation setting for long-context egocentric video understanding in AR. We release our dataset at https://sai-lab-nyu.github.io/EgoEverything/.

Abstract:
Multimodal Large Language Models have achieved signif-icant success in offline video understanding, yet their application tostreaming videos is severely limited by the linear explosion of visual to-kens, which often leads to Out-of-Memory (OOM) errors or catastrophicforgetting. Existing visual retention and memory management methodstypically rely on uniform sampling, low-level physical metrics, or pas-sive cache eviction. However, these strategies often lack intrinsic seman-tic awareness, potentially disrupting contextual coherence and blurringtransient yet critical semantic transitions. To address these limitations,we propose CurveStream, a training-free, curvature-aware hierarchicalvisual memory management framework. Our approach is motivated bythe key observation that high-curvature regions along continuous featuretrajectories closely align with critical global semantic transitions. Basedon this geometric insight, CurveStream evaluates real-time semantic in-tensity via a Curvature Score and integrates an online K-Sigma dynamicthreshold to adaptively route frames into clear and blurred memorystates under a strict token budget. Evaluations across diverse tempo-ral scales confirm that this lightweight framework, CurveStream, consis-tently yields absolute performance gains of over 10% (e.g., 10.69% onStreamingBench and 13.58% on OVOBench) over respective baselines,establishing new state-of-the-art results for streaming video perception.

Abstract:
Autoregressive video diffusion models have demonstrated re-markable progress, yet they remain bottlenecked by intractable linearKV-cache growth, temporal repetition, and compounding errors duringlong-video generation. To address these challenges, we present Pack-Forcing, a unified framework that efficiently manages the generationhistory through a novel three-partition KV-cache strategy. Specifically,we categorize the historical context into three distinct types: (1) Sinktokens, which preserve early anchor frames at full resolution to maintainglobal semantics; (2) Mid tokens, which achieve a massive spatiotempo-ral compression (∼32× token reduction) via a dual-branch network fusingprogressive 3D convolutions with low-resolution VAE re-encoding; and(3) Recent tokens, kept at full resolution to ensure local temporal co-herence. To strictly bound the memory footprint without sacrificing qual-ity, we introduce a dynamic top-k context selection mechanism for themid tokens, coupled with a continuous Temporal RoPE Adjustment thatseamlessly re-aligns position gaps caused by dropped tokens with negli-gible overhead. Empowered by this principled hierarchical context com-pression, PackForcing can generate coherent 2-minute, 832×480 videosat 14–16 FPS on a single H200 GPU. It achieves a bounded KV cacheenables a remarkable 24× temporal extrapolation (5 s → 120 s), oper-ating effectively trained on merely 5-second clips. Extensive results onVBench demonstrate state-of-the-art temporal consistency (26.07) anddynamic degree (56.25), proving that short-video supervision is sufficientfor high-quality, long-video synthesis.

Abstract:
360 video object segmentation (360VOS) aims to predicttemporally-consistent masks in 360 videos, offering full-scene coverage,benefiting applications, such as VR/AR and embodied AI. Learning360VOS model is nontrivial due to the lack of high-quality labeled dataset.Recently, Segment Anything Models (SAMs), especially SAM2 – withits design of memory module – shows strong, promptable VOS capabil-ity. However, directly using SAM2 for 360VOS yields implausible resultsas 360 videos suffer from the projection distortion, semantic inconsis-tency of left-right sides, and sparse object mask information in SAM2’smemory. To this end, we propose PanoSAM2, a novel 360VOS frame-work based on our lightweight distortion- and memory-aware adaptationstrategies of SAM2 to achieve reliable 360VOS while retaining SAM2’suser-friendly prompting design. Concretely, to tackle the projection dis-tortion and semantic inconsistency issues, we propose a Pano-AwareDecoder with seam-consistent receptive fields and iterative distortionrefinement to maintain continuity across the 0°/360° boundary. Mean-while, a Distortion-Guided Mask Loss is introduced to weight pixelsby distortion magnitude, stressing stretched regions and boundaries. Toaddress the object sparsity issue, we propose a Long–Short Mem-ory Module to maintain a compact long-term object pointer to re-instantiate and align short-term memories, thereby enhancing temporalcoherence. Extensive experiments show that PanoSAM2 yields substan-tial gains over SAM2: +5.6 on 360VOTS and +6.7 on PanoVOS, show-ing the effectiveness of our method. Code is available at https://github.com/Eric-Bumaro/PanoSAM2.

Abstract:
Diffusion models (DMs) have achieved remarkable successin image and video generation. However, there are still challenges in(1) physical alignment and (2) counterfactual generation. To diagnosethe root causes, we introduce the Causal Scene Graph (CSG) to modelDM’s generative process, and build the Physical Alignment Probe (PAP)dataset to quantify the failure modes. Our analysis yields three key in-sights: (1) DMs struggle with physical elements not explicitly determinedin the prompt; (2) the prompt embedding contains disentangled repre-sentations for texture and physics; (3) visual causal structure is dispro-portionately established during the initial, computationally constraineddenoising steps. Based on these findings, we introduce Lina (LearningINterventions Adaptively), a novel framework that learns to predictprompt-specific interventions. Lina employs (1) targeted guidance in theprompt and visual latent spaces, and (2) a reallocated, causality-awaredenoising schedule. Without modifying pre-trained DM weights, Linaenforces both physical alignment and counterfactual generation in imageand video DMs, achieving state-of-the-art performance on challengingcausal generation tasks and the Winoground dataset. Our project pageis at https://opencausalab.github.io/LINA

Abstract:
Recent work has shown that Chain-of-Thought (CoT) rea-soning improves text-to-motion generation, yet generating explicit rea-soning tokens at inference time introduces significant latency. We presentLaCT-Motion (Latent Chain-of-Thought for Motion), the first methodto transfer latent reasoning from language to LLM based human mo-tion generation, achieving explicit-CoT-level quality without its infer-ence overhead. Our three-stage recipe mirrors human skill acquisition:practice learns explicit CoT reasoning via supervised fine-tuning; in-ternalization progressively compresses variable-length reasoning stepsinto compact continuous latent tokens through a stage-based curricu-lum with stochastic scheduling; and perfection refines the fully latentpolicy with GRPO using format, motion similarity, and semantic simi-larity rewards. At inference, the model reasons entirely through latenttokens with no explicit text generated. Experiments on HumanML3Dshow that latent internalization not only matches but surpasses explicitCoT reasoning (R-Precision Top-1: 0.577 vs. 0.515 for Motion-R1, FID:0.167), suggesting that continuous latent tokens encode spatio-temporalreasoning more effectively than discrete text. Code will be released athttps://github.com/Erwin2233/LaCT-Motion.

Abstract:
Pre-trained Vision Transformers (ViTs) have shown strongperformance across diverse tasks, creating increasing demand for efficientfine-tuning and compression for downstream deployment. Existing VectorQuantization (VQ) methods achieve high compression ratios, yet strug-gle to balance fine-tuning efficiency and downstream performance. In thiswork, we propose Vector Quantization Tuning (VQT), a novel frameworkfor efficient fine-tuning and compression of pre-trained ViTs. We iden-tify Coupled Vector Quantization Noise (CVQN) as a key factor limitingperformance. It arises from the interaction between Low-Rank Adapta-tion (LoRA) and the Straight-Through Estimator (STE), which degradesoptimization effectiveness under vector quantization. To address this,VQT employs a VQ-aware fine-tuning process that stochastically replacesvector-quantized weights with continuous counterparts to reduce CVQN,while progressively aligning the model toward fully vector-quantized rep-resentations to ensure consistency between fine-tuning and deployment.Furthermore, VQT enables efficient storage for multi-task deploymentby storing only lightweight task-specific parameters in the cloud, withless than 1 bit per parameter needed to reconstruct compressed models.Extensive experiments show the advantages of VQT over state-of-the-art baselines in performance and efficiency at high compression ratios.For example, at 1-bit compression, VQT surpasses the advanced baselineby 9.5% on the VTAB-1K benchmark while fine-tuning only 0.8% of theparameters. The code is available: https://github.com/leenas233/VQT.

Abstract:
Monocular depth estimation (MDE) is a fundamental yet inherently ill-posed task. Recent vision foundation models (VFMs), particularly DINO-based transformers, have significantly improved accuracy and generalization for dense prediction. Prior works generally follow a unified paradigm: sampling a fixed set of intermediate transformer layers at uniform intervals to build multi-scale features. This common practice implicitly assumes that geometric information is uniformly distributed across layers, which may underutilize the structural 3D cues encoded in VFMs. In this study, we present a systematic layer-wise analysis of DINOv3, revealing that 3D information is distributed nonuniformly: deeper layers exhibit stronger depth predictability and better capture inter-sample geometric variation. Motivated by this, we introduce a Last-Layer-Centric Feature Recombination (LFR) module to enhance geometric expressiveness. LFR treats the final layer as a geometric anchor and adaptively selects complementary intermediate layers according to a minimal-similarity criterion. Selected features are fused with the last-layer representation via compact linear adapters. Extensive experiments show that LFR module consistently improves MDE accuracy and achieves state-of-the-art performance. Our analysis sheds light on how geometric knowledge is organized within VFMs and offers an efficient strategy for unlocking their potential in dense 3D tasks. Code is released at https://github.com/book-book24/LFR.

Abstract:
In this paper, we study depth perception of vision-language models (VLMs) to isolate the effects of pictorial depth cues and disentangle vision and language influences on model performance. To this end, we combine depth-ordering and odd-one-out psychophysical tasks: the VLMs are presented with images where one object is at different depth relative to other, otherwise identical, objects, and must determine whether the odd-one-out target is closer or farther to the observer. To create stimuli, we generate 2D views from simulated and real 3D scenes while controlling the presence of individual pictorial depth cues, enabling a fine-grained analysis of cue-level contributions. Language effects are examined by varying referring expression clarity. We also introduce a novel metric to quantify vision-vs-language sensitivities. Applying this methodology, we create the Odd-One-Out Depth (O3-D) dataset with 37K real and synthetic images and 147K image-question pairs. Evaluation of 12 open-source and commercial models on O3-D shows under-utilization of depth cues and depth-ordering accuracies between 47% and 56%, with no model above chance level. At the same time, our metric reveals strong linguistic bias in the answers. Neither chain-of-thought (CoT) nor incontext learning (ICL) significantly improves performance, suggesting that static image data alone may be insufficient for depth understanding. All code, the image generation pipeline, and the O3-D dataset are publicly released at https://github.com/lyiqian/o3-d.

Abstract:
Recent advances in foundation models have shown greatpromise in domains such as natural language processing and computervision, and similar efforts are now emerging in the Earth Observationcommunity. These models aim to generalize across tasks with limitedsupervision, reducing the need for training separate models for eachtask. However, current strategies, which largely focus on scaling modelsize and dataset volume, require prohibitive computational and data re-sources, limiting accessibility to only a few large institutions. Moreover,this paradigm of ever-larger models stands in stark contrast with theprinciples of sustainable and environmentally responsible AI, as it leadsto immense carbon footprints and resource inefficiency. In this work,we present a novel and efficient alternative: EoS-FM1 , an Ensemble-of-Specialists framework for building Remote Sensing Foundation Models(RSFMs). Our method decomposes the pre-training process into train-ing lightweight, task-specific ConvNeXtV2 specialists that can be frozenand reused. This modular approach offers strong advantages in efficiency,interpretability, and extensibility. Moreover, it naturally supports feder-ated training, pruning, and continuous specialist integration, making itparticularly well-suited for collaborative and resource-constrained set-tings. Our framework sets a new direction for building scalable and effi-cient RSFMs.

Abstract:
Reinforcement Learning from Verifiable Rewards (RLVR)has substantially enhanced the reasoning capabilities of large languagemodels in abstract reasoning tasks. However, its application to LargeVision-Language Models (LVLMs) remains constrained by a structuralrepresentational bottleneck. Existing approaches generally lack explicitmodeling and effective utilization of visual information, preventing vi-sual representations from being tightly coupled with the reinforcementlearning optimization process and thereby limiting further improvementsin multimodal reasoning performance. To address this limitation, wepropose KAWHI (Key-Region Aligned Weighted Harmonic Incentive),a plug-and-play reward reweighting mechanism that explicitly incorpo-rates structured visual information into uniform reward policy optimiza-tion methods (e.g., GRPO and GSPO). The method adaptively localizessemantically salient regions through hierarchical geometric aggregation,identifies vision-critical attention heads via structured attribution, andperforms paragraph-level credit reallocation to align spatial visual ev-idence with semantically decisive reasoning steps. Extensive empiricalevaluations on diverse reasoning benchmarks substantiate KAWHI as ageneral-purpose enhancement module, consistently improving the perfor-mance of various uniform reward optimization methods. Code is availableat https://github.com/kawhiiiileo/KAWHI.

Abstract:
Accurate dialogue description in audiovisual video captioning is crucial for downstream understanding and generation tasks. However, existing models generally struggle to produce captions that faithfully reflect spoken content and speaker dynamics. To mitigate this limitation, we propose DiaDem, a powerful audiovisual video captioning model capable of generating captions with more precise dialogue descriptions while maintaining strong overall performance. We first design a dedicated pipeline to synthesize high-quality dialogue-aware audiovisual captions for supervised fine-tuning (SFT), and then introduce a difficulty-partitioned two-stage reinforcement learning (RL) strategy to further enhance dialogue descriptions. To enable systematic evaluation of dialogue description capabilities, we present DiaDemBench, a comprehensive benchmark designed to evaluate models across diverse dialogue scenarios, focusing on both speaker attribution accuracy and utterance transcription fidelity in audiovisual captions. Extensive experiments on DiaDemBench reveal even commercial models still exhibit substantial room for improvement in dialogue-aware captioning. Notably, DiaDem not only outperforms the Gemini series in dialogue description accuracy but also achieves competitive performance on general audiovisual captioning benchmarks, demonstrating its overall effectiveness. Our project is available at https://diadem-captioner.github.io/.

Abstract:
Multimodal large language models (MLLMs) have made rapidprogress, yet they still exhibit object hallucination, generating plausiblebut incorrect descriptions that are inconsistent with the visual input. Di-rect Preference Optimization (DPO) mitigates this by training modelsto prefer non-hallucinated responses over hallucinated ones, and recentefforts further enrich the preference data with relevant context. How-ever, it remains unclear whether DPO actually leverages such context.To investigate this, we propose Contextual Preference Gain (CPG), asimple metric that measures how much a model’s preference strengthenswhen relevant context is provided. We find that higher CPG consistentlycorresponds to lower hallucination, yet standard DPO and its variantsexhibit only limited CPG, indicating that they underutilize contextualinformation and thus remain prone to hallucination. To address this, wepropose Context-Calibrated DPO (C2 -DPO), which directly maximizesCPG while preserving the original preference ordering. Across multiplebenchmarks, C2 -DPO substantially reduces hallucination without com-promising general reasoning, relatively reducing the Object HalBenchhallucination rate of Qwen2-VL-Instruct-2B by 36%. Code is availableat https://github.com/mlvlab/C2-DPO

Abstract:
Building a unified visual tokenizer is essential for bridgingthe gap between visual understanding and generation. Yet existing ap-proaches struggle with the inherent conflict between these tasks, as asingle token space is forced to support both high-level semantic abstrac-tion and low-level pixel reconstruction. We propose WinTok, a concisehybrid tokenizer that achieves a win-win performance by explicitly de-coupling the two objectives. WinTok supplements pixel tokens with aset of learnable semantic tokens, effectively mitigating cross-task inter-ference without incurring the computational overhead of dual tokenizers.To further enhance understanding capability, we introduce an asymmet-ric token distillation mechanism: the semantic tokens are guided by pre-trained semantic embeddings from any visual foundation model, enablingthem to inherit strong discriminative power while maintaining flexibility.Across 10 challenging benchmarks, WinTok delivers consistent improve-ments in reconstruction, understanding, and generation. Trained on only50M open-source data, WinTok surpasses the strong baseline UniTok by11.2% in classification accuracy and achieves a competitive reconstruc-tion rFID of 0.41, despite using substantially less training data. Code isreleased at https://github.com/WeChatCV/WinTok.

Abstract:
Thermal infrared (TIR) imaging is indispensable for all-weatherperception and energy management, yet existing 3D thermal modelingmethods remain primarily confined to static environmental snapshots.These approaches typically struggle to capture complex temporal tem-perature variations and lack the capacity to simulate scenes under unseenenvironmental conditions. To bridge this gap, we propose ThermoGS,a unified framework that couples 4D Gaussian Splatting (4DGS) withfundamental thermodynamic principles to achieve physically-consistentthermal field emulation. Unlike previous data-driven methods that di-rectly regress radiance, ThermoGS decouples intrinsic material proper-ties, including emissivity (e), convective heat transfer coefficient (h), vol-umetric heat capacity (c), and absorptivity (α)—from transient temper-ature states. By integrating Physics-Informed Neural Networks (PINNs),our framework ensures that the predicted spatiotemporal thermal distri-bution strictly adheres to energy balance equations, enabling generalizedsimulation across arbitrary viewpoints and time periods. Furthermore,we introduce the Thermo Dataset, a comprehensive, drone-captured col-lection featuring synchronized multi-modal data and diverse environmen-tal parameters. Extensive experiments demonstrate that ThermoGS sig-nificantly outperforms comparative methods in thermal simulation accu-racy, maintaining a predicted temperature error within 1 degree Celsius.The code is available at https://github.com/NPU-CVPG/ThermoGS.

Abstract:
Existing 3D Gaussian Splatting (3DGS) frameworks rely oncamera-speci x001C c rasterization, su x001B ering from inconsistent solid-angle sam-pling and degraded performance across heterogeneous camera models(e.g., perspective, x001C sheye, omnidirectional). To address this limitation,we propose UniTriSplat, a uni x001C ed 3DGS framework for universal camerasthat reformulates Gaussian splatting on the unit sphere via HEALPixdiscretization. Leveraging the equal-area property of HEALPix, we con-struct a spherical sampling grid aligned with the angular resolution ofinput images. We derive the forward rendering and gradient propagationof Gaussians directly in the spherical radian domain, yielding uniform op-timization behavior from narrow-FoV images to full 360-degree panora-mas. To enhance perceptual reconstruction quality, we additionally intro-duce a HEALPix-aware SSIM loss that respects spherical neighborhoodstructure. Extensive experiments across diverse camera models demon-strate that UniTriSplat consistently improves cross-camera generaliza-tion while preserving geometric x001C delity and rendering quality. Projectpage: https://yipengzhu0809.github.io/UniTriSplat/

Abstract:
Metric 3D object detection is a core capability for embodied agents, yet most reliable systems lean on depth sensors, trading away cost, power, and integration simplicity. This motivates monocular 3D detection, which avoids additional constraints, yet it faces a major obstacle: from a single image, depth, and especially absolute scale, are underconstrained. As a result, the prevailing pattern of detecting in 2D and then predicting 3D attributes is often brittle, since modest range errors can dominate 3D localization, and the learned scale prior can fail when cameras, motion, or environments undergo domain shifts. To address this, we propose Map-Det3D, an online multi-view 3D object detection model that brings detection directly into a 3D space reconstructed from RGB. We map a short temporal window into multiple views and repurpose a feed-forward metric 3D reconstruction model as our geometric backbone while tuning its object-aware capabilities. Building on this representation, Map-Det3D directly predicts boxes in metric 3D space, without the widely used 2Dto-3D lifting. Experiments across different benchmarks show that this design supports strong online performance and robust transfer without adaptation, suggesting that training reconstruction priors for detection is a practical route to stable metric 3D detection from monocular video. Code and models are available at royyang0714.github.io/Map-Det3D.

Abstract:
Point clouds are one of the most fundamental and widelyused 3D representations, serving as the most basic geometric represen-tation of 3D shapes. Nevertheless, most existing 3D printing pipelinesrequire a watertight mesh as input, preventing the direct use of pointclouds for fabrication. A common workaround is to reconstruct meshesfrom point clouds; however, the resulting meshes often contain geomet-ric artifacts, such as incorrect faces or topological inconsistencies, thatare difficult to repair and may lead to printing failures. To overcomethese limitations, we propose PrintAnything, a novel framework thatlearns to produce executable 3D printing G-code directly from 3D pointclouds without requiring mesh reconstruction. To enable point cloudsto serve as direct input for slice-wise toolpath generation, we introducea slice-wise point projection strategy that transforms unstructured 3Dpoint clouds into slice-aligned 2D representations consistent with layer-by-layer nature of fused deposition modeling in 3D printing. To eliminatemesh dependency and provide a unified representation that bridges pointclouds and G-code, we propose Geometric plan (G-plan) map, a com-pact 2D representation composed of occupancy, region, and flow mapsthat encode the geometric and extrusion properties required for toolpathsynthesis in 3D printing. As a result, our proposed method accuratelygenerates printable G-code directly from point clouds, enabling a prac-tical and fully mesh-free pipeline for 3D printing. The code is publiclyavailable at https://github.com/Sangminhong/PrintAnything.

Abstract:
Integrating macro-scale radiology and micro-scale pathologyprovides complementary insights crucial for precision oncology. How-ever, this integration is often hindered by modality discrepancy. The fea-ture embeddings from heterogeneous foundation models reside on poorlyaligned latent manifolds, and naive static fusion may therefore mix unre-lated semantic neighborhoods, leading to topological degeneracy and se-mantic entanglement. To bridge this gap, we propose a novel multimodallearning framework for Synthesizing radiology and pathology EvidencEvia Knowledge-guided FLOW matching. Specifically, we construct ashared feature manifold anchored by learnable clinical prototypes, en-abling the disentanglement and synthesis of shared and modality-specificfeature evidence. We then reformulate multimodal integration as a trans-port problem via knowledge-guided flow matching and seek continuousflows that smoothly transport features onto this semantic substrate. Thisparadigm preserves the intrinsic topology of the feature spaces and re-duces feature collapse. Furthermore, an uncertainty-aware evidential ag-gregator is incorporated to synthesize cross-modal feature evidence forreliable decision-making. Extensive experiments on three cancer datasets(i.e., TCGA Glioma, Gastric Cancer, and Chondrosarcoma) demonstratethe effectiveness of SeekFlow. Notably, SeekFlow consistently achievesstate-of-the-art performance across all benchmarks, outperforming ex-isting multimodal fusion methods in both diagnosis and prognosis tasks.The code is available at https://github.com/HKU-MedAI/SeekFlow.

Abstract:
Due to the sparsity of LiDAR measurements, RGB-assisteddense depth reconstruction is widely adopted to provide structural pri-ors for autonomous driving. However, the inherent sensitivity of RGBimaging to illumination conditions makes dense depth reconstruction un-der low-light scenarios still a critical challenge. Specifically, under long-exposure imaging, motion blur in low-light RGB frames significantlydegrades the accuracy of depth reconstruction. To address this issue,we exploit the high-temporal-resolution motion cues captured by eventcameras and propose Event-guided Restoration and Upsampling Network(ERU-Net), a unified framework that tightly couples event-guided featurerestoration with depth completion. The Event-guided Feature Restora-tion (EFR) module combines implicit neural representation (INR) andself-recursive optimization to remove motion blur from long-exposure in-puts and recover artifact-free features. These features serve as structuralpriors for the Restoration-Aware Depth Upsampling (RDU) module, en-abling accurate completion of sparse LiDAR measurements with finegeometric details. Extensive experiments on challenging synthetic andreal-world captured datasets demonstrate that ERU-Net significantlyoutperforms state-of-the-art approaches, producing accurate and tem-porally consistent dense depth sequences in severe low-light conditions.Our code will be available at https://github.com/qzm777/Night-RIDE-ECCV2026.

Abstract:
Atmospheric turbulence severely degrades thermal infraredimaging quality, yet most existing mitigation methods focus solely on im-age restoration while ignoring underlying physical characteristics. In thisstudy, we construct a large-scale infrared turbulence imaging dataset andproposed the Turbulence-oriented Mutual Progressive Layered Extrac-tion (TurboMPLE), a joint network for infrared turbulence mitigationand physical fields estimation. Through Mutual PLE Blocks, TurboM-PLE achieves progressive feature-level collaboration, introducing turbu-lence priors into the mitigation process while incorporating richer visualinformation into physical fields estimation. In addition, embedding theLambert–Beer law and turbulent physics constraints enables more in-terpretable results. With a lightweight and computationally efficient de-sign, TurboMPLE achieves state-of-the-art performance on both tasksand demonstrates strong generalization capability on real-world data.The code is available at https://github.com/Ayt777/TurboMPLE.

Abstract:
We introduce Spatial Amsan, a benchmark for evaluatingstate-based spatial reasoning and action evaluation in egocentric manip-ulation videos. The term amsan (Korean for mental arithmetic) reflectsthe core challenge: constructing and updating an internal spatial state toevaluate whether each manipulation advances toward a goal. The bench-mark comprises a tangram task (7 colored blocks) and a wooden puz-zle task (16 blocks), totaling 282 scenarios with 1,801 evaluation turns.Models are evaluated through a multi-turn protocol that diagnosticallyisolates four capabilities: visual perception, temporal tracking, spatialcontact reasoning, and goal-directed action evaluation. Our zero-shotevaluation of nine VLMs spanning frontier API models and open-weightlocal models reveals that contact reasoning is a universal bottleneck (12–16% on the wooden puzzle despite 53–96% on block identification) andthat action evaluation degrades from above 79% to below 16% over 8turns while perception remains stable. These results highlight a fun-damental gap between perceiving scene changes and reasoning abouttheir correctness over time. We release all data, annotations, and codeat https://github.com/Blanchard-lab/SpatialAmsan.

Abstract:
Visuotactile sensors are indispensable for contact-rich roboticmanipulation tasks. However, policy learning with tactile feedback insimulation, especially for online reinforcement learning (RL), remains acritical challenge, as it demands a delicate balance between physics fi-delity and computational efficiency. To address this challenge, we presentTac2Real, a lightweight visuotactile simulation framework designed toenable efficient online RL training. Tac2Real integrates the Precondi-tioned Nonlinear Conjugate Gradient Incremental Potential Contact (PNCG-IPC) method with a multi-node, multi-GPU high-throughput par-allel simulation architecture, which can generate marker displacementfields at interactive rates. Meanwhile, we propose a systematic approach,TacAlign, to narrow both structured and stochastic sources of domaingap, ensuring a reliable zero-shot sim-to-real transfer. We further evalu-ate Tac2Real on the contact-rich peg insertion task. The zero-shot trans-fer results achieve a high success rate in the real-world scenario, verifyingthe effectiveness and robustness of our framework. The project page is:https://ningyurichard.github.io/tac2real-project-page/

Abstract:
Multimodal Large Language Models (MLLMs) excel in di-verse vision tasks, but full-parameter retraining is computationally ex-pensive as real-world knowledge evolves. Existing continual learning meth-ods often suffer from semantic entanglement in parameter spaces acrosstasks, impeding the continuous deployment of models. This challenge isespecially pronounced in Anomaly Detection (AD), which exhibits tripleheterogeneity across modalities, domains, and defect scale variability,significantly complicating multi-task knowledge transfer. In this paper,we propose CL-Anomaly, a parameter-efficient fine-tuning frameworkbased on an isolation-sharing collaboration to enable continual learningfor anomaly detection with MLLMs. We introduce the task-private ex-pert PrivLoRA, which physically isolates task-specific subspaces in theparameter space to prevent semantic entanglement of anomaly knowl-edge in diverse scenarios. The Layer-Adaptive Shared Experts maintaincross-task representations within a unified feature space, enabling knowl-edge sharing between previous and new tasks. Furthermore, we proposea Layer-Adaptive Knowledge Transfer strategy that automatically se-lects and dynamically updates the layer-wise key shared experts of eachtask via a momentum-based mechanism, promoting effective knowledgetransfer across related anomaly detection tasks. Extensive experimentsacross three continual learning scenarios for anomaly detection, includ-ing class-incremental, cross-domain, and cross-modal, demonstrate thatCL-Anomaly outperforms state-of-the-art methods. Code is available athttps://github.com/WenDongyp/CL-Anomaly.

Abstract:
Faithfully capturing the intricate relationship between motion and geometry in dynamic scenes is essential for 4D reconstruction. Recent state-of-the-art methods rely on monolithic deformation networks or global time-basis factorizations that struggle to represent complex, non-rigid topological changes. We propose MoBa-GS, a framework that resolves this entanglement by introducing a structural inversion: a spatially-factorized motion field coupled with adaptive geometric optimization. First, our model learns a low-frequency dynamic canonical space to represent coarse scene motion. Next, it decomposes complex, non-rigid motion into a Spatially-Varying Motion Basis of local kinematics, predicted from the canonical geometry, which is then linearly combined using dynamic blending weights. This formulation directly acts as an implicit neural scaffold, recovering both geometry and motion from random initialization, thereby removing the dependency on SfM point cloud priors. This design is further augmented with motion-guided densification and positional annealing to reduce geometry overfitting. Extensive experiments show that our framework surpasses prior state-of-theart methods in reconstruction fidelity. Enabled by time-invariant caching, MoBa-GS requires an order-of-magnitude shorter training time (<18 minutes), a compact storage (∼ 11 MB), and achieves real-time rendering speeds (>163 FPS). Our work establishes a new foundation for highfidelity, efficient 4D representations without relying on explicit geometric priors. Code is available at https://github.com/tgy1221/MoBa-GS.

Abstract:
3D Morphable Models (3DMMs) remain the standard parametric shape priors for many state-of-the-art 3D face reconstruction algorithms. However, as these models are derived from a finite number of 3D face samples, they inherit the morphological biases of their training data, potentially limiting their generalizability across diverse global populations. In this paper, we propose a novel framework to analyze 3DMM reconstructions through the lens of surface curvature, with the objective to discover, quantify and visualize biases. While standard evaluation metrics often rely on Euclidean distances, our reconstruction error captures subtle surface nuances such as local topology or undulations. To do so, we leverage the Laplace-Beltrami Operator (LBO) to generate high-resolution curvature error maps, providing a localized and geometrically meaningful visualization of discrepancies between ground truth faces and reconstructed meshes. We derive from it an error metric that we validated through a user study, observing a significantly higher correlation to human perception compared to traditional methods. Furthermore, we conduct extensive experiments across several 3DMM bases and fitting algorithms, uncovering systematic age-related biases and providing preliminary evidence of biases associated with gender and ethnicity. Our findings highlight the necessity of adopting curvature-aware evaluation protocols to ensure demographic fairness and geometric precision in future 3D face reconstruction research. The code and annotation data are available at https://github.com/artefactory/3dface-fairness.

Abstract:
Language guided image editing has gained significant pop-ularity, yet existing methods struggle with intricate object intersectionsand fine grained spatial relationships due to the absence of an explicitvisual reasoning process. While Chain of Thought (CoT) has been ex-plored to enhance reasoning, purely textual CoT or coordinate basedprompts are fundamentally limited in representing intricate visual lay-outs and lack the pixel level cues necessary for precise editing. To addressthese challenges, we propose Unlocking Complex Image Editing viaMultimodal Reasoning Edit (MURE), a natively multimodal frame-work that shifts the editing process from purely verbal reasoning to asequence of native interleaved textual and visual rationales. As aunified monolithic model, MURE enables seamless end to end optimiza-tion by sharing a latent space and reusing KV cache, which provides amore flexible intermediate visual process and mitigates the error prop-agation typical of multi tool systems. To further ensure reasoning reli-ability, we introduce the Multimodal Deep Confidence (MMDC)paradigm. This paradigm explores a tree of visual reasoning paths ateach step, pruning low quality branches via a deep confidence score froma reward model to ensure a high fidelity trajectory toward the final re-sult. We define the formal representation for native interleaved chainsand release CoT-Edit 14K, the first dataset comprising 14K high qual-ity multimodal reasoning examples. Extensive experiments demonstratethat MURE establishes a new state of the art in complex editing, achiev-ing an average gain of 7.5% on the MagicBrush benchmark and show-casing superior spatial reasoning capabilitiesThe code and CoT-Edit-14Kdataset are released at https://github.com/zhentao-zou/MURE.

Abstract:
This paper introduces a scalable paradigm for supervisedstyle transfer by inverting the problem: instead of learning to stylizedirectly, we learn to destylize, reducing stylistic elements from artisticimages to recover their natural counterparts and thereby producing au-thentic, pixel-aligned training pairs at scale. To realize this paradigm, wepropose DeStylePipe, a progressive, multi-stage destylization frameworkthat begins with global general destylization, advances to category-wiseinstruction adaptation, and ultimately deploys specialized model adap-tation for complex styles that prompt engineering alone cannot han-dle. Tightly integrated into this pipeline, DestyleCoT-Filter employsChain-of-Thought reasoning to assess content preservation and style re-moval at each stage, routing challenging samples forward while discard-ing persistently low-quality pairs. Built on this framework, we constructDeStyle-350K, a large-scale dataset aligning diverse artistic styles withtheir underlying content. We further introduce BCS-Bench, a benchmarkfeaturing balanced content generality and style diversity for systematicevaluation. Extensive experiments demonstrate that models trained onDeStyle-350K achieve superior stylization quality, validating destyliza-tion as a reliable and scalable supervision paradigm for style transfer. Ourproject page: https://wangyephd.github.io/projects/DeStyle/index.html

Abstract:
Although diffusion-based real-world image restoration (Real-IR) has achieved remarkable progress, efficiently leveraging ultra-large-scale pre-trained text-to-image (T2I) models and fully exploiting theirpotential remain significant challenges. To address this issue, we proposeResFlow-Tuner, an image restoration framework based on the state-of-the-art flow matching model, FLUX.1-dev, which integrates unifiedmulti-modal fusion (UMMF) with test-time scaling (TTS) to achieveunprecedented restoration performance. Our approach fully leveragesthe advantages of the Multi-Modal Diffusion Transformer (MM-DiT) ar-chitecture by encoding multi-modal conditions into a unified sequencethat guides the synthesis of high-quality images. Furthermore, we in-troduce a training-free test-time scaling paradigm tailored for imagerestoration. During inference, this technique dynamically steers the de-noising direction through feedback from a reward model (RM), therebyachieving significant performance gains with controllable computationaloverhead. Extensive experiments demonstrate that our method achievesstate-of-the-art performance across multiple standard benchmarks. Thiswork not only validates the powerful capabilities of the flow match-ing model in low-level vision tasks but, more importantly, proposes anovel and efficient inference-time scaling paradigm suitable for largepre-trained models. The source code is publicly available at https://github.com/Rorschach-1010/ResFlow-Tuner.

Abstract:
Zero-Shot Compositional Action Recognition (ZS-CAR) re-quires recognizing novel verb–object combinations composed of previ-ously observed primitives. In this work, we tackle a key failure mode:models predict verbs via object-driven shortcuts (i.e., relying on the la-beled object class) rather than temporal evidence. We argue that sparsecompositional supervision and verb–object learning asymmetry can pro-mote object-driven shortcut learning. Our analysis with proposed di-agnostic metrics shows that existing methods overfit to training co-occurrence patterns and underuse temporal verb cues, resulting in weakgeneralization to unseen compositions. To address object-driven short-cuts, we propose Robust COmpositional REpresentations (RCORE) withtwo components. Co-occurrence Prior Regularization (CPR) adds ex-plicit supervision for unseen compositions and regularizes the modelagainst frequent co-occurrence priors by treating them as hard nega-tives. Temporal Order Regularization for Composition (TORC) enforcestemporal-order sensitivity to learn temporally grounded verb representa-tions. Across Sth-com and EK100-com, RCORE reduces shortcut diagnos-tics and consequently improves compositional generalization. The codeis available at https://github.com/KHU-VLL/RCORE.

Abstract:
Few-shot 3D point cloud semantic segmentation aims to rec-ognize novel object categories with limited labeled examples. Existingmethods typically rely on point-based prototypes extracted from sup-port sets, but such prototypes inevitably contain background noise andlack semantic richness, leading to suboptimal segmentation quality. In-spired by chemical distillation that iteratively removes impurities, wepropose CascadeProto, which employs multi-step refinement to distillhigh-quality prototypes from noisy initializations. At its core lies theEntropy-aware Prototype Purification Module (EPPM), which lever-ages information-theoretic principles to suppress high-entropy backgroundfeatures while amplifying low-entropy foreground information. To fur-ther enrich prototype representations, we introduce Learnable ModalityAdapters (LMA) that independently align each of three CLIP modal-ities — text, audio, and image — with point cloud features throughforeground-background decoupled distribution matching, enabling flexi-ble single-modality semantic enrichment that bridges the 2D-3D domaingap. Furthermore, we propose an Attention-based Dynamic RoutingMechanism (ADRM) that adaptively aggregates predictions from mul-tiple cascade stages, allowing simple regions to benefit from early-stageoutputs while complex regions leverage deeper purification. Extensiveexperiments on S3DIS and ScanNet benchmarks demonstrate the effec-tiveness and superiority of CascadeProto across all three modalities overstate-of-the-art methods. The code is available at https://github.com/changshuowang/CascadeProto.

Abstract:
Out-of-Distribution (OOD) detection is essential for ensur-ing the robustness and reliability of object detection systems deployed insafety-critical applications. While prior research has mainly focused onuni-modal detectors or vision-language model (VLM) based classifiers,the potential of VLM-based object detectors in OOD scenarios remainsunderexplored. In this work, we take the first step toward building OODobject detection methods upon VLMs. We identify two key challenges:(i) learning discriminative features to separate in-distribution (ID) fromOOD instances, and (ii) designing scoring functions consistent with VLMprobabilistic outputs. Hence, we introduce Negative Label Guided Atten-tion and Scoring (NegAS). To address (i), we propose a negative labelguided attention module (NegA), where LLM-generated, visually-similarbut semantically-different negative labels are used to guide attention to-ward potential OOD background regions. To address (ii), we introducea novel sigmoid-based OOD scoring function (NegS) that leverages bothID and negative labels, producing strong responses for ID instances andsuppressed responses for OOD ones. Extensive experiments demonstratethat our approach improves OOD detection performance by a large mar-gin while maintaining competitive ID accuracy, e.g., reducing the FPR95by 11.4% on the COCO dataset and 25.5% on the OpenImages datasetcompared to the baseline model. While initially designed for dense VLMdetectors like YOLO-World, we successfully adapt NegAS to GroundingDINO, a query-based VLM transformer, achieving significant improve-ments, demonstrating the generalizability of our framework. The code isavailable at https://github.com/yjzzyj/NegAS.

Abstract:
Infrared small target detection (IRSTD) aims to localizesmall targets in cluttered infrared scenes. Extensive research follows thepixel-level supervision-guided ‘encoder–decoder’ segmentation paradigm.Although these methods have achieved promising performance, they of-ten overlook that infrared small targets occupy only a few pixels andare usually surrounded by blurred, low-contrast boundaries caused bycluttered backgrounds. Based on this observation, we argue that thefirst principle of IRSTD should be target localization rather than densereconstruction of target regions entangled with indistinguishable back-ground noise. In this paper, we reformulate IRSTD as a centroid re-gression task and propose a novel Single-Point Supervision guided In-frared Probabilistic Response Encoding method (namely, SPIRE), whichis non-trivial because point-level supervision must produce detectionoutputs comparable to dense supervision. Specifically, we design Point-Response Prior Supervision (PRPS) to transform single-point annota-tions into probabilistic response maps consistent with infrared point-target response characteristics, and combine it with a High-ResolutionProbabilistic Encoder (HRPE) that performs encoder-only probabilis-tic regression followed by lightweight peak-based inference, without de-coder reconstruction. By preserving high-resolution features and increas-ing effective supervision density, SPIRE alleviates optimization instabil-ity under sparse target distributions. Finally, extensive experiments ontwo public IRSTD benchmarks, SIRST-UAVB and SIRST4, demonstratethat SPIRE achieves competitive target-level detection performance withconsistently low false alarm rate (Fa) and significantly reduced compu-tational cost.

Abstract:
Multi-person pose estimation (MPPE), which involves detecting body joint positions (keypoints) for every person in an image, is a fundamental task in computer vision. Despite recent advances, no transformer-based model currently achieves real-time performance. This work addresses the latency challenge by introducing DETRPose, the first family of real-time, end-to-end transformer models for multi-person 2D pose estimation. DETRPose significantly enhances the GroupPose decoder, enabling real-time inference. For training, a novel denoising keypoint technique is proposed to accelerate convergence. The varifocal loss is also extended for keypoints, termed Keypoint Similarity VariFocal loss, to improve query quality. Extensive evaluation demonstrates that DETRPose models achieve accuracy comparable to or exceeding that of leading alternatives while requiring five to ten times fewer training epochs. DETRPose-S matches the accuracy of YOLOv8-Pose-X and YOLO11- Pose-X on the COCO dataset (67.0 vs 67.3 and 67.2 in AP) with 81% fewer parameters (11.5M vs 69.4M and 58.8M) and 52% faster inference speed (2.39ms vs 5.23ms and 4.93ms). On the CrowdPose dataset, DETRPose-X has 13.1× fewer FLOPs (232.3G vs 3048.1G) and only 2% fewer precision (75.1 vs 76.6 in AP) than ED-Pose-SwinL-5S. On the OCHuman dataset, DETRPose-S surpasses all previous models, showing the robustness of DETRPose on out-of-distribution datasets. Code is available at https://github.com/SebastianJanampa/DETRPose

Abstract:
Due to the extensive application of UAVs in modern war-fare, anti-UAV single object tracking has garnered increasing researchinterest. However, prevailing multi-modal methods heavily rely on theassumption of perfectly calibrated sensors, leaving the critical challengeof tracking under realistic spatio-temporal misalignment largely unad-dressed. To tackle this issue, we formally introduce the novel task ofuncalibrated RGBT anti-UAV tracking and mathematically formulateits observation process as a unified state-space model. Based on thistheoretical foundation, we propose OCTA-SOT, an Online Cross-modalTrajectory Adjustment Single Object Tracking framework. As a com-pletely training-free and plug-and-play module, it can be directly appliedto off-the-shelf base trackers. During inference, it adaptively selects reli-able trajectory information to update inter-modality mappings in realtime, while simultaneously calibrating unconfident trackers and theirresults. At its core, a customized Kalman-driven mechanism dynami-cally adjusts these mappings, thereby achieving robust target tracking.Extensive experiments on the challenging Anti-UAV300 dataset demon-strate the exceptional effectiveness of our method. Without requiring anytraining, OCTA-SOT effectively enhances the DIMP tracker, achievingabsolute improvements of 9.3% in AUC, 12.9% in Precision, and 11.9%in Normalized Precision, while simultaneously outperforming a series ofcurrent state-of-the-art methods. The source code is publicly availableat https://github.com/xkliu-eps/AntiUAV RGBT Tracking/.

Abstract:
Ensuring the reliability of deep learning models in real-timeindustrial defect detection is critical for high-stakes quality inspection.To mine uncertain samples within continuous industrial media streams,thereby enhancing the reliability of the detection system, this paperproposes a streaming active learning method based on the Fuzzy Dual-dimensional Uncertainty (FuDU) framework. Specifically, we first designa Prototype-based Global Uncertainty Quantification (PGUQ) moduleon the backbone to evaluate image-level uncertainty via normal/defectivefeature prototypes. A Dual-entropy defect Uncertainty Evaluator (DeUE)is then integrated into the detection head to quantify box-level uncer-tainty. Finally, by modeling uncertainty as systematic error, we pro-pose a fuzzy dual-dimensional uncertainty-aware strategy that lever-ages fuzzy inference to fuse dual-dimensional uncertainties, enabling ex-pert knowledge-driven adaptive sampling decisions. Comprehensive ex-periments demonstrate that FuDU is efficient and flexible, making itwell-suited for challenging industrial inspection tasks such as the de-tection of nuclear fuel rod defects. Our code is publicly available at:https://github.com/wangzhaoyang-508/FuDU.

Abstract:
Recent training-free Neural Architecture Search (NAS) meth-ods have introduced zero-cost proxies (ZCPs) to automate architecturedesign without expensive training or expert intervention. However, exist-ing ZCPs either completely ignore training dynamics or rely on static orinitialization-time characterizations of optimization that fail to capturefine-grained correlations with performance. Moreover, they overlook theheterogeneous importance of different model components, which limitstheir ability to generalize across datasets and search spaces. To mitigatethese limitations, we propose an Effective Rank Score called ER-Scoreas a novel ZCP to quantify stability in the training dynamics ofover-parameterized networks. Experiments show that ER-Score con-sistently outperforms existing ZCPs across multi-scale tasks and diversearchitectures, including convolutional neural networks (CNNs) and Vi-sion Transformers (ViTs). We further introduce topology-weighted strate-gies for CNNs that incorporate topological information via operation-wise feature map aggregation, resulting in a topology-weighted vari-ant termed TER-Score. Extensive experiments demonstrate that TER-Score achieves state-of-the-art ranking consistency and the best pop-ulation convergence when integrated with evolutionary search on theNAS-Bench-301 benchmark. Finally, our method achieves the lowesttest errors of 2.41% and 23.55% on the DARTS search space for theCIFAR-10 and ImageNet-1k datasets, respectively. Code is released athttps://github.com/Thiswycf/TER-Score.

Abstract:
As Vision–Language Models (VLMs) increasingly power au-tonomous agents—from coding assistants that interpret terminal outputand execute shell commands, to Computer-Use Agents with full desktopcontrol, to HR screening and loan evaluation bots—these agents act di-rectly over visual artifacts from untrusted sources. While prior work hasstudied direct multimodal jailbreaks, text-based indirect prompt injec-tion, and HTML-embedded attacks, the security implications of Multi-modal Indirect Prompt Injection (M-IPI) in realistic agentic workflowsremain insufficiently understood. We ask a central question: does pro-cessing an artifact visually, rather than as text, degrade an agent’s abil-ity to resist embedded attacks? To study this, we introduce the M-IPIBenchmark—a suite of 2,600 high-fidelity visual artifacts encompass-ing two attack families: (1) Technically-Framed Attacks, where maliciouscommands are interwoven with genuine debugging workflows in terminalscreenshots, and (2) Ethics-Framed Attacks, a novel vector where adver-saries exploit alignment priors such as fairness mandates to override eval-uation policies—and use it to systematically analyze modality-dependentfailures. We evaluate five model families in paired vision and text-onlyconfigurations that receive identical content, isolating the modality effect.Our results reveal that VLMs consistently underperform text-only coun-terparts at detecting embedded attacks—a “visual authority” effect whererendered presentation suppresses critical evaluation. For end-to-end at-tack success, both modalities show comparable susceptibility (∼50% fortechnical, >80% for ethics-framed), with qualitatively distinct failureprofiles: VL models disproportionately trust visually-rendered commandswhile text-only models are more vulnerable to natural-language direc-tives. These findings indicate that the vulnerabilities are rooted in theshared language backbone and that alignment priors represent a dis-tinct, exploitable attack surface. Our benchmark artifacts are releasedunder gated, research-only access at https://github.com/adityakm24/OpenYourEyes to support future defensive research.

Abstract:
Spatial reasoning remains a persistent challenge for manyvision–language models (VLMs), and improving it typically requires fine-tuning with substantial additional parameters. Our preliminary analysisreveals that rescaling activations in selected transformer layers—withoutmodifying pretrained weights—can significantly influence downstreamperformance. Motivated by this observation, we propose ScAle, an ultra-lightweight adaptation method that learns a small set of scalar coeffi-cients to modulate last-token attention and MLP activations in a fullyfrozen backbone. We evaluate our method on the synthetic spatial rea-soning benchmark SpatialEval and on real-world VQA datasets (CO-COQA and VGQA) across multiple model families. Our method, ScAle,achieves up to 134.1% relative accuracy gains using only 1K train-able parameters without requiring millions of trainable parameters as instandard LoRA. Despite its extreme compactness, our approach recov-ers a substantial fraction of standard PEFT performance while preserv-ing strong non-spatial VQA accuracy. These results demonstrate thatbounded activation reweighting provides a simple, architecture-agnostic,and highly parameter-efficient alternative for adapting pretrained VLMs.Code is available at https://github.com/rchowdhubnor/ScAle.git.

Abstract:
Accurate survival modeling using whole slide images (WSI) is crucial for guiding cancer treatment and improving patient outcomes, yet it remains challenging due to the gigapixel resolution, small data cohorts, and limited annotations. In recent years, pathology foundation models (FM) have shown impressive performance improvements over the previous benchmarks set by the ImageNet-based backbones across a variety of WSI analysis tasks. However, the performance of individual FMs, especially in a secondary analysis task such as survival modeling, varies depending on the dataset, without a clear consensus for a superior FM. With over 25 publicly available pathology FMs so far, in this work, we address the growing challenge of model selection for survival analysis in digital pathology. We propose SurvMILKD, the first multi-teacher knowledge distillation (MKD) framework for WSIbased survival analysis. To efficiently enable MKD with high resolution of the WSIs, we integrate the weakly supervised multiple instance learning (MIL) adapters in our framework. The objective of the framework is to distill the complementary task-specific knowledge from multiple teacher FMs into the student FM, which, during inference, has collective knowledge of all FMs involved during training. Additionally, we introduce the risk-aware distillation loss to distill outcome-specific knowledge from multiple teachers into the student model. Our experiments on eight public datasets (including two external evaluation datasets) demonstrate that the proposed approach outperforms the individual FMs, their ensemble, as well as the prior MKD approaches that we benchmarked on survival analysis for the first time in literature. The codes are available at https://github.com/AIMLab-UBC/SurvMILKD.

Abstract:
Bird’s-Eye View (BEV) representations derived from multi-camera input have become a central interface for online HD map con-struction. However, most approaches rely solely on ego-centric super-vision, requiring large-scale scene structure to be inferred from incom-plete observations, occlusions, and diminishing information density atlong range, where perspective effects and spatial sparsity hinder consis-tent structural reasoning. We introduce Cross-View Supervision (CVS),a representation learning paradigm that transfers geometric and topolog-ical priors from an ego-aligned overhead perspective into camera-basedBEV encoders. Rather than adding auxiliary semantic losses, CVS alignsrepresentations in a shared BEV feature space and distills globally con-sistent structural knowledge from a perspective-privileged teacher intothe ego-centric backbone. This supervision enhances structural coher-ence without modifying the inference architecture or requiring overheadinput at test time. Experiments on nuScenes using ego-aligned aerialimagery from the AID4AD cross-view extension demonstrate consistentimprovements over StreamMapNet while maintaining identical camera-only inference. CVS yields +3.9 mAP in the standard 60 × 30 m regionand +9.9 mAP in the extended 100 × 50 m setting, corresponding toa 44% relative gain at long range. These results highlight perspective-privileged structural supervision as a promising training principle forimproving BEV representation learning in HD map construction. Theproject repository is available athttps://github.com/DriverlessMobility/CrossViewSupervision.

Abstract:
Effective Human-Robot Collaboration (HRC) requires robotsto accurately infer human intents under partial observations. However,existing benchmarks mostly rely on idealized views, overlooking the spa-tiotemporal incompleteness such as low-angle truncation and dynamicocclusions inherent in real-world quadruped robot perception. Further-more, current evaluations lack diagnostic transparency, failing to distin-guish whether Vision-Language Models (VLMs) perform reliable com-pensatory reasoning or merely hallucinate based on statistical biaseswhen visual features are degraded. To bridge these dual gaps, we in-troduce CoT-HRC, a large-scale benchmark built in Habitat 3.0 thatcouples simulated robot-centric views with explicit hierarchical Chain-of-Thought (CoT) annotations. We propose a diagnostic protocol featur-ing the Step-wise Consistency Assessment (SCAR ) to penalize spuriousaccuracy by enforcing intermediate logical consistency, and the Condi-tional Reasoning Accuracy (CRA) to explicitly decouple models’ un-derlying reasoning capabilities from visual perception failures. Exten-sive experiments across state-of-the-art VLMs reveal that while currentmodels inherently possess strong physical commonsense, their reason-ing chains are severely disrupted by the dynamic geometric constraintsof egocentric views. Our fine-grained ablations further highlight criti-cal bottlenecks in temporal aggregation and the compensatory role ofsemantic priors, establishing CoT-HRC as a vital stepping stone for ro-bust embodied intent inference.The full benchmark is publicly availableat https://github.com/Thus-cx/CoT-HRC.

Abstract:
Object-Goal Navigation (ObjectNav) is a key capability fordeploying mobile robots in everyday environments such as homes, schools,and workplaces. In this task, an agent must locate an instance of a targetobject category in previously unseen environments using only onboardperception, requiring the integration of semantic understanding, spatialreasoning, and long-horizon planning. Reinforcement learning (RL) hasbecome a dominant paradigm for ObjectNav, yet modern systems involvenumerous design choices across perception modules, policy architectures,and inference-time strategies. The relative impact of these components,however, remains poorly understood. In this work, we present a large-scale empirical study of modular RL-based ObjectNav systems. We de-compose the navigation pipeline into three key components: perception,policy, and test-time enhancement, and conduct extensive controlled ex-periments to analyze their individual contributions. Our results suggestthat improvements in perception quality and test-time strategies oftenyield larger performance gains than policy improvements alone, high-lighting the importance of understanding how different components inter-act within modular navigation systems. Motivated by these findings, weintroduce a unified framework for systematically studying modular Ob-jectNav systems. Guided by our analysis, we build an enhanced systemthat achieves state-of-the-art performance on the Gibson benchmark, im-proving SPL by 6.6% and success rate by 2.7% over prior methods. Wealso introduce a human expert baseline, achieving 98% success, high-lighting the significant gap between current RL agents and human-levelnavigation. Finally, we provide practical insights and design recommen-dations for each module to help guide future research. Project page:https://honwang0054.github.io/What-matters-in-RL-ObjNav-web/.

Abstract:
In embodied intelligence, the embodiment gap between roboticand human hands brings significant challenges for learning from humandemonstrations. Although some studies have attempted to bridge thisgap using reinforcement learning, they remain confined to merely re-producing human manipulation, resulting in limited task performance.Moreover, current methods struggle to support diverse robotic hand con-figurations. In this paper, we propose UniBYD, a unified framework thatuses a dynamic reinforcement learning algorithm to discover manipula-tion policies aligned with the robot’s physical characteristics. To enableconsistent modeling across diverse robotic hand morphologies, UniBYDincorporates a unified morphological representation (UMR). Building onUMR, we design a dynamic PPO with an annealed reward schedule, en-abling reinforcement learning to transition from offline-informed imita-tion of human demonstrations to online-adaptive exploration of policiesbetter adapted to diverse robotic morphologies, thereby going beyondmere imitation of human hands. To address the severe state drift causedby the incapacity of early-stage policies, we design a hybrid Markov-based shadow engine that provides fine-grained guidance to anchor theimitation within the expert’s manifold. To evaluate UniBYD, we proposeUniManip, the first benchmark for cross-embodiment manipulation span-ning diverse robotic morphologies. Experiments demonstrate a 44.08%average improvement in success rate over the current state-of-the-art.Our project page is https://zhanheng-creator.github.io/UniBYD/.Mocap Data2-fingered3-fingeredUMRReinforcement learningShadow Engine5-fingeredDynamic PPOUniBYD Our results Imitation-based reinforcement learningFig. 1: Leveraging human demonstrations, UniBYD learns manipulation strategiesthat transcend mere imitation and are tailored to a broad spectrum of robotic handmorphologies.

Abstract:
We address the inverse problem of blind 3D scene recon-struction from extremely motion-blurred images, a scenario where tradi-tional Structure-from-Motion (SfM) pipelines fail. Existing approachestypically circumvent this bottleneck by relying on impractical sharp-imagesupervision. In this work, we introduce PRISM3D, a unified frameworkenabling robust reconstruction directly from severely degraded inputs.To overcome the lack of a reliable starting point, we propose a RobustInitialization strategy utilizing deep dense tracking method (VGGSfM)to recover global topology where feature matching fails. To the best of ourknowledge, we are the first to effectively leverage this paradigm to boot-strap 3D Gaussian Splatting from extreme motion blur. However, whilerobust, this initialization yields sparse and noisy geometry that causes de-terministic optimization to diverge. To resolve this, we propose a coupledsolution driven by probability and physics: we adopt a probabilisticformulation for geometric densification via Markov Chain Monte Carlo(MCMC) to robustly populate the sparse priors, while simultaneouslymodeling physical image formation via continuous Bézier Trajectories.Furthermore, while PRISM3D establishes a highly robust standalonepipeline, the availability of complementary event streams offers an op-portunity to push the reconstruction fidelity further. To exploit this, weintroduce PRISM3D-E, a multi-modal (RGB + Events) extension thatseamlessly integrates high-temporal-resolution events as structural priorsto maximize geometric recovery. Because existing datasets lack pairedevent streams under such severe degradation, we concurrently contributethe PRISM3D-E Benchmark to facilitate rigorous evaluation. Exten-sive experiments demonstrate that both our standalone RGB frameworkand its multi-modal extension establish new state-of-the-art performance.To facilitate future research in extreme motion deblurring, our projectpage, featuring extensive qualitative comparisons, video results, and theupcoming releases of our codebase and benchmark dataset, is availableat https://gopirajumatta.github.io/PRISM3D/.

Abstract:
While controllable image generation has made significantstrides by incorporating visual reference conditions, existing methodspredominantly operate as open-loop systems. They inject control sig-nals in a strictly feed-forward manner, failing to guarantee strict fidelityto the reference due to the absence of active feedback and error cor-rection mechanisms. To address this fundamental limitation, we pro-pose a novel test-time iterative optimization framework that reformu-lates reference-consistent generation as a closed-loop dynamic trackingproblem. By treating the pre-trained generative model as a control plant,our framework employs a sensor-controller architecture driven by a mod-ified Proportional-Integral-Derivative (PID) algorithm. This mechanismiteratively optimizes the latent control signals at test time based on thesensed discrepancy between the generated output and the reference tar-get. Notably, this approach is entirely training-free, model-agnostic, andintegrates seamlessly around existing diffusion pipelines. Extensive eval-uations across ID-preserving, pose-controlled, and depth-controlled gen-eration tasks validate the universality of our method. Empirical resultsdemonstrate improvements over computation-matched open-loop base-lines, achieving relative performance gains of up to 25.36% for facialsimilarity, alongside spatial error reductions of up to 27.71% for posealignment and 28.50% for depth consistency. More broadly, this workoffers a new conceptual perspective: it demonstrates that controllablegeneration can be effectively managed as a dynamic feedback system,bringing the rigorous principles of classical control theory into the op-timization of generative models. Code is available at https://github.com/zzdrill/From-Open-Loop-to-Closed-Loop.

Abstract:
Although large vision-language models (LVLMs) have demonstrated remarkable capabilities, they are prone to hallucinations in multiimage tasks. We attribute this issue to limitations in existing attention mechanisms and insufficient cross-image modeling. Inspired by this, we propose a structured hallucination mitigation framework involving Cross-Image Attention calibration and Preference Learning (CAPL). CAPL explicitly enhances inter-image interactions at the architectural level while reinforcing reliance on genuine cross-image evidence during training, thereby improving the model’s perception and modeling of cross-image associations. Specifically, we (i) introduce a selectable image token interaction attention mechanism to establish fine-grained crossimage entity alignment and information flow; (ii) design a cross-image modeling–based preference optimization strategy that contrasts reasoning outcomes under full inter-image interaction and those obtained when images are mutually invisible, encouraging the model to ground its predictions in authentic visual evidence and mitigating erroneous inferences driven by textual priors. Experimental results demonstrate that CAPL consistently improves performance across multiple model architectures, achieving stable gains on both multi-image hallucination and general benchmarks. Notably, performance on single-image visual tasks remains stable or slightly improves, indicating strong generalization capability. The code is available at https://github.com/yyyxcleo/CAPL.

Abstract:
Deploying AI-generated video detectors in real-world ser-vices demands an ultra-low false positive rate (FPR) on real videosto avoid falsely rejecting authentic content, a regime where standardmetrics such as AUROC fail to reflect actual operating behavior. Weintroduce Spatial Patch-Level Incoherence and Temporal Roughness(SPLIT), a training-free detector that operates on patch tokens froma frozen vision encoder to detect both fully generated and partiallyedited videos. SPLIT computes two complementary signals: Two-stepTemporal Roughness (TTR), capturing non-smooth patch trajectoriesvia one-step and two-step feature variation contrast, and Local SpatialMotion Incoherence (LSMI), measuring spatially inconsistent temporalchanges through gradients of a feature-space motion field. The two arefused multiplicatively with gamma correction to sharpen real–fake sepa-ration at strict thresholds. We further propose a service-aligned evalua-tion protocol based on Fake Recall at fixed FPR with real-only thresholdcalibration and cross-real threshold transfer. Across three benchmarks—FakeParts, GenVideo, and ViF-Bench—SPLIT achieves the highest FakeRecall at FPR = 0.1%, substantially outperforming supervised and training-free baselines while remaining robust to post-processing with negligi-ble overhead. The code is publicly available at https://github.com/mldljyh/SPLIT.

Abstract:
Conventional one-hot encodings often yield poorly calibrated models, being overconfident under attack, and letting entropy-based detection algorithms fail. Previous image classification works have demonstrated that Hadamard-coded output representations can improve adversarial robustness. However, attempts to integrate Hadamard codes into semantic segmentation fall far behind state-of-the-art models in mean intersection-over-union performance. Regarding object detection, such output encodings have not yet been investigated at all. Further, no prior art addressed intrinsic codeword inconsistencies or actually exploited intrinsic codeword redundancy. Accordingly, we first derive a novel decoding procedure for Hadamard codewords towards optimal class-wise probabilities, solving the underlying optimization problem by using the projection onto the probability simplex. Second, our optimization delivers a measure of prediction inconsistency. Third, we are the first to show how to exploit these inconsistencies for adversarial attack and disturbance detection. Fourth, we introduce HadamardNet, a framework employing Hadamard codes as output representations for semantic segmentation and object detection models and tasks. We conduct a comprehensive evaluation both on disturbances and adversarial attacks, achieving state-of-the-art perturbation detection performance for both tasks in only a single detection pass, while delivering equivalent or close-by reference performance on clean data. Code is available at https://github.com/ifnspaml/HadamardPerturbationDetection.

Abstract:
We present AHOY, a method for reconstructing complete,animatable 3D Gaussian avatars from in-the-wild monocular video de-spite heavy occlusion. Existing methods assume unoccluded input: afully visible subject, often in a canonical pose. This excludes the vastmajority of real-world footage where people are routinely occluded by fur-niture, objects, or other people. Reconstructing from such footage posesfundamental challenges: large body regions may never be observed, andmulti-view supervision per pose is unavailable. We address these challengeswith four contributions: (i) a hallucination-as-supervision pipeline thatuses identity-finetuned diffusion models to generate dense supervisionfor previously unobserved body regions; (ii) a two-stage canonical-to-pose-dependent architecture that bootstraps from sparse observations tofull pose-dependent Gaussian maps; (iii) a map-pose/LBS-pose decou-pling that absorbs multi-view inconsistencies from the generated data;(iv) a head/body split supervision strategy that preserves facial iden-tity. We evaluate on YouTube videos and on multi-view capture datawith significant occlusion and demonstrate state-of-the-art reconstruc-tion quality. We also demonstrate that the resulting avatars are robustenough to be animated with novel poses and composited into 3DGSscenes captured using cell-phone video. Our project page is available athttps://miraymen.github.io/ahoy/.

Abstract:
Generating precise multi-entity 3D trajectories is fundamen-tal for interactive video generation and embodied world models. However,existing text-to-trajectory mappings face two bottlenecks: severe featureleakage from coupled multi-modal inputs, and physical collisions causedby blind end-to-end 3D coordinate regression. We propose ProgTraj-Director, a framework driven by physical property disentanglement andhierarchical spatial planning. To resolve the “who does what” attribu-tion ambiguity, our Structured Vision-Language Alignment (StructVLA)module disentangles static identity masks from dynamic spatial motionflows, binding them via orthogonal factorized tensor fusion in the latentspace. This formulation encourages entity-specific semantic separationand mitigates cross-entity feature leakage. To bridge the “where to go”semantic-physical gap, we formulate trajectory generation as hierarchicalspatial planning. The model first constructs a Geometry-Aware Bird’s-EyeView (BEV) to resolve anti-collision topological boundaries. Guided bythis structural prior, 3D trajectories are derived via progressive spatialdenoising to improve geometric and temporal consistency. To supportthis research, we introduce Stepwise-ME, a large-scale dataset of over31,000 interactive video clips (8M frames) with fine-grained stepwiseannotations. Extensive experiments show that our approach enhancesaction attribution and spatial coherence, providing a reliable structuralprior for downstream video generation. Code and dataset are available athttps://github.com/wzbos-token/Who-Does-What-and-Where-to-Go.

Abstract:
While Video Virtual Try-on (VVT) has achieved remark-able progress in synthesizing realistic garment overlays on dynamic sub-jects, existing paradigms remains fundamentally constrained by a pas-sive dependency on source camera trajectories, failing to accommodatethe requisite interactive freedom for omnidirectional viewpoint explo-ration. To address this limitation, we define a pioneering research fron-tier: Camera-controllable Video Virtual Try-on (CaM-VVT). Unlike con-ventional VVT, CaM-VVT not only necessitates viewpoint-agnostic tex-ture hallucination but also strict structural synchronization between non-rigid human dynamics and background contexts under arbitrary, un-constrained camera movements. To tackle these challenges, we presentTryOnCrafter, the first unified DiT-based framework specifically archi-tected for the CaM-VVT task. Departing from implicit pixel-space ma-nipulation, we introduce a Renderable 4D Try-on Proxy that explicitlydecouples the human subject from the environment. This is achieved bydistilling high-fidelity 2D try-on priors into a clothed 3DGS-based avatar,which is subsequently animated via SMPL-X sequences and metric-alignedinto a reconstructed background point cloud. This proxy establishes a ro-bust structural foundation with superior texture density and motion in-tegrity. Our Proxy-Anchored Video DiT leverages this robust structuralfoundation as a primary geometric anchor, ensuring that the synthesizedphotorealistic videos are strictly constrained by prescribed trajectoriesand physically plausible deformations. Benefiting from the inherent ed-itability of the 4D proxy, TryOnCrafter facilitates diverse downstreamapplications, including human relocalization, “bullet time” effects, and360-degree orbital viewing. Extensive experiments on our establishedCaM-VVTBench demonstrate that TryOnCrafter significantly outper-forms existing baselines in preserving structural consistency and garmentidentity across complex camera maneuvers.

Abstract:
Visual fall detection models are usually trained on small,staged datasets. Their real-world utility remains unclear; such data lacksdiversity and evaluation protocols differ from paper to paper. We proposeOmniFall, a unified benchmark of 15k videos (80 hours) with frame-levelannotations in a single 16-class taxonomy. It spans three domains: OF-Staged unifies eight staged datasets with cross-subject and cross-viewsplits; OF-Synthetic adds 12k videos (17 h) with controlled demographicand environmental diversity; and OF-In-the-Wild provides a test-onlyset of genuine accident videos. We evaluate fine-tuned models as wellas much larger zero-shot multimodal LLMs. On in-the-wild fall events,both do comparably well. The clinically critical fallen state is wherethey part: zero-shot models keep confusing fallen with lying, whereasmodels fine-tuned on synthetic data with explicit fallen-state scenes dosubstantially better. We release the unified annotations, the syntheticdata, and the in-the-wild test set to foster the development of fall andfallen-state detectors for uncontrolled environments.Dataset: https://hf.co/datasets/simplexsigil2/omnifall

Abstract:
Spatial reasoning is a core aspect of human intelligence forperception and inference in 3D environments. However, current vision-language models (VLMs) struggle to maintain geometric coherence andcross-view consistency for spatial reasoning in multi-view settings. Inthis paper, we aim to understand the successes and failure of VLMs inmulti-view spatial reasoning, via structured analysis of how VLMs en-code perceptual evidence, integrate relational information, construct in-termediate spatial representations and perform perspective transforma-tion. Guided by cognitive science theories of human spatial reasoning,our analysis examines VLM’s consistency and representational persis-tence across reasoning phases that align with human’s spatial cognition,in explicit and implicit manners. Using a self-constructed spatial bench-mark, namely ReMindView-Bench, which consists of >50,000 VQA sam-ples with complementary views and controlled viewpoint configurations,spatial patterns and query types, our explicit analysis using an LLM-as-graph-parser workflow and self-consistency prompting shows that VLMsremain reliable for in-frame perceptual encoding but degrade sharplywhen integrating information across views. Our implicit analysis of linearprobing and entropy dynamics further reveals progressive loss of task-relevant spatial information and increasing uncertainty over reasoningphases. These findings provide important insights of how multi-view spa-tial mental models are formed, destabilized, and degraded across reason-ing phases in VLMs. The ReMindView-Bench benchmark is available athttps://huggingface.co/datasets/Xue0823/ReMindView-Bench, andthe source codes of benchmark construction and VLM reasoning analy-sis are available at https://github.com/pittisl/ReMindView-Bench.

Abstract:
Large vision-language models (LVLMs) are vulnerable to ty-pographic attacks, where misleading text inserted into an image can over-ride visual understanding. However, existing evaluation protocols anddefenses are largely focused on object recognition and do not considertext-reading capability. This is a critical oversight: real-world scenariosoften require both recognizing objects and reading scene text (e.g., rec-ognizing pedestrians while reading tra!c signs), where simply ignoringall text for robustness is unacceptable in practice. To address this gap,we introduce a novel task, Read-or-Ignore VQA (RIO-VQA), whichjointly evaluates both requirements: models must decide, from context,when to read scene-text and when to ignore inserted distractor text. Toevaluate this capability, we present RIO-Bench, a same-scene counter-factual benchmark that holds the scene fixed while varying only questionintent (object vs. text) and text condition (clean vs. attack), enablingdirect comparisons of model behaviors with reduced confounding fac-tors. Using RIO-Bench, we highlight a trade-o": representative defensesdeveloped in object-centric settings can achieve robustness by suppress-ing text sensitivity, at the cost of text-reading performance (i.e., “ignor-ing” text). Motivated by this trade-o", we provide a data-driven defensebaseline that improves both requirements on RIO-Bench, complementingprior text-ignoring baselines. Overall, this work highlights a fundamen-tal misalignment between the current object-centric robustness scope andreal-world multimodal requirements, providing a principled path towardreliable LVLMs.

Abstract:
Current CT report generation frameworks predominantlyrely on global feature representations, often failing to capture region-specific details and potentially missing certain abnormalities. To over-come this limitation, we propose MedRegion-CT, a region-focused mul-timodal large language model framework featuring three key innova-tions. First, we revisit the SlowFast strategy to jointly model globaland fine-grained information and adapt it to the medical domain via aRegion-based SlowFast Tokenizer that extracts tokens guided by clin-ically meaningful regions. Second, generated pseudo-masks guide themodel to attend to diagnostically important anatomical regions, facilitat-ing a systematic understanding of the overall scan context. Third, quanti-tative lesion information, including size, diameter, and spatial location,is encoded as structured textual prompts, enabling context-aware andclinically informed report generation. To enable rigorous evaluation, wevalidate our framework on multi-institutional structured report genera-tion benchmarks. Experimental results demonstrate that MedRegion-CTachieves state-of-the-art performance, outperforming existing approachesin both linguistic quality and clinical accuracy. All code is publicly avail-able at: https://github.com/babbu3682/MedRegion-CT.

Abstract:
Communities are fundamental spatial units that shape urbanform and social life. Whether a residential compound is spatially openor enclosed affects mobility, access to public services, and equity, yetstudies of Chinese fengbi xiaoqu remain largely qualitative or small-scale,limiting reproducible city-scale analysis. We address this gap by introduc-ing GBA-GCs, a metropolitan-scale multimodal benchmark for locallygrounded gated/open community recognition in China’s Greater BayArea, covering 37,444 residential compounds with aligned boundary poly-gons, high-resolution satellite imagery, Chinese metadata, and structuredattributes, together with expert-verified labels, inter-annotator reliability,and official evaluation splits. Built on this benchmark, we present Multi-modal Classifier for Gated Community (MCGC), a vision-centricmultimodal framework based on DINOv3-SAT that fuses imagery, text,and structured cues via modality-aware cross-attention and adaptivegating to mitigate modality imbalance. MCGC consistently outperformsstrong unimodal and multimodal baselines. Finally, we apply the validatedmodel to metropolitan-scale mapping and report equity-oriented findingsincluding spatial clustering of GCs, privatized green space, and reducedpedestrian connectivity. The benchmark, code, and release documentationare available at https://github.com/MinweiZhao/GBA-GCs.

Abstract:
With the increase in model parameters and training data, the instruction following and generalization capabilities of Large VisionLanguage Models (LVLMs) have been significantly improved. Based on the Mixture-of-Experts (MoE) architecture, LVLMs expand their parameter capacity while maintaining the inference cost. However, traditional MoE methods employ a Top-k static routing strategy, which fails to account for variations in the input and to adaptively select the number of experts, resulting in suboptimal resource utilization. In this paper, we propose viewing token routing as an information encoding task, framing dynamic routing as a Minimum Description Length (MDL) problem in encoding. By validating the connection between MDL and gating entropy in the MoE scenario, we introduce Gating Entropy-based Uncertaintyaware Adaptive Routing (GeMoE) for MoE. Unlike traditional static or heuristic-based dynamic routing methods, GeMoE explicitly models the trade-off between model complexity and performance. By using gating entropy to assess the complexity of tokens, GeMoE adaptively determines the number of experts each token should engage. On a wide range of backbones and benchmarks, our method achieves 99.5% average performance retention compared to the original static routing, while improving average expert activation sparsity by 36.5%. The code will be publicly available at https://github.com/caichaoxiang/GeMoE.

Abstract:
We study a critical yet overlooked failure mode in Grounded Video Question Answering: question-invariant grounding, where models predict nearly identical temporal segments for different questions about the same video. We trace this behavior to two structural limitations in prior common designs: (i) modality isolation that fixes video representations before they receive question semantics, and (ii) weak question injection inside the grounding module. To address this, we propose †Corresponding authors. 2 J. Seo et al. GroundFormer, which conditions video features on question intent before localization via learnable communication tokens that mediate directed visuo-lingual interaction. On top of the question-conditioned features, a factorized MIL cross-attention couples answer selection with temporal evidence under candidate-level supervision, while Gaussian smoothing converts peaked attention into temporally coherent segments. We further introduce a hierarchical multi-modal contrastive loss that aligns video, question, and answer embeddings across a two-pass training pipeline. GroundFormer achieves state-of-the-art grounded VideoQA performance on NExT-GQA and STAR, substantially improving question-discriminative temporal grounding.

Abstract:
We present Point2Pose, a model-free method for causal 6Dpose tracking of multiple rigid objects from monocular RGB-D video.Initialized only from sparse image points on the objects, our approachtracks multiple unseen objects without requiring object CAD modelsor category priors. Point2Pose leverages a 2D point tracker to obtainlong-range correspondences, enabling instant recovery after complete oc-clusion. Simultaneously, the system incrementally reconstructs an on-line Truncated Signed Distance Function (TSDF) representation of thetracked targets. Alongside the method, we introduce a new multi-objecttracking dataset comprising both simulation and real-world sequences,with motion-capture ground truth for evaluation. Experiments show thatPoint2Pose trades some single-object pose accuracy for broader model-free tracking capabilities, including multi-object tracking and recoveryfrom complete occlusion. Project page: https://point2pose.github.io/.

Abstract:
Multimodal large language models (MLLMs) have demon-strated strong performance in offline video understanding tasks, but mostremain constrained to offline inference or exhibit weak online reasoningability, rendering online multi-turn interaction over continuously arriv-ing video streams challenging. Existing streaming approaches adopt aninterleaved perception-generation paradigm, which precludes concurrentperception and generation and induces early memory decay with grow-ing streams, degrading long-range dependency modeling. We proposeThink While Watching, a memory-anchored streaming video reason-ing framework that maintains continuous segment-level memory duringmulti-turn interaction. We construct a three-stage, multi-round, chain-of-thought (CoT) dataset with a stage-matched training strategy while en-forcing strict causality in streaming reasoning via a segment-level stream-ing causal mask and streaming positional encoding. At inference, we de-sign an efficient pipeline that overlaps watching and thinking and adap-tively selects the optimal attention backend. We evaluate our methodunder single-round and multi-round streaming input protocols. Basedon Qwen3-VL, we improve single-round accuracy by 2.6% on Streaming-Bench and 3.79% on OVO-Bench. In the multi-round protocol, we main-tain performance while reducing output tokens by 56%. Code is avail-able at: https://github.com/wanglu-cs/Think While Watching.

Abstract:
Vision Transformers (ViTs) are strong backbones for seman-tic segmentation, but their computational cost limits deployment. Recenttoken compression methods for efficient transformer-based segmentationreduce this cost by decreasing the number of tokens. However, exist-ing evaluations primarily focus on low-to-moderate compression, leavingtheir behavior under aggressive compression and corrupted inputs un-clear. Meanwhile, structural pruning provides an orthogonal route toefficiency by removing redundant components in the ViT architecture,but is rarely compared to token compression under a unified protocol.To bridge this gap, we benchmark representative token compression andstructural pruning methods for ViT-based semantic segmentation un-der matched FLOPs on ADE20K and Cityscapes, together with theircommon-corruption variants ADE20K-C and Cityscapes-C. Our resultsreveal a consistent trend on both clean and corrupted inputs: token com-pression is highly effective at mild reductions but degrades sharply whencompression becomes severe, consistent with substantial information lossfrom overly aggressive token reduction. In contrast, structural prun-ing exhibits a smoother degradation curve and is more stable at highcompression. Motivated by these findings, we study a prune-then-mergepipeline that applies moderate token compression on top of a moder-ately pruned backbone. At comparable FLOPs, this combined strategyconsistently achieves a better accuracy-robustness trade-off at high com-pression, offering a practical recipe for deployment-oriented ViT segmen-tation. Code is available at https://github.com/phatnguyencs/vit-seg-compression.

Abstract:
The physical world is not merely visual; it is governed byrigorous structural and procedural constraints. Yet, the evaluation ofvision-language models (VLMs) remains heavily skewed toward percep-tual realism, prioritizing the generation of visually plausible 3D lay-outs, shapes, and appearances. Current benchmarks rarely test whethermodels grasp the step-by-step processes and physical dependencies re-quired to actually build these artifacts—a capability essential for au-tomating design-to-construction pipelines. To address this, we introduceDreamHouse, a novel benchmark for physical generative reasoning:the capacity to synthesize artifacts that concurrently satisfy geomet-ric, structural, constructability, and code-compliance constraints. Weground this benchmark in residential timber-frame construction, a do-main with fully codified engineering standards and objectively verifiablecorrectness. We curate over 26,000 structures spanning 13 architecturalstyles—each verified to construction-document standards (LOD 350)—and develop a deterministic 10-test structural validation framework. Un-like static benchmarks that assess only final outputs, DreamHouse sup-ports iterative agentic interaction. Models observe intermediate buildstates, generate construction actions, and receive structured environ-mental feedback, enabling a fine-grained evaluation of planning, struc-tural reasoning, and self-correction. Extensive experiments with state-of-the-art VLMs reveal substantial capability gaps that are largely invis-ible on existing leaderboards. These findings establish physical validityas a critical evaluation axis orthogonal to visual realism, highlightingphysical generative reasoning as a distinct and underdeveloped fron-tier in multimodal intelligence. The benchmark is available at https://luluyuyuyang.github.io/dreamhouse/.

Abstract:
Existing tampering benchmarks largely rely on object masks, which severely misalign with the true edit signal: many pixels inside a mask are untouched or only trivially modified, while subtle yet consequential edits outside the mask are treated as natural. We reformulate VLM image tampering from coarse region labels to a pixel-grounded, meaning and language-aware task. First, we introduce a taxonomy spanning edit primitives (replace/remove/splice/inpaint/attribute/colorization, etc.) and their semantic class of tampered object, linking low-level changes to high-level understanding. Second, we release a new benchmark with perpixel tamper maps and paired category supervision to evaluate detection and classification within a unified protocol. Third, we propose a training framework and evaluation metrics that quantify pixel-level correctness with localization to assess confidence or prediction on true edit intensity, and further measure tamper meaning understanding via semantics-aware classification and natural language descriptions for the predicted regions. We also re-evaluate the existing strong segmentation/localization baselines on recent strong tamper detectors and reveal substantial overand underscoring using mask-only metrics, and expose failure modes on micro-edits and off-mask changes. Our framework advances the field from masks to pixels, meanings and language descriptions, establishing a rigorous standard for tamper localization, semantic classification and description. Code and data are available at https://github.com/VILA-Lab/PIXAR.

Abstract:
We introduce a novel mathematical framework for analyz-ing and generating complex tree-shaped 3D objects, such as botanicaltrees and plants, which deform both in their 3D geometry and branchingstructure. Unlike previous works, which either consider only the skeletalstructure of tree-like objects or approximate their 3D geometry usingbranch thickness, the proposed framework accurately models both the3D geometry of the tree branches and the way they are interconnected.In this paper, we first generalize the Square Root Normal Fields (SRNF)representation, originally proposed for the statistical analysis of genus-0surfaces, to tree-shaped 3D objects. We then treat tree-shaped 3D ob-jects as points on a novel Riemannian tree-shape space equipped with anovel Riemannian metric that measures the amount of surface bendingand stretching, and structural changes one needs to apply to one 3D tree-shape to align it with another. This way, deformations become trajecto-ries in this novel tree-shape space. We analyze the theoretical propertiesof this novel tree-shape space and the corresponding metric and developalgorithms for computing point-wise and branch-wise correspondencesand geodesic paths between complex 3D trees. We finally show how touse these building blocks for (1) computing statistical summaries, i.e.means and modes of variation, of collections of tree-shaped 3D objects,and (2) synthesizing novel tree-shaped 3D objects by sampling fromprobability distributions fitted to a population of tree-shaped 3D objects.We demonstrate the performance and utility of the proposed frameworkon real and synthetic plants and botanical trees and show that it sig-nificantly outperforms the state-of-the-art. Additional results and sourcecode are available at https://tahmina979.github.io/Tree in SRNF/

Abstract:
Robust 3D object detection in adverse weather conditions is challenging due to sensor limitations. Although combining complementary modalities such as LiDAR and 4D RADAR has shown promise, the sparsity of these sensors becomes apparent in adverse weather with reduced reflections, leading to objects with few or no point cloud returns. To address this limitation, camera sensors provide visual cues even when LiDAR and RADAR signals are weakened. However, cameras themselves are also vulnerable to adverse weather, where some regions become unreliable due to snow or rain occluding the camera lens. While some camerafusion methods designed for adverse weather learn to weigh image regions via confidence maps, these maps receive no direct supervision and are learned solely through the detection loss. We introduce Reliability-Aware Fusion (RAF), which explicitly supervises per-pixel reliability estimation and provides a direct learning signal for identifying and suppressing unreliable visual cues. Our framework leverages pretrained LiDAR–RADAR networks, keeping their backbones frozen while only training the added camera branch, BEV fusion encoder, and detection head. Extensive experiments on the K-Radar and VoD datasets demonstrate that integrating RAF consistently improves detection accuracy over LiDAR–RADAR baselines, achieving up to +6.5 AP and +7.4 AP gains. Code is BEV 3D available at https://github.com/parkie0517/RAF.

Abstract:
Cortical surface reconstruction (CSR) is widely used in neuroimag-ing and is crucial for quantitative analyses of cerebral cortical thickness andsulcal morphology. While CSR is a mature technology when applied routinelyto adult T1 -weighted brain MRI data of the cerebrum, it remains underexploredfor a broad range of MRI contrasts, resolutions, ages, species, and brain struc-tures such as the cerebellum. To address this challenge, we propose XSurfer,a contrast- and resolution-agnostic CSR framework that performs optimizationon single images using an untrained neural network such that training data arenot needed. Specifically, XSurfer starts from segmentations of 3D MRI data,applies sulcal cerebrospinal fluid (CSF) recovery in sulci to address limitationscaused by partial-volume effects, identifies CSF voxels in deep sulci, and extractstopologically correct initial white matter (WM) surfaces and pseudo-target pialsurfaces. It then employs an untrained neural network to predict velocity fields,which are driven by mesh-based and geometrically-constrained loss functionterms to achieve diffeomorphic surface deformation, thereby reconstructing accu-rate pial surfaces. We evaluated XSurfer across different contrasts, resolutionsand seven datasets spanning adult, fetal, infant, ex vivo human, and non-humanprimate brain; XSurfer provides accurate CSR for all cases, surpassing state-of-the-art learning-based methods and recon-all-clinical recently providedby FreeSurfer as a more general-purpose CSR method. By enabling precise,contrast- and resolution-agnostic CSR without pretraining, XSurfer facilitatescross-domain multi-modal neuroimaging and standardizes morphometric analysisfor neuroscience research and clinical applications. The source code is publiclyavailable at: https://github.com/birthlab/XSurfer.

Abstract:
Recently, visual token pruning has been studied to handlethe vast number of visual tokens in Multimodal Large Language Models.However, we observe that while existing pruning methods perform reliablyon simple visual understanding, they struggle to effectively generalizeto complex visual reasoning tasks, a critical gap underexplored in previ-ous studies. Through a systematic analysis, we identify Relevant VisualInformation Shift (RVIS) during decoding as the primary failure driver.To address this, we propose Decoding-stage Shift-aware Token Pruning(DSTP), a training-free add-on framework that enables existing pruningmethods to align visual tokens with shifting reasoning requirements duringthe decoding stage. Extensive experiments demonstrate that DSTP signifi-cantly mitigates performance degradation of pruning methods in complexreasoning tasks, while consistently yielding performance gains even acrossvisual understanding benchmarks. Furthermore, DSTP demonstrates ef-fectiveness across diverse state-of-the-art architectures, highlighting itsgeneralizability and efficiency with minimal computational overhead. Oursource code is available here.

Abstract:
Vision–Language Encoders (VLEs) are widely adopted as the backbone of zero-shot referring image segmentation (RIS), enabling textguided localization without task-specific training. However, prior works underexplored the underlying biases within mid-layer representations that preserve positional and language-specific information. Through layerwise investigation, we reveal that the conventionally used final-layer multimodal embeddings prioritize global semantic alignment, leading to two coupled consequences. First, vision embeddings exhibit weak sensitivity to positional cues. Second, multilingual text embeddings form language-dependent geometric shifts within the shared space. Motivated by these findings, we identify an underexplored pathway within VLE mid-layers to construct a spatial map, applicable for improving zero-shot RIS by 1–7 mIoU on nine RefCOCO benchmarks. Furthermore, leveraging mixed-language mid-layer embeddings yields enhanced spatial grounding accuracy (+7–8 mIoU and IoU@50), albeit with increased inference cost, and also improves performance on the zero-shot text-to-image retrieval task. Our work opens up the discussion about the effects of effective representational bias probing of VLEs for enhanced spatial grounding. The code is available at https://github.com/kaist-cvml/Biased2Grounded.

Abstract:
We present GuidedSceneGen, a text-to-3D generation frame-work that produces metrically accurate, globally consistent, and semanti-cally interpretable indoor scenes. Unlike prior text-driven methods thatoften suffer from geometric drift or scale ambiguity, our approach main-tains an absolute world coordinate frame throughout the entire generationprocess. Starting from a textual scene description, we predict a global 3Dlayout encoding both semantic and geometric structure, which serves as aguiding proxy for downstream stages. A semantics- and depth-conditionedpanoramic diffusion model then synthesizes 360° imagery aligned with theglobal layout, substantially improving spatial coherence. To explore un-observed regions, we employ a video diffusion model guided by optimizedcamera trajectories that balances coverage and collision avoidance, achiev-ing up to 10× faster sampling compared to exhaustive path exploration.The generated views are fused using 3D Gaussian Splatting, yielding aconsistent and fully navigable 3D scene in absolute scale. GuidedSceneGenenables accurate transfer of object poses and semantic labels from lay-out to reconstruction, and supports progressive scene expansion withoutre-alignment. Quantitative results and a user study demonstrate greater3D consistency and layout plausibility compared to recent text-to-3Dbaselines. Project page: https://d3ixi.github.io/GuidedSceneGen/

Abstract:
Modern vision generators transport a base distribution to data through time-indexed measures, implemented as deterministic flows (ODEs) or stochastic diffusions (SDEs). Despite strong empirical performance, standard flow-matching objectives do not directly control the information geometry of the trajectory, allowing low-entropy bottlenecks that can transiently deplete semantic modes. We propose Entropy-Controlled Flow Matching (ECFM): a constrained variational principle over continuityequation paths enforcing a global entropy-rate budget d H(µ ) ≥ −λ. dt t ECFM is a convex optimization in Wasserstein space with a KKT/Pontryagin system, and admits a stochastic-control representation equivalent to a Schrödinger bridge with an explicit entropy multiplier. In the pure transport regime, ECFM recovers entropic OT geodesics and Γ-converges to classical OT as λ → 0. We further obtain certificate-style mode-coverage and density-floor guarantees with Lipschitz stability, and construct nearoptimal collapse counterexamples for unconstrained flow matching.

Abstract:
Foundation models are vital tools in various Computer Vision applications. They take as input a single RGB image and output a deep feature representation that is useful for various applications. However, in case we have multiple views of the same 3D scene, they operate on each image independently and do not always produce consistent features for the same 3D point. We propose a way to lift a Foundation Model into a Multi-View Foundation Model. Such a model takes as input a set of images and outputs a feature map for each image such that the features of corresponding points are as consistent as possible. This approach bypasses the need to build a consistent 3D model of the features. Specifically, we show how to augment any Transformer-based foundation model (i.e., DINO, SAM, CLIP) with intermediate 3D-attention layers that help match features across different views. Quantitative evaluations demonstrate that our approach achieves superior 3D-awareness while best preserving the native semantic space of the original foundation model, outperforming existing lifting techniques.

Abstract:
We present a dense RGB-D SLAM system using differen-tiable triangles as the 3D map representation. While 3D Gaussian Splat-ting has emerged as the leading method for novel-view synthesis, trianglesremain the standard primitive for traditional rendering hardware, gameengines, and downstream tasks requiring explicit geometry such as simu-lation, collision, and editing. Recent offline methods have demonstratedthat an unstructured ‘triangle soup’ can be optimised into a photorealis-tic mesh via Delaunay triangulation across a set of posed images. Build-ing upon this insight, we present the first dense SLAM system to employTriangle Splatting to perform both tracking and mapping through on-line differentiable rendering of a triangle soup. The map can be convertedinto a connected mesh on-the-fly via restricted Delaunay triangulation,enabling new online capabilities such as mesh deformation and collisionchecking. On Replica and TUM-RGBD, our system outperforms base-lines on 3D geometry, matches the camera-tracking accuracy, and enablesonline mesh-based scene editing.

Abstract:
Color constancy algorithms estimate and remove the effect of the illuminant from a captured image, enabling consistent color perception under varying illumination conditions. Learning-based methods achieve high accuracy but tend to be biased toward the spectral characteristics of training cameras. Traditional algorithms such as white-patch Retinex are computationally efficient and camera-independent, but underperform when scene statistics deviate from their assumptions. Therefore, in this paper, we propose a hybrid approach that combines classical assumptions with learned features. Our model processes images in a block-based manner through two complementary branches. The backbone branch extracts scene features and identify reliable regions for illuminant estimation. Guided by these saliency maps, the statistics branch computes per-block illumination priors using a soft maximum formulation that provides a differentiable approximation to the classical white-patch Retinex. Features from both branches are fused and refined through multi-head self-attention, which enables blocks to exchange information across the image, addressing the aggregation bias common to block-based methods. Experiments on five benchmarks demonstrate that our model achieves state-of-the-art performance with a compact architecture (∼1.1 MB) that requires no camera-specific calibration or metadata.

Abstract:
Image matting is highly ill-posed, especially when both theforeground and background are richly textured. While single-image mat-ting methods learn strong priors from data, they often struggle on thesechallenging cases. Existing approaches improve results by requiring ad-ditional signals such as green screens, polarized lighting, or clean back-ground images, but these typically rely on specialized capture setups.We present Parallax Portrait Matting, a practical two-frame mattingmethod that uses a second image captured with slight viewpoint change.Such a setting arises naturally in burst photography, where small cameramotion induces foreground-background parallax and provides comple-mentary observations for matting. Our pipeline estimates trimaps andforeground/background motion, then constructs aligned views for pre-diction. To handle imperfect motion estimation, the network uses thebackground-aligned pair for direct fusion and the foreground-aligned cuethrough cross-attention for error compensation. Experiments show thatour method recovers finer details and more accurate foreground colorsthan strong single-image matting baselines on challenging portrait cases.

Abstract:
In real-world clinical practice, medical images face open-world shifts: (i) long-tailed rare diseases, (ii) subtle lesions dominated bynormal anatomy, and (iii) hierarchical taxonomies. Yet most open-worldparadigms assume flat, balanced label spaces, leaving these biomedi-cal demands unresolved. We introduce Generalized Biomedicine Dis-covery (GBD) and a unified benchmark spanning long-tail, anomaly,and taxonomy-aware discovery. Our key insight is that dominant knownpatterns form a visual manifold that masks subtle novelty. Inspired byexpert diagnosis, we propose SCAN (Surprise-evoked ComplementaryAccommodatioN), which follows a cognition-inspired perceptual progres-sion: it applies predictive suppression to filter expected norms, trig-gers surprise-evoked salience to highlight unexpected deviations, andperforms complementary accommodation to integrate these shifts intoglobal representations. Extensive experiments show that SCAN improvesnovel concept discovery while generally preserving established clinicalknowledge, and it plugs into existing architectures to better navigatethe known–unknown trade-off in medical imaging. Code is available atgithub.com/lytang63/generalized-biomedicine-discovery.

Abstract:
Affordance prediction serves as a critical bridge between perception and action in embodied AI. However, existing research is confined to pinhole camera models, which suffer from narrow Fields of View (FoV) and fragmented observations, often missing critical holistic environmental context. In this paper, we present the first exploration into Panoramic Affordance Prediction, utilizing 360-degree imagery to capture global spatial relationships and holistic scene understanding. To facilitate this novel task, we first introduce PAP-12K, a largescale benchmark dataset containing over 1,000 ultra-high-resolution (12k, 11904×5952) panoramic images with over 12k carefully annotated QA pairs and affordance masks. Furthermore, we propose PAP, a trainingfree, coarse-to-fine pipeline inspired by the human foveal visual system to tackle the ultra-high resolution and severe distortion inherent in panoramic images. PAP employs recursive visual routing via grid prompting to progressively locate targets, applies an adaptive gaze mechanism to rectify local geometric distortions, and utilizes a cascaded grounding pipeline to extract precise instance-level masks. Experimental results on PAP-12K reveal that existing affordance prediction methods designed for standard perspective images suffer severe performance degradation and fail due to the unique challenges of panoramic vision. In contrast, PAP framework effectively overcomes these obstacles, significantly outperforming state-of-the-art baselines and highlighting the immense potential of panoramic perception for robust embodied intelligence.

Abstract:
In the real world, fashion is about layering: adding a jacketover a shirt, or a sequence of adding and removing layers, rather thanjust a single-layer swap. This fundamental real-world task remains achallenge in existing Virtual Try-On (VTON) methods, which excel atsingle-layer replacement but are not designed to layer or de-layer anexisting outfit. This paper proposes Layering Virtual Try-On (LVTON),a layering benchmark and method that preserves an existing outfit whileenabling sequential layering. We find that current VTON paradigms arefundamentally ill-equipped for LVTON, as their reliance on cloth-agnosticrepresentations and single-item datasets discards essential layering context.Our key insight is that the LVTON challenge must be disentangled intotwo distinct competencies: (1) General VTON Priors (e.g., deformation,identity preservation) and (2) Specific Layering Knowledge (e.g., layeringorder and occlusion reasoning). First, our model obtains general VTONpriors by being trained on data produced by an automatic data generationpipeline that synthesizes samples from fashion videos via segmentationand inpainting. Second, the model is fine-tuned on a small, dedicatedLVTON dataset to learn the layering logic. Our method achieves state-of-the-art results on our LVTON benchmark and demonstrates superiorgeneralizability on traditional VTON benchmarks, setting new state-of-the-art results when fine-tuned and exhibiting zero-shot capabilities.

Abstract:
The slow iterative sampling nature remains a major bottle-neck for the practical deployment of diffusion and flow-based genera-tive models. While consistency models (CMs) represent a state-of-the-art distillation-based approach for efficient generation, their large-scaleapplication is still limited by two key issues: training instability andinflexible sampling. Existing methods seek to mitigate these problemsthrough architectural adjustments or regularized objectives, yet over-look the critical reliance on trajectory selection. In this work, we firstconduct an analysis on these two limitations: training instability orig-inates from loss divergence induced by unstable self-supervised term,whereas sampling inflexibility arises from error accumulation. Based onthese insights and analysis, we propose the Dual-End ConsistencyModel (DE-CM) that selects vital sub-trajectory clusters to achievestable and effective training. DE-CM decomposes the PF-ODE trajec-tory and selects three critical sub-trajectories as optimization targets.Specifically, our approach leverages continuous-time CMs objectives toachieve few-step distillation and utilizes flow matching as a boundaryregularizer to stabilize the training process. Furthermore, we propose anovel noise-to-noisy (N2N) mapping that can map noise to any point,thereby alleviating the error accumulation in the first step. Extensiveexperimental results show the effectiveness of our method: it achieves astate-of-the-art FID score of 1.70 in one-step generation on the ImageNet256×256 dataset, outperforming existing CM-based one-step approaches.

Abstract:
Capturing spatial relationships from visual inputs is a cornerstone of human-like general intelligence, yet current Vision-Language Models (VLMs) still struggle to perform spatial understanding from everyday visual observations. To enhance the spatial ability within general architectures, we introduce Visual Spatial Tuning (VST), a comprehensive framework to cultivate VLMs with human-like visuospatial competence spanning spatial perception and reasoning. VST dataset comprises VST-P, a large-scale perception dataset with 4.1 million samples spanning 19 skills across single views, multiple images, and videos; and VSTR, a reasoning dataset with 135K samples that explicitly instruct models to reason in space. For spatial reasoning, we design Reconstruction with Text in Chain-of-Thought (RT-CoT), which represents scene layout as a textual intermediate and performs reasoning grounded on this reconstruction. We further propose a strategy termed prompting with BEV annotations to improve the faithfulness and coherence of spatial reasoning traces. Then, we adopt a progressive training pipeline: supervised fine-tuning to build foundational spatial perception abilities, followed by reinforcement learning to develop spatial reasoning, where a 3D-aware layout reward is proposed. Without the side-effect on general capabilities, the proposed VST consistently achieves state-of-the-art results on several spatial benchmarks, including 34.8% on MMSI-Bench and 61.2% on VSIBench. Moreover, VST substantially improves Vision-LanguageAction (VLA) models, paving the way for more physically grounded AI.

Abstract:
We present Generative Relightable Avatars (GRA), a personspecific method for photorealistic free-view rendering and environmentmap relighting of full-body humans. We postulate that modeling finegrained appearance details is inherently a one-to-many problem that can benefit from a generative formulation. In contrast to fully regressive relightable avatar methods, GRA follows a hybrid approach that combines controllable, physics-grounded relighting with probabilistic refinement. Starting from a tracked animated mesh, we optimize material parameters in UV-space and render a coarse relit appearance under a target HDR environment map. Next, we refine the textures with a feedforward model to capture pose-dependent texture dynamics and illumination effects beyond simplified reflectance assumptions. Finally, a finetuned video-to-video diffusion model transforms the physically grounded renderings into temporally coherent, high-detail videos while preserving 3D control, with an error-recycling strategy for generating long videos. Experimental evaluations demonstrate our method’s improved perceptual quality over prior relightable avatar baselines. We urge the readers to watch the supplementary video. See the project page for more details.

Abstract:
Visual intelligence requires anticipating the future behaviorof agents, yet vision systems lack a general representation for motion andbehavior. We propose dense point trajectories as visual tokens for behavior,a structured mid-level representation that disentangles motion fromappearance and generalizes across diverse non-rigid agents, such as animalsin-the-wild. Building on this abstraction, we design a diffusion transformerthat models unordered sets of trajectories and explicitly reasons aboutocclusion, enabling coherent forecasts of complex motion patterns. Toevaluate at scale, we curate 300 hours of unconstrained animal motion fromvideo through robust shot detection and camera-motion compensation.Experiments show that forecasting trajectory tokens achieves category-agnostic, data-efficient prediction, outperforms state-of-the-art baselines,and generalizes to rare species and morphologies, providing a foundationfor predictive visual intelligence in the wild.

Abstract:
Recent advances in video generation have revealed an un-expected capability: diffusion-based video models can perform reason-ing through Chain-of-Frames (CoF), suggesting that reasoning unfoldssequentially across video frames. In this work, we revisit this assump-tion and uncover a different mechanism. Through systematic analysis,we show that video reasoning primarily develops along the diffusion de-noising steps instead, where early steps exhibit multiple hypotheses thatgradually converge. We term this mechanism Chain-of-Steps (CoS),which is validated through qualitative analysis and probing tests. Ourinvestigation reveals several intriguing emergent behaviors: 1) Modelsdemonstrate a form of persistent working memory that supports tasksrequiring consistent reference, such as object permanence. 2) They canself-correct intermediate mistakes during generation instead of com-mitting to incorrect trajectories. 3) Early steps function differently fromlater steps, exemplified by a perception before action phenomenon.Moreover, analysis of Diffusion Transformer layers shows that middlelayers conduct key reasoning procedures. Motivated by these insights,we propose Training-Free Ensemble (TFE), a simple strategy thatintegrates reasoning paths by merging latents from identical models withdifferent random seeds at inference time. This approach encourages theexploration of diverse reasoning trajectories and improves reasoning per-formance. Together, our findings provide the first systematic dissectionof the mechanisms underlying video reasoning and offer practical insightsfor developing more capable video reasoning models.

Abstract:
Translating continuous, noisy egocentric video streams into discrete, temporally ordered action steps is fraught with visual challenges. Heavy ego-motion, transient occlusions, and the high intra-class variability of unscripted human-object interactions cause standard framelevel online temporal models to struggle, often resulting in severe oversegmentation and structural collapse. To bridge the gap between unstable low-level perception and high-level procedural logic, we present VidParse, a online, training-free framework that treats activity understanding as a graph-constrained inference problem. Rather than relying on learned temporal filters, we dynamically identify semantic transitions using a temporal similarity matrix over manipulation-anchored features, which are extracted from frozen foundation models to prioritize foreground hand-object interactions. A beam search decoder then leverages an induced procedural task graph to explicitly enforce valid action transitions and prune impossible trajectories. By anchoring robust visual segments to hard procedural constraints, our approach preserves longrange state transitions and achieves up to a 10x improvement in complex multi-step parsing accuracy over strong online baselines, all without requiring a single gradient update.

Abstract:
In human visual perception, uppercase lettering serves as anatural salience cue that captures attention within lowercase text. Inthis paper, we present a systematic empirical characterization study re-vealing that Large Language Models (LLMs) exhibit an analogous prop-erty: letter casing modulates internal attention allocation. Through anal-ysis across 13 models—nine LLMs and four Vision-Language Models(VLMs)—with diverse tokenization schemes, we show that formattingtarget information in alternating or uppercase against a lowercase con-text concentrates attention on those textual spans. In text this e!ectis universal, holding across every evaluated non-reasoning model. Weframe it as a previously under-explored latent property of pretrainedtransformers rather than a prescriptive method. Our investigation re-veals a central attention–performance divergence: while this “casing ef-fect” robustly shifts attention, its impact on downstream accuracy isnon-trivial—increased concentration does not inherently improve taskaccuracy and, in high-entropy contexts like alternating case, can degradeit. We further identify a boundary condition: the deliberative “thinking”phase in reasoning models acts as a semantic bu!er that mitigates ty-pographic sensitivity in text. Extending the study to VLMs, we find thee!ect transfers partially: the same prompt-side casing reorganizes cross-modal attention along two coupled axes—predominantly a macroscopicdisengagement from the image toward the text prompt, and secondar-ily a concentration of the residual visual attention on the target region.By isolating casing as a zero-shot mechanism for attention steering thatrequires no model access or fine-tuning, we provide a new foundationalunderstanding of how pretraining internalizes typographic emphasis.

Abstract:
The O(N 2 ) complexity of attention over N tokens remainsa computational bottleneck in transformer models. Vector-Quantized(VQ) attention reduces this to O(M N ) by representing keys with Mcodewords, but applies uniform codebook capacity regardless of whereattention mass concentrates: high-attention regions of key space maybe coarsely approximated while low-attention regions waste representa-tional capacity. We propose Adaptive Vector-Quantized (AVQ) Atten-tion, which adaptively allocates codebook capacity based on attentionimportance. Starting from a small set of codewords, our method identifiesthe most important codes during the forward pass and refines them withpre-learned child codewords, achieving fine-grained quantization whereit matters most while maintaining coarse quantization elsewhere. We de-velop an implementation using custom Triton kernels that enables the fulladaptive refinement process, including importance scoring, child code-word insertion, and parent contribution replacement, to be carried outwithin the tiled computation paradigm of Flash Attention with minimaloverhead. Our approach maintains O(M N ) complexity while achievingimproved accuracy-efficiency trade-offs compared to fixed-codebook VQ-attention.

Abstract:
Physically based differentiable rendering has proven to be apowerful tool for inverse rendering problems (e.g., 3D reconstruction, re-flectance estimation, lighting estimation). However, most existing meth-ods operate solely on radiometric intensity, discarding valuable polariza-tion cues that constrain scene geometry and material properties. Whileforward simulation of polarized light is well-defined via Mueller-Stokescalculus, extending reverse-mode differentiation to this domain presentssignificant challenges. The rank-deficient nature of common polarimet-ric operators, such as linear polarizers and diffuse reflections, violatesthe invertibility assumptions of standard gradient estimators like pathreplay backpropagation and results in numerical instability. We addressthis by proposing a robust, polarization-aware differentiable path tracingmethod. Our approach estimates unbiased gradients through a combina-tion of path replay and local caching. This formulation enables efficientand stable optimization of material and lighting parameters in complexscenes, broadening the applicability of physically based inverse rendering.Project page: https://vcai.mpi-inf.mpg.de/projects/DPPT/

Abstract:
Collaborative perception serves as a pivotal solution to en-hance the perception capability of individual agents in autonomous driv-ing, where a core challenge lies in seeking reliable evidence to quantifyand weight the contribution of each participating agent. Existing meth-ods typically rely on a confidence map (co-trained with the detectionhead), which is, however, inherently correlated with the detection re-sults and thus fails to provide unbiased physical evidence. Furthermore,how to deeply integrate evidence into the cooperative fusion process re-mains an open question. To address these issues, this paper first pro-poses uncertainty map, a physically grounded and unambiguous met-ric for evaluating perception quality. This map is directly supervisedby real-time sensor signals (i.e., LiDAR point density), ensuring de-coupling from detection noise and thereby providing physical scenario-aware evidence for weighting agent contribution. Based on this map,we develop the Uncertainty-Enhanced Collaborative Perception (UECP)framework, centered on the Uncertainty-Aware Pyramid Fusion (UAPF)module. UAPF uses a coarse-to-fine strategy, with two key components:Uncertainty-Weighted Downsampling (UWD) for high-fidelity featurepreservation, and Uncertainty-Guided Residual Fusion (UGRF) to rein-force ego features, suppressing noise and ensuring robust fusion. Exten-sive experiments on real-world datasets show UECP outperforms SOTAmethods in effectiveness and robustness by embedding the uncertaintymap into fusion. Code will be publicly available.

Abstract:
Accurate trajectory forecasting of surrounding traffic par-ticipants is a core capability for autonomous driving, enabling vehiclesto anticipate behavior and plan safe maneuvers. We observe that cur-rent state-of-the-art forecasting models on Argoverse 2 and the WaymoOpen Motion Dataset tailor their training objectives to the differentbenchmark metrics. Because these metrics encourage conflicting behav-ior, we propose a paradigm change for trajectory forecasting: trainingmodels with metric-agnostic probabilistic objectives and treating metricoptimization as a downstream task applied to the predictive distribution.Concretely, we introduce Trajectory Distribution Evaluation (TraDiE)policies, metric-specific policies that map a predictive distribution to theset of K trajectories and confidences required by trajectory forecast-ing metrics. We evaluate this framework by introducing DONUT-NLL,which adapts the training objective of the state-of-the-art trajectory fore-casting model DONUT to directly optimize the predictive distribution.Using our policies, DONUT-NLL achieves state-of-the-art results on allmetrics of the Waymo motion prediction benchmark.

Abstract:
Deep neural networks are highly vulnerable to adversarialperturbations, raising serious safety concerns in the real-world systems.While prior work mainly explores single-task attacks or jointly degradingall tasks in multi-task models, practical scenarios often demand moreselective and stealthy attack strategies. To address this challenge, wepropose Stealthy Multi-Task Adversarial Attack (SMTA2 ), a novelframework that selectively degrades a targeted task while strictly pre-serving the performance of non-targeted tasks. We formulate this objec-tive as a constrained multi-objective optimization problem and designtask-aware adversarial perturbations that maximize degradation on thetargeted task without causing collateral damage on non-targeted tasks.To enhance practicality, we further introduce an automated loss-weighttuning strategy that dynamically balances attack and preservation objec-tives. Experiments on two multi-task benchmarks NYUv2 and Cityscapesdemonstrate that SMTA2 achieves strong attack performance on tar-geted tasks while maintaining non-targeted tasks intact on both unde-fended and adversarially trained models, establishing the first systematicframework for stealthy and selective multi-task attack framework.

Abstract:
Data augmentation is known to improve generalization ofdeep visual models. Recent methods favor mixup strategies that gener-ate interpolated samples to improve model performance. However, thesetechniques not only incur significant computational overhead, they alsolead to semantic disruption of augmentation data due to cross-samplemixing. We first propose Self-Saliency (S 2 ) Mixup, which constructschallenging yet label-consistent samples by extracting multi-scale salientpatches and reinserting them into non-salient regions of the same im-age. This promotes scale-invariant feature learning while avoiding cross-sample interference. To further enhance model robustness, we intro-duce FracMix, a mixing scheme that injects self-similarity patterns intosalient regions using adaptive ratios. Collectively, our unified framework,S 2 -FracMix, enables simultaneous learning from fractal and non-fractalstructures within a single image, yielding a targeted and structurally co-herent augmentation strategy. We theoretically analyze the advantageof our technique, and empirically establish its superiority over the ex-isting methods by achieving state-of-the-art performance in extensiveevaluation with seven benchmarks across classification (coarse and fine-grained), robustness, calibration, object detection, and transfer learningtasks. Project page is available at fracmix-data-augmentation.github.io

Abstract:
We propose continuous adversarial flow models, a type ofcontinuous-time flow model trained with an adversarial objective. Unlikeflow matching, which uses a fixed mean-squared-error criterion, our ap-proach introduces a learned discriminator to guide training. This changein objective induces a different generalized distribution, which empiri-cally produces samples that are better aligned with the target data dis-tribution. Our method is primarily proposed for post-training existingflow-matching models, although it can also train models from scratch.On the ImageNet 256px generation task, our post-training substantiallyimproves the guidance-free FID of latent-space SiT from 8.26 to 3.63 andof pixel-space JiT from 7.17 to 3.57. It also improves guided generation,reducing FID from 2.06 to 1.53 for SiT and from 1.86 to 1.80 for JiT.We further evaluate our approach on text-to-image generation, where itachieves improved results on both the GenEval and DPG benchmarks.

Abstract:
Current Video Large Language Models (Video LLMs) excel in question answering (QA) but largely operate as black boxes, providing textual answers without verifiable visual grounding. Existing explainability efforts rely on textual rationales or sparse bounding boxes, which struggle to capture complex video dynamics such as occlusions and nonrigid deformations. We propose Evidence-Backed Video Question Answering (E-VQA), a novel task requiring models to jointly output a semantic answer and precise spatio-temporal evidence: temporal segments and dense, tracked object segmentation masklets. To support this, we introduce ST-Evidence, the first human-verified benchmark for both discriminative and generative pixel-level grounding. Evaluations of state-of-the-art models reveal a critical decoupling between QA accuracy and true visual perception that scaling alone fails to bridge. To address this, we develop scalable, automated generation pipelines to create ST-Evidence-Instruct, a 160k-scale dataset bridging highlevel reasoning with fine-grained grounding. Fine-tuning grounded Video LLMs on this data yields substantial gains over the corresponding sizematched UniPixel baselines (e.g., +27.2 t-mean and +13.8 J&F on a 7B model), establishing a robust baseline for explainable, evidencebacked video understanding. Code and data are available at https: //github.com/SalesforceAIResearch/EVQA.

Abstract:
We introduce a new problem domain for human action recog-nition: the fine-grained analysis of children’s gait behaviors from stan-dard RGB video. We specifically target the ambulatory patterns of chil-dren aged 3–17 years. Such behaviors arise naturally in the diagnosis andtreatment of several critical developmental and neuromuscular disorders,such as cerebral palsy and hemiplegia. Despite their clinical value, cur-rent 3D sensor-based gait analysis systems are expensive, intrusive, andoften impractical for young subjects. To address this, we introduce anew dataset comprising over 1,100 high-frame-rate (60 FPS) video se-quences from 110 subjects, accompanied by synchronized, anonymizedpose sequences. In each session, the child performs a 5-second "walk-around" task, capturing the gait cycle from multiple viewpoints. Cru-cially, we demonstrate that current state-of-the-art approaches, includ-ing gait foundation models and Multimodal Large Language Models(MLLMs), fail to effectively resolve these clinical nuances. We identifythe key technical challenges in analyzing these erratic and subtle mo-tor patterns and describe a unified end-to-end framework for decodingfundamental components of pediatric gait. Through comprehensive ex-perimental results, we demonstrate the potential of this dataset to drivenovel research questions and establish a rigorous baseline for automatedchild gait assessment. Project page: pediamedai.com/ChildrenGait

Abstract:
While humans naturally gesture during speech, only a sparse subset of these co-speech gestures are visually depictive and semantically linked to specific spoken words. In this paper, we introduce a large-scale dataset – Gesture Recognition in the Wild (GRW), comprising co-speech gestures corresponding to a diverse vocabulary of 155 words. GRW contains 140k manually annotated video clips where the word is spoken, with 17k instances of semantic co-speech gestures including their frame-level temporal boundaries. The video clips are collected ‘in the wild’ from public-facing discourse, including lectures, talk shows, and interviews, covering a diverse range of speakers and visual conditions. We also introduce video models to: (a) classify gestures as semantic or not; (b) recognize the word corresponding to a co-speech gesture; and (c) temporally localize the gesture. These models are trained and evaluated on the GRW dataset and compared against a range of strong baselines, establishing benchmark results for all three tasks. The dataset, annotations, and trained models are publicly available on the project website.

Abstract:
Camera pose matters. The position and orientation of eachviewpoint define a shared spatial coordinate frame that relates observa-tions across video frames. Yet this signal is absent from multimodal LLMs(MLLMs) for video understanding, which process frames as isolated 2Dsnapshots, instead of the persistent scene humans perceive. We revisitpose as a lightweight supervisory signal and introduce Cambrian-P , avideo MLLM augmented with per-frame learnable camera tokens and apose regression head. With a carefully designed sampling scheme, themodel achieves substantial gains of 4.5–6.5% on spatial reasoning bench-marks such as VSI-Bench, generalizes across eight additional spatial andgeneral video QA benchmarks, and, as a byproduct, achieves state ofthe art streaming pose estimation on ScanNet. Surprisingly, training onpseudo-annotated poses from in-the-wild video further improves generalvideo QA benchmarks, showing pose helps beyond spatial reasoning. To-gether, these results position camera pose as a fundamental signal forvideo models that reason about the physical world.

Abstract:
Visual reasoning in immersive environments is critical forVR, AR, and robotics, yet is fundamentally challenged by partial ob-servability. Existing Vision-Language Models (VLMs) are ill-suited forthis task, as their rigid “look first, think later” paradigm decouples per-ception from reasoning. They either process distorted, low signal-to-noisepanoramas or reason over a fixed set of pre-determined views, preventingdynamic, task-driven exploration. We reframe immersive visual reason-ing as an iterative process of probabilistic belief updating. We introduceDiffusion-Based Immersive Visual Reasoning (DIVR), a novel frameworkthat models reasoning as a denoising diffusion process, progressively re-fining a latent reasoning hypothesis from a state of high uncertainty toa denoised low-uncertainty state. DIVR comprises two tightly coupledmodules: a Latent Hypothesis Refiner (LHR) that uses observed visualevidence to denoise the current hypothesis, and a Reasoning-Guided Ob-server (RGO) that uses the denoised hypothesis to actively select the nextmost informative viewport. This creates a closed-loop system where theevolving reasoning hypothesis continuously guides perception, distinctfrom the rigid, open-loop paradigm of prior work. Extensive experimentson multiple 360° VQA benchmarks demonstrate that DIVR establishesa new state-of-the-art, significantly outperforming strong baselines, par-ticularly on complex questions that require dynamic, multi-step evidencegathering.

Abstract:
Learning robot manipulation policies from large-scale human videos is challenging due to the viewpoint mismatch between thirdperson observations and egocentric robot control. To this end, we introduce Exo2EgoPolicy, a geometry-aware framework that learns egocentric manipulation policies from monocular exocentric video via viewpointaligned pose translation. Unlike prior Exo→Ego approaches that rely on diffusion-based pixel synthesis and often produce temporally inconsistent predictions, our method operates directly on a structured pose manifold and explicitly models the geometric ambiguity inherent in monocular observation. We formulate exocentric-to-egocentric translation as a viewpoint-conditioned latent variable model on the SE(3) manifold that disentangles pose from camera transformation and captures the ambiguity induced by unknown camera extrinsics. Under bounded viewpoint motion, the formulation yields pose trajectories that are identifiable up to a global rigid transformation and supports stable sequential inference through temporal regularization. Beyond representation alignment, we show that for quasi-static manipulation tasks whose rewards depend primarily on relative hand–object geometry, pose captures the key task-relevant information required for policy learning. This enables policy co-training on pose-translated human demonstrations alongside limited robot teleoperation data within a unified human–robot state space. Empirically, Exo2EgoPolicy improves temporal consistency, cross-view alignment, and out-of-distribution policy transfer compared to pixelbased translation and other cross-view co-training baselines, achieving 20–30% higher task success on manipulation benchmarks. These results suggest that geometry-aligned pose representations provide a scalable foundation for cross-view policy learning from human video. Website

Abstract:
3D representations are fundamental for scene rendering, un-derstanding, and interaction. Approaches like Neural Radiation Fieldsand 3D Gaussian Splatting achieve impressive photorealistic novel-viewsynthesis, but lack the ability to easily decompose scene elements intoobject level, requiring additional segmentation or grouping. We presentMLP-Splatting, a method that enables scene decomposition via a fewexpressive light-field primitives while providing photorealistic novel-viewsynthesis.MLP-Splatting models each primitive as an independent compact MLPwith localized spatial support that predicts radiance and opacity. In con-trast to low-level Gaussian primitives or a single global radiance field,our neural primitives provide greater expressive capacity while remain-ing spatially localized. Rendering is performed through efficient sparsevolumetric compositing over ray–primitive interactions.Our primitives are supervised using RGB supervision alone, which yieldsprimitives that represent local scene regions often corresponding to ob-jects or object parts, enabling interactive object-level editing withoutsegmentation masks by selecting a handful of primitives. Our method,augmented with optional semantic feature distillation, enables open-vocabulary scene interaction and open-set instant segmentation. Com-pared to state-of-the-art semantic 3DGS methods, we achieve substan-tially lower memory usage (1/7×) and faster rendering (5×).Project Page: https://shinjeongkim.com/mlp-splatting

Abstract:
Generating large dynamic 3D worlds is important for appli-cations such as virtual reality, simulation, and robotics. However, the lackof large-scale 3D dynamic training data prevents training world-scale 3Dgenerative models directly. Recent progress in video generation providespowerful priors for generating 3D dynamic visual content. As a result,current approaches typically generate videos from multiple camera viewsusing image or video diffusion models and fuse them into a 3D represen-tation. However, these models generate each view independently, makingit difficult to maintain motion consistency across viewpoints. Existing so-lutions address this problem by sequentially generating each view whileupdating an explicit 3D representation, which introduces a significantcomputational bottleneck. In this work we propose D YN W ORLD, a frame-work that enables parallel generation of motion-consistent videos acrossmultiple views. Our key insight is that the latent tokens of transformer-based video generators implicitly encode spatial and dynamic informa-tion that can be geometrically related across cameras. We thus intro-duce a Spatial Token Memory that stores latent video tokens along withtheir estimated 3D positions. During generation, tokens from one vieware back-projected into 3D and re-projected into other views, allowingmultiple video generators to share consistent motion context through at-tention. This mechanism enforces cross-view motion consistency whileallowing all views to be generated simultaneously. Experiments showthat our method produces large, diverse, view-consistent dynamic worldswhile reducing generation time by an order of magnitude compared tosequential pipelines.

Abstract:
While foundation models have emerged as general-purposevisual backbones, their representations are primarily optimized for se-mantics and lack explicit modeling of physical factors, such as reflectance,hindering their efficacy in tasks requiring explicit material reasoning. Weintroduce Φeat, a novel material-grounded visual backbone that encour-ages a representation sensitive to material identity, including reflectanceand mesostructure. Instead of relying on generic data augmentations,we pretrain our model by contrasting observations of the same materialunder controlled variations in lighting and geometry. This encouragesinvariance to extrinsic factors while preserving sensitivity to intrinsicmaterial properties. We show that the resulting representation providesstrong priors for material-centric tasks, including feature-based mate-rial selection and classification. Our results demonstrate that physicallyinspired weak supervision is an effective strategy for learning represen-tations tailored to material perception.

Abstract:
Fine-tuning Multimodal Large Language Models (MLLMs)on specialized tasks often leads to catastrophic forgetting of their gen-eral capabilities. Existing model merging methods to combat this areoften heuristic or use sub-optimal objectives. We propose Curvature-Guided Mixing (CGM), a theoretically grounded framework that mergespre-trained and fine-tuned models. CGM formulates a joint optimiza-tion objective and uses a second-order (Hessian) approximation of theloss landscapes to analytically derive an optimal, closed-form “soft mix-ing” ratio. This ratio intelligently blends parameters based on their rel-ative task-specific curvatures. We also introduce CGM† , a robust “hardmixing” variant that performs sparse parameter selection guided by anovel, curvature-aware score. Experiments on LLaVA-1.5 and Qwen-2.5VL across multiple downstream tasks show that CGM and CGM†consistently improve the trade-off between task specialization and gen-eral knowledge retention over existing methods. Code is available atgithub.com/zzsyjl/CGM-ECCV-2026.

Abstract:
When different objectives conflict with each other in multi-task learning, gradients begin to interfere and slow convergence, therebypotentially reducing the final model’s performance. To address this, weintroduce SON-GOKU, a scheduler that computes gradient interference,constructs an interference graph, and then applies greedy graph-coloringto partition tasks into groups that align well with each other. At eachtraining step, only one group (color class) of tasks are activated, and thegrouping partition is constantly recomputed as task relationships evolvethroughout training. By ensuring that each mini-batch contains onlytasks that pull the model in the same direction, our method improvesthe effectiveness of any underlying multi-task learning optimizer withoutadditional tuning. Since tasks within these groups will update in compati-ble directions, multi-task learning will improve model performance ratherthan impede it. Empirical results on six different datasets show thatthis interference-aware graph-coloring approach consistently outperformsbaselines and state-of-the-art multi-task optimizers. We provide extensivetheory showing why grouping and sequential updates improve multi-tasklearning, with guarantees on descent, convergence, and the ability toaccurately identify what tasks conflict or align.

Abstract:
Text-to-image diffusion models are increasingly deployed increative contexts, yet remain impersonal, optimized for aggregate aes-thetics rather than individual taste. Human preferences are inherentlypluralistic: one user who favors muted, nostalgic portraits may prefer vi-brant compositions for street photography, while another gravitates to-ward dreamy, overexposed film aesthetics. Existing personalization meth-ods require dense interaction histories or per-user fine-tuning, failing incold-start settings and collapsing each user’s context-dependent prefer-ences into a single static representation. We introduce zero-shot imagepersonalization from personas (ZIPP), a paradigm that conditions im-age generation on natural-language personas (concise descriptors of auser’s identity, interests, and aesthetic sensibilities) without any user-specific data or weight updates. ZIPP uses an LLM in a roleplay set-ting to rewrite input prompts from the perspective of a given persona,steering diffusion models toward personalized outputs. To mine per-sonas at scale, we develop an inductive Graph Attention Network over a23M-user Reddit interaction graph with dual contrastive objectives thatalign graph structure with users’ visual behavior, and verbalize learnedrepresentations into coherent natural-language personas via an MLLM.We further introduce ZIP-Bench the first zero-shot image personal-ization benchmark, pairing 1.5K users with graph-mined personas and40K generated images. Across four benchmarks and 14 LLMs spanningfive model families, persona conditioning yields consistent improvementsin both zero-shot and few-shot settings, with frontier models achievingthe strongest gains (13–20%). In the few-shot setting, ZIPP matchesor exceeds fine-tuned baselines requiring per-user adapters trained on100+ examples. Unlike baselines that collapse preferences into a fixedstyle, ZIPP preserves intra-user preference diversity, achieving the low-est distributional divergence from users’ true preference distributions(CMMD 0.16 vs. 0.55 for fine-tuned alternatives). IPF-normalized eval-uation against global population demographics further reveals that ex-isting methods exhibit substantial bias toward narrow subpopulations,which persona conditioning significantly mitigates. A human evaluationconfirms these findings: ZIPP achieves a 79% win rate over genericgeneration and outperforms all fine-tuned baselines (58–65% win rate)without any user-specific training. We release ZIP-Bench at: https://huggingface.co/datasets/behavior-in-the-wild/zip-bench

Abstract:
We introduce Vision Bridge Transformer (ViBT), alarge-scale instantiation of Brownian Bridge Models designed for con-ditional generation. Unlike traditional diffusion models that transformnoise into data, Bridge Models offer an efficient data-to-data translationparadigm by directly modeling the trajectory between inputs and out-puts. We first demonstrate the advantages of this paradigm through acontrolled apples-to-apples comparison against diffusion models, reveal-ing improved efficiency and performance in this setting. Building on this,we successfully scale the Bridge paradigm up to a 20B-parameter modelto tackle complex image and video translation tasks. To enable stabletraining at this scale, we adopt a Transformer architecture equipped witha variance-stabilized velocity matching objective. Together, these ad-vances unlock the potential of scaling Bridge Models, establishing strongperformance for instruction-based image editing and video stylization.

Abstract:
We present Spanning Tree Autoregressive (STAR) modeling, which can incorporate prior knowledge of images, such as center bias and locality, to maintain sampling performance while also providing sufficiently flexible sequence orders to accommodate image editing at inference time. Approaches that expose conventional autoregressive (AR) models in visual generation to arbitrary sequence orders via random permutation suffer from degraded sampling performance or compromise the flexibility in sequence order choice at inference time. Instead, STAR utilizes traversal orders of uniform spanning trees in a lattice defined by the positions of image patches. Traversal orders are obtained via breadth-first search, allowing us to efficiently construct a spanning tree via rejection sampling whose traversal order ensures that the connected partial observation of the image appears as a prefix for native image inpainting support. Through the tailored yet structured sequence order randomization strategy, STAR preserves the capability of postfix completion while maintaining sampling performance, without any significant changes to the model architecture widely adopted in language AR modeling.‡

Abstract:
Global geolocation, the task of predicting precise coordi-nates from street-view imagery, is inherently plagued by visual ambi-guity. Resolving such ambiguity necessitates a transition from static,one-shot predictions to an interactive geolocation paradigm drivenby multi-turn deductive reasoning. However, most of existing geoloca-tion models and general-purpose MLLMs fail to support this dynamicprocess, mainly due to the lack of interaction capabilities and the ge-ographic knowledge gap. Driven by the imperative to actualize thisinteractive paradigm, we introduce MG-Geo, the first large-scale mul-timodal geolocation dataset explicitly structured for spatial reasoning.Comprising 4.87M geo-tagged Meta entries, 70K image-grounded Cluesamples, and 73K multi-turn Dialog samples across 210 countries andterritories, MG-Geo separates large-scale geographic alignment fromreasoning-oriented supervision. Experiments demonstrate that GaGAachieves SOTA performance across several benchmarks. Notably, on theGWS15k dataset, it surpasses the strong Hybrid Model by 4.57% and2.92% at the country and city levels, respectively, while securing thehighest city-level accuracy (7.46%) on OSV-5M-test. More importantly,we formalize the “Similarity Trap”—a phenomenon where distributivevisual features mislead static models—and demonstrate that GaGA ef-fectively navigates this challenge through a Tiered Interaction Protocol.By dynamically integrating user-provided geographic anchors, GaGAachieves significant localization improvements. Our dataset is accessiblevia: https://huggingface.co/datasets/kendouvg/MG-Geo.

Abstract:
Current instruction-based image retrieval systems are powerful but limited to single-turn interactions, failing to capture the iterative nature of complex, real-world visual searches. To overcome this limitation, we introduce Contextual Composed Image Retrieval (CoCo-IR), a novel task that enables users to progressively refine search results through interactions. We address this new task by proposing a new model based on a Large Multimodal Model (LMM) that functions as a context-aware reasoner for CoCo-IR. Our model interprets the entire interaction history to generate Transformable Image Embeddings (TIE) that evolve across turns. To fuel the model training without expensive human annotations, we develop a fully autonomous, scalable data engine that leverages LMMs to generate high-quality contextual retrieval data, and uses model-guided verification to mine challenging hard negatives. Extensive experiments demonstrate that our approach establishes new state-of-the-art performance: We achieve 39.4 mAP@5 on the challenging single-turn benchmark CIRCO; furthermore, on our new CoCo-IR benchmark, our model maintains robust performance with 44.1 R@1 on 4-turn dialogues, dramatically outperforming existing methods (28.2 4-turn R@1) that fail to handle multi-turn context. Project page: https://CoCo-IR.github.io.

Abstract:
Vision–language models (VLMs) have become a paradigmfor multimodal learning, yet remain unstable due to object hallucination,weak visual grounding, and catastrophic forgetting after full-parameterinstruction tuning. We claim these failures result from a lack of explicitcontrol over visual representation learning during the standard next-token prediction objective. As a result, visual embeddings thus becomepassively optimized and prone to injecting redundant or spurious signals.To counter this, we introduce Information-Regularized Attention (IRA),a stochastic attention mechanism that explicitly regulates the amount ofvisual information injected into the hidden states of intermediate trans-former layers. This local reparameterization translates uncertainty aboutvisual representations into local noise that is independent across datapoints. Beyond evaluating model performance, we also quantify embed-ding properties, where IRA produces smoother curvature trajectoriesand suppresses attention-sink across all layers, indicating a more stabletransformation of the visual signal. Our results suggest that stochasticattention is not merely a regularizer but a key contributor to represen-tation learning in a generative architecture, offering a new direction forbuilding more reliable VLMs.

Abstract:
Multimodal document retrieval aims to retrieve relevant pages while preserving both textual and visual content from the original document. However, existing benchmarks primarily evaluate simple lexical or semantic matching, and most methods encode pages independently. Consequently, they overlook the contextual information in the document required to resolve queries that aggregate information across multiple pages. In this paper, we introduce CMDR and CMDR-Bench, a new multimodal document retrieval task and benchmark that require modeling document context. To address this challenge, we propose CMDR-Embed, a contextual multimodal embedding framework that explicitly incorporates document context by jointly encoding multiple pages and deriving page-level embeddings from a shared contextual representation. Furthermore, we introduce CMCL, a contextual multimodal contrastive learning objective that effectively trains CMDR-Embed and balances contextual modeling with page-level discriminability. Experiments demonstrate that CMDR-Embed significantly outperforms non-contextual embeddings, highlighting the importance of context-aware multimodal embeddings for advancing document retrieval1.

Abstract:
Personalized object detection aims to adapt a general-purposedetector to recognize user-specific instances from only a few examples.Lightweight models often struggle in this setting due to their weak se-mantic priors, while large vision-language models (VLMs) offer strongobject-level understanding but are too computationally demanding forreal-time or on-device applications. We introduce MOCHA (Multi-modalObjects-aware Cross-arcHitecture Alignment), a distillation frameworkthat transfers multimodal region-level knowledge from a frozen VLMteacher into a lightweight vision-only detector. MOCHA extracts fusedvisual and textual teacher’s embeddings and uses them to guide stu-dent training through a dual-objective loss that enforces accurate localalignment and global relational consistency across regions. This processenables efficient transfer of semantics without the need for teacher modi-fications or textual input at inference. MOCHA consistently outperformsprior baselines across four personalized detection benchmarks under strictfew-shot regimes, yielding a +10.1 average improvement, with minimalinference cost.

Abstract:
Multi-modal semantic segmentation (MSS) is essential forrobust perception in complex environments, yet its potential remainslargely untapped due to the prohibitive cost of human annotations. Whileunsupervised semantic segmentation (USS) has seen success on single RGBmodality, its naive extension to multi-modal data is hamperedby fusion degradation. This is because, in the absence of explicitsupervision, existing frameworks struggle to reconcile the heterogeneousstructural patterns captured by different sensors, failing to effectivelyexploit their complementary information. In this paper, we make thefirst attempt to address the novel problem of Unsupervised Multi-modal Semantic Segmentation (UMSS), aiming to effectively exploitcomplementary sensor information in a fully label-free setting. To thisend, we propose UniM2 (Unified Multi-Modal), a novel frameworkbuilt upon DINOv3 that transforms conventional fusion methods intoconsistent performance gains. Our key idea is to learn a unified latentspace driven by Cross-modal Correspondence Synergy (CMCS) toextract intrinsic shared semantic cues, bypassing the need for label-guidedadaptive fusion. To mitigate inherent inter-modal conflicts, we introducea Cross-modal Harmonizer (CMH) that designates RGB as a stablereference, effectively suppressing inconsistent relational supervision whileguiding the model to exploit complementary structural features. Extensiveexperimental results on NYU-Depth-v2 and MFNet show that UniM2improves mIoU by 6.4% and 9.8%, respectively, demonstrating clearadvantages over existing frameworks in UMSS task.

Abstract:
Monitoring canopy height change is essential for understand-ing carbon sinks and forest dynamics. Remote sensing enables consis-tent, large-scale observations of such changes, increasingly integratedwith deep learning architectures such as Geospatial Foundation Models(GFMs). However, existing methods and datasets frame the problem asbinary change detection, which overlooks both the continuous nature ofchange, especially for vegetation, and the inherent uncertainty in labels.We present the Canopy Height Change (CHC) dataset, providing 3 mresolution continuous canopy height differences and associated spatiallyresolved uncertainties across 10 598 km2 of northern and western Spain.The dataset is paired with a co-located time series of PlanetScope satel-lite imagery. Based on the dataset, we introduce the task of uncertainty-aware change regression, associated metrics and strategies for fine-tuningGFMs. Furthermore, we evaluate state-of-the-art GFMs and highlightpromising directions and remaining challenges for advancing continuouscanopy height change estimation.

Abstract:
Deep models are successfully applied to various fields, whiledemanding large amount of annotated data for high-performance recog-nition. To remedy the data-hunger issue, self-supervised learning, espe-cially masked autoencoder (MAE), is a promising approach to effectivelypre-train the deep models. The MAE leverages random masking to con-struct pretext tasks where masked image patches are reconstructed byusing unmasked (visible) ones. In vision domain, however, inherent imageproperties of high redundancy and correlation in local neighbors couldinterfere with the mask-based pretext tasks. In this study, we analyze twomain processes of masking and reconstruction in MAE through the lensof difficulty of the pretext task. The analysis inspires us to propose simpleyet effective approaches based on filtering to improve random maskingas well as raw-pixel reconstruction by properly controlling difficulty ofMAE task with a negligible extra computation cost. In the experimentson image classification, the proposed method renders favorable perfor-mance improvement to MAE using ViTs.

Abstract:
We introduce ArcDeck, a multi-agent framework that for-mulates paper-to-slide generation as a structured narrative reconstruc-tion task. Unlike existing methods that directly summarize raw text intoslides, ArcDeck explicitly models the source paper’s logical flow. It firstparses the input to construct a discourse tree and establish a global com-mitment document, ensuring the high-level intent is preserved. Thesestructural priors then guide an iterative multi-agent refinement process,where specialized agents iteratively critique and revise the presentationoutline before rendering the final visual layouts and designs. To evaluateour approach, we also introduce ArcBench, a newly curated benchmarkof academic paper-slide pairs. Experimental results demonstrate that ex-plicit discourse modeling, combined with role-specific agent coordination,significantly improves the narrative flow and logical coherence of the gen-erated presentations. The ArcDeck codebase and ArcBench benchmarkare available on our project webpage.

Abstract:
We present Split-then-Merge (StM), a controllable genera-tive video composition framework that minimizes reliance on annotateddatasets and handcrafted rules. Instead of requiring manual supervision,StM decomposes unlabeled videos into dynamic foreground and back-ground layers. By self-composing these elements, the model learns tosynthesize complex interactions between moving subjects and diversescenes, capturing the intricate dynamics necessary for high- x001C delity videogeneration. Speci x001C cally, StM introduces a transformation-aware train-ing pipeline utilizing multi-layer fusion and augmentation to address af-fordance challenges in video composition. An identity-preservation lossfurther maintains foreground x001C delity during blending. Extensive exper-iments show that StM outperforms state-of-the-art methods on bothquantitative benchmarks and qualitative evaluations, including humanstudies and Vision-Language Model assessments. Finally, we release StM-50K, the x001C rst multi-layer video dataset, to facilitate future research ingenerative video composition. Data, code, and more details are availableat our project page.Fig. 2: Video Composition. Given input foreground and background videos, image-based methods (a) x0015 (b) use only the x001C rst frame, while (c) x0015 (e) take full video inputs.(a) Object insertion [59] followed by Image-to-Video (I2V) and (b) end-to-end I2Vcomposition SkyReels [13] fails to retain motion due to lack of video access. (c) Manualcopy-paste preserves motion but violates a x001B ordance (swan placed on ground). (d) Naivegenerative composition yields appearance and motion drift (e.g ., black swan turnswhite). (e) Our method preserves identity and motion, and achieves a x001B ordance-awareplacement with realistic blending (swan placed in water with wave and shadows).

Abstract:
The modern-day surge in popularity of wearable devices posesa fundamentally unique motion capture problem: reconstructing full-body movement from any set of sensing hardware worn at a given mo-ment. Yet, most research efforts assume fixed sensor configurations (e.g.,IMU suits or HMD-centric rigs) and cannot generalize across them.In contrast, we argue that motion capture should prioritize unobtru-sive and lightweight devices such as smartphones, smartwatches, smartglasses, and smart insoles, and study the interplay between them. To thisend, we make three contributions. First, we present a large-scale multi-modal dataset synchronizing these consumer-grade sensors with ground-truth 3D motion, spanning 50 diverse activities including everyday tasks,sports, and social interactions. Second, we propose WHIP, a baseline gen-erative model that reconstructs motion from arbitrary subsets of avail-able sensors, robustly handling missing modalities and producing physi-cally plausible motions. Third, we conduct a systematic study of sensorcomplementarity, quantifying how different modalities complement oneanother. Code and dataset are available at this URL.

Abstract:
Music-driven dance generation aims to produce human mo-tion that is both rhythmically synchronized and semantically consis-tent with music. While recent neural approaches have achieved impres-sive visual realism, they typically model motion as a continuous signaland neglect its compositional nature, making generated dances struc-turally incoherent and difficult to control. In this work, we introducea structure-aware framework that models choreography as a sequenceof atomic movements—semantically interpretable motion events thatserve as the building blocks of dance. To construct this atomic move-ment vocabulary, we first segment large-scale dance data and clusterthem into atomic movement groups. We then employ a large languagemodel to semantically relabel and refine the clusters, yielding a set ofinterpretable and reusable atomic movements. Based on these atomicmovement annotations, we design a two-stage generation framework thatmirrors the human choreography process. In the atomic movement plan-ning stage, the model predicts the type, duration, and timing of atomicmovements conditioned on the input music, forming a symbolic danceallocation. In the completion stage, a transition-aware generator syn-thesizes smooth and stylistically coherent motion conditioned on theplanned structure. Extensive experiments demonstrate that our methodproduces dances with significantly improved structural coherence, rhyth-mic alignment, and perceptual naturalness compared to existing base-lines, while providing enhanced interpretability and controllable edit-ing through explicit structural representation. The code is available at(A) Real music and choreography exhibits highly structural formation.Repeated patterns Repeated patternsSimilar or symmetrical atomic movements(B) Existing end-to-end works overlook such structural organization.Fig. 1: (A) There exists explicit repeated patterns in music and choreography. (B)Existing methods, usually in an end-to-end form, treat music-to-dance generation as asimple sequence-to-sequence task, neglecting the inner structure.

Abstract:
Action anticipation, i.e., predicting future actions from past video observations, is fundamental to intelligent systems that assist humans. Despite progress in model architectures, current evaluation practices exhibit critical limitations: methods evaluate exclusively in closedset settings where the training and test action vocabularies are identical, thereby preventing an understanding of generalization to novel action classes encountered in real-world deployment. We therefore introduce the first open-vocabulary evaluation framework for action anticipation, where models are trained on one egocentric dataset and tested on entirely different egocentric datasets with novel action vocabularies. Since our thorough evaluation shows that adapting existing approaches to this task is insufficient, we propose a novel approach that employs horizonspecific learnable queries and a lightweight text encoder adaptation for open-vocabulary long-term action anticipation. It substantially outperforms other approaches that we have adapted to this task.

Abstract:
Understanding camera movement in natural language is crit-ical for training and evaluating video generation models, among other ap-plications. However, we demonstrate that existing vision-language mod-els (VLMs) fail this task in surprising ways, frequently confusing trans-lation with rotation, left with right, and object movement with cameramovement. To address these limitations, we establish natural languagecamera movement understanding as a standalone research task. We in-troduce a two-level cinematographic taxonomy and an extensive, atomicbenchmark featuring both real and synthetic videos. Furthermore, we cu-rate a large-scale, multi-source training set enhanced by targeted cameramovement augmentation. Our fine-tuned VLM-8B outperforms Gemini3.1 Pro by 10% and 11% on our benchmark’s real and synthetic videos,respectively. Despite these gains, a significant gap remains relative to hu-man performance, underscoring the need to promote and facilitate futureresearch on natural language camera movement understanding.

Abstract:
In real scenarios, the videos can span several minutes or evenhours, yet existing research on spatio-temporal video grounding (STVG),given a textual query, mainly focuses on localizing the target from a videoof tens of seconds, typically less than one minute, hindering its applica-tions. In this paper, we explore Long-Form STVG (LF-STVG), whichaims to locate the target from long-term videos. In LF-STVG, long-termvideos encompass a much longer temporal span and more irrelevant infor-mation, making it challenging for current short-form STVG approachesthat process all the frames at once. Addressing these, we propose a novelAutoRegressive Transformer architecture for LF-STVG, dubbed ART-STVG. Unlike current STVG methods requiring seeing the entire videosequence to make a full prediction at once, our ART-STVG regards thevideo as a streaming input and processes its frames sequentially, makingit capable of easily handling the long videos. To capture spatio-temporalcontext in ART-STVG, spatial and temporal memory banks are devel-oped and applied to decoders of ART-STVG. Considering that memoriesat different moments are not always relevant for localizing the target incurrent frame, we introduce simple yet effective memory selective strate-gies that enable the more relevant information for decoders, greatly im-proving the performance. Moreover, rather than parallelizing spatial andtemporal localization as done in existing approaches, we introduce a novelcascaded spatio-temporal design that connects spatial decoder to tempo-ral decoder during grounding, which allows ART-STVG to leverage morefine-grained target information to assist with complicated temporal lo-calization in complex long videos, further boosting performance. On thenewly extended datasets for LF-STVG, ART-STVG largely outperformscurrent approaches, while showing competitive results on Short-FormSTVG. Our code is at: xxx.

Abstract:
Reasoning video object segmentation predicts pixel-level masksin videos from natural-language queries that may involve implicit andtemporally grounded references. However, existing methods are devel-oped and evaluated in an offline regime, where the entire video is availableat inference time and future frames can be exploited for retrospective dis-ambiguation, deviating from real-world deployments that require strictlycausal, frame-by-frame decisions. We study Online Reasoning Video Ob-ject Segmentation (ORVOS), where models must incrementally interpretqueries using only past and current frames without revisiting previouspredictions, while handling referent shifts as events unfold. To supportevaluation, we introduce ORVOSB, a benchmark with frame-level causalannotations and referent-shift labels, comprising 210 videos, 12,907 anno-tated frames, and 512 queries across five reasoning categories. We furtherpropose a baseline with continually-updated segmentation prompts anda structured temporal token reservoir for long-horizon reasoning underbounded computation. Experiments show that existing methods struggleunder strict causality and referent shifts, while our baseline establishesa strong foundation for future research.

Abstract:
Large-scale Vision–Language Models (VLMs) such as CLIP learn powerful semantic representations but operate in Euclidean space, which fails to capture the inherent hierarchical structure of visual and linguistic concepts. Hyperbolic geometry, with its exponential volume growth, offers a principled alternative for embedding such hierarchies with low distortion. However, existing hyperbolic VLMs use entailment losses that are unstable: as parent embeddings contract toward the origin, their entailment cones widen toward a half-space, causing catastrophic cone collapse that destroys the intended hierarchy. Additionally, hierarchical evaluation of these models remains unreliable, being largely retrieval-based and correlation-based metrics and prone to taxonomy dependence and ambiguous negatives. To address these limitations, we propose an adaptive entailment loss paired with a norm regularizer that prevents cone collapse without heuristic aperture clipping. We further introduce an angle-based probabilistic entailment protocol (PEP) for evaluating hierarchical understanding, scored with AUC-ROC and Average Precision. This paper introduces a stronger hyperbolic VLM baseline ARGENT, Adaptive hieRarchical imaGe-tExt represeNTation. ARGENT improves the SOTA hyperbolic VLM by 0.7, 1.1, and 0.8 absolute points on image classification, text-to-image retrieval, and proposed hierarchical metrics, respectively.

Abstract:
Vision language action models (VLAs) have advanced general purpose robotic grasping and manipulation by enabling robots to interpret visual observations and natural language instructions to generate executable action sequences. However, existing VLAs often implicitly assume gripper invariance, despite grasping strategies being inherently embodiment-dependent. Different gripper types, such as parallel-jaw and suction, usually require distinct interaction strategies to achieve the same grasping objective. Moreover, current datasets for VLAs predominantly rely on parallel-jaw grippers, limiting gripper-aware learning. To address this gap, we introduce MiGA, a multi-gripper-aware dataset spanning five distinct gripper types across multiple robots with 103,000 demonstrations, explicitly capturing strategy divergence under shared task objectives. We further propose GVLA, which combines a new multi-gripper tokenizer with adapter-based policy routing. Our new gripper encoding induces structured embedding information that balances parameter sharing and strategy differentiation, while layer-wise probing confirms meaningful gripper-conditioned representations for VLAs. Intensive experiments in both simulation and real-world robots show that our GVLA outperforms the current baselines across evaluated settings. Our method also improves zero-shot generalization or few-shot adaptation to new objects or unseen tasks, and enable more efficient gripper adaptation.

Abstract:
Despite impressive advances in image matting, video mattingremains challenging due to the inherent gap between high-level tracking,which requires frame-wise understanding, and low-level matting, whichfocuses on extremely fine-grained details. Existing methods attempt thisusing costly domain-specific video matting datasets, which may limittheir out-of-domain generalization and leave tracking robustness vulner-able. We rethink this paradigm with SAM2Matting, a novel frameworkthat decouples the task by enhancing the foundational tracker of SAM2with a region-proposal bridge and dedicated matting heads. This en-ables uncompromised SAM2 to handle tracking while the matting com-ponents focus exclusively on resolving fine-grained intricate details. No-tably, despite being trained only on images, SAM2Matting establishesnew state-of-the-art performance on video matting, with robust general-ization across both human-centric and in-the-wild matting scenarios.

Abstract:
Modern dense predictors largely follow a pyramidal pixel-decoding paradigm: they progressively lift low-resolution representationsto dense, high-resolution outputs. While effective, this design tightlycouples computation with output resolution, making dense predictionincreasingly costly to scale. In this work, we argue that strong denseprediction can be achieved by separating capacity from resolution: per-forming high-capacity reasoning in a compact latent space, and readingout to pixels with a lightweight operator. To instantiate this principle,we introduce Latent Dense Prediction Transformer (L-DPT), a minimalarchitecture that decouples dense decoding from pixel-space rendering.Instead of progressively decoding toward the pixel grid, L-DPT performsattention-based dense decoding entirely in latent space, keeping compu-tation largely stable as resolution increases while enabling more flexiblefeature integration from the encoder. The final output is produced viaa lightweight pixel readout, such as pixel shuffle, so scaling to extremeresolutions does not amplify decoding cost. As a result, L-DPT achievesstronger dense prediction accuracy while enabling inference on profes-sional ultra-high-resolution images up to 100 megapixels. More broadly,we view L-DPT as an instance of a scalable design principle rather thana task-specific decoder for dense prediction.

Abstract:
Scalable Vector Graphics are a fundamental medium for reso-lution-independent visual content, yet the deep learning community lacksa continuous, dense, and invertible latent space for vector representa-tions, the kind of foundational building block that Variational Autoen-coders and their descendants have long provided for raster images. Weintroduce SLS (SVG Latent Space), a Transformer-based autoencoderthat learns compact dense representations of individual SVG paths, theatomic visual elements from which any SVG image can be composed. Bymodeling SVG commands, coordinate data, and visual properties withina unified BPE-based token vocabulary, SLS learns fixed-size latent rep-resentations that jointly capture structure and appearance, and can bedecoded back into valid, style-consistent SVG paths with high fidelity.The resulting embedding space is robust, invertible, and structured: em-beddings lie on a unit hypersphere, enabling efficient similarity search,composition, and downstream conditioning through simple vector-spaceoperations. Finally, we demonstrate that SLS generalizes across diversetasks reducing their FLOPs by over 150× compared to token-based ap-proaches, and establishing a general-purpose latent foundation for vectorgraphics research.

Abstract:
Generative models based on the Flow Matching objective,particularly Rectified Flow, have emerged as a dominant paradigm forefficient, high-fidelity image synthesis. However, while existing researchheavily prioritizes generation quality and architectural scaling, the un-derlying dynamics of how RF models memorize training data remainlargely underexplored. In this paper, we systematically investigate thememorization behaviors of RF through the test statistics of MembershipInference Attacks (MIA). We progressively formulate three test statistics,culminating in a complexity-calibrated metric (Tmc_cal ) that successfullydecouples intrinsic image spatial complexity from genuine memorizationsignals. This calibration yields a significant performance surge—boostingattack AUC by up to 15% and the privacy-critical TPR@1%FPR metricby up to 45%—establishing the first non-trivial MIA specifically tailoredfor RF. Leveraging these refined metrics, we uncover a distinct temporalpattern: under standard uniform temporal training, a model’s suscepti-bility to MIA strictly peaks at the integration midpoint, a phenomenonwe justify via the network’s forced deviation from linear approximations.Finally, we demonstrate that substituting uniform timestep samplingwith a Symmetric Exponential (U-shaped) distribution effectively min-imizes exposure to vulnerable intermediate timesteps. Extensive evalu-ations across three datasets confirm that this temporal regularizationsuppresses memorization while preserving generative fidelity.

Abstract:
Representation Autoencoders (RAEs) enable diffusion mod-els to operate in the feature spaces of pretrained visual encoders. How-ever, many off-the-shelf encoders are not optimized for faithful recon-struction and often discard fine-grained visual details. Finetuning theseencoders for image reconstruction can recover such details, but we showthat it also reduces the effective dimensionality of the resulting represen-tation space. We analyze how this altered geometry affects generation inhigh-dimensional feature spaces. Under this geometry, standard veloc-ity prediction in flow matching can require the model to fit orthogonalnoise directions outside the low-dimensional signal manifold, making op-timization inefficient. This motivates the clean data parameterization(x0 -prediction), which focuses learning on the underlying signal mani-fold. Across experiments with multiple reconstruction-finetuned featuresets, we show that x0 -prediction consistently improves text-to-image gen-eration performance.

Abstract:
Streaming 4D reconstruction has been demonstrated onlyindoors, on dense camera rigs surrounding subjects that move at hu-man pace. Outdoor 4D reconstruction exists but relies either on camerasmounted on the moving vehicle itself, or on limited-coverage arrays ob-serving quasi-static subjects offline. The case that actually matters forspectators is a fast-moving subject, watched from a sparse ring of allocen-tric cameras, streaming. No method targets this, and no benchmark ex-ists to evaluate one. To this end, we introduce FastFlowGS, a streaming4D Gaussian Splatting method for reconstructing fast-moving subjectsfrom a small set of fixed external cameras, and Monaco4D, a photoreal-istic Unreal Engine 5 benchmark for high-speed outdoor reconstruction.FastFlowGS fuses sparse matches, semi-dense tracks, and dense opticalflow by lifting each signal to 3D with geometric uncertainty and combin-ing them through a Kalman-style temporal update. Monaco4D providesFormula 1 sequences under varied illumination from trackside, onboard,and drone viewpoints with dense ground truth. On CMU-Panoptic (apublic dataset), FastFlowGS exceeds the strongest baseline by 12.6%VMAF at 35% greater efficiency. On Monaco4D, where existing stream-ing methods degrade severely, it improves dynamic-region PSNR by upto 18.6% with 28.3% lower per-frame optimization time.

Abstract:
Geometry transformers such as VGGT achieve strong per-formance by jointly reasoning over multiple views with global attention.However, scaling them to large view collections remains challenging dueto the quadratic cost of attention. Moreover, our empirical analysis re-veals that the reconstruction quality in VGGT is sensitive to the distri-bution of viewpoints. Simply increasing the number of views without suf-ficient viewpoint diversity can even degrade performance, as redundantviews introduce highly similar tokens that dilute informative geometricsignals in the attention mechanism. Motivated by this observation, wepropose a training-free and plug-and-play VGGT inference frameworkthat organizes views into diversity-aware balanced chunks. The chunksare constructed through combinatorial graph partitioning over visualdissimilarity and spatial dispersion. This view organization allows thetransformer to focus attention on geometrically informative views whilereducing redundant attention interactions. To estimate spatial dispersionwithout full pose estimation, we approximate spatial relationships via asoft pose propagation strategy based on visual similarity from a smallset of seed frames. Extensive experiments demonstrate improved perfor-mance in camera pose estimation, multi-view depth prediction, and 3Dreconstruction while reducing memory usage and inference latency. Ourframework also complements existing VGGT variants, enabling scalablemulti-view reconstruction without sacrificing geometric fidelity.

Abstract:
In active reconstruction, an embodied agent must decidewhere to look next to efficiently acquire views that support high-qualitynovel-view rendering. Recent work on active view planning for neuralrendering largely derives next-best-view (NBV) criteria by backpropa-gating through radiance fields or estimating information entropy over3D Gaussian primitives. While effective, these strategies tightly cou-ple view selection to heavy, representation-specific mechanisms and failto account for the computational and resource constraints required forlightweight online deployment. In this paper, we revisit active recon-struction from a renderability-centric perspective. We propose R3CON,a radiance-fields-free active reconstruction framework that induces an im-plicit, pose-conditioned renderability field over SE(3) from a lightweightvoxel map. Our formulation aggregates per-voxel online observation statis-tics into a unified scalar renderability score that is cheap to update andcan be queried in closed form at arbitrary candidate viewpoints in mil-liseconds, without requiring gradients or radiance-field training. This ren-derability field is strongly correlated with image-space reconstruction er-ror, naturally guiding NBV selection. We further introduce a panoramicextension that estimates omnidirectional (360◦ ) view utility to acceleratecandidate evaluation. In the standard indoor Replica dataset, R3CONachieves more uniform novel-view quality and higher 3D Gaussian splat-ting (3DGS) reconstruction accuracy than recent active GS baselineswith matched view and time budgets. .

Abstract:
Recovering dense 3D geometry from unposed images remainsa foundational challenge in computer vision. Current state-of-the-artmodels are predominantly trained on perspective datasets, which implic-itly constrains them to a standard pinhole camera geometry. As a result,these models suffer from significant geometric degradation when appliedto wide-angle imagery captured via non-rectilinear optics, such as fisheyeor panoramic sensors. To address this, we present CAM3R, a Camera-Agnostic, feed-forward Model for 3D Reconstruction capable of process-ing images from wide-angle camera models without prior calibration.Our framework consists of a two-view network which is bifurcated into aRay Module (RM) to estimate per-pixel ray directions and a Cross-viewModule (CVM) to infer radial distance with confidence maps, pointmaps,and relative poses. To unify these pairwise predictions into a consistent3D scene, we introduce a Ray-Aware Global Alignment framework forpose refinement and scale optimization while strictly preserving the pre-dicted local geometry. Extensive experiments on various camera modeldatasets, including panorama, fisheye and pinhole imagery, demonstratethat CAM3R establishes a new state-of-the-art in pose estimation andreconstruction.

Abstract:
Recent advances in 3D Gaussian Splatting (3DGS) have ach-ieved significant success in high-fidelity Novel View Synthesis (NVS),yet the optimization process still introduces noisy Gaussian primitivesdue to the sparse and incomplete initialization from Structure-from-Motion (SfM) point clouds. Most existing methods focus only on adjust-ing the positions of primitives during optimization, while ignoring theunderlying spatial structure. To this end, we introduce a new perspec-tive by formulating the optimization of 3DGS as a primitive denoisingprocess and propose Denoising-GS, a spatial-aware denoising frame-work for Gaussian primitives by taking both the positions and spatialstructure into consideration. Specifically, we design an optimizer thatpreserves the spatial optimization flow of primitives, facilitating coher-ent and directed denoising rather than random perturbations. Buildingupon this, the Spatial Gradient-based Denoising strategy jointlyconsiders the spatial supports of primitives to ensure gradient-consistentupdates. Furthermore, the Uncertainty-based Denoising module es-timates primitive-wise uncertainty to prune redundant or noisy primi-tives, while the Spatial Coherence Refinement strategy selectivelysplits primitives in sparse regions to maintain structural completeness.Experiments conducted on three benchmark datasets demonstrate thatDenoising-GS consistently enhances NVS fidelity while maintaining rep-resentation compactness, achieving state-of-the-art performance acrossall benchmarks. Source code and models will be made publicly available.(a) (b) (c) (d)Point Cloud Spatial Gradient- Uncertainty-basedPosition DenoisingDenoising based Denoising Denoising↓ optimize and denoise ↓ ↓calculate uncertainty and prune↓Fig. 1: Illustrative comparison of different denoising methods: (a) Point cloud denois-ing, which moves noisy points toward the GT surface; (b) Position denoising of Gaus-sian primitives, which adjusts the mean positions of noisy primitives toward the GTsurface; (c) Spatial gradient-based denoising of Gaussian primitives, which optimizesboth the mean positions and the spatial structure of primitives based on gradients; (d)Uncertainty-based denoising of Gaussian primitives, which estimates the uncertaintyof each primitive and prunes those with high uncertainty.

Abstract:
We tackle the problem of sparse novel view synthesis (NVS)using video diffusion models; given K (≈ 5) posed images of a scene,we predict the view from a target camera pose. Many prior approachesleverage generative image priors encoded via diffusion models. However,models trained on single images lack multi-view knowledge, and we in-stead argue that video models contain implicit multi-view knowledge andare therefore easier to adapt for NVS. Our key insight is to formulatesparse NVS as a low-frame-rate video completion task. However, onechallenge is that sparse NVS is defined over an unordered set of inputimages, often too sparse to admit a meaningful order, so the modelsshould be invariant to permutations of that input image set. To thisend, we present FrameCrafter, which adapts video models (naturallytrained with coherent frame orderings) to permutation-invariant NVSthrough simple but effective architectural modifications, including per-frame latent encoding, query-centered camera conditioning and removalof temporal positional embeddings. Our results suggest that video modelscan be easily modified to “forget” about time with minimal supervision,producing state-of-the-art performance on sparse-view NVS benchmarks.

Abstract:
We introduce a novel neural representation for maps between3D shapes based on flow-matching models, which is computationally ef-ficient and supports cross-representation shape matching without large-scale training or data-driven procedures. 3D shapes are represented asthe probability distribution induced by a continuous and invertible flowmapping from a fixed anchor distribution. Given a source and a targetshape, the composition of the inverse flow (source to anchor) with theforward flow (anchor to target), we map points between the two surfaces.By encoding the shapes with a pointwise task-tailored embedding, thisconstruction provides an invertible and modality-agnostic representationof maps between shapes across point clouds, meshes, signed distancefields (SDFs), and volumetric data. The resulting representation con-sistently achieves high coverage and accuracy across diverse benchmarksand challenging settings in shape matching. Beyond shape matching, ourframework shows promising results in other tasks, including UV mappingand registration of raw point cloud scans of human bodies.

Abstract:
We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images. Existing feedforward models such as DUSt3R, VGGT, and AnySplat typically predict per-pixel point maps or pixel-aligned Gaussians, which remain fundamentally 2.5D and limited to visible surfaces. In contrast, UniQueR formulates reconstruction as a sparse 3D query inference problem. Our model learns a compact set of 3D anchor points that act as explicit geometric queries, enabling the network to infer scene structure— including geometry in occluded regions—in a single forward pass. Each query encodes spatial and appearance priors directly in global 3D space (instead of per-frame camera space) and spawns a set of 3D Gaussians for differentiable rendering. By leveraging unified query interactions across multi-view features and a decoupled cross-attention design, UniQueR achieves strong geometric expressiveness while substantially reducing memory and computational cost. Experiments on Mip-NeRF 360 and VR-NeRF demonstrate that UniQueR surpasses recent feedforward baselines in both rendering quality and geometric accuracy, using an order of magnitude fewer primitives than dense alternatives.

Abstract:
Cross-attention is a crucial component in learning-based imageto-point-cloud (I2P) registration. Although existing cross-attention mechanisms have achieved promising progress, attention ambiguity remains a fundamental challenge that hinders the learning of discriminative 2D- 3D correspondences. To address this problem, we revisit cross-attention and establish ordinary differential equations (ODEs) to model the ideal I2P feature interaction. Based on this formulation, we develop an ODEdriven cross-attention (OCA) module that refines feature representations and attention matrices through ODEs. In practice, OCA can be seamlessly integrated into existing I2P registration frameworks. To validate its effectiveness, we incorporate OCA into five state-of-the-art baselines and evaluate on four public benchmark datasets. Experimental results demonstrate that OCA improves registration recall by up to 5%, 9%, and 15% under the standard, fine-tuning, and zero-shot settings, respectively. Code is released at github.com/anpei96/oca-i2p-demo.

Abstract:
We present SynCity 3000, a framework for generating 3Dscenes that are globally coherent while enabling fine-grained layout con-trol. Building on the ability of current image-to-3D generators to pro-duce complex 3D assets from a single image, we extend this capabilityto the scale of entire scenes by adapting the generator to be applicableas a convolutional operator. We achieve this by fine-tuning the modelon scene-like data generated by a new synthetic data engine, which wepropose to address the scarcity of 3D scene data for training. The convo-lutional generator is then applied to a dimetric image of the entire scene,generated from the user prompt, resulting in 3D scenes of arbitrary sizeand complexity. Across diverse prompts and layouts, SynCity 3000 pro-duces large, coherent, and detailed scenes, addressing the shortcomingsof prior approaches to 3D scene generation.

Abstract:
We address the problem of recovering high-speed videos from dynamic scenes under extreme photon sparsity. Existing methods rely on aggregating photon detections in local spatiotemporal windows to improve signal-to-noise ratio; however, this local grouping discards global structure and fails in low-light regimes where photon detections are sparse in space and time. In this work, we show that the information needed to recover both motion and illumination is encoded in correlations over the full space-time pattern of photon arrivals. Building on this insight, we develop a spatiotemporal flux probing theory and an algorithm that estimates the Fourier coefficients of the underlying intensity directly from the photon stream. We demonstrate that our approach (1) recovers fast motion and temporal illumination dynamics with substantially fewer photons than prior methods, (2) enables velocity-selective videography that automatically refocuses video onto specific detected motions, and (3) generalizes across sensing modalities including singlephoton, event, and spike cameras.

Abstract:
Deep neural networks often produce poorly calibrated confi-dence estimates, overstating their certainty even when predictions are in-correct. Temperature Scaling (TS) remains the most widely used post-hoccalibration method due to its simplicity and e!ectiveness, yet its global,uniform rescaling of logits fails to correct the highly heterogeneous struc-ture of miscalibration observed across the confidence spectrum. In par-ticular, the largest correctness–confidence discrepancies arise in di!er-ent quantile regions depending on the setting, which standard TS leaveslargely unaddressed. We introduce Quantile-Adaptive Temperature Scal-ing (QaTS), a simple and e"cient post-hoc calibration method thatadapts the temperature as a function of a prediction’s empirical con-fidence quantile. By mapping confidences into the quantile space, QaTSnormalizes the calibration problem, makes the structure of miscalibrationexplicit, and enables a monotone temperature function that adapts acrossquantiles while leaving well-calibrated high-confidence predictions largelyunchanged. This quantile-aware formulation aligns naturally with a repa-rameterized Expected Calibration Error (ECE) objective and yields asample-wise temperature that is robust across a variety of challengingscenarios, such as class imbalance and distributional shifts. Across abroad range of datasets, architectures, evaluation scenarios and diversetasks, QaTS consistently, and substantially, outperforms state-of-the-artpost-hoc calibration methods, delivering more reliable and trustworthyconfidence estimates without modifying model predictions.

Abstract:
Single-Photon Avalanche Diode (SPAD)-based Light Detec-tion and Ranging (LiDAR) is emerging for autonomous vehicles due toits high sensitivity and precise depth sensing capabilities. However, flarecaused by excessive photon returns or pile-up effects can lead to incorrectdepth estimation and exaggerated boundaries in point clouds, resultingin severe distortions of geometric measurements, making flare suppres-sion essential for safety-critical applications. Existing flare mitigationmethods primarily operate at the hardware or signal-processing levels.While effective under specific configurations, they are largely rule-basedand configuration-dependent, lacking learnable representations that gen-eralize across diverse sensing scenarios. In this work, we reformulate flaresuppression as a semantic segmentation problem, enabling data-drivenlearning of geometric and photometric cues directly from SPAD mea-surements. We first benchmark representative segmentation models onthe newly introduced SPAD flare dataset and observe that they strug-gle to exploit the intrinsic multi-echo characteristics of SPAD signals.Motivated by this observation, we propose Physically-Informed seg-mentation for LiDAR Flare (PILF), a learning-based approach thattreats the first and second echoes, together with ambient illumination,as distinct modalities, aggregating cross-echo information while jointlyencoding geometric and photometric features. Experiments across mul-tiple real-world scenes demonstrate that PILF significantly outperformscompared segmentation models, achieving up to 79.32% mIoU, andproviding an effective solution for SPAD-based LiDAR flare suppression.

Abstract:
World–Action Models (WAMs) initialized from pre-trainedvideo generation backbones have demonstrated remarkable potential forrobot policy learning. However, existing approaches face two critical bot-tlenecks: joint reasoning over future visual dynamics and actions incurssubstantial inference overhead, and joint modeling entangles visual andmotion representations, making motion prediction dependent on videoforecasts. To address these issues, we introduce GigaWorld-Policy, anaction-centered WAM that learns 2D pixel–action dynamics while en-abling efficient action decoding with optional video generation. Specif-ically, the model predicts future action sequences conditioned on thecurrent observation and, during training, generates future videos condi-tioned on the predicted actions and the same observation. Supervisionfrom both action prediction and video generation provides richer learn-ing signals and encourages physically plausible actions through visual-dynamics constraints. With a causal design that prevents future-videotokens from influencing action tokens, explicit future-video generationis optional at inference time, enabling faster deployment. To supportthis paradigm, we curate a diverse, large-scale robot dataset to pre-train the model. Experiments on real-world robotic platforms show thatGigaWorld-Policy runs 9× faster than the WAM baseline Motus whileimproving task success rates by 7%; compared with π0.5 , it improvesperformance by 95% on RoboTwin 2.0.

Abstract:
Reliable localization of non-line-of-sight (NLOS) pedestriansis critical for safe urban autonomous driving, yet it remains highly chal-lenging in ego-dynamic outdoor environments, where ego-vehicle motionmakes radar multipath propagation complex and noisy. In this paper, wepresent a reflection-aware framework for NLOS pedestrian localizationwith a moving ego-vehicle in outdoor testbed scenarios. Our frameworkfuses front-view camera images and 2D radar point clouds to infer reflec-tion orders and reflective surface distributions in bird’s-eye-view space.It then uses physics-guided ray tracing to reconstruct distorted reflec-tion paths and localize the hidden pedestrian. We validate the frame-work in outdoor testbed scenarios under ego-dynamic conditions. The re-sults demonstrate the effectiveness of the proposed framework for NLOSpedestrian localization with a moving ego-vehicle.

Abstract:
Adversarial patch attacks that aim to fool deep neural network models in the real world prove effective in ‘every’ possible network type, including vision-language models (VLMs). Surprisingly, most of the existing adversarial patch generation algorithms develop patches of fixed shape (presumably rectangular or circular in a few instances). Due to such biases toward the shape of an adversarial patch, existing adversarial patch detectors, including VLMs, which yield high accuracy, provide a false sense of security. Our extensive experiments equivalently showcase the bias towards the fixed shape of adversarial patches and their high detection, which might leave a hole in adversarial patch defense against adaptive patches. In response, we have first proposed a novel deformable adversarial patch generation algorithm with multifold aims: (i) to highlight the vulnerabilities of state-of-the-art (SOTA) image classification algorithms and (ii) to help provide a strong foundation for developing a universal adversarial patch detector. Introducing smooth and deformable patches that significantly increase practical threats in real-world scenarios. Using the proposed algorithm, we developed large-scale adversarial patch detection datasets comprising 140,000 adversarial patch images for rigorous robustness evaluation and generalizable adversarial detection. The proposed deformable patches effectively fool several SOTA image classification models. While the defense is not the primary focus, our preliminary study highlights that detecting the proposed deformable patches is hard. Still, they can enhance security against various patch shapes, including fixed and deformable patches.

Abstract:
Test-Time Adaptation (TTA) seeks to improve model robust-ness under distribution shifts by adapting parameters using unlabeledtarget data. However, in the absence of supervision, entropy-based adap-tation is fundamentally underconstrained: multiple distinct parameterupdates can achieve similarly low entropy while inducing drasticallydifferent decision boundaries. This phenomenon, known as underspecifi-cation, renders standard TTA brittle and prone to collapse into spuriousmodes. In this work, we reinterpret TTA through a posterior-inspired lensinduced by entropy minimization, where low-entropy solutions define apseudo-likelihood over parameters. Instead of committing to a single pointestimate, we introduce a particle-based diversification framework thatexplores multiple plausible adaptation trajectories simultaneously. Ourmethod can be viewed as a structured exploration of multiple plausibleadaptation solutions, implemented through multi-level diversification atthe output, parameter, optimizer, and input levels. Crucially, the frame-work acts as a plug-and-play wrapper compatible with existing TTAmethods. Extensive experiments on challenging benchmarks demonstrateconsistent gains in stability and robustness, achieving improvements of3–4% under mixed shifts, 2–3% with batch size one, and 1–2.5% underlabel shifts, outperforming state-of-the-art baselines. Our results suggestthat treating TTA as a multi-hypothesis inference problem, rather thana single-point optimization task, is key to mitigating underspecificationand enabling reliable real-world deployment.

Abstract:
Adversarial Robustness Distillation (ARD) aims to trans-fer both natural and robust knowledge from a strong teacher modelto a lightweight student model, thereby achieving robust performancein resource-constrained settings. The dual-teacher robustness distilla-tion paradigm further provides an inspiring solution for optimizing the‘accuracy-robustness’ trade-off. However, existing dual-teacher distilla-tion frameworks fail to fully align the supervision provided by teachergroup, resulting in suboptimal performance. To fully utilize the poten-tial of dual-teacher robustness distillation, we propose a novel multi-teacher framework called global consistency multi-teacher robustness dis-tillation (GCMRD), which improves the existing dual-teacher paradigmfrom three perspectives. First, for internal maximization, we introducean integrated soft-label attack objective to replace hard-label or single-teacher-guided objective, leading to consistent attack preferences acrossboth natural and robust teachers. Second, for external minimization, wedesign a fully-repulsion regularization mechanism based on the erroneousadversarial outputs of the natural teacher, which enables more compre-hensive utilization of teacher supervision. Finally, we employ the outputsof the natural teacher to guide the fine-tuning of the robust teacher, in-directly balancing the student model’s accuracy-robustness trade-off byenforcing consistency with the teacher ensemble. Experimental resultsdemonstrate that our GCMRD achieves state-of-the-art performance inboth natural accuracy and adversarial robustness.

Abstract:
Building interactive digital twins requires recovering both 3Dgeometry and the kinematic structures that govern how objects articu-late. Yet existing methods for articulated object reconstruction requireexplicitly observable motion from multiple articulation states. We intro-duce a rest-state formulation that reconstructs articulated objects froma single closed configuration, an inherently ill-posed setting where geom-etry, semantics, and motion priors compensate for the absence of motioncues. Our framework adopts an explicit mesh as an intermediate repre-sentation for cross-model verification and fusion, reconciling noisy out-puts from vision-language and segmentation models into spatially con-sistent part structures. To estimate joint parameters without observedmotion, we use a video diffusion model to synthesize articulation hy-potheses and validate them through geometric consistency. Our approachachieves accurate part decomposition and physically plausible articu-lation, performing competitively with motion-observing reconstruction-based, generation-based, and modular pretrained-model baselines.

Abstract:
Deep learning based object detectors require trustworthinessbeyond competitive detection performance, but deep neural networks areprone to overconfident predictions, assigning high confidence scores topredictions that are likely to be inaccurate. To improve the alignmentbetween confidence scores and prediction accuracy, existing methodscalibrate confidence scores based on box-level localization, such as pre-cision or intersection over union with the ground truth bounding box.However, box-level localization reflects only a measure of agreement be-tween the predicted box and the ground truth, resulting in calibratedconfidence scores for box-level accuracy failing to capture the localizationaccuracy of coordinates of box. To tackle this issue, we propose a novelpost-hoc calibration framework, rethinking detection calibration (ReDC),which provides reliable coordinate-level confidence scores, including di-rectional information. The proposed framework defines coordinate-wisealignment and deviation direction between predictions and ground truth.Based on the alignment measure, confidence re-encoding produces reliablecoordinate-level confidence scores, while directional displacement estima-tion predicts coordinate-wise deviation directions. Extensive experimentsunder in-domain and out-domain scenarios demonstrate that the proposedapproach expresses the coordinate-wise localization of detected objectsmore precisely than existing methods. Furthermore, our method coversthe representational scope of prior calibration approaches by aggregatingcoordinate-level confidence scores into box-level localization.

Abstract:
Performance in face and speaker verification is largely drivenby margin-penalty softmax losses such as CosFace and ArcFace. Recentlyintroduced α-divergence loss functions offer a compelling alternative, par-ticularly due to their ability to induce sparse solutions (when α > 1).However, standard geometric margins are designed for the softmax func-tion and do not naturally extend to this generalized probabilistic frame-work. In this paper we propose Q-Margin, a novel α-divergence lossthat introduces a principled probabilistic margin. Unlike conventionalmethods that apply geometric penalties to the logits (unnormalized log-likelihoods), Q-Margin encodes the margin penalty directly into the ref-erence measure (prior probabilities). This formulation naturally encour-ages discriminative embeddings while preserving the beneficial sparsityproperties of the α-divergence. We demonstrate that Q-Margin achievescompetitive or superior performance on the challenging IJB-B and IJB-C face verification benchmarks and similarly strong results in speakerverification on VoxCeleb. Crucially, against ArcFace and CosFace base-lines trained under an identical recipe, Q-Margin consistently improvesat low False Acceptance Rates (FARs), a capability critical for practicalhigh-security applications. Finally, the extreme sparsity of the Q-Marginposteriors enables exact and memory-efficient training, offering a scalablesolution for datasets with millions of identities.

Abstract:
Realistic and diverse multi-agent driving scenes are crucial for evalu-ating autonomous vehicles, but safety-critical events which are essential for thistask are rare and underrepresented in driving datasets. Data-driven scene genera-tion offers a low-cost alternative by synthesizing complex traffic behaviors fromexisting driving logs. However, existing models often lack controllability or yieldsamples that violate physical or social constraints, limiting their usability. Wepresent OMEGA, an optimization-guided, training-free framework that enforcesstructural consistency and interaction awareness during diffusion-based samplingfrom a scene generation model. OMEGA re-anchors each reverse diffusion stepvia constrained optimization, steering the generation towards physically plausibleand behaviorally coherent trajectories. Building on this framework, we formu-late ego–attacker interactions as a game-theoretic optimization in the distributionspace, approximating Nash equilibria to generate realistic, safety-critical adver-sarial scenarios. Experiments on nuPlan and Waymo show that OMEGA improvesgeneration realism, consistency, and controllability, increasing the ratio of physi-cally and behaviorally valid scenes from 32.35% to 72.27% for free explorationcapabilities, and from 11% to 80% for controllability-focused generation. Our ap-proach can also generate 5× more near-collision frames with a time-to-collisionunder three seconds while maintaining the overall scene realism.

Abstract:
Recent text-to-image (T2I) models generate semantically co-herent images from textual prompts, yet evaluating how well they alignwith individual user preferences remains an open challenge. Conventionalevaluation methods, general reward functions or similarity-based metrics,fail to capture the diversity and complexity of personal visual tastes. Wepresent PIGReward, a personalized reward modeling framework that dy-namically generates user-conditioned evaluation dimensions and assessesimages through CoT reasoning. To address the scarcity of user data, PI-GReward adopts a self-bootstrapping strategy that reasons over limitedreference data to construct rich user contexts, enabling personalizationwithout user-speci x001C c training. Beyond evaluation, PIGReward providespersonalized feedback that drives user-speci x001C c prompt optimization, im-proving alignment between generated images and individual intent. Ex-tensive experiments demonstrate that PIGReward outperforms existingmethods in both accuracy and interpretability, establishing a scalable,reasoning-based foundation for personalized T2I evaluation and opti-mization. Project page: https://jeongeunnn-e/PIGReward/

Abstract:
Instruction-guided image editing has a training-time blindspot. Generative editors are never required to semantically verify whethertheir outputs actually satisfy the instruction. Supervision stops at recon-struction and input textual-level conditioning. This produces incompleteedits, spatial spillover, and poor localization. We present IABEdit, amodel-agnostic framework that embeds differentiable semantic verifica-tion into training. A frozen vision-language model extracts spatially-aware descriptors from the ground-truth edit. A trainable aligner thenreproduces them from the generated output. The residual between thetwo becomes a gradient that teaches the generator both what to editand where, with no inference-time VLM cost. IABEdit is compatiblewith diverse backbones, including U-Net (Stable Diffusion) and MMDiT(FLUX), without altering their inference pipelines. On MagicBrush, itimproves structural fidelity by +3.49 DINO-I over the best diffusion base-line and +1.26 over the best overall baseline, while remaining competitiveon instruction alignment. It also achieves state-of-the-art instruction ad-herence performance on RealEdit and EMU Edit benchmarks based onembedding-based metrics. Most consequentially, on the D-LORD surveil-lance benchmark, it surpasses the proprietary Gemini agent by +5.13DINO-P under heavy occlusion, where preserving identity is hardest.This shows that gradient-aligned VLM distillation holds up under real-world-like surveillance and occlusion conditions. Human and GPT-4oevaluations confirm perceptually precise, well-localized edits.

Abstract:
Recent pre-trained text-to-image flow models have enabledremarkable progress in text-based image editing. Mainstream approachesadopt a corruption-then-restoration paradigm, where the source im-age is first corrupted into an editable “intermediate state” and then re-stored to the target image under the prompt guidance. However, currentmethods construct this intermediate state in a target-agnostic manner,i.e., they mainly focus on realizing source image reconstruction whileneglecting the semantic gaps towards the specific editing target. Thisdesign inherently results in limited editability or inconsistency when thedesired modifications substantially deviate from the source. In this pa-per, we argue that the intermediate state should be target-aware, i.e.,selectively corrupting editing-relevant contents while preserving editing-irrelevant ones. Thus, we propose FlowCycle, an inversion-free and flow-based editing framework that parameterizes corruption with learnablenoises and optimizes them through a cycle-consistent process. By itera-tively editing the source to the target and recovering back to the sourcewith dual consistency constraints, FlowCycle learns to produce a target-aware intermediate state, enabling faithful modifications while preservingsource consistency. For efficiency, we further accelerate the optimizationby dynamically adjusting the sampling steps. Extensive ablations demon-strated that FlowCycle achieves superior editing performance.

Abstract:
With the recent rapid progress in generative models, im-age editing has made remarkable advances, yet achieving faithful editsthat precisely modify only the target regions while strictly preservingall other regions remains challenging. Since externally provided regionannotations are often difficult to obtain in practice, a growing body ofwork seeks to improve preservation by automatically inferring edit andnon-edit regions, and then enforcing consistency on the latter. However,these approaches still suffer from inaccurate region estimation and heuris-tic correction strategies that distort the native inference process, makingmethods designed for fidelity themselves a new source of artifacts. Wepropose SR-Edit, an image editing framework that overcomes these is-sues via iterative self-refinement. Specifically, at each iteration, SR-Editfirst (i) extracts progressively precise and self-consistent region separa-tion from the model’s own predictions by lightweight post-processing,and then (ii) enforces preservation in non-edit areas through correctionupdates that remain aligned with the original sampling dynamics. Ex-tensive experiments demonstrate that SR-Edit achieves superior preser-vation and overall image quality compared to existing editing techniques.

Abstract:
The emergence of large-scale text-to-image diffusion (T2ID)models has led to significant advancements in generating high-qualityvisual content from textual prompts. However, these powerful capabil-ities have also raised growing concerns about the generation of harm-ful and copyrighted material. While existing concept erasure techniquescan effectively block the production of specific unwanted concepts fromprompts, they often fall short when it comes to erasing an entire cate-gory (including subcategories) and are typically limited to handling onlya few concepts at a time. In this paper, we introduce SubcategoricalUnlearning via Regularized Erasure (SURE), a novel method for re-moving entire subcategories from text-to-image diffusion models usingonly a single parent category as the target. Unlike prior approaches,SURE does not rely on sets of synonyms. Instead, it employs conceptspace to discover and eliminate the target category while preserving themodel’s overall utility. To further enhance erasure, SURE incorporatesLipschitz regularization, which encourages smoother model responses toperturbations around the target category. Specifically, the regularizationpromotes consistent behavior in the model’s latent space when exposedto slight variations of the category to be forgotten. This smoothnessconstraint aids in erasure while maintaining the model’s ability to gen-erate unrelated content. Extensive experiments conducted across threetasks—object removal, suppression of explicit content, and elimination ofartistic styles demonstrate that SURE achieves balanced performance inboth effective category erasure and preservation of non-target concepts.

Abstract:
Text-to-image models produce images that align well withnatural language prompts, but compositional generation has long beena central challenge. Models often struggle to satisfy multiple conceptswithin a single prompt, frequently omitting some concepts and resultingin partial success. Such failures highlight the difficulty of jointly op-timizing multiple concepts during reward optimization, where compet-ing concepts can interfere with one another. To address this limitation,we propose Correlation-Weighted Multi-Reward Optimization (CMO),a framework that leverages the correlation structure among concept re-wards to adaptively weight each attribute concept in optimization. Byaccounting for interactions among concepts, CMO balances competingreward signals and emphasizes concepts that are partially satisfied yetinconsistently generated across samples, improving compositional gener-ation. Specifically, we decompose multi-concept prompts into pre-definedconcept groups (e.g., objects, attributes, and relations) and obtain re-ward signals from dedicated reward models for each concept. We thenadaptively reweight these rewards, assigning higher weights to conflictingor hard-to-satisfy concepts using correlation-based difficulty estimation.By focusing optimization on the most challenging concepts within eachgroup, CMO encourages the model to consistently satisfy all requestedattributes simultaneously. We apply our approach to train state-of-the-art diffusion models, SD3.5 and FLUX.1-dev, and demonstrate consis-tent improvements on challenging multi-concept benchmarks, includingConceptMix, GenEval 2, and T2I-CompBench.

Abstract:
Editing multiple elements of an image in a single forward pass is a practical alternative to multi-turn image manipulation, offering improved efficiency and potentially better harmonization. However, when several instructions target different regions, semantic interference often leads to attribute leakage and poor edit disentanglement, especially as the number of edits increases. In this work, we propose MICE (Multi-Instance Concurrent Editing), a training-free strategy for scalable multi-instance image editing with Multimodal Diffusion Transformers. MICE modifies the additive bias of joint attention to regulate interactions between instance-specific edit instructions, latent, and context tokens identified via user-provided segmentation masks. Specifically, MICE allows intra-instance attention, penalizes interactions between neighboring region tokens, and suppresses unrelated cross-instance attention. As a result, our method enforces attribute binding while preserving global visual consistency. We evaluate MICE on LoMOE-Bench and introduce MICE-Bench, a more challenging benchmark with an average of 8.5 concurrent edits per image. The experiments demonstrate that our approach outperforms strong baselines and recent competitors in terms of visual quality preservation and faithfulness to the editing instructions.

Abstract:
Cameras capture scene-referred linear raw images, which areprocessed by onboard image signal processors (ISPs) into display-referred8-bit sRGB outputs. Although raw data is more faithful for low-levelvision tasks, collecting large-scale raw datasets remains a major bottle-neck, as existing datasets are limited and tied to specific camera hard-ware. Generative models offer a promising way to address this scarcity;however, existing diffusion frameworks are designed to synthesize photo-finished sRGB images rather than physically consistent linear representa-tions. This paper presents RawGen, to our knowledge the first diffusion-based framework enabling text-to-raw generation for arbitrary targetcameras, alongside sRGB-to-raw inversion. RawGen leverages the gener-ative priors of large-scale sRGB diffusion models to synthesize physicallymeaningful linear outputs, such as CIE XYZ or camera-specific raw rep-resentations, via specialized processing in latent and pixel spaces. Tohandle unknown and diverse ISP pipelines and photo-finishing effectsin diffusion-model training data, we build a many-to-one inverse-ISPdataset where multiple sRGB renditions of the same scene generatedusing diverse ISP parameters are anchored to a common scene-referredtarget. Fine-tuning a conditional denoiser and specialized decoder on thisdataset allows RawGen to obtain camera-centric linear reconstructionsthat effectively invert the rendering pipeline. We demonstrate RawGen’ssuperior performance over traditional inverse-ISP methods that assume afixed ISP. Furthermore, we show that augmenting training pipelines withRawGen’s scalable, text-driven synthetic data can benefit downstreamlow-level vision tasks.

Abstract:
Recalling that diffusion models map noisy images to cleanones, the choice of noise addition (i.e., corruption) critically influencesthe denoising process. However, prior diffusion-based image restoration (IR)methods often employ naïve corruption strategies—such as randomlysampling noise, fixing the timestep, or even omitting corruption alto-gether—which may not accurately reflect the actual degradation of eachimage. To handle this, we propose Input-Aware Corruption for IR (IAC-IR) framework, which maps each low-quality (LQ) input into an optimalnoisy sample that lies on the pretrained diffusion trajectory. Instead ofchoosing corruption heuristically, we predict the timestep and noise us-ing supervision derived from the properties of the pretrained diffusionmodel. Specifically, the predicted timestep aligns the corrupted samplewith the Gaussian noise corrupted distribution, while the predicted noisepreserves the recoverable content of the input. Moreover, we use theseinput-aware corruption factors to improve conventional score-based dis-tillation. Rather than relying on random corruption, which often produceunreliable target scores and weak gradients, we perform distillation withinput-aware corruption, yielding more reliable score estimates and morestable distillation. By modeling input-aware corruption and integratingit into distillation, our method better leverages the pretrained diffusionprior, achieving superior perceptual quality in image restoration.

Abstract:
Diffusion distillation is central to accelerating image andvideo generation, yet existing methods are fundamentally limited by thedenoising process, where step reduction has largely saturated. Partial-timestep, low-resolution generation can further accelerate inference, butit suffers from noticeable quality degradation due to cross-resolutiondistribution gaps. We propose Cross-Resolution Distribution MatchingDistillation (RMD), a novel distillation framework that bridges cross-resolution distribution gaps for high-fidelity, few-step multi-resolutioncascaded inference. Specifically, RMD divides the timestep intervals foreach resolution using logarithmic signal-to-noise ratio (logSNR) curves,and introduces logSNR-based mapping to compensate for resolution-induced shifts. Distribution matching is conducted along resolution tra-jectories to reduce the gap between low-resolution generator distributionsand the teacher’s high-resolution distribution. In addition, a predicted-noise re-injection mechanism is incorporated during upsampling to stabi-lize training and improve synthesis quality. Quantitative and qualitativeresults show that RMD preserves high-fidelity generation while acceler-ating inference across various backbones. Notably, RMD achieves up to33.4× speedup on SDXL and 25.6× on Wan2.1-14B, while preservinghigh visual fidelity.

Abstract:
Joint image–feature generative modeling has recently emergedas an effective strategy for improving diffusion training by coupling low-level VAE latents with high-level semantic features extracted from pre-trained visual encoders. However, existing approaches rely on a fixedrepresentation space, constructed independently of the generative objec-tive and kept unchanged during training. We argue that the represen-tation space guiding diffusion should itself adapt to the generative task.To this end, we propose Coevolving Representation Diffusion (CoReDi),a framework in which the semantic representation space evolves duringtraining by learning a lightweight linear projection jointly with the diffu-sion model. While naïvely optimizing this projection leads to degeneratesolutions, we show that stable coevolution can be achieved through acombination of stop-gradient targets, normalization, and targeted reg-ularization that prevents feature collapse. This formulation enables thesemantic space to progressively specialize to the needs of image synthesis,improving its complementarity with image latents. We apply CoReDi toboth VAE latent diffusion and pixel-space diffusion, demonstrating thatadaptive semantic representations improve generative modeling acrossboth settings. Experiments show that CoReDi achieves faster convergenceand higher sample quality compared to joint diffusion models operatingin fixed representation spaces.

Abstract:
While diffusion models dominate the field of visual genera-tion, they remain computationally inefficient, as they allocate uniformcomputational effort to samples with varying levels of complexity. In con-trast, autoregressive (AR) models are inherently complexity-aware, asevidenced by their variable likelihoods, but are often hindered by lossydiscrete tokenization and error accumulation. In this work, we introduceGenerative Refinement Networks (GRN), a next-generation visual syn-thesis paradigm to address these issues. At its core, GRN addresses thediscrete tokenization bottleneck through a theoretically near-lossless Hi-erarchical Binary Quantization (HBQ), achieving a reconstruction qual-ity comparable to continuous counterparts. Built upon HBQ’s latentspace, GRN fundamentally upgrades AR generation with a global re-finement mechanism that progressively perfects and corrects artworks —like a human artist painting. Besides, GRN integrates an entropy-guidedsampling strategy, enabling complexity-aware, adaptive-step generationwithout compromising visual quality. On the ImageNet benchmark, GRNestablishes new records in image reconstruction (0.56 rFID) and class-conditional image generation (1.81 gFID). We also scale GRN to morechallenging text-to-image and text-to-video generation, delivering supe-rior performance on an equivalent scale. We release all models and codeto foster further research on GRN.

Abstract:
Reference-to-video (R2V) generation is a controllable videosynthesis paradigm that constrains the generation process using bothtext prompts and reference images, enabling applications such as per-sonalized advertising and virtual try-on. In practice, existing R2V meth-ods typically introduce additional high-level semantic or cross-modal fea-tures alongside the VAE latent representation of the reference image andjointly feed them into the diffusion Transformer (DiT). These auxiliaryrepresentations provide semantic guidance and act as implicit alignmentsignals, which can partially alleviate pixel-level information leakage inthe VAE latent space. However, they may still struggle to address copy–paste artifacts and multi-subject confusion caused by modality mismatchacross heterogeneous encoder features. In this paper, we propose Re-fAlign, a representation alignment framework that explicitly aligns DiTreference-branch features to the semantic space of a visual foundationmodel (VFM). The core of RefAlign is a reference alignment loss thatpulls the reference features and VFM features of the same subject closerto improve identity consistency, while pushing apart the correspond-ing features of different subjects to enhance semantic discriminability.This simple yet effective strategy is applied only during training, incur-ring no inference-time overhead, and achieves a better balance betweentext controllability and reference fidelity. Extensive experiments on theOpenS2V-Eval benchmark demonstrate that RefAlign outperforms cur-rent state-of-the-art methods in TotalScore, validating the effectivenessof explicit reference alignment for R2V tasks.

Abstract:
Humans can efficiently parse continuous sensory streams,from hours to years, scaffolding an internal world model that groundsspatial reasoning and prediction. To mimic this capacity, spatial super-sensing [59] challenges multimodal models to move beyond linguistic un-derstanding toward true world modeling. However, their benchmark re-lies on synthetic long videos—formed by concatenating random shortclips—and is mostly limited to household scenes, leaving real-world con-tinuity and diversity underexplored. To address the gap, we introduceVSI-Super-Wild, a large-scale spatial supersensing benchmark builtfrom genuinely long, in-the-wild videos across diverse scenarios. Notably,inspired by cognitive studies on how humans structure experience, wesystematically probe the full triad of world state: the agent (observer),objects (scene items), and the environment (places and global layout). †Equal contribution. Corresponding author.In total, VSI-Super-Wild comprises 442 real-world long-form videosacross 8 scene categories and 6,980 human-verified question-answer pairs.Evaluating multimodal models on VSI-Super-Wild exposes a funda-mental disconnect: despite advances in static image understanding, mod-els consistently fail at tasks that require coherent world-state trackingover time. We characterize how performance degrades with world-statecomplexity and temporal horizon, and diagnose four failure modes: spa-tial collapse, semantic shortcuts, insufficient update, and instance con-fusion. This taxonomy reveals that models lack the mechanisms to bindobjects, agents, and environments into a unified spatial world model—afundamental gap that defines the path forward for spatial supersensing.

Abstract:
High temporal resolution is essential for capturing fine-grained dynamics in video understanding, yet current video large language models (VLLMs) and benchmarks predominantly rely on low-frame-rate sampling, discarding dense temporal information. This design compromise is driven by the high cost of frame-wise tokenization, which incurs redundant computation and linear growth in token count with video length. While effective for slowly changing content, such engineering trade-offs severely limit tasks where information is distributed across nearly every frame, such as educational or high-motion videos that require frameby-frame reasoning. To address this gap, we introduce the novel task of High-FPS Video Understanding, the first task specifically designed to evaluate video comprehension at high frame rates, where information is present in nearly every frame. We further propose the first benchmark, DIVE-Bench (Dense Information Video Evaluation Benchmark), to expose the limitations of existing benchmarks whose question– answer pairs are not sensitive to dense temporal variations. To enable efficient high-FPS processing, we propose Gated Residual Tokenization (GRT), a codec-style inter-tokenization framework that operates not only after but also during tokenization. First, Motion-Compensated Gated Inter-Tokenization applies pixel-level motion estimation to gate static regions during tokenization, achieving sub-linear growth in both tokenization time and token count. Second, Semantic-Scene Token Merging further reduces redundancy by merging semantically similar static tokens while preserving dynamic content. Extensive experiments on DIVEBench show that GRT improves high-FPS efficiency while preserving or improving answer quality under dense temporal perception tasks. These results highlight the importance of dense temporal modeling and establish codec-style inter-tokenization as a practical direction for scalable high-FPS video understanding.

Abstract:
Perception-centric systems are typically implemented with a modular encoderdecoder pipeline: a vision backbone for feature extraction and a separate decoder (or late-fusion module) for task prediction. We ask whether this separation is necessary for tasks such as openvocabulary segmentation and OCR. We present Falcon Perception, a single, early-fusion dense Transformer that processes image patches, text, and task tokens in a single shared parameter space. Falcon Perception uses a hybrid attention mask: bidirectional for image tokens and causal for text/task tokens, and keeps dense outputs practical by emitting only a small number of task tokens per instance and decoding continuous spatial outputs with lightweight heads, enabling parallel high-resolution mask prediction. On SA-Co, Falcon Perception achieves 68.0 F , compared to 62.3 for a strong state-of-the-art promptable segmentation baseline. 1 To measure compositional grounding beyond saturated referring benchmarks, we introduce PBench and show large gains on spatial and dense long-context regimes (e.g., 53.5 vs. 31.6 on spatial and 72.6 vs. 58.4 on Dense). Finally, we extend the same architecture to document OCR: a compact Falcon-OCR model with 300M parameters achieves 80.3% on olmOCR and 88.64% on OmniDocBench. Code: GitHub, PBench: HF Dataset, Blog: Project Blog.

Abstract:
Generalized Category Discovery (GCD) aims to simultaneously recognize known categories and discover novel ones from partially labeled data without annotations. Existing methods rely on single-path global embeddings, implicitly performing only hypothesis formation and omitting an explicit verification mechanism, which leads to over-reliance on pretrained semantics and confident misclassification of unknown categories. We reformulate GCD as a Hypothesis–Verification representation framework and propose HVGCD, a structured representation framework that decomposes inference into a global hypothesis, hypothesisconditioned verification evidence, and statistical grounding. The verification branch reconstructs discriminative evidence via content-adaptive prototypes, enabling the model to verify and refine its hypotheses under open-world uncertainty, while a maximum-entropy grounding term stabilizes inference in open-world settings. As an efficient module, HVGCD integrates seamlessly into existing GCD pipelines and consistently improves performance on both coarseand fine-grained benchmarks.

Abstract:
Class-agnostic segmentation enables open-world object seg-mentation beyond predefined categories, and extending it to the eventdomain helps enhance perception in high-speed motion and extreme illu-mination scenarios. However, the sparsity and low redundancy of eventdata lead to weak intra-class consistency, making event-based segmen-tation typically underperform compared to RGB-based counterparts.To address this issue, we propose IC2 -ESeg, an Intra-class Consistencyguided, Class-agnostic Event Segmentation framework, including threemodules: intra-class consistency mining (ICM), lightweight informationinjection (LIJ), and cross-modal distillation (CMD). Grounded in theevent generation mechanism, the ICM module constructs noise, motion,and brightness representations by analyzing statistical noise frequency,spatio-temporal distribution, and brightness gradients, thereby enrichingthe informational content of event data. The LIJ module efficiently inte-grates these representations into the image encoder, while the CMD mod-ule transfers the general segmentation capability of Segment AnythingModel (SAM) to the event domain. Experiments on RGBE-SEG demon-strate that IC2 -ESeg achieves state-of-the-art (SOTA) performance inclass-agnostic segmentation. We also construct ComScene, a high reso-lution RGB-event segmentation dataset encompassing diverse scenarios.Results on MVSEC and ComScene demonstrate the strong generaliza-tion of IC2 -ESeg.

Abstract:
In real-world deployment under strict real-time constraints,weather and imaging variations induce significant distribution shifts,severely degrading detectors. Single-Domain Generalized Object Detec-tion aims to mitigate this issue, yet existing methods rarely investi-gate—at the level of problem formulation—the generalization capabil-ity of real-time detectors under such constrained inference budgets. Tothis end, we introduce Real-Time Single-Domain Generalized ObjectDetection (RT-SDGOD), which focuses on how real-time detectors canachieve cross-domain generalization under zero extra inference overheadby relying solely on training-time representation learning. We observethat, under domain shift, DETR-based real-time detectors mainly de-grade through increased missed detections, rooted in limited and un-stable object-level discriminative evidence. Based on this, we proposeRT-SDGDet, a multi-evidence collaborative modeling framework for RT-SDGOD. The core idea is to enable multiple queries of the same ob-ject to collaboratively cover more sufficient discriminative evidence whilemaintaining the stability of such evidence modeling across views. Specifi-cally, we use one-to-many (O2M) supervision to construct stable object-specific query groups, and further design Discriminative Evidence Di-versity Learning (DEDL) and Dual-view Evidence Consistency Learning(DvECL) to expand object-level evidence coverage and improve evidencestability under appearance perturbations, respectively. Since all compo-nents are introduced only during training, our method incurs no ex-tra inference overhead. Extensive experiments show that the proposedmethod achieves better generalization performance than existing ap-proaches across multiple unseen target domains.

Abstract:
Group Activity Detection (GAD) aims to jointly infer groupmemberships and collective activities from videos. Existing approachestypically rely on actor-centric features to model group activities, assum-ing that collective semantics can be inferred solely from inter-actor re-lations. This overlooks critical context-interactive information, such asexplicit spatial constraints and actor–scene dependencies, which are es-sential in crowded and multi-view environments. To address these lim-itations, we propose a Context-Interactive Reasoning framework thatjointly models spatial topology and group semantic context. Specifically,a Short-Term Interaction Regularizer (STIR) softly constrains inter-actor spatial relations with learnable frame-level topology priors, sup-pressing spurious connections and promoting spatially coherent group-ing. Complementarily, a Long-Range Context Conditioner (LRCC) se-lectively incorporates global scene semantics via data-dependent gating,enabling activity-aware context utilization while avoiding unnecessarynoise. Extensive experiments on challenging benchmarks demonstratethat our method achieves state-of-the-art performance in both groupmembership identification and collective activity recognition.

Abstract:
Multi-object tracking degrades in crowded scenes. The reliability of geometry and appearance shifts under occlusion, uniform appearance, and fast motion. Consequently, association scores suffer from mode-dependent miscalibration. We observe that these failures concentrate into a small set of recurring error modes. This pattern renders single global thresholds ineffective. To address this issue, we propose a diagnose-then-correct strategy via Mode-Conditioned Residual Calibration (MCRC). It is a lightweight auxiliary module that identifies the dominant failure mode from pairwise and set-level geometric cues. The module then applies a mode-conditioned residual correction to the association cost matrix. For highly ambiguous tracking pairs, MCRC selectively activates an uncertainty-gated semantic refiner. This refiner incorporates simple acceptance checks to ensure representation stability, thereby avoiding global computational overhead. Extensive evaluations on DanceTrack, SportsMOT, MOT17, and MOT20 confirm its effectiveness. MCRC consistently improves identity preservation when inserted into diverse trackers, maintaining minimal added overhead.

Abstract:
Adversarial examples exhibit cross-model transferability, en-abling threatening black-box attacks on commercial models. Model en-sembling, which attacks multiple surrogates, is a known strategy to im-prove this transferability. However, prior studies typically use small, fixedensembles, which leaves open an intriguing question of whether scalingthe number of surrogate models can further improve black-box attacks.In this work, we conduct the first large-scale empirical study of thisquestion. We show that by resolving gradient conflict with advanced op-timizers, we overcome the quantitative limitations of idealized theoreticalbounds to empirically discover a robust log-linear scaling law, demon-strating that the Attack Success Rate scales linearly with the logarithmof the ensemble size T . We rigorously verify this law across standardclassifiers, SOTA defenses, and MLLMs, and find that scaling distills ro-bust, semantic features of the target class. Consequently, we apply thisfundamental insight to benchmark SOTA MLLMs. This reveals both theattack’s devastating power and a clear robustness hierarchy, as evidencedby achieving an over 80% transfer attack success rate on proprietarymodels like GPT-4o, while also highlighting the exceptional resilienceof Claude-3.5-Sonnet. Our findings urge a shift in focus for robustnessevaluation: from designing intricate algorithms on small ensembles tounderstanding the principled and powerful threat of scaling.

Abstract:
Vision-language pre-trained models (VLPs) are widely used in real-world applications. However, they remain vulnerable to adversarial attacks. Although adversarial detection methods have demonstrated success in single-modality settings (either vision or language), their effectiveness and reliability in multimodal models such as VLPs remain largely unexplored. In this work, we study the geometry of VLP embedding spaces and observe structured anisotropy that differs from unimodal vision models. Our theoretical analysis shows that under this anisotropic structure, adversarial attacks increase the expected geometric separation between clean and adversarial examples (AEs). Specifically, we demonstrate that AEs consistently exhibit greater expected distances to randomly sampled points than their clean counterparts, indicating that AEs tend to push representations out of manifold regions. Building on these insights, we propose GeoDetect, which leverages these off-manifold deviations via geometric scores to identify AEs. Through comprehensive evaluations, we show that our approach reliably detects AEs across diverse VLP architectures and threat settings, covering unimodal and multimodal attacks as well as adaptive attacks, thereby providing a robust and practical approach to improving the safety and reliability of these models.

Abstract:
Continual learning aims to progressively learn from a sequenceof tasks, each containing a disjoint subset of classes, while preservingpreviously learned knowledge. Prompt-based continual learning methodspropose to learn a small set of parameters, i.e., prompts, by associatingthem with a query feature of an input image. These methods optimize theprompts, attempting to represent diverse patterns of images. However,we have observed that existing prompt-based methods suffer from aprompt collapse problem, that is, the prompts tend to be highly similarto each other, thereby failing to capture the diverse data distributionsin continual learning scenarios. To address this issue, we propose in thispaper a novel prompt-based continual learning framework that capturesdiverse patterns of images across a sequence of tasks. To this end, wemodel each prompt as a probabilistic distribution and construct a mixtureof these distributions, from which we sample diverse prompts. This enablesour model to effectively capture highly diverse image distributions in thecontinual learning process. We also present a distribution regularizationloss to prevent abrupt changes in the prompt distributions throughoutthe training process. We show extensive experimental results for continuallearning on standard benchmarks, including ImageNet-R, CIFAR-100,and CUB-200, demonstrating the effectiveness of our framework.

Abstract:
Modern video generation models, despite high-quality frame synthesis, still struggle with motion fidelity and temporal consistency, causing artifacts and semantic drift; current solutions typically require extensive backbone retraining. We introduce a training-free framework for inference-time motion calibration that intervenes in the sampling trajectory of pretrained models to correct nascent inconsistencies efficiently. Our framework has two complementary components. First, Dynamic Latent Control (DLC) at selected steps estimates a clean latent, evaluates a VFM-based motion–semantic loss on proxy frames, and applies a corrective update to steer the trajectory. To control overhead, Amortization Trajectory Rectification (ATR) interleaves sparse calibration with fast amortized steps by caching a rectification field. Second, Dynamic RoPE Control (DRC) modulates temporal RoPE in attention blocks from the same motion signals, strengthening long-range coherence or sharpening short-range dynamics when flicker is detected. Extensive experiments on VBench and VBench 2.0 show that our method improves advanced motion fidelity metrics while incurring a small trade-off on superficial temporal smoothness for some backbones.

Abstract:
Modeling human-object interactions (HOI) from an egocen-tric perspective is a critical yet challenging task, particularly when re-lying on sparse signals from wearable devices like smart glasses andwatches. We present ECHO, the first unified framework to jointly recoverhuman pose, object motion, and contact dynamics solely from head andwrist tracking. To tackle the underconstrained nature of this problem,we introduce a novel tri-variate diffusion process with independent noiseschedules that models the mutual dependencies between the human, ob-ject, and interaction modalities. This formulation allows ECHO to oper-ate with flexible input configurations, making it robust to intermittenttracking and capable of leveraging partial observations. Crucially, it en-ables training on a combination of large-scale human motion datasetsand smaller HOI collections, learning strong priors while capturing in-teraction nuances. Furthermore, we employ a smooth inpainting inferencemechanism that enables the generation of temporally consistent interac-tions for arbitrarily long sequences. Extensive evaluations demonstratethat ECHO achieves state-of-the-art performance, significantly outper-forming existing methods lacking such flexibility.

Abstract:
Existing video object removal methods excel at inpaintingcontent “behind” the object and correcting appearance-level artifactssuch as shadows and reflections. However, when the removed object hasmore significant interactions, such as collisions with other objects, cur-rent models fail to correct them and produce implausible results. Wepresent VOID, a video object removal framework designed to performphysically-plausible inpainting in these complex scenarios. To train themodel, we generate a new paired dataset of counterfactual object re-movals using Kubric and HUMOTO, where removing an object requiresaltering downstream physical interactions. During inference, a vision-language model identifies regions of the scene affected by the removedobject. These regions are then used to guide a video diffusion model thatgenerates physically consistent counterfactual outcomes. Experiments onboth synthetic and real data show that our approach better preservesconsistent scene dynamics after object removal compared to prior videoobject removal methods. We hope this framework sheds light on howto make video editing models better simulators of the world throughhigh-level causal reasoning.

Abstract:
In this paper, we tackle the problem of performing consistent,unified modifications to a multi-shot video sequence. This task is par-ticularly challenging because multi-shot videos consist of discontinuoustemporal segments that vary significantly in viewpoint, camera scale, andsubject pose, leading to severe identity drift and cumulative error propa-gation. Achieving coherent edits requires establishing reliable cross-shotsemantic awareness to maintain stable subject appearance and visualcontinuity across these disjointed boundaries. To address this, we proposeMSEditor, the first framework designed specifically for consistent multi-shot video editing. To overcome the scarcity of high-quality multi-shottraining data, we repurpose existing multi-view video datasets to providerobust cross-shot supervision. Architecturally, we introduce a Supervi-sory Adapter that injects this cross-shot information into the diffusionbackbone, enabling the model to learn identity-consistent representa-tions. Furthermore, to effectively mitigate cumulative errors and ensurelong-range temporal coherence, we design a Cross-Shot Packing strat-egy that dynamically aggregates information from semantically relatedshots within the self-attention window. Extensive experiments demon-strate that MSEditor significantly outperforms existing methods on ourcurated multi-shot video editing benchmark in terms of identity preser-vation, temporal stability, and overall visual quality.

Abstract:
High-fidelity 4D physical simulation requires accurate phys-ical parameter assignments that align with diverse material properties.Previous methods struggle with critical bottlenecks: they either rely oncomputationally intensive per-scene optimization or predict parametersfrom purely visual inputs. The latter suffers from inherent ambiguity,e.g., the inability to distinguish the stiffness of rubber from its ap-pearance alone. To bridge this gap, we propose AutoPhyX, a text-conditioned framework for predicting spatially-varying physical proper-ties. Since training robust feed-forward models requires fine-grained su-pervision, we first introduce a novel part-controllable data generationpipeline that decomposes complex 3D assets into semantically distinctcomponents and pairs them with physically plausible, diverse parame-ters and corresponding text descriptions. Leveraging this dataset, Auto-PhyX employs a cross-modal modulation mechanism where text featuresdynamically modulate visual features for precise physical grounding. Byformulating property prediction within a voxel field, AutoPhyX en-sures compatibility with diverse 3D representations, including meshes,point clouds, Gaussian Splatting, and NeRF. Experiments demonstratethat our method enables physically plausible, text-driven parameter as-signment in a single forward pass. It achieves high accuracy and robustgeneralization to in-the-wild objects, paving the way for future physics-intensive embodied and robotic applications.

Abstract:
Video diffusion models have achieved remarkable visual fi-delity, yet often struggle to adhere to basic physical constraints, suchas support, motion consistency, and object permanence, leading to im-plausible dynamics even in short clips. We revisit this challenge throughthe lens of the Information Bottleneck principle, arguing that physicallygrounded video generation should prioritize time-predictive representa-tions over high-entropy appearance variations. Building on this perspec-tive, we propose Latent Dynamics Optimization (LDO), a post-trainingframework that leverages a predictive latent world model (V-JEPA 2)to guide a pretrained video diffusion model towards dynamics-consistentgeneration without altering its sampling process. LDO operates on twolevels: (1) aligning Gram-matrix geometry of intermediate diffusion fea-tures with the latent world model’s casually predicted dynamics, and(2) encouraging temporally consistent rollouts with Group-Relative Pol-icy Optimization (GRPO) using the world model as a dynamics critic.Experiments on VideoPhy and PisaBench show that LDO substantiallyimproves physical commonsense, object permanence, and trajectory fi-delity while preserving visual quality, suggesting that predictive latentsupervision offers a practical route to make video generators not onlyphotorealistic but also physically legible.

Abstract:
We present DynHair, a novel method for tracking and mod-eling dynamic hair for human head avatars. From video input, we recon-struct a dynamic head avatar with an explicit strand-based hair represen-tation using structured 3D Gaussian Splatting. In contrast to the faceregion of human head avatars, which can be modeled with 3D Gaus-sians that are attached or generated with respect to some expressive3D head model, hair is particularly challenging as it exhibits dynamicmotion effects. Therefore, we present a novel method that models thedynamic deformations of the hair strands using a temporal network thatis conditioned on angular velocity and acceleration of the head, as wellas relative gravity. Specifically, an LSTM encodes the motion historyand modulates per-point strand features via FiLM conditioning whichis then used by an MLP to produce physically plausible displacementsto the canonical hairstyle. We jointly optimize this motion and appear-ance representation of the hair, with a 3DGS-based representation ofthe face-region, via differentiable Gaussian splatting with photometric,geometric, and physics-based supervision. As a result, we obtain hair⋆ Equal contribution. † Equal supervision.tracking of the training video data and an animatable head avatar withcontrollable hair dynamics. In our experiments, we demonstrate state-of-the-art performance in terms of hair dynamics, temporal consistency,and generalization across subjects. Our data and code are available athttps://dynhair.is.tue.mpg.de/.

Abstract:
This paper proposes the Distribution-Alignment Bridge (DAB), a framework that reconceptualizes text-to-video retrieval as a distribution alignment task rather than traditional deterministic point matching. By modeling both text and video embeddings as Gaussian distributions defined by mean and variance, DAB explicitly accounts for modalityspecific uncertainty. We employ a deterministic, diffusion-inspired bridge to iteratively refine text distributions toward their target video distributions through a truncated refinement process. This approach unifies probabilistic embedding and distributional transformation into a cohesive, end-to-end trainable system. To optimize cross-modal similarity, we introduce a distribution-aware contrastive loss based on Kullback–Leibler divergence. Extensive evaluations on MSR-VTT, MSVD, and VATEX benchmarks confirm that DAB significantly outperforms existing probabilistic and diffusion-based baselines, while providing calibrated uncertainty-aware ranking through bridge-induced distributional margins.

Abstract:
Diffusion models achieve strong image and video generation quality but remain slow at inference due to multi-step denoising. Caching accelerates inference by reusing intermediate features across timesteps, yet its effectiveness depends critically on refresh scheduling (when to perform full recomputation). Existing schedules fall into three paradigms: fixed-interval, online heuristic, and global optimization. The first two may miss temporal sensitivity structure or accumulated segment-level degradation, while the last incurs substantial offline cost. We propose Equal-Risk Caching (ERC), which builds a one-dimensional temporal risk profile from a single fixed-length probing cache curve and uses cumulative risk mass as a global surrogate for segment sensitivity. ERC places refresh boundaries by equal-mass partitioning on the cumulative risk axis, automatically allocating denser recomputation to high-risk regions and longer reuse to low-risk regions. This reduces the offline profiling cost of schedule construction from O(T ·L) to O(T). Experiments on multiple diffusion models demonstrate an overall competitive quality– speed trade-off.

Abstract:
Large-scale dyadic interactive audio-visual dialogue (DIAD)datasets provide fundamental data resources for developing humanoidinteractive virtual agents and digital humans. However, collecting suchdata is time-consuming, expensive, and ethically sensitive. To addressthis, we propose CHAT, a new dyadic interactive audio-visual dialoguegeneration (DIADG) framework that generates diverse, paired, and mu-tually responsive speech-face dialogue clips from a single textual prompt.CHAT unifies large language models and talking face models with inter-active audio and facial behaviour refinement modules, enabling the gen-eration of aligned dyadic dialogue clips with diverse contents and facialidentities. Experiments show that CHAT outperforms existing relatedmethods designed for similar tasks under both objective and subjectiveevaluations. Moreover, our synthesised CHAT-AVD-50k dataset servesas effective pre-training data for downstream interactive head genera-tion, consistently improving PerFRDiff and ReactDiff on REACT 2024.CHAT offers a scalable alternative to the costly and ethically sensitivecollection of real dyadic interaction data.

Abstract:
Interactive video-to-video applications require real-time gen-eration while maintaining long-range temporal consistency. However, re-cent methods achieve speed by restricting attention to a fixed-lengthcausal window. This limits long-range consistency whenever relevant his-tory falls outside the context window. To address this limitation, we in-troduce vid2vid-long, a correspondence-based approach grounded in asimple observation. Video-to-video translation provides an explicit cross-frame reference: the regions that match in the input video should matchin the output video too. Concretely, we compute patch-level correspon-dences in the input, guiding retrieval of previously generated outputs inthe corresponding regions, and adding them to the context window. Ourexperiments demonstrate that vid2vid-long consistently improves long-range temporal consistency across recent video models while preservinggeneration quality and real-time performance.

Abstract:
Audio-visual continual test-time adaptation involves contin-ually adapting a source audio-visual model at test-time, to unlabelednon-stationary domains, where either or both modalities can be distribu-tionally shifted, which hampers online cross-modal learning and eventuallyleads to poor accuracy. While previous works have tackled this problem,we find that SOTA methods suffer from catastrophic forgetting wherethe model’s performance drops well below even the source model dueto continual parameter updates at test-time. In this work, we first showthat adapting only the modality fusion layer to a target domain not onlyimproves performance on that domain but can also enhance performanceon subsequent domains. Based on this strong cross-task transferability ofthe fusion layer’s parameters, we propose a method, AVReCAP, that im-proves test-time performance of the models without access to any sourcedata. Our approach works by using a selective parameter retrieval mech-anism that dynamically retrieves the best fusion layer parameters from abuffer using only a small batch of test data. These parameters are thenintegrated into the model, adapted to the current test distribution, andsaved back for future use. Extensive experiments on benchmark datasetsinvolving unimodal and bimodal corruptions show our proposed AVReCAPsignificantly outperforms existing methods while minimizing catastrophicforgetting.

Abstract:
Modern Video Object Segmentation (VOS) involves track-ing and segmenting user-specified targets. While recent approaches haveachieved remarkable performance in single-target scenarios, extendingthem to multi-target settings typically involves replicating the single-target processing for each individual object, resulting in reduced framerates (FPS) with unbounded latency as target density scales. Built uponSegment Anything 2 (SAM2), we propose SAM-MT, which addressesthis by transforming the model into an interactive framework for real-time Multi-Target video segmentation. SAM-MT uses explicit queriespropagated and updated across frames to represent different individualtargets, in parallel with a shared representation for global context. It em-ploys decoupled masked attention to keep individual identities distinctfrom cross-target interference, and sparse memory for stable temporalevolution, along with specialized strategies for occlusion handling andoverlap prevention. SAM-MT successfully decouples latency from thenumber of targets, achieving real-time speed on par with single-targetbaselines (>36 FPS for 10 targets) while maintaining SAM2’s robustvideo segmentation performance.

Abstract:
Existing Referring Video Object Segmentation tasks focus on referring expressions describing events, actions or appearances of relevant objects within the observed frames, lacking evaluation in scenarios that require pre-decisive spatio-temporal reasoning, thereby limiting their applicability. To address this, we propose Foresight Expression Video Object Segmentation, a task that queries future events in upcoming video segments and requires masks of the objects in the observed frames as visual answers. For example, in ego-centric scenes, the question “What tool will be used?” demands reasoning over spatio-temporal cues to predict the masks of the next tool to be used, which helps with the understanding of future actions and decisions. To support this task, we introduce FeVOS, a dataset with 968 video clips, 14,525 foresight expressions, and 2,904 chain-of-thought annotations to provide explicit and interpretable reasoning steps. We further develop FeVOS-R1, an MLLM-based model trained on our dataset via a two-stage pipeline of supervised fine-tuning and reinforcement learning. FeVOS-R1 not only achieves state-of-the-art performance on FeVOS, but also demonstrates strong generalization to existing RVOS benchmarks. We hope this work can inspire more research on predictive reasoning in video perception.

Abstract:
Automatically retrieving videos from large camera-trap data-sets remains challenging. Text-to-Video retrieval (TVR) methods basedon large video-language models (VLMs) have potential to retrieve eventsof interest by describing them with simple text queries. However, cur-rent methods often lack spatiotemporal understanding and do not gen-eralize well to ecological data. In this work, we introduce Prompting-MammAlps, the first camera-trap TVR benchmark, and propose a fine-grained and interpretable TVR method. Specifically, we trained a vi-sion transformer to perform spatiotemporal action localization, and con-vert its output to structured text, describing each video. Independently,ethology-inspired queries are processed by a Large-Language Model (LLM)based coding agent to parse the structured text per video and retrievevideos accordingly. We harnessed the LLM to use functions from a cus-tom parsing library to minimize the risk of LLM hallucinations and toimprove method interpretability. This retrieval approach applied on thePrompting-MammAlps benchmark achieved a set-based F1-score of 34%on a test set of 135 ecologically-relevant queries and 775 candidate videos.In comparison the best zero-shot VLM achieved a F1-score of 18%, whilealso lacking interpretability.Project page: eceo-epfl.github.io/prompting-mammalps/

Abstract:
Understanding human hand motion requires reasoning thatgoes beyond coarse action semantics to capture detailed finger articula-tion and interaction dynamics. However, existing Video-MLLMs primar-ily capture global action intent and often fail to represent fine-grainedfinger-level movements and contact semantics. In this work, we proposeFine-grained Finger-level Hand Motion Captioning (FingerCap), whichaims to generate textual descriptions that capture detailed finger-levelsemantics of hand actions. To support this task, we curate FingerCap-40K, a large-scale corpus of 40K paired hand-motion videos and captionsspanning two complementary sources: concise instruction-style finger mo-tions and diverse, naturalistic hand–object interactions. Furthermore,we design HandJudge, an LLM-based rubric that measures finger-level correctness and motion completeness for effective evaluation. Toestablish a strong yet lightweight baseline under the FingerCap task,we introduce FiGOP (Finger Group-of-Pictures), a module tailoredfor Video-MLLM settings with sparse RGB sampling. FiGOP augmentskeyframes with intermediate 2D hand keypoints, enabling complemen-tary motion cues without increasing the density of RGB frames. Exper-iments on FingerCap-40K show that strong open- and closed-sourceVideo-MLLMs still struggle with finger-level reasoning, while our FiGOP-enhanced model yields consistent gains under both HandJudge and hu-man studies. The dataset and benchmark are available at § FingerCap.

Abstract:
In this paper, we present Generative Language-Image Pre-training (GenLIP), a simplified generative pretraining approach for Vi-sion Transformers (ViTs) tailored to multimodal large language models(MLLMs). To better align ViTs with the autoregressive nature of LLMs,GenLIP trains a ViT to predict language tokens directly from visual to-kens using a standard language modeling objective—without contrastivebatch construction or an additional text decoder. GenLIP offers three keyadvantages: (1) Simplicity: a single transformer jointly models visualand linguistic tokens; (2) Scalability: it scales efficiently with both dataand model size; (3) Performance: it achieves competitive or superiorresults across diverse multimodal benchmarks. With only 1/5 of theseen samples, GenLIP matches or surpasses strong baselines such asSigLIP2. With continued pretraining on multi-resolution images at nativeaspect ratios, GenLIP further excels at detail-sensitive tasks such as OCR,chart understanding, and visual question answering, making it a strongfoundation for vision encoders in MLLMs.

Abstract:
Scaling the width of Vision Transformers (ViT) often triggerssevere instability issues. This work identifies that these instabilities stemfrom the “high-activation domination” phenomenon frequently encoun-tered during feature normalization in high-dimensional spaces. Specifi-cally, the norm of a high-dimensional vector can be easily dominated by afew outlier elements with disproportionately large values. Upon normal-ization, the remaining elements are suppressed toward zero, leading tosignificant gradient propagation issues. To address this, this work intro-duces Multi-Head RMSNorm (MH-RMSNorm), which partitions a high-dimensional feature vector into multiple chunks and applies RMSNormto each segment independently. This simple, drop-in replacement sig-nificantly enhances the training stability and predictive performance ofhigh-dimensional ViTs. For example, Our 1280-dimensional ViT achieves85.5% top-1 accuracy on ImageNet-1K, whereas baseline models usingstandard LayerNorm or RMSNorm suffer from complete training col-lapse; when applied to DiT on ImageNet-256, our method achieves a1.95 FID, significantly outperforming standard ViT counterparts.

Abstract:
Self-supervised learning (SSL) has produced a diverse land-scape of vision transformers (ViTs) whose pretrained representations sup-port a wide range of downstream tasks. Towards a better understandingof these models, a body of works has assessed the mechanics of their self-attention as well as which types of information they capture across theirrepresentations, revealing, e.g., stark differences between models trainedwith contrastive learning (CL) vs. masked image modeling (MIM). How-ever, the total of these advances on model understanding has to datenot yet fully permeated a larger community, where, e.g., insights thatare specific to CL models are still at times generalized to MIM models.To make model understanding straightforward and intuitive for a broadcommunity, we propose a simple and easily interpretable visualizationprotocol. Our protocol is based on visualizing unsupervised semanticsegmentation results – yet by no means do we focus on top segmenta-tion performance. Instead, our protocol allows us to easily convey modelbehavior that consistently emerges across images. Benchmarked on a di-verse set of SSL models across layers and representations, our protocolallows us to gain novel insights into distinct positional biases and scalingbehaviors, including, e.g., strong boundary artifacts in DINOv3-Largemodel tokens. These novel insights come on top of more easily conveyinga range of previous findings. Our protocol further allows us to clearly vi-sually convey and distinguish between positional effects and the closelyrelated yet distinct locality bias, the latter being much more extensivelystudied in the literature so far. Our protocol is publicly available6 , serv-ing to catalyze further model understanding for a broad community.

Abstract:
We introduce LATTE (Linearized Attention with Tunable Taylor Series Expansion), a training-free attention linearization method that selectively applies linearization to a subset of self-attention blocks in a pretrained vision transformer. LATTE approximates the softmax function via a second-order Taylor expansion centered on tunable anchor points, thereby enabling a hybrid architecture that seamlessly integrates standard self-attention with linear attention. This method preserves model performance without requiring any fine-tuning or retraining but only minimal calibration on the target dataset. LATTE is highly scalable, linearizing more than half of the attention layers retains more than 95% of the original model’s performance and reduces attention GFLOPs by up to 15× (e.g., from 16.384 to 1.082 at T=1000 for the CLIP attention block). We evaluate LATTE across diverse benchmarks, including image classification, object detection, and vision–language multimodal tasks, demonstrating consistent superiority over current state-of-the-art training-free linearization methods. Code will be released here.

Abstract:
Visual grounding is an essential capability of Visual Language Models (VLMs) to understand the real physical world. Previous state-of-the-art grounding visual language models usually have large model sizes, making them heavy for deployment and slow for inference. However, we notice that the sizes of visual encoders are nearly the same for small and large VLMs and the major difference is the sizes of the language models. Small VLMs fall behind larger VLMs in grounding because of the difference in language understanding capability rather than visual information handling. To mitigate the gap, we introduce ‘Efficient visual Grounding language Models’ (EGM): generate many mid-quality tokens (from small models) to match the performance of large VLMs with few high-quality but expensive tokens. This method is deploymentfriendly, and yields better end-to-end latency: On the RefCOCO benchmark, our EGM-Qwen3-VL-8B demonstrates 91.4 IoU with an average of 737ms (5.9× faster) latency while Qwen3-VL-235B demands 4,320ms to reach 90.5 IoU. To validate our approach’s generality, we further set up a new amodal grounding setting that requires the model to predict both the visible and occluded parts of the objects. Experiments show our method consistently improves both vanilla and amodal grounding capabilities of small models to match or outperform larger models, thereby improving efficiency for visual grounding.

Abstract:
While Change Captioning systems have garnered substantialattention to respond to our evolving world, their true performance on di-verse real-world change contexts remains largely unexplored due to thelack of comprehensive evaluation frameworks. To fill this gap, we proposeC3-Bench, a comprehensive benchmark for evaluating Context-awareChange Captioning. C3-Bench features: (1) 4,996 human-labeled imagepairs of 51 real-world change contexts across four domains (e.g., naturalscenes, remote sensing imagery, image editing, and anomalies), each withdiverse, carefully curated scenarios derived from multiple change-centriccommunities; and (2) the first LLM-as-Judge evaluation framework in thechange captioning task that measure fine-grained dimensions (e.g., cor-rectness, specificity, fluency, and relevance), along with a novel reversibil-ity metric exploring whether models understand changes with symmetricconsistency. Based on C3-Bench, we benchmark 32 models—including Corresponding authorconventional change captioning models, proprietary Large MultimodalModels (LMMs), and 2B-90B open-source LMMs. We reveal a fundamen-tal blind spot in the prevailing change captioning paradigm: Once thechange context departs from training-style regimes, conventional mod-els collapse, and even state-of-the-art LMMs such as GPT-5.2 exhibitsystematic domain- and position-dependent errors that distort reliablechange understanding. By making these hidden failure modes explicitand measurable, we delineate the next frontier for building generalizableand trustworthy change captioning systems. All codes and datasets arepublicly available on the project page.

Abstract:
Recent progress in CLIP-like vision-language models (VLMs)has greatly advanced medical image analysis. However, most existingmedical VLMs still rely on coarse image-text contrastive objectives andfail to capture the systematic visual knowledge encoded in well-definedmedical phenotype ontologies. To address this limitation, we constructPhenoKG, the first large-scale, phenotype-centric multimodal knowl-edge graph that encompasses around 524K high-quality image-text pairslinked to more than 3,000 phenotypes. Building upon PhenoKG, we pro-pose PhenoLIP, a pretraining framework that explicitly incorporatesstructured phenotype knowledge into medical VLMs through a two-stage process. We first learn a knowledge-enhanced phenotype embed-ding space that captures the hierarchical structure of phenotype ontolo-gies and then distill this textual knowledge into multimodal pretrainingvia a teacher-guided knowledge distillation objective. To support eval-uation, we further introduce PhenoBench, an expert-verified bench-mark designed for phenotype recognition, comprising over 7,800 image-caption pairs covering more than 1,000 phenotypes. Extensive experi-ments demonstrate that PhenoLIP outperforms previous state-of-the-artbaselines, improving upon BIOMEDICA in phenotype classification ac-curacy by 8.06% and in cross-modal retrieval by 15.03%, underscoringthe value of integrating phenotype-centric priors into medical VLMs forstructured and interpretable medical image understanding.

Abstract:
Spatial reasoning is a critical capability for intelligent robots,yet current vision-language models (VLMs) still fall short of human-levelperformance in video-based spatial reasoning. This gap mainly stemsfrom two challenges: a semantic-geometric misalignment that preventsconsistent 3D understanding, and the absence of persistent memory toretain 3D representation and understanding across frames. To addressthese limitations, we present VLM2 , a Vision-Language Model with per-sistent Memory for spatial reasoning with a view-consistent, 3D-awarerepresentation purely from 2D videos. Specifically, we incorporate a dual-memory module consisting of a working memory that operates as a slid-ing window to focus on immediate context, and an episodic memory thatconsolidates and stores critical information across frames. This designenables bounded and efficient spatial reasoning under a fixed computa-tional cost. Extensive experiments on multiple benchmarks show thatVLM2 achieves state-of-the-art performance among video-based models,significantly advancing the frontier of visual-spatial intelligence.

Abstract:
Visual recognition models often fail when deployed in newenvironments. Domain Generalization (DG) addresses this by learningrepresentations that remain invariant to environment-specific variations.Recent approaches increasingly rely on large vision-language models, as-suming that preserving their expressive visual representations improvesrobustness. However, we show that such visual expressiveness can insteadpropagate spurious cues that tie representations to the training environ-ments, hindering invariant learning. We therefore discard visual guidanceand instead treat the language embedding space as the primary sourceof domain invariance, naturally acting as an information bottleneck thatpreserves core semantics while suppressing domain-specific variations.Extensive experiments across diverse backbones exhibit state-of-the-artperformance and further analyze what makes guidance effective for ro-bust generalization. These findings shift the focus of DG from improvingrepresentations to designing supervision that enforces invariance.

Abstract:
Large Vision-Language Models (LVLMs) encounter significant challenges in image understanding and visual reasoning, leading to critical perception failures. Visual prompts, which incorporate image manipulation code, have shown promising potential in mitigating these issues. While visual prompts have emerged as a promising direction, previous methods for visual prompt generation have focused on tool selection rather than diagnosing and mitigating the root causes of LVLM perception failures. Because of the opacity and unpredictability of LVLMs, optimal visual prompts must be discovered through empirical experiments, which have relied on manual human trial-and-error. In this work, we propose an automated semantic exploration framework for discovering task-wise visual prompts. Unlike previous methods, our approach enables diverse yet efficient exploration through agent-driven experiments, minimizing human intervention and avoiding the inefficiency of per-sample generation. We introduce a semantic exploration algorithm named SEVEX, which addresses two major challenges of visual prompt exploration: (1) the distraction caused by lengthy, low-level code and (2) the vast, unstructured search space of visual prompts. Specifically, our method leverages an abstract idea space as a search space, a novelty-guided selection algorithm, and a semantic feedback-driven ideation process to efficiently explore diverse visual prompts based on empirical results. We evaluate SEVEX on the BlindTest and BLINK benchmarks, which are specifically designed to assess LVLM perception. Experimental results demonstrate that SEVEX significantly outperforms baseline methods in task accuracy, inference efficiency, exploration efficiency, and exploration stability. Notably, our framework discovers sophisticated and counterintuitive visual strategies that go beyond conventional tool usage, offering a new paradigm for enhancing LVLM perception through automated, task-wise visual prompts.

Abstract:
Physical adversarial camouflage poses a significant threatto object detectors in safety-critical applications. However, synthesiz-ing robust 3D textures remains non-trivial due to the structural mis-match between static texture parameters and dynamic physical obser-vations. In this paper, we identify two fundamental optimization hur-dles: spatially inconsistent gradients arising from sparse rendering vis-ibility; and cross-view gradient misalignment, where contradictory sig-nals from diverse viewpoints cause destructive interference. To overcomethese, we propose a unified framework that reconciles spatial continu-ity with optimization stability. First, the Deformable Texture Field pa-rameterizes textures as continuous fields via learnable flow grids, en-forcing intrinsic smoothness while enabling adaptive geometric defor-mation to enhance the texture’s representational flexibility. Second, aGradient-Aligned Meta-Optimization strategy leverages multi-step ex-ploration trajectories to implicitly maximize gradient alignment acrossconflicting viewpoints. Extensive digital and physical experiments demon-strate that our method achieves state-of-the-art robustness and transfer-ability against advanced detectors under diverse viewing conditions.

Abstract:
Most existing mesh-based methods for garment simulationsuffer from sensitivities tied to mesh discretisation and resolution. Re-cent approaches such as NeuralClothSim employ continuous neural fieldsas a promising alternative, but remain constrained by a 2D curvilinearparameterisation that models a single cloth panel, limiting their abil-ity to represent garments with multiple panels stitched together. Thispaper introduces NeuralGarSim, a quasistatic garment simulator for-mulated directly in 3D Euclidean space. Our method accepts diverseinput representations—including distance fields, meshes, point clouds,and Gaussians—as undeformed garment states, making it compatible withoff-the-shelf reconstruction frameworks without additional preprocessing.We then represent the garment deformation as a neural field and define anonlinear Kirchhoff–Love shell model directly in R3 by applying tangentialdifferential calculus on the garment surface. This ensures that the neuralgarment simulation is parameterisation-space-free and naturally extendsto arbitrary topologies. By minimising a potential energy functional, ourmethod learns a 3D neural deformation field that predicts physicallyconsistent deformations across garments with multiple panels, seams,and holes. It supports multiple material models and trains 2–4× fasterthan its predecessor, NeuralClothSim, while retaining the continuous,consistent, and memory-adaptive behaviour1 .

Abstract:
We present Soft Tail-dropping Adaptive Tokenizer (STAT),a discrete tokenizer that learns adaptive visual representations. STATadjusts the number of tokens allocated to each image according to itsperceptual complexity. Specifically, it encodes an image into discrete to-kens together with token-wise keep probabilities indicating whether eachtoken is necessary for faithful reconstruction or can be safely dropped.Through this learned adaptivity, STAT achieves state-of-the-art recon-struction quality while using fewer tokens on average. When integratedwith vanilla causal autoregressive (AR) modeling, STAT enables a content-aware generative model with adaptive-length sampling. The model achievescompetitive or superior visual generation quality compared with othergenerative model families while exhibiting favorable scaling behavior thathas been elusive in prior vanilla AR visual generation attempts.

Abstract:
Normalizing Flows (NFs) is a principle generative framework,which learns to generate images by establishing a bijective mapping tra-jectory between noise and data with likelihood maximization. In thispaper, we leverage the self-play paradigm, a general trajectory fine-tuningmethod to improve generative capability of NFs without auxiliary fine-tuning data. The general self-play mechanism formulates competitionbetween model and its historical counterpart in favoring real data trajec-tory (i.e., “wining trajectory”) while forgetting trajectory of self-generatedsynthesized data (i.e., “losing trajectory”), improving model capabilitythrough preference alignment. However, NF-based self-play faces twochallenges. (1) Enforcing NF to forget data synthesized with historicalmodel contradicts with NF’s original training goal, leading to potentialtraining collapse. (2) Self-play is naturally plausible for generative modelwith pre-defined data trajectory (e.g., diffusion models), but faces com-patibility problem with NFs due to its implicit wining data trajectorydefinition. We thus propose jigsaw self-play optimization for NFs (JSON)to overcome these challenges. Our core contribution is the integration ofa jigsaw puzzle reassembly task to serve as an intrinsic spatial coherenceevaluator, identifying “winning” trajectories that exhibit superior spatiallogic for self-play fine-tuning. We further propose a bounded self-play lossand a nested-loop optimization strategy to mitigate training collapse andensure stable fine-tuning. By aligning the model with high-quality struc-tural anchors mined during self-play, JSON achieves promising results onImageNet-1K, providing a robust framework for high-fidelity NF-basedgeneration.

Abstract:
How to accurately predict a high-fidelity future world? Whilethe visual world is inherently continuous, existing deterministic videoprediction models operate in discrete pixel space and are mainly opti-mized with pixel-wise mean squared error (MSE), which often leads toover-smoothed predictions and a lack of fine-grained visual details. Toaddress these limitations, we propose Predictive Differentiable Rendering(PDR), a novel end-to-end video prediction paradigm that bridges thegap between discrete and continuous representations. Inspired by recentprogress in 3D reconstruction with 3D Gaussian Splatting, we introducePredGS, a lightweight and plug-and-play adapter based on 2D Gaus-sian representation, which could be seamlessly integrated with existingpixel space predictors, significantly improving spatial detail preserva-tion with negligible computational overhead. Furthermore, we developpredgsplat, a CUDA-accelerated differentiable 2D Gaussian renderersupporting arbitrary channels. Each Gaussian is defined by 5 + C learn-able parameters (position, scale, rotation, and C channel amplitudes)and achieves up to 10× faster rendering than the baseline. Optimizedby a combined L1 and SSIM loss, PDR overcomes the inherent blurringtendencies of MSE Loss, significantly enhancing the prediction perfor-mance. Extensive experiments on diverse real-world benchmarks, includ-ing TaxiBJ, WeatherBench, KTH, and Human3.6M, demonstrate thatPDR consistently surpasses existing methods, delivering superior detailpreservation, visual fidelity, and predictive accuracy.

Abstract:
How can we infer a 3D hand pose when large portions of the handare heavily occluded by itself or by objects? Humans often resolve such ambigui-ties by leveraging contextual knowledge—such as affordances, where an object’sshape and function suggest how the object is typically grasped. Inspired by thisobservation, we propose a generative prior for 3D hand pose modeling guidedby affordance-aware textual descriptions of hand-object interactions (HOI). Ourmethod employs a diffusion-based generative model that learns the distributionof plausible hand poses conditioned on contextual signals, such as affordancedescriptions and image features. The affordance descriptions are designed to rep-resent the semantic intent and geometric structure of HOI, using the reasoningfrom a vision-language model (VLM) and grasp classification. We leverage thediffusion prior to refine the 3D pose predictions in hand reconstruction into moreaccurate and functionally coherent estimation. Our experiments demonstrate thatour affordance-guided refinement significantly improves 3D hand pose estima-tion performance on 3D hand affordance datasets, HOGraspNet and HO3D, overstate-of-the-art methods such as foundation models for hand reconstruction andthe latest diffusion priors for 3D hands.

Abstract:
Procedural material creation underpins applications in digitalcontent creation, visual effects, and 3D asset design. Achieving high-quality results requires more than reproducing node graphs—it demandsunderstanding the process by which experts construct materials. We for-mulate procedural material generation as retrieval-time process reasoningover expert demonstrations, elevating process to a first-class represen-tation beyond graph-only synthesis. Concretely, we represent expertworkflows as process traces: textual records of construction steps, param-eters, and design intent. To instantiate this idea, we use a pretrainedLLM-based ProcessSynthesizer to synthesize a process trace alignedwith a user’s intent and a pretrained LLM-based Compiler to groundthe process trace into an executable Blender material graph. Becauseprocedural expertise is most naturally conveyed through demonstrations,we leverage tutorial videos as a source of process knowledge and extracttextual, LLM-compatible traces using automated video analysis tools.In an expert study with five Blender artists (avg. 7.5 years of expe-rience), materials generated by reflecting expert demonstrations werefound to produce workflows requiring fewer edits, and more closely matchprofessional design strategies than methods operating solely on staticartifacts. A user study with 150 participants further shows that ourapproach achieves superior generation and editing performance comparedto prior procedural systems. All code, models, and data will be availableat https://materialapprentice.github.io.

Abstract:
Multimodal Large Language Models (MLLMs) have shown promising capabilities in generating Scalable Vector Graphics (SVG) via direct code synthesis. However, existing paradigms typically adopt an open-loop “blind drawing” approach, where models generate symbolic code sequences without perceiving intermediate visual outcomes. This methodology severely underutilizes the powerful visual priors embedded in MLLMs’ vision encoders, treating SVG generation as a disjointed textual sequence modeling task rather than an integrated visuo-spatial one. Consequently, models struggle to reason about partial canvas states and implicit occlusion relationships, which are visually explicit but textually ambiguous. To bridge this gap, we propose Render-in-the-Loop, a novel generation paradigm that reformulates SVG synthesis as a stepwise, visual-context-aware process. By rendering intermediate code states into a cumulative canvas, the model explicitly observes the evolving visual context at each step, leveraging on-the-fly feedback to guide subsequent generation. However, we demonstrate that applying this visual loop naively to off-the-shelf models is suboptimal due to their inability to leverage incremental visual-code mappings. To address this, we first utilize fine-grained path decomposition to construct dense multi-step visual trajectories, and then introduce a Visual Self-Feedback (VSF) training strategy to condition the next primitive generation on intermediate visual states. Furthermore, a Render-and-Verify (RaV) inference mechanism is proposed to effectively filter degenerate and redundant primitives. Our framework, instantiated on a multimodal foundation model, outperforms strong open-weight baselines on the standard MMSVGBench. This result highlights the remarkable data efficiency and generalization capability of our Render-in-the-Loop paradigm for both Text-to-SVG and Image-toSVG tasks.

Abstract:
Complex-valued Gaussian primitives have recently been ex-plored for representing holographic radiance x001C elds in 3D novel view syn-thesis. In this work, we extend this line of research to the hologramoptimization domain and propose a structured representation based oncomplex-valued 2D Gaussian primitives. Inspired by Gabor's theory,we show that our primitive attains the minimum space x0015 frequency un-certainty and reduces the parameter search space by 5:1 compared toper-pixel parameterization. To enable end-to-end training, we develop adi x001B erentiable rasterizer for our representation, integrated with a GPU-optimized light propagation kernel in free space. Extensive experimentsshow that our method reduces VRAM usage by up to 30% and acceler-ates optimization by 50% over standard autodi x001B -based implementations,delivers up to 13 dB higher PSNR than prior Gaussian-based methods,and achieves up to 3200× faster rendering while maintaining reconstruc-tion quality on par with existing CGH approaches. For evaluation, weintroduce a conversion procedure that adapts our representation to prac-tical hologram formats, including smooth and random phase-only holo-grams. By reducing the hologram parameter search space, our representa-tion enables a more scalable hologram estimation in the next-generationcomputer-generated holography systems.

Abstract:
Gaussian splatting (GS) has garnered significant attention in VR/AR and digital content creation due to its explicit parameterization and efficient rendering capabilities. However, existing GS-based methods for deformable objects face two key limitations: (i) illumination is erroneously baked into textures, causing physically inconsistent responses under dynamic deformations and lighting changes; (ii) snapshot-based reconstruction restricts post-reconstruction material editing. To address these challenges, we propose Deformable and Relightable GS (DR-GS), a unified Gaussian framework that integrates physically-based inverse rendering, relighting, and deformation-aware manipulation. Through explicitly disentangling geometry, illumination, and material representations, DR-GS overcomes the limitations of static snapshots, resolving unrealistic appearance under varying conditions while enabling postreconstruction parameter editing. Extensive experiments show that DRGS achieves leading visual quality across static reconstruction, dynamic deformation, and relighting, reliably preserving reflections and specular highlights on glossy surfaces. It further establishes a fully decoupled geometry-illumination-material pipeline, enabling high-quality 3D asset creation and comprehensive post-editing.

Abstract:
3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have demonstrated remarkable capabilities in photo-realistic novel view synthesis. However, their practical adoption is often hindered by substantial storage requirements and limited rendering efficiency, particularly for scenes with repetitive structures. While existing acceleration methods primarily focus on optimizing individual Gaussian primitives or neural network architectures, they fail to address the fundamental redundancy inherent in repetitive content. To overcome this limitation, we introduce InstGS, the first Gaussian instancing-based accelerated rendering framework. To eliminate redundancy at the representation level, we perform gradient-driven cross-frame instance segmentation to group similar Gaussians into reusable components. A shared Gaussian template with instance-specific offsets is optimized to replace all similar instances, yielding substantial memory saving with negligible loss in visual fidelity. Extensive experiments demonstrate that InstGS achieves high-quality, high-frame-rate, and low-memory rendering performance.

Abstract:
Segmenting thin structures such as infrastructure cracks andanatomical vessels is hampered by topology-sensitive geometry, highannotation cost, and poor cross-domain generalization. We propose FMS2 ,a unified flow-matching framework with two complementary modules.SegFlow is a 2.96M-parameter encoder–decoder segmentation modelthat recasts prediction as continuous image→mask transport: it learnsa time-indexed velocity field with a single flow-matching regression lossand obtains the mask through deterministic ODE integration, ratherthan supervising only end-state logits. This trajectory-level supervisionimproves thin-structure continuity and sharpness over topology-awareloss baselines, without auxiliary topology heads, post-processing, or multi-term loss engineering. SynFlow is a mask-conditioned mask→imagegenerator that produces pixel-aligned image–mask pairs by injectingmask geometry at multiple scales, emphasizing boundary bands withedge-aware gating, and expanding sparsity, width, and branching througha controllable mask generator. On five crack and vessel benchmarks,SegFlow alone outperforms CNN, Transformer, Mamba, and generativebaselines, improving mean IoU from 0.511 to 0.599 (+17.2%) and reducingBetti matching error from 82.145 to 51.524 (−37.3%). With limitedlabels, SynFlow recovers near-full performance using 25% real annotationsand improves cross-domain IoU by 0.11. Unlike classical augmentation,SynFlow provides pixel-aligned supervision with controllable structuralshifts (e.g., sparsity, width, branching), making it effective under domainshift. We release 10k crack and 1k vessel image–mask pairs. The projectpage is available at FMS2 .

Abstract:
Classic 3D scene graph generation approaches fail to workin real-time due to the heavy computational cost of environment map-ping and the need to generate intermediate point-cloud representations.To alleviate this issue, a recent work eschews point clouds in favor ofa lightweight Gaussian distribution for each object. This approximationdrastically speeds up inference and enables real-time 3D scene graph gen-eration. However, the representation has two key weaknesses. 1) Each ob-ject is approximated by a single 3D Gaussian, which causes a severe lossof 3D geometric detail. 2) The discrepancy between this approximationand the true object geometry exacerbates the inaccurate merging of ob-ject candidates during online inference. To address these issues, we pro-pose NoPA, which represents each object as a separate non-parametricdistribution. This formulation retains 3D geometric information whilepreserving real-time inference of the parametric Gaussian formulation. Tobuild upon our novel object representation, we propose a tailored merg-ing strategy to recover coherent object instances. Specifically, we leveragemaximum mean discrepancy on kernel density estimates to enable robustmerging of object candidates during online exploration while minimizingadded computational complexity. The key is to maintain a fixed particleset per object. Furthermore, to rectify the relation loss caused by misclas-sified objects, NoPA propagates relationships between objects with highaffinity. Experiments show that NoPA substantially outperforms currentmethods without sacrificing real-time inference speed.

Abstract:
We present Free-Range Gaussians, a multi-view reconstruc-tion method that predicts non-pixel, non-voxel-aligned 3D Gaussiansfrom as few as four images. This is done through flow matching overGaussian parameters. Our generative formulation of reconstruction allowsthe model to be supervised with non-grid-aligned 3D data, and enables itto synthesize plausible content in unobserved regions. Thus, it improveson prior methods that produce highly redundant grid-aligned Gaussians,and suffer from holes or blurry conditional means in unobserved regions.To handle the number of Gaussians needed for high-quality results, weintroduce a hierarchical patching scheme to group spatially related Gaus-sians into joint transformer tokens, halving the sequence length whilepreserving structure. We further propose a timestep-weighted renderingloss during training, and photometric gradient guidance and classifier-freeguidance at inference to improve fidelity. Experiments on Objaverse andGoogle Scanned Objects show consistent improvements over pixel andvoxel-aligned methods while using significantly fewer Gaussians, withlarge gains when input views leave parts of the object unobserved.

Abstract:
Reflections from shiny objects and glass façades naturally extend the field of view of a camera, capturing the surrounding environment without the need to pan the camera or acquire a full panorama. We propose PanoLess, a Gaussian-splat–based framework that reconstructs the surrounding environment as a distant-illumination map from images captured on only one side of a reflective surface. PanoLess leverages surface-aligned 2D Gaussian splats with deferred shading to recover accurate per-pixel normals and reflection cues, which are fused into a neural cubemap representation of the environment. In addition, PanoLess produces a visibility map that explicitly denotes which regions of the environment are supported by the partial reflective observations. Unlike existing inverse-rendering and reflection-aware Gaussian-splatting approaches—which typically require full 360◦ coverage and struggle under incomplete views—PanoLess enables consistent, physically grounded illumination estimation from partial-view input. We show that PanoLess achieves high-fidelity and geometrically consistent environment reconstruction, outperforming reflection-aware baselines on a new custom synthetic benchmark and publicly available datasets, and demonstrating generalization to real-world reflective captures.

Abstract:
We propose a method to reconstruct dynamic fire in 3D froma limited set of camera views with a Gaussian-based spatiotemporal rep-resentation. Capturing and reconstructing fire and its dynamics is highlychallenging due to its volatile nature, transparent quality, and multitudeof high-frequency features. Despite these challenges, we aim to recon-struct fire from only three views, which consequently requires solvingfor under-constrained geometry. We solve this by separating the staticbackground from the dynamic fire region by combining dense multi-viewstereo images with monocular depth priors. The fire is initialized as a 3Dflow field, obtained by fusing per-view dense optical flow projections. Tocapture the high-frequency features of fire, each 3D Gaussian encodesa lifetime and linear velocity to match the dense optical flow. To en-sure sub-frame temporal alignment across cameras, we employ a customhardware synchronization pattern – allowing us to reconstruct fire withaffordable commodity hardware. Our quantitative and qualitative vali-dations across numerous reconstruction experiments demonstrate robustperformance for diverse and challenging real and simulated fire scenarios.

Abstract:
When does 3D Gaussian Splatting (3DGS) recover the truescene surface rather than just overfitting view-dependent appearance? Weanswer this by developing a mathematical framework—based on a first-hitrendering abstraction—that cleanly isolates geometry from appearance.We prove that geometric misalignment forcefully converts spatial texturesinto high-frequency angular signals via parallax. This establishes a strictidentifiability window: if angular capacity is bounded, surface-consistentsolutions are mathematically preferred; if unrestricted, the same imagescan be perfectly explained by an incorrect, opaque “billboard” geometry.Experiments on synthetic stress tests confirm this prediction, showingbillboard failures emerge precisely at high angular capacities. Conversely,in the real-world datasets we evaluate under standard capture proto-cols, reconstructions remain surface-consistent even at high SH degrees,which is consistent with the prediction that rich spatial texture can pushbillboard solutions outside the tested angular-capacity range.

Abstract:
Reconstructing high-fidelity 3D scenes from sparse views re-mains a central problem in generalizable neural rendering. Existing gen-eralizable 3D Gaussian Splatting (3DGS) methods often exhibit geomet-ric artifacts in sparse-view settings, since supervision based solely on 2Dphotometric losses cannot resolve depth and correspondence ambigui-ties. To address this issue, we propose MAC-Splat, a training frameworkbuilt around direct 3D consistency supervision. MAC-Splat builds onthe MASt3R geometric backbone and a frozen DINOv3 encoder to ob-tain semantically informed 2D correspondences, which serve as geometricanchors for 3D supervision. Using these anchors, we define the Multi-Attribute Consistency (MAC) loss. This objective jointly regularizes the3D attributes of matched Gaussians, including their position, shape,and appearance, by enforcing agreement in a common world coordinateframe. The formulation is robust to outliers and respects the geometry ofcovariance matrices, which leads to stable training under sparse-view con-ditions. Experiments on ScanNet++ show that MAC-Splat outperformsstrong baselines, with particularly large gains under different overlapregimes. In particular, it improves average PSNR over Splatt3R by morethan 4.5 dB, reduces LPIPS, and maintains performance as the camerapose gap increases. These results indicate that a direct, multi-attribute3D consistency objective, when combined with high-quality correspon-dences, is effective for addressing the ill-posed sparse-view reconstructionproblem.

Abstract:
We study sinusoidal recurrence as an iterative mechanism forharmonic spectral enrichment in implicit neural representations (INRs).Our analysis reveals that sinusoidal activations induce a harmonic linespectrum, providing a spectral account of how recurrent unrolling en-riches the effective spectral support. We realize this principle with ashared sinusoidal block that iteratively refines the latent representation.We empirically validate the resulting spectral behavior against feed-forward INRs, non-sinusoidal recurrent variants, and equilibrium-stylesinusoidal models. Complementing this analysis, we evaluate the pro-posed architecture across image and 3D representation tasks. On RGBimage benchmarks, our method achieves higher fidelity than feed-forwardbaselines with fewer parameters and fewer optimization steps, and it fur-ther transfers favorably to super-resolution, NeRF, and SDF tasks.

Abstract:
We present ’RenderFormer-V2’, a unified learned transformer-based neural rendering model, complementary to modern physics-basedrendering systems, that can handle diverse light-transport effects suchas caustics, volumetric scattering, environment lighting, textured anddisplaced surfaces and out-of-distribution materials without per-scenetraining or specialized code. RenderFormer-V2 models global light trans-port as a sequence-to-sequence transformation. Following its predecessor,RenderFormer-V2 also employs a two stage process: a view-independentstage that resolves intra-scene primitive to primitive transport, and aview-dependent stage that transforms the internal neural scene repre-sentation into image pixels. Different from RenderFormer, our modelemploys a novel combined windowed-attention and rendering-informedattention sink in the view-independent stage to improve scalability whilemaintaining render accuracy. To further improve versatility, RenderFormer-V2 supports heterogeneous scene primitives, including environment mapsand participating media, and it employs a material encoding indepen-dent of the underlying surface reflectance model that encodes materialappearance via a novel neural embedding. We demonstrate the versatil-ity of RenderFormer-V2 on a variety of scenes and perform an extensiveablation of the improved attention mechanism.

Abstract:
Feedforward geometric foundation models achieve strongshort-window reconstruction, yet scaling them to minutes-long videos isbottlenecked by quadratic attention complexity or limited effective mem-ory in recurrent designs. We present LoGeR (Long-context GeometricReconstruction), a novel architecture that scales dense 3D reconstruc-tion to extremely long sequences without post-optimization. LoGeRprocesses video streams in chunks, leveraging strong bidirectional priorsfor high-fidelity intra-chunk reasoning. To manage the critical challengeof coherence across chunk boundaries, we propose a learning-based hybridmemory module. This dual-component system combines a parametric Test-Time Training (TTT) memory to anchor the global coordinate frame andprevent scale drift, alongside a non-parametric Sliding Window Attention(SWA) mechanism to preserve uncompressed context for high-precisionadjacent alignment. Notably, this memory architecture allows LoGeR togeneralize to sequences of thousands of frames during inference, despitebeing trained on only 128 frames. When evaluated on standard bench-marks and a repurposed VBR dataset with sequences up to 19k frames,LoGeR substantially outperforms current state-of-the-art feedforwardmethods. It reduces the ATE on KITTI by over 74% while achievingrobust, globally consistent reconstruction across unprecedented horizons.

Abstract:
Superquadrics have proven to provide a compact, geometri-cally meaningful representation for 3D objects. However, existing meth-ods suffer from limited reconstruction accuracy, are restricted to rigidprimitives, and lack robustness to partial point clouds. In this work,we present SuperFlex, an enhanced framework that expands the expres-sive power and applicability of superquadric decompositions. First, weintroduce a novel loss formulation which significantly improves recon-struction accuracy. Second, we include bending and tapering deforma-tions, enabling high-fidelity representation of curved and asymmetricgeometries. Finally, we leverage these high-quality decompositions assupervision to train a model that is robust to partial real-world pointclouds. Experiments demonstrate substantial improvements in recon-struction accuracy over both optimization- and learning-based baselineswhile maintaining a highly compact primitive representation. Projectpage: https://superflex3d.github.io.Point Cloud

Abstract:
The rise of home-deployed embodied AI systems is driving agrowing need for fast, metric 3D reconstruction of residential spaces tosupport navigation, interaction, and long-horizon task execution. How-ever, the commonly used pinhole-camera 3D reconstruction pipelinesstruggle to model large indoor residences efficiently due to their limitedfield of view, to which achieving full coverage across multiple rooms oftenrequires thousands of images and incurs drift from long chains of incre-mental alignment. In this work, we present CasaMaestro (Spanish wordsmeaning “house” and “master”), a feedforward model that can take onlytwenty to fifty sparse multi-view indoor panoramas as input and directlypredicts metric depth along with camera poses, allowing fast point-cloudDA3-AUC30 Pi3-AUC30 VGGT-AUC30Yaw Step 90° Yaw Step 45° Yaw Step 15° DA3-time Pi3-time VGGT-timeMulti Viewpoints Single Viewpoint0.9 8000.8 7000.7 6000.65000.54000.43000.30.2 2000.1 1000 0 (s)AUC Yaw step 90 Yaw step 45 Yaw step 15 TimesFig. 2: Illustration of existing problems. Left visualization shows pinhole modelseither face limited FoV in sparse capture or accumulative error in dense sequence. Rightfigure shows pose accuracy and processing time under different input density.reconstruction of the entire house with full coverage. CasaMaestro isthe first model that supports house-scale reconstruction with multi-viewpanoramas. Experiments show that CasaMaestro can robustly providehigh quality results in both real-world and synthetic scenes, which canserve as a strong foundation for acquiring house-scale 3D indoor assetsto be applied in close-loop simulation.

Abstract:
The generation of plausible layouts is a critical step in textdriven 3D scene synthesis. While Large Language Models (LLMs) have shown promise in high-level organization in this task, their native finegrained spatial placement remains a significant bottleneck, often requiring in-context guidance or complex agentic reflections to fix errors. This paper focuses directly on strengthening the core spatial ability by finetuning LLM to generate structured scene code (codematic indoor scene generation). We identify and address two fundamental issues hindering the LLM from doing this: data scarcity and poor instruction following. First, to mitigate data scarcity, we consolidate multiple heterogeneous datasets, spanning procedural, real-world, and professionally designed scenes, into a unified, large-scale training corpus. This dataset comprises 52K rooms and 280k data pairs, featuring diverse descriptions at both the scene and object levels. Second, even with abundant data, LLMs exhibit poor instruction following due to an information imbalance, where the tokenheavy layout history overwhelms the concise user prompt. To resolve this, we propose SceneSpinner, a framework that introduces a language-based planning stage to provide high-level reasoning and a novel Conditional Mutual Information (CMI) regularization to explicitly force the model to focus on user instructions. Experiments demonstrate that our approach significantly improves the ability of LLMs to generate plausible, diverse, and instruction-aligned 3D layouts.

Abstract:
State-of-the-art vision models process images in their en-tirety, lacking the ability to selectively zoom in on relevant regions. Thislimitation is particularly acute in scenarios where processing must beconditioned on a specific task – such as instance detection, which re-quires localizing a specific object in a high-resolution, cluttered scene. Insuch settings, critical details are easily lost as images are often resizedto match the model dimensions and computational constraints. We in-troduce Coarse-to-Fine Glimpse-based Active Perception (CF-GAP), atask-driven front-end that enhances high-resolution processing of exist-ing instance detectors. CF-GAP selectively directs a sequence of limited-view glimpses across the scene, utilizing task information to iterativelyrefine focus on the most relevant regions. These localized regions arethen processed at high resolution by a downstream instance detector.By avoiding full-image processing and eliminating irrelevant confound-ing information, CF-GAP improves Average Precision (AP) by up to20% across various state-of-the-art instance detectors on the HR-InsDetand Robotools benchmarks, while further enabling lightweight detectorsto outperform their larger counterparts.

Abstract:
Medical image visualization requires volumetric rendering al-gorithms that preserve anatomical fidelity while maintaining high ren-dering speeds. To address the high computational cost of large volumet-ric datasets, we propose a Gaussian-based volumetric representation forefficient visualization of dense medical volumes without compromisingstructural and radiometric details. We optimize the proposed represen-tation using Monte Carlo volumetric estimation, which enables trainingon a highly sparse subset of voxels while maintaining consistency withthe dense volumetric objective. In addition, we introduce a curriculumlearning strategy that progressively incorporates structured slice-basedsampling during training. Sparse voxel samples provide an early globalcoverage of the volume, while slice samples capture spatially correlatedregions that aid geometric structure and texture continuity. This combi-nation enables the Gaussian representation to learn anatomical detailsof various structures and corresponding textures from sparse supervisionwhile significantly reducing the computational cost associated with densevoxel processing. The learned representation supports slice-based ren-dering methods such as shear–warp volume rendering, enabling efficientvisualization of multimodal medical datasets including MRI and Cryosec-tion volumes while preserving anatomical structures. Using sparse super-vision, our method achieves up to 43.86 FPS rendering with a compres-sion ratio of 11.31:1.

Abstract:
We introduce a novel self-supervised learning framework thatautomatically learns representations from input computer-aided design(CAD) models for downstream tasks, including part classification, mod-eling segmentation, and machining feature recognition. To train our net-work, we construct a large-scale, unlabeled dataset of boundary repre-sentation (BRep) models. The success of our algorithm relies on two keycomponents. The first is a masked graph autoencoder that reconstructsrandomly masked geometries and attributes of BReps for representa-tion learning to enhance the generalization. The second is a hierarchi-cal graph Transformer architecture that elegantly fuses global and locallearning by a cross-scale mutual attention block to model long-range ge-ometric dependencies and a graph neural network block to aggregatelocal topological information. After training the autoencoder, we replaceits decoder with a task-specific network trained on a small amount oflabeled data for downstream tasks. We conduct experiments on varioustasks and achieve high performance, even with a small amount of labeleddata, demonstrating the practicality and generalizability of our model.Compared to other methods, our model performs significantly better ondownstream tasks with the same amount of training data, particularlywhen the training data is very limited.

Abstract:
Depth estimation in multi-illumination scenes with multi-ple, spatially varying light sources remains a crucial yet less-exploredproblem. Illumination changes introduce shadows, specular highlights,and exposure shifts that distort local appearance cues, causing severedepth inconsistency or even failure. Existing depth foundation models,trained predominantly on uniformly lit data, degrade sharply under suchconditions. However, direct adaptation is challenging because groundtruth depth is typically limited for multi-illumination datasets, whilesynthetic relighting often incurs geometric distortions. To address thesechallenges, we propose LumiDepth, a framework that learns from multi-illumination RGB images. First, a Disagreement-Calibrated ProbabilisticPseudo Supervision (DCPS) module constructs high-quality pseudo la-bels while preserving diversity. Second, a Frequency-aware Consistencyand Distillation (FaCD) module improves cross-illumination stabilitywithout over-smoothing by enforcing low-frequency geometric consis-tency and distilling high-frequency structural details bi-directionally. Toenable systematic evaluation, we introduce ReMID, a real-world multi-illumination RGB-D benchmark, together with stability metrics thatquantify average and worst-case depth variation. Experiments acrossdiverse datasets demonstrate that LumiDepth achieves state-of-the-artoverall performance, markedly improving both consistency and accuracyby reducing depth variation by 30.2% and absolute relative error by24.8%. We further show our target-domain label-free design remains ef-fective for depth under other appearance shifts such as weather and sen-⋆sor noise.

Abstract:
Intraoperative 2D/3D registration, which aligns live X-rayimages with preoperative volumes, is essential for image-guided interven-tions. Previous regression-based methods suffer from limited generaliza-tion, thus requiring time-consuming patient-specific retraining. Inspiredby recent geometry foundation models such as DUSt3R, we proposeXPos3R, a generalizable pose regression method that eliminates preop-erative preparation. Unlike existing geometry models designed for homo-geneous inputs, XPos3R extends this paradigm to multi-modal inputs,namely 2D X-rays and 3D volumes. Specifically, we introduce an asym-metric encoder–decoder architecture that improves cross-modal featurealignment while maintaining computational efficiency. To scale trainingunder limited medical data, we adopt an anatomy-specific data cura-tion strategy and construct million-scale synthetic datasets. Evaluatedon real-world benchmarks, a single pretrained XPos3R surpasses patient-specific methods in both accuracy and robustness. With test-time opti-mization completed in seconds, it further reduces the 3D error to <4 mmand the reprojection error to <1 mm. The strong generalization, accu-racy, and efficiency of XPos3R highlight its clinical potential, while itsasymmetric framework may inspire broader cross-modal vision geometrytasks.

Abstract:
We study learning from limited supervision (LLS) for hyperspectral imagery (HSI): learning transferable representations from abundant unlabeled HSIs in the target sensing domain and then adapting with only a handful of labels per class. Joint-Embedding Predictive Architectures (JEPAs) are appealing for LLS because they learn via latent prediction rather than pixel reconstruction; however, they remain unexplored for HSI. Generic masking or cropping is ill-suited to HSI: band masking corrupts high-dimensional spectra, spatial masking removes entire pixel-level semantics, and dense spatial–spectral extraction can yield highly correlated, degenerate prediction targets. We propose MorphJEPA, the first JEPA tailored to HSI, which uses morphology as the pretext signal by predicting embeddings of pixel-aligned targets generated by per-band morphological opening/closing. This induces a directional structural bottleneck that preserves core topology while suppressing high-frequency nuisances. MorphJEPA couples this objective with a compact disentangled spatial–spectral encoder fused by crossattention and a context-anchored Sketched Isotropic Gaussian Regularizer (SIGReg) to prevent collapse and maintain a well-conditioned latent manifold. Extensive 1/5/10-shot evaluations show consistent gains in intra-scene settings (Houston 2013, Trento) and under cross-scene temporal/spatial shifts (Houston 2013 → 2018, HyRank) for both zero-shot transfer and low-shot adaptation. Project page: https://github.com/ amartya-ray/MorphJEPA.

Abstract:
Depth completion aims to predict a dense depth map froma color image with sparse depth measurements. Although deep learningmethods have achieved state-of-the-art (SOTA), effectively handling thesparse and irregular nature of input depth data in deep networks remainsa significant challenge, often limiting performance, especially under highsparsity. To overcome this limitation, we introduce the Gaussian BeliefPropagation Network (GBPN), a novel hybrid framework synergisticallyintegrating deep learning with probabilistic graphical models for end-to-end depth completion. Specifically, a scene-specific Markov Random Field(MRF) is dynamically constructed by the Graphical Model ConstructionNetwork (GMCN), and then inferred via Gaussian Belief Propagation(GBP) to yield the dense depth distribution. Crucially, the GMCN learnsto construct not only the data-dependent potentials of MRF but also itsstructure by predicting adaptive non-local edges, enabling the capture ofcomplex, long-range spatial dependencies. Furthermore, we enhance GBPwith a serial & parallel message passing scheme, designed for effectiveinformation propagation, particularly from sparse measurements. Exten-sive experiments demonstrate that GBPN achieves SOTA performanceon the NYUv2 and KITTI benchmarks. Evaluations across varying spar-sity levels, sparsity patterns, and datasets highlight GBPN’s superiorperformance, notable robustness, and generalizable capability.

Abstract:
Hallucinations in medical vision language models arise whenlanguage priors override subtle visual evidence during decoding. Existingtraining-free contrastive decoding methods suppress these priors by con-trasting logits against a globally perturbed image, but global perturba-tions destroy the very diagnostic cues they are meant to protect —stan-dard VCD blur flips nearly one in five CheXpert labels on held-out chestradiographs. We propose Proximity-Constrained CounterfactualDecoding (PCCD), which admits a counterfactual view only whenit satisfies both a global and an object-masked feature-space similaritybound, constraining diagnostic drift while increasing the contrastive mar-gin for visually grounded tokens to first order under explicitly stated as-sumptions. Two complementary branches address heterogeneous halluci-nation drivers: Object-Aware VCD preserves attribution-indicated lesionregions while attenuating contextual co-occurrence priors, and Latent-Disentangled VCD applies structure-preserving style edits to suppressattribute biases. When neither branch passes the proximity gate, PCCDfalls back to greedy decoding, guaranteeing it never degrades below theunmitigated baseline. Across radiology VQA, chest X-ray report gener-ation, and a new CXR existence probe (Med-POPE), PCCD improvesGREEN AUC, QAAS, and RadGraph F1 by up to →+2.5 pp over SOTAwith one auxiliary forward pass per token and no weight updates.

Abstract:
Pretrained deep denoisers can be used to solve a wide rangeof model-based image reconstruction tasks via Plug-and-Play (PnP) andRegularization-by-Denoising (RED) algorithms, without retraining pertask. These denoisers are trained only for single-step denoising. Usingthem as Image Reconstruction (IR) regularizers in an iterative processcan destabilize reconstruction. A common failure mode is the peak-and-collapse behaviour: metrics such as PSNR improve for early iterations andthen abruptly degrade, making these algorithms unreliable in practice.We propose a data-driven stabilization framework that (i) formalizes thisinstability of any IR operator through a local quantity and (ii) preventscollapse by regularizing this quantity adaptively, requiring no retrainingor modification of the given pretrained network. Our key idea is tocontrol the potentially unstable IR operator with a contractive operatorwhose stable iterates act as an anchor and prevent collapse. We furtherintroduce an efficient family of trainable contractive operators that serve asstrong anchors while remaining lightweight. Extensive experiments acrossproximal algorithms, denoiser architectures, noise levels, and imagingtasks show consistent, collapse-free performance and improved reliabilityof PnP and RED reconstruction.

Abstract:
On-the-fly category discovery requires deciding for each in-coming test sample whether to assign it to an existing category or spawna new one. Existing methods typically implement this decision throughmatching-based heuristics, such as radius- or hash-based rules. While ef-fective in practice, these methods usually treat category birth implicitlyas a fallback when no existing category matches confidently, rather thanas an explicit alternative supported by its own statistical evidence. Toaddress this, we propose DP-BOA, a posterior-predictive decision frame-work based on an online Dirichlet-process Gaussian mixture model witha Normal–Inverse–Wishart prior. During training, we use labeled datato calibrate a shared NIW prior over category Gaussians and warm-startthe known-category posteriors. At test time, for each incoming sample,DP-BOA compares the posterior predictive evidence for assignment toexisting categories against the evidence for spawning a new category in-duced by the DP prior, and then updates category statistics online afterthe decision. The method captures anisotropic category geometry andnaturally adapts decision confidence as evidence accumulates. Acrossstandard OCD benchmarks, DP-BOA consistently outperforms strongbaselines and delivers particularly strong novel-class discovery perfor-mance while maintaining competitive known-class accuracy. The projectpage is available at DP-BOA.

Abstract:
Non-line-of-sight (NLOS) imaging aims to reconstruct hid-den scenes from time-resolved light transport, enabling vision around cor-ners. While classical physics-based methods provide principled inversion,learning-based approaches often rely on dense 3D backbones with cubiccomputational complexity. We propose a hybrid deep learning frame-work that combines a physics-guided initialization with a triplane-basedbackbone for high-fidelity volumetric reconstruction. The initialization isprovided by a learnable Enhanced Light-Cone Transform (ELCT), whichproduces a stable physics-consistent coarse volume, while the learnedbackbone replaces expensive O(N 3 ) 3D processing with scalable O(N 2 )triplane feature extraction. ConvNeXt-style residual convolutions, Restormerattention, and axis-aware cross-attention jointly refine structure and re-cover missing geometry. Experiments on synthetic and real NLOS datademonstrate improved reconstruction fidelity compared to representativephysics-based and learning-based baselines.

Abstract:
With the growing demand for realistic virtual humans, para-metric body models have become a cornerstone of modern medicine, sportsor entertainment applications. However, most of these models are inherentlylimited: they only capture the 3D surface of the skin, offering no insightinto the complex bio-mechanical structures that generate motion. As moreapplications expand towards biomechanics, the need for virtual humanmodels that go beyond the skin has become increasingly evident. Traditionalsoft-tissue simulations, such as FEM, are accurate but non-scalable and toocomputationally expensive for most common applications. Alternatively,existing biomechanical tools can simulate muscular forces and activations,but do not model changes in external shape, restricting how activationscorrelate with actual observable anatomy. This motivates a novel inverseresearch problem: recovering muscle deformations directly from visiblesurface observations - i.e., from the skin, and thus the pose. In this work,we present SOMA (from Surface Observations to Muscle Anatomy), aperson-specific model that infers spatio-temporal muscle behavior fromsurface signals obtained using RGB cameras, and SKIM, a subject-specificsoft-tissue deformation dataset. To the best of our knowledge, this is thefirst method that attempts to recover muscle deformations from multi-viewRGB data. We show how our method provides anatomically groundedanimations without the complexity of traditional simulations, leading to ascalable and cost-effective solution. Data and code are available.

Abstract:
Plane segmentation from a single RGB image remains chal-lenging due to imprecise region grouping and geometrically inconsistentsupervision, often leading to over-segmentation and false planar detections.We propose instead a pixel-wise planarity prediction framework for ro-bust monocular plane segmentation. Building on a pretrained monoculargeometric backbone predicting depth and surface normals, we introducea dedicated planarity head that estimates per-pixel planarity confidence.During inference, predicted depth, normals, and planarity are combinedin a lightweight region-growing procedure that enforces geometric consis-tency when forming plane segments. We further analyze existing planeground-truth annotations and demonstrate substantial geometric incon-sistencies under strict distance thresholds. Across multiple datasets, ourmethod achieves improved geometric precision and segmentation qualitycompared to prior state-of-the-art approaches, while improving computa-tional efficiency.

Abstract:
Most PAN-sharpening networks are trained on synthetically degraded panchromatic (PAN) and multispectral (MS) pairs because real high-resolution MS (HRMS) labels are rarely available. However, real satellite acquisitions often suffer from cross-modal misregistration and sensor-dependent optical responses that are absent from synthetic training pipelines, leading to spectral distortions and double-edge artifacts at test time. This suggests that the key bottleneck lies not only in network design but also in the supervision paradigm. In this work, we show that natural videos can serve as a spatial teacher for PAN-sharpening: nearby HR frame pairs provide abundant high-frequency structures and exhibit frame-to-frame shifts that mimic realistic misregistration, enabling strong spatial supervision without HRMS labels. Based on this insight, we propose ViPS, a cross-domain training framework under the paradigm of “Video can teach PAN-Sharpening”. ViPS disentangles supervision sources by learning spatial detail restoration from video-derived pseudo PAN–MS pairs, while enforcing spectral fidelity from real satellite PAN–MS pairs. To align optical degradations across domains, we utilize point spread function (PSF) banks and randomly sample a kernel during training, enabling physically grounded cross-domain learning. Extensive experiments across multiple sensors show that ViPS outperforms very recent state-of-the-art methods under both reducedand full-resolution settings, while maintaining fast inference.

Abstract:
Galaxy clusters are powerful probes of astrophysics and cosmology through gravitational lensing: their mass, dominated by 85% dark matter, distorts background light. Yet, mass reconstruction lacks the scalability and large-scale benchmarks to process the hundreds of thousands of clusters expected from forthcoming wide-field surveys. We introduce a fully automated method to reconstruct clustersurfacemassdensityfromphotometryandgravitationallensingobservables. Central to our approach is DARKCLUSTERS-15K, our new dataset of 15,000 mock cluster observations with paired mass and photometry maps, the largest to date, spanning multiple redshifts and simulation frameworks. We train a plug-and-play diffusion prior on DARKCLUSTERS-15K that learns the statistical relationship between mass and light, and draw approximate posterior samples constrained by weakand strong-lensing observables, yielding principled reconstructions with well-calibrated empirical uncertainties. Our approach requires no expert tuning, runs in minutes rather than hours, achieves higher accuracy, and matches experttuned reconstructions of the MACS 1206 cluster. We release our method and DARKCLUSTERS-15K to support upcoming wide-field cosmological surveys.

Abstract:
Modern autonomous driving systems increasingly rely ontightly coupled Camera–LiDAR fusion pipelines to achieve robust sceneunderstanding. While this multi-modal redundancy is designed to counterindependent single-sensor failures, it implicitly assumes that physicalobjects cannot simultaneously deceive both modalities. In this paper, weexpose the vulnerability of this assumption by proposing Agent-OBJ,a generative framework that synthesizes physically plausible 3D adver-saries via a prompt-driven agent. Unlike prior works that mainly focus onsensor-specific perturbations, our pipeline instantiates a physically plau-sible 3D adversary by generating a canonical pedestrian geometry from abase prompt and then modulating its shape via an additional semanticprompt, while controlling appearance with a pretrained multi-view per-sonalization model conditioned on multi-view images and a style promptto produce view-consistent appearance. To simultaneously compromiseboth 2D monocular and 3D fusion detectors, the agent dynamically op-timizes the generated adversary using confidence-score feedback fromboth detectors. Meanwhile, to ensure stealthiness, we regularize the gen-erated 3D point cloud by enforcing similarity to a pedestrian featurebank, aligning it with the distribution of natural objects and makingit visually and geometrically indistinguishable from benign instances.Furthermore, we incorporate a multi-view consistency constraint duringoptimization to promote cross-view robustness, ensuring the adversaryremains effective under diverse viewpoints in real-world driving scenes.Extensive experiments on nuScenes demonstrate high attack success ratesagainst both monocular and fusion detectors while preserving strongperceptual plausibility.

Abstract:
Vision-Language Navigation (VLN) requires an agent to navigate through 3D environments by following natural language instructions. While recent Video Large Language Models (Video-LLMs) have largely advanced VLN, they remain highly susceptible to State Drift in long scenarios. In these cases, the agent’s internal state drifts away from the true task execution state, leading to aimless wandering and failure to execute essential maneuvers in the instruction. We attribute this failure to two distinct cognitive deficits: Progress Drift, where the agent fails to distinguish completed sub-goals from remaining ones, and Memory Drift, where the agent’s history representations degrade, making it lose track of visited landmarks. In this paper, we propose a Dual-Anchoring Framework that explicitly anchors the instruction progress and history representations. First, to address progress drift, we introduce Instruction Progress Anchoring, which supervises the agent to generate structured text tokens that delineate completed versus remaining sub-goals. Second, to mitigate memory drift, we propose Memory Landmark Anchoring, which utilizes a Landmark-Centric World Model to retrospectively predict object-centric embeddings extracted by the Segment Anything Model, compelling the agent to explicitly verify past observations and preserve distinct representations of visited landmarks. Facilitating this framework, we curate two extensive datasets: 3.6 million samples with explicit progress descriptions, and 937k grounded landmark data for retrospective verification. Extensive experiments in both simulation and real-world environments demonstrate the superiority of our method, achieving a 15.2% improvement in Success Rate and a remarkable 24.7% gain on long-horizon trajectories. To facilitate further research, we will release our code, data generation pipelines, and the collected datasets.

Abstract:
Simulation is indispensable for autonomous driving, yet thepronounced visual Sim2Real gap severely limits the cross-domain gen-eralization of perception models. To bridge this gap, we propose Cross-Domain Control Transfer (CDCT), a diffusion-based Sim2Real frame-work for translating synthetic images into realistic counterparts withoutpaired supervision. CDCT features a novel cross-domain score composi-tion mechanism that injects domain-agnostic geometry guidance, derivedfrom synthetic-domain score differences, into a real-world appearanceprior. This ensures strict geometric and semantic consistency withoutpaired real-world spatial conditions. However, naively incorporating mul-tiple geometric conditions often incurs a prohibitive parameter overhead.To address this, our Lightweight Multi-Condition Adapter (LMCA) mod-ule processes diverse rendering buffers simultaneously, eliminating thecomputational redundancy of standard multi-branch architectures. Ex-tensive experiments on the CARLA-to-Cityscapes benchmark demon-strate that CDCT yields substantial zero-shot performance gains ondownstream perception tasks. Crucially, joint training with our generateddata surpasses the performance of real-world-only training, showcasingits immense potential for autonomous driving.

Abstract:
Real-world collaborative perception agents typically deploydiverse sensor configurations and network architectures. While feature-level fusion methods address this heterogeneity by aligning feature spaces,they require collaborative training and large communication overhead. Incontrast, object-level fusion sidesteps these constraints, as transmittedbounding boxes are directly usable across varying architectures. How-ever, standard late fusion relies only on box coordinates and confidencescores, which provide insufficient information for robust association andleave the system vulnerable to localization noise and asynchronous de-lays. To address this, we propose HERO, an uncertainty-guided object-level collaborative perception method that equips proposals with evi-dential statistics and decouples the two fusion decisions, aggregating se-mantic evidence across matched proposals while selecting geometry fromthe most reliable source, to perform robust association and fusion underpose noise and asynchrony. Experiments on OPV2V-H and DAIR-V2Xshow that HERO matches the SOTA feature-level fusion methods whiletransmitting only about 2 KB per frame of sparse object proposals. Un-der localization error and latency scenarios, HERO further outperformsfeature-level SOTA models.

Abstract:
Generalization is a central challenge in autonomous driv-ing, as real-world deployment requires robust performance under unseenscenarios, sensor domains, and environmental conditions. Recent world-model-based planning methods have shown strong capabilities in sceneunderstanding and multi-modal future prediction, yet their generaliza-tion across datasets and sensor configurations remains limited. In addi-tion, their loosely coupled planning paradigm often leads to poor video-trajectory consistency during visual imagination. To overcome these limi-tations, we propose DriveVA, a novel autonomous driving world modelthat jointly decodes future visual forecasts and action sequences in ashared latent generative process. DriveVA inherits rich priors on mo-tion dynamics and physical plausibility from well-pretrained large-scalevideo generation models to capture continuous spatiotemporal evolutionand causal interaction patterns. To this end, DriveVA employs a DiT-based decoder to jointly predict future action sequences (trajectories)and videos, enabling tighter alignment between planning and scene evolu-tion. We also introduce a video continuation strategy to strengthen long-duration rollout consistency. DriveVA achieves an impressive PDM-based planning performance of 90.9 PDM score on the NAVSIM bench-mark. Extensive experiments also demonstrate the zero-shot capabilityand cross-domain generalization of DriveVA, which reduces average L2error and collision rate by 78.9% and 83.3% on nuScenes and 52.5%and 52.4% on the Bench2Drive built on CARLA v2 compared with thestate-of-the-art world-model-based planner.

Abstract:
Autonomous driving operates in partially observable envi-ronments where actors may become fully occluded by other vehicles orinfrastructure. Most end-to-end driving systems implicitly couple actorexistence to instantaneous observations, causing actor hypotheses to de-grade or disappear during prolonged occlusion and removing potentiallycritical agents from downstream prediction and planning. We introduceBeyondSight, a permanence-aware end-to-end driving framework thatdecouples actor existence from observability by maintaining persistentactor hypotheses over time. BeyondSight propagates actor queries tem-porally and updates them with observation-conditioned evidence, en-abling joint perception, prediction, and planning to reason about ac-tors even when they are temporarily unobservable. To enable principledtraining and evaluation of persistence-aware models, we further introducenuScenes-Permanence, an extension of nuScenes that provides super-vision and observability-conditioned evaluation for unobservable actors.Experiments show that BeyondSight substantially improves reasoningunder occlusion, increasing detection performance for unobservable ac-tors from 0 to 0.249 mAP while reducing planning error from 0.61 to0.54 L2avg . These results highlight object permanence as an importantmodeling principle for robust end-to-end autonomous driving.

Abstract:
Single-photon LiDAR (SPL) based on single-photon avalanche diode (SPAD) sensing enables time-resolved photon measurements with extreme sensitivity, offering unique potential for active 3D perception. However, real-world single photon perception remains fundamentally challenging due to unique measurement noise and complex multi-return transient phenomena, which jointly complicate geometric reconstruction and semantic scene understanding. Despite growing interest in SPAD-based sensing, existing studies are largely limited to simulated data or smallscale controlled captures. As a result, systematic evaluation of real-world single photon perception across depth estimation, multi-view reconstruction, and 3D semantic understanding remains underexplored. To bridge this gap, we introduce SP-TransientBench (STB), a real-captured multitask benchmark for single photon perception. STB provides 256 × 192 transient data for three tasks: 10 depth estimation samples, 9 reconstruction scenes, and 27 semantic sequences with 10,297 samples. Each view provides full time-of-flight histograms with multi-return behavior, standardized metadata, and calibrated camera poses for multi-view evaluation. We further provide 13-class 3D semantic annotations and histogram-domain multi-return annotations for selected scenes, enabling the study of raw transient cues. By providing dedicated data splits and evaluation protocols for each task, STB enables consistent and reproducible benchmarking of real-world single photon perception across multiple 3D vision problems. The dataset and code are available at: https: //huggingface.co/datasets/shuinb/SP-TransientBench

Abstract:
Reinforcement learning for end-to-end autonomous drivingin closed-loop simulations is gaining increasing attention, yet most sim-ulators differ substantially from real-world conditions, leading to a sig-nificant sim2real gap. Recent methods use scene reconstruction to buildphotorealistic simulators, improving sensor realism but remaining con-strained by the training data distribution, which limits their ability torender novel trajectories and corner cases. To address this, we proposeReconDreamer-RL, a framework that integrates video diffusion priorsinto scene reconstruction for reinforcement learning, enabling more re-alistic and diverse closed-loop autonomous driving training. Specifically,we introduce ReconSimulator, which combines video diffusion priors forappearance modeling with a kinematic model for physical modeling, re-constructing driving scenarios from real-world data and narrowing thesim2real gap. To cover more corner cases, we propose the Dynamic Ad-versary Agent (DAA), which adjusts surrounding vehicles’ trajectoriesrelative to the ego vehicle to generate challenging scenarios such as cut-ins. Finally, we introduce the Cousin Trajectory Generator (CTG) tomitigate the bias of training trajectories toward simple straight-line mo-tion. Experiments show that ReconDreamer-RL improves end-to-end au-tonomous driving training and outperforms imitation learning methodswith a 5× reduction in Collision Ratio.

Abstract:
6-DoF LiDAR-based localization is a fundamental capabilityfor autonomous systems operating in large-scale outdoor environments.Many deep-learning-based localization methods have achieved promisingperformance so far. However, as one of the always-on modules competingfor limited on-board computational resources, the localization module isexpected to consume only a small portion of the overall compute bud-get. Most existing learning-based methods are still too heavy for thispurpose. In contrast, binary neural networks (BNNs) offer an appealingsolution, but the 1-bit compression causes severe information loss andperformance drop. In this paper, we address this challenge by propos-ing Binarized LiDAR-based Localization (BiLoc), the first binary neuralnetwork framework for 6-DoF LiDAR localization. Specifically, we rein-terpret the training of BNNs from the perspective of the information-bottleneck principle, aiming at retaining minimal yet sufficient represen-tations for pose estimation while suppressing redundant variations. Andwe introduce an auxiliary objective that adaptively regulates informationretention in the binary encoder, effectively mitigating the informationloss caused by binarization. This auxiliary objective provides additionaloptimization signals that compensate for the limited representationalcapacity and the gradient mismatch inherent in BNNs. Extensive exper-iments on large-scale outdoor LiDAR datasets demonstrate that BiLocestablishes a new state of the art for LiDAR localization with BNNs.

Abstract:
How can we bridge the numerical chasm between full-precisionCNN models and highly quantized targets when original training dataare unavailable? As the demand for deploying deep neural networkson resource-constrained edge hardware grows, achieving extreme modelcompression without compromising the privacy of training data has be-come a critical challenge. However, direct transitions to ultra-low-bitregimes introduce extreme optimization instability and activation col-lapse, where high quantization noise causes deep layers to lose their dis-criminative capacity and representational power.In this paper, we propose Zest (ZSQ Enhancement via Stepping-stoneTransition), an accurate zero-shot quantization framework for CNNs thatbridges the precision gap between full-precision models and ultra-low-bit targets through a unified two-stage distillation and reconstructionpipeline. Rather than attempting a direct and unstable jump from fullprecision to an ultra-low bit quantized model, Zest introduces a stableintermediate bit stage to tame the numerical distribution and regular-ize the model. Furthermore, we propose a two-phase distillation strategythat initializes high-fidelity semantic skeletons from the full-precisionmodel and subsequently recalibrates them through an intermediate bitstage to ensure calibration data are numerically aligned with quantized-domain constraints. Experimental results demonstrate that this progres-sive transition leads to better distillation, achieving 70.50% top-1 accu-racy on ResNet-18 at W 4A4 precision and showing higher performancecompared to existing SOTA methods in various models.

Abstract:
Vision-language models (VLMs) pre-trained on large-scaledatasets demonstrate strong generalization in few-shot scenarios, oftensurpassing conventional visual models on natural samples. However, theyremain vulnerable to adversarial perturbations, and directly applyingadversarial training may disrupt the cross-modal alignment, degradinggeneralization to novel classes. To mitigate these issues, we proposean Inverse-Adversarial and Difficulty-Adaptive Robust Vision-LanguageModel (IADA-RVLM) for robust few-shot classification. Specifically, toconstrain the optimization trajectory, our method jointly leverages ad-versarial and inverse-adversarial samples, where inverse-adversarial sam-ples serve as semantic anchors to guide representations toward high-confidence regions. To improve robustness, we incorporate visual prompttuning and text adapters to inject task-specific knowledge into bothmodalities. To balance robustness and generalization under varying sce-narios, we quantify adversarial transfer difficulty via semantic distancesand dynamically adjust the fusion of general and specialized knowl-edge. Extensive experiments across multiple datasets and attack settingsdemonstrate that our method achieves a superior overall trade-off be-tween robustness and generalization.

Abstract:
Visual Prompting (VP) has emerged as a parameter-efficient paradigm for adapting pre-trained models to downstream tasks. While existing VP methods predominantly operate in the input space, extending VP to deep activations confronts a fundamental Capacity-Integrity Paradox: increasing prompt capacity for better task adaptation inevitably triggers a parameter explosion and severe semantic disruption, where deep prompts override pre-trained representations. To strike a delicate tradeoff within this Capacity-Integrity Paradox, we conducted a series of preliminary experiments. Interestingly, these experiments reveal a strong correlation among prompt channels: applying shared prompts across grouped channels not only drastically reduces parameters but also improves performance. Motivated by this structural correlation and the need to systematically master the tradeoff, we propose Low-Rank Tensor Visual Prompting (LoTeP). By modeling deep prompts as low-rank tensors, LoTeP elegantly achieves extreme parameter compression. Concurrently, it enables precise control over the prompt capacity via layerwise rank decaying, effectively preserving the semantic integrity of deep activations. Extensive experiments demonstrate the superior generality, effectiveness, and efficiency of our approach. Overall, LoTeP consistently outperforms the state-of-the-art LoR-VP by an average margin of over 2.5% across all evaluated scenarios, while adding less than 0.1% of the backbone parameters over the LoR-VP baseline.

Abstract:
While point cloud foundation models have significantly ad-vanced point cloud video understanding, existing parameter-efficient fine-tuning (PEFT) methods still suffer from two critical limitations: pro-hibitive annotation costs for large-scale point cloud datasets and severememory bottlenecks. In this paper, we aim to mine richer supervisionsignals from existing data rather than blindly scaling datasets. A furtherkey principle is that the memory footprint of fine-tuning must be dras-tically reduced compared to full fine-tuning, which remains elusive forcurrent PEFT techniques. Driven by these challenges, we identify threecore desiderata: data-, parameter-, and memory efficiency, and presentPoinTriE, a unified framework that excels along all three dimensions.For pre-training, pseudo-motion trajectories are synthesized via rigidtransformations, paired with text corpora and 2D projections derivedfrom raw point clouds. We then propose a Geometric-Motion DualityNetwork optimized via multimodal contrastive learning, rigid rotationprediction, and motion distribution divergence to produce dense self-supervision. During fine-tuning, we freeze the pretrained backbone andonly update a lightweight Spatio-temporal Side Network built with LoRAunits. Equipped with a gradient flow masking strategy, PoinTriE simul-taneously reduces memory consumption and parameter overhead. Exten-sive experiments confirm that PoinTriE establishes new state-of-the-artresults on action recognition and semantic segmentation tasks.

Abstract:
Vision-language dataset distillation (VLDD) compresses alarge image-text paired dataset into a small set of synthetic pairs thatcan efficiently train contrastive vision-language models under strict dataand compute budgets. Most existing methods match expert trajectoriesor cross-modal statistics, yet still enforce full-dimensional alignment in aEuclidean embedding space. This is often overly restrictive due to rank-deficient image–text correlation, with shared semantics concentrated in alow-dimensional range and remaining variation spread across a weaklycorrelated residual subspace. LoRS relaxes alignment at the similaritylevel by low-rank factorization, but does not explicitly control domi-nant alignment capacity and structure in the representation space. Wethus propose a rank-aware hyperbolic alignment (RAHA) that combineshierarchical geometry with explicit alignment-capacity control. RAHAlifts multimodal representations to hyperbolic space and optimizes dis-tilled pairs with asymmetric objectives that enforce geodesic alignmentin the shared range while regularizing the residual subspace to preservemodality-private diversity and improve transfer robustness. Experimentson benchmarks show that RAHA demonstrates competitive cross-modalretrieval and improved transfer indicators under fixed budgets.

Abstract:
Detecting out-of-distribution (OOD) data is crucial for reliable ma-chine learning deployment. Among detection strategies, post-hoc methods areparticularly attractive due to their efficiency, as they operate directly on pre-trainednetworks without requiring retraining. Within this paradigm, one promising di-rection exploits loss-landscape curvature to estimate model uncertainty; however,such methods incur substantial computational cost and rely on implicit assump-tions about how landscape flatness differs between in-distribution (ID) and OODdata. In this work, we provide the first systematic investigation of this curvaturediscrepancy and show that OOD inputs exhibit larger Hessian curvature than IDdata, with the gap widening under stronger distributional shifts. Motivated by theseobservations, we propose F OLD, a lightweight flatness-modulated OOD detectorthat leverages the feature Hessian and partial feature normalization to improveID-OOD separability while avoiding costly parameter-space curvature approxi-mations. To optimally adapt this normalization across diverse datasets, we furtherintroduce AUTO F OLD, a self-supervised tuning scheme that synthesizes pseudo-OOD samples via ID logit masking for automatic calibration without requiringexternal data. Experiments on OOD benchmarks show that F OLD outperformsprior methods, improving the average AUROC by 1.63% and reducing FPR95by 2.30%, while maintaining computational efficiency comparable to a standardforward pass. Supported by theoretical analysis and extensive ablations, F OLDprovides a principled and practical solution for robust real-world deployment.

Abstract:
Domain-incremental object detection (DIOD) requires mod-els to continually adapt to new domains while preserving prior knowl-edge. Recently, parameter-efficient fine-tuning offers a promising avenue,wherein a pre-trained model is frozen and a small number of learnable pa-rameters are injected for downstream tasks. However, these methods riskoverwriting critical past knowledge, triggering inter-domain interferenceand performance degradation. To address this challenge, we propose Or-thogonal Knowledge Refreshing (OKR), a simple yet effective frameworkfor DIOD. OKR incrementally constructs independent domain-specificsubspaces via dedicated low-rank branches for each domain, which areseamlessly fused for a holistic decision, enabling conflict-free capacity ex-pansion without domain selection during inference. To minimize knowl-edge interference during fusion, we present a gradient-based orthogo-nal refreshing strategy that projects gradient updates of new domainsonto the orthogonal complement of the fused historical subspace, sup-porting continual adaptation without forgetting. Moreover, to mitigatesemantic fragmentation across domains, we enforce topology-aware con-sistency, aligning the semantic structures of old and new domains. Ex-tensive experiments validate the superiority of OKR, outperforming thebest exemplar-free method by significant margins of +5.6% and +6.5%mAP on the Pascal VOC and BDD100K series, respectively.

Abstract:
As real-world prediction systems often face missing modali-ties at inference, incomplete multimodal learning (IML) remains a prac-tical challenge. While prior methods aim to learn representations robustto missing inputs, representations from incomplete modalities inevitablydeviate from their full-modality counterparts due to missing evidence. Toexplicitly leverage these deviations, we propose MARS (Missingness-Aware Residual-guided Specialization), a mixture-of-experts frameworkthat guides expert specialization based on how representations are re-shaped by missingness. By contrasting task representations derived fromincomplete inputs with their complete counterparts during training, wederive a privileged residual signal that captures this representational gap.The residual signal guides a residual router to assign samples to the ex-perts specialized for the corresponding deviation patterns. In parallel, afeature router learns to imitate this routing behavior using only incom-plete inputs, enabling deployment without access to full modalities. Tomitigate this train–test router gap, we develop a discrepancy-aware noiseregularization that adaptively perturbs the residual router’s decisionswhen the feature router deviates, enhancing the expert robustness underimperfect imitation. Experiments on multimodal classification (CASIA-SURF, CREMA-D, UPMC Food-101) and segmentation (MCubeS) un-der missing scenarios show that MARS consistently surpasses baselines,while remaining efficient and extensible to diverse backbones and tasks.

Abstract:
Vision-language models (VLMs) such as CLIP enable zero-shot classification by comparing image features with text prompts in ashared embedding space. A fundamental property underlying this ca-pability is the global comparability of logits across arbitrary candidateclasses. However, VLMs are often adapted to fine-grained domains usingtechniques such as LoRA. While this improves in-domain accuracy, out-of-domain accuracy degrades. This leads to a higly fragmented modelecosystem, with thousands of specialized models. Multi-Expert-Domain(MED) classification seeks to address this problem, by merging LoRAstrained independently on specialized domains. However, due to the inde-pendent training, the various domain experts no longer produce globallycalibrated logits. As a result, when evaluating over the union of multipledomain-specific class sets, heterogeneous logit scales induce cross-domaininterference and artificially high confidence for out-of-domain classes,inducing prediction errors. In this work, we identify domain supervi-sion and cross-domain logit miscalibration as the key issue to scalablemulti-domain zero-shot recognition. We propose a mixture-of-expertsMED architecture, MED-DSLC, combining domain supervised training anddomain-wise logit scaling, to explicitly restore global logit comparability.MED-DSLC is a lightweight solution for MED classification, which is shownto preserve within-domain discrimination while reducing cross-domainlogit interference with minimal data. Extensive experiments across di-verse fine-grained benchmarks demonstrate that it substantially improvesmean accuracy (+15%), cross-domain robustness, and scalability in thesize of MED classification problem. Our results show that restoring output-level calibration is essential under highly data imbalanced settings forachieving a truly zero-shot VLM under multi-domain specialization. Codeis publicly available at MED-DSLC.

Abstract:
Large-scale video diffusion models (VDMs) deliver strong generation performance, but full fine-tuning for downstream tasks incurs prohibitive computational costs. Existing parameter-efficient finetuning (PEFT) methods have two critical flaws on billion-scale models: they still require substantial trainable parameters, and reward-based training suffers from noise-induced optimization instability in conditionguided tasks. We propose MagicPrompt, a lightweight framework that achieves extreme parameter efficiency and stable reward optimization. It first adopts Attention-Embedded Prompt Tuning, which steers generation via lightweight soft prompts with orders of magnitude fewer parameters while preserving pre-trained knowledge. It further introduces Dual-Space Reward Feedback Optimization, which uses self-supervised latent objectives to improve condition-guided reward training. Experiments show MagicPrompt reaches competitive performance with less than 1% trainable parameters and notably reduces training costs.

Abstract:
Event cameras emit asynchronous events in response to environmental appearance changes. The scarcity of real-world event datasets makes simulation essential. However, most simulators infer event timestamps from frame sequences, forcing many threshold crossings to share a small set of discrete times; a failure mode we term timestamp batching that worsens under fast motion and occlusion. We present TIDES, a continuous-time event simulator built on dynamic Gaussian splatting. Because TIDES operates on an explicit 3D scene representation with learnt geometry and motion, it can derive per-pixel intensity dynamics directly from the scene, rather than by differencing rendered frames. This enables accurate threshold-crossing prediction, including multiple crossings per rendering step, without temporal upsampling or frame interpolation. The same 3D scene model reveals where objects partially occlude one another; TIDES uses this to guide adaptive time stepping, concentrating computation only in regions where occlusion dynamics make simple models of brightness change unreliable. Finally, we model finite sensor bandwidth using a tile-level arbiter whose throughput, jitter, and event drops reproduce realistic sensor artifacts. Across paired RGB-event benchmarks, TIDES attains state-of-the-art event-stream fidelity. We also show that events simulated by TIDES transfer more effectively to real downstream tasks than competitors’.

Abstract:
Few-shot classification from a frozen backbone collapses when the target domain lies far from the pre-training distribution, because global embeddings map unrelated classes to nearly identical CLS tokens. Local patch descriptors are more resilient: an edge or texture gradient activates similar features whether it appears in a photograph or an X-ray. Yet part-level matching remains largely unexplored for few-shot recognition. The few methods that attempt it formulate matching as optimal transport or bipartite assignment, requiring iterative solvers whose cost grows rapidly with shots. We take a different approach. Instead of solving a matching optimization, we formulate patch-based classification as probabilistic evidence accumulation under a discriminative variational model. Each query patch carries a Bernoulli latent variable; minimizing the resulting assignment free energy yields a closed-form sigmoid gate and a softplus evidence score whose per-patch contribution saturates at −log(1−π), bounding clutter influence without iterative solvers or learned parameters. We evaluate extensively across twelve datasets spanning satellite, medical, fine-grained, and natural-image domains. Without any gradient updates, VPG outperforms the best fine-tuned baseline on five of ten dataset-settings and exceeds the previous training-free state of the art by +5.35 points on CDFSL. With lightweight episodic fine-tuning, VPG reaches 71.06% on CDFSL and 81.8% on four unseen Meta-Dataset domains.

Abstract:
While formal robustness verification has seen significant success in image classification, scaling these guarantees to object detection remains notoriously difficult due to complex non-linear coordinate transformations and Intersection-over-Union (IoU) metrics. As a fundamental step towards verifying complete detection pipelines, we introduce IoUCert, a novel formal verification framework designed specifically to overcome these core mathematical bottlenecks. By isolating the object localisation task in single-object settings, we propose a coordinate transformation that circumvents precision-degrading relaxations of non-linear box prediction functions. This approach allows us to optimise bounds directly with respect to anchor box offsets, enabling a novel Interval Bound Propagation method that derives optimal IoU bounds. We demonstrate that IoUCert enables, for the first time, the robustness verification of foundational, anchor-based architectures including tractable variants of SSD, YOLOv2, and YOLOv3 against various input perturbations, providing a rigorous theoretical basis for future end-to-end detector verification.

Abstract:
Detector–free matchers (e.g., EDM, JamMa) offer a strong accuracy– latency trade-off for pairwise matching. However, their implicit keypoint selection—where keypoints emerge as a by-product of matching—often leads to multi-view inconsistency and fragmented tracks. This limitation hinders applications that require connected tracks, such as object tracking, SfM, and SLAM. Prior attempts to mitigate this issue rely on coarsely quantized matches combined with heavy post-processing for multiple images; thus, they are typically limited to batch-based SfM pipelines. We take a different approach and directly address multi-view inconsistency through a Detect-by-Tracking (DeT) mechanism. Given an image pair I , I , and a sub-pixel query q , DeT steers the local A B A similaritymatrixofadetector-freematchersothattherow-wisesoftmaxrepresents the correspondence probability of an arbitrary sub-pixel query q . This produces A connected tracks across views without heavy post-processing, while preserving the core strength of detector-free matching: implicit keypoint selection driven by matching. Moreover, DeT can be incorporated into off-the-shelf matchers without additional training. Experiments demonstrate that DeT maintains the base detector-free matcher’s matching accuracy while producing connected tracks.

Abstract:
Recovering a sharp image from a motion-blurred observation remains challenging due to the inherently ill-posed nature of blind deblurring, especially under real-world non-uniform blur caused by complex camera and scene motion. Event cameras, with their microsecond-level temporal resolution and inherent sensitivity to motion, provide rich motion cues that can serve as strong priors for resolving such ambiguities. However, existing event-guided deblurring methods either rely on simplified blur assumptions or treat event data merely as generic features, without fully exploiting their physical relationship with the image formation process. In this paper, we propose a complete event-driven nonuniform blind deblurring framework that explicitly models the physical formation of spatially varying blur kernels. Specifically, we first estimate dense pixel-wise motion trajectories from the event stream via a differentiable event-alignment objective. These trajectories are then used to construct per-pixel point spread functions (PSFs), which provide a physically grounded blur operator. Based on this operator, we formulate a joint optimization framework that enforces blur consistency, event consistency, and image regularization. Finally, we design an unrolled optimization network that alternates between data-consistency reconstruction and learned image priors. Experiments on both synthetic and real-world datasets demonstrate that the proposed method improves deblurring performance while maintaining low computational complexity.

Abstract:
Detailed image captioning demands both factual ground-ing and fine-grained coverage, yet existing methods have struggled toachieve them simultaneously. We address this tension with ReflectiveNote-Guided Captioning (ReflectCAP), where a multi-agent pipeline an-alyzes what the target large vision-language model (LVLM) consistentlyhallucinates and what it systematically overlooks, distilling these pat-terns into reusable guidelines called Structured Reflection Notes. At in-ference time, these notes steer the captioning model along both axes—whatto avoid and what to attend to—yielding detailed captions that jointlyimprove factuality and coverage. Applying this method to 8 LVLMs span-ning the GPT-4.1 family, Qwen series, and InternVL variants, Reflect-CAP reaches the Pareto frontier of the trade-off between factuality andcoverage, and delivers substantial gains on CapArena-Auto, where gen-erated captions are judged head-to-head against strong reference models.Moreover, ReflectCAP offers a more favorable trade-off between captionquality and compute cost than model scaling or existing multi-agentpipelines, which incur 21–36% greater overhead. This makes high-qualitydetailed captioning viable under real-world cost and latency constraints.

Abstract:
In the pursuit of robust and generalizable category-level ob-ject pose estimation, most existing methods adopt parametric formula-tions that learn effective representations from data, yet they primarilyencode category-level patterns into fixed shape priors or static param-eter weights, which limits their scalability to highly diverse instances.In this paper, we rethink category-level pose estimation from a memory-centric perspective and present MemPose, a memory-augmented frame-work that explicitly incorporates category-level geometric memory intothe pose estimation pipeline. We introduce an external memory bufferthat stores and dynamically updates structural representations frompreviously observed instances, enabling the model to leverage accumu-lated experience to support current perception. Extensive experimentson four challenging benchmarks (REAL275, CAMERA25, Housecat6Dand Wild6D) demonstrate the superiority of our proposed method overprevious state-of-the-art approaches.

Abstract:
Reference-based inpainting utilizes the information in thereference image to inpaint the missing region in the target image, aimingto generate the fact object behind the mask rather than a reasonable one.The key challenge is how to accurately locate the region in the referenceimage and maximally utilize it. We proposed UNet-Twice, a simple yetnovel framework. UNet-Twice uses the same UNet two times, combinedwith a well-constructed data pair to build a close connection betweenthe target and reference image. The two passes fuse spatially comple-mentary features generated from the target and the reference image,which provides intuitively explicit guidance for image generation. Ourproposed framework is simple and easy to extend to a multi-referencesetting. Extensive experiments demonstrate that our proposed frame-work outperforms existing reference-based inpainting methods in bothsingle-reference and multi-reference settings.

Abstract:
Diffusion models have recently advanced text-to-video (T2V)generation, yet they still struggle with fine-grained compositional align-ment, such as attribute binding, spatial relations, and object interactions.While reward-based fine-tuning improves alignment, it is susceptible toreward hacking and adapts poorly to new prompt distributions. In thiswork, we propose NoisEasier, a test-time scaling framework that im-proves T2V generation through differentiable reward-guided noise opti-mization without modifying the underlying model. By combining effi-cient short-step generators with a multi-objective reward formulation,NoisEasier enables stable and practical test-time optimization under re-alistic inference budgets. Our key insight is that jointly optimizing theentire stochastic trajectory accelerates reward convergence and improvescompositional alignment over optimizing only the initial latent, with neg-ligible additional computational and time cost. Experiments on VBenchand T2V-CompBench demonstrate consistent improvements across mul-tiple backbones, achieving over 10% average gains on challenging dimen-sions such as attribute binding, object interaction, and numeracy. Over-all, NoisEasier serves as both a flexible alternative and a complementaryenhancement to reward-based fine-tuning, establishing test-time scalingas an effective paradigm for controllable text-to-video generation.

Abstract:
Recent advances in Image-to-Video (I2V) generation allow asingle image to be animated into a convincing video under text guidance,raising serious copyright and privacy risks. We propose Anti-Prompt,an image protection approach that injects imperceptible perturbationsinto an image, inducing visible inconsistencies and structural failures intext-guided I2V generation. Our method is motivated by a simple em-pirical observation: when text guidance is removed from modern I2Vmodels, generation quality degrades markedly, not only in motion re-alism but also in subject preservation, structural coherence, and tem-poral consistency. Building on this insight, Anti-Prompt exploits themodel’s reliance on textual guidance by attenuating text-conditioned in-teractions during denoising while strengthening visual-only pathways.To further systematically evaluate protection effectiveness, we introducea Video-LLM–assisted evaluation protocol that provides interpretable,frame-grounded analyses of generation artifacts and inconsistencies. Ex-periments on two representative I2V architectures demonstrate that ourmethod achieves strong protection performance while improving effi-ciency and cross-model transferability.

Abstract:
Prior studies have demonstrated that diffusion classifiers achieverobust zero-shot classification performance. However, their effectivenessis strongly tied to the pretraining data distribution: they perform well inmajority, high-density regions of the data manifold, but are significantlyless accurate in minority, low-density regions. Although prior works onminority sampling have focused on generating more minority-like im-ages, what minority sampling fundamentally enables beyond generationremains underexplored. In this paper, we reveal a direct relationshipbetween minority sampling in generation and the perception capabil-ity of diffusion classifiers. Specifically, we show that enhancing minoritysampling broadens the coverage of underrepresented regions on the datamanifold, thereby improving diffusion-based recognition. To exploit thisconnection, we propose Self-Improving Diffusion Classifiers with Minor-ity Preference Optimization (MiPO), which fine-tunes a pretrained dif-fusion model using minority preference rewards. Using only arbitrarycaption data, MiPO generates candidate samples, rewards those thatbetter cover minority regions, and optimizes the model with LoRA andGroup Relative Policy Optimization, without additional image data, ex-ternal foundation models, or external reward models. This enables stable,prompt-adaptive minority sampling and translates low-density genera-tive coverage into improved zero-shot diffusion classification. To sum up,we show that diffusion classifier perception is biased toward majorityregions, demonstrate that this bias can be alleviated through minoritypreference optimization, and evaluate MiPO on five standard datasets.

Abstract:
Localizing a font into new languages is a highly intricate task requiring precisedesign adaptation of glyphs, color/texture, and spacing/kerning, from source to target lan-guages. Most existing methods focus on single glyph generation with limited capability inhandling multilingual font rendering. In this work, we propose LoGAN, a VLM-based agenticframework for few-shot multilingual font localization, which takes in a small number of indi-vidual glyphs from a font or letters from a logo and uses them to generate complete charactersets in other languages. LoGAN breaks down this task into multiple components: a glyph-leveldiffusion model, a style finetuning module, a spacing and kerning transfer algorithm, and atexture expansion model, with a VLM agent coordinator. LoGAN achieves broad languagecoverage for font localization with various styles, including Chinese/Korean/Japanese (CJK).We evaluate our approach on both font and real-world logo datasets spanning more than27 languages and compare it against both specialized font generation and state-of-the-artimage editing models with strong text rendering capabilities (e.g., FLUX, Nano-Banana).Our approach yields higher glyph fidelity while maintaining better style, texture, and kerningconsistency according to both quantitative and qualitative evaluations.

Abstract:
Although autoregressive (AR) models have demonstrated re-markable success in image generation, extending these models to layout-conditioned generation remains challenging due to the sparse natureof layout conditions and the risk of feature entanglement. We presentStructured Masking for AR-based Layout-to-Image (SMARLI), a novelframework that effectively integrates spatial layout constraints into theAR generation process. To equip AR models with layout control, a struc-tured masking strategy is applied to the attention computation to governthe interaction among the global prompt, layout, and image tokens. Thisdesign prevents the misassociation of different regions with their corre-sponding descriptions while enabling the sufficient injection of layoutconstraints into the generation process. To alleviate the exposure bias ofAR models and further enhance generation quality and layout accuracy,we incorporate a Group Relative Policy Optimization (GRPO) post-training scheme. We adapt it to the next-set-based paradigm and intro-duce a specifically designed layout reward, which is coordinated with animage quality reward to guide policy optimization in a balanced manner.Experimental results demonstrate that SMARLI seamlessly integrateslayout tokens with text and image tokens without compromising genera-tion quality, and the proposed masking strategy and post-training schemecan also be transferred to standard next-token-based AR models. Theproposed framework achieves superior layout control while maintainingthe structural simplicity and generation efficiency of AR models.

Abstract:
Reinforcement Learning from Human Feedback has emergedas a standard for aligning di!usion models. However, we identify a fun-damental limitation of the standard DPO formulation when optimizingmulti-dimensional preferences: by relying on the Bradley-Terry model,it aggregates heterogeneous evaluation axes (e.g., aesthetic quality andsemantic alignment) into a single scalar reward. This aggregation createsa reward conflict where the model is forced to unlearn desirable featuresof a specific dimension if they appear in a globally non-preferred sam-ple. To address this issue, we propose Multi Reward Conditional DPO(MCDPO) which resolves reward conflicts by introducing a disentangledBradley-Terry objective. MCDPO explicitly injects a preference outcomevector as a condition during training, which allows the model to learnthe correct optimization direction for each reward axis independentlywithin a single network. Extensive experiments on Stable Di!usion 1.5and SDXL demonstrate that MCDPO achieves superior performance onbenchmarks even with 18% and 3% training data respectively. Notably,our conditional framework enables dynamic and multiple-axis control atinference time using Classifier Free Guidance to amplify specific rewarddimensions without additional training or external reward models.

Abstract:
Video generation models produce visually coherent content but struggle with tasks requiring spatial reasoning and multi-step planning. Reinforcement learning (RL) offers a path to improve generalization, but its effectiveness in video reasoning hinges on reward design—a challenge that has received little systematic study.WeinvestigatethisproblembyadaptingGroupRelativePolicyOptimization (GRPO)toflow-basedvideomodelsandtrainingthemonmaze-solvingandrobotic navigationtasks.Wefirstshowthatmultimodalrewardmodelsfailcatastrophically in this setting. To address this, we design verifiable reward functions grounded in objective task metrics. For structured game environments, we introduce a multicomponent trajectory reward that is verifiable against ground-truth optimal paths. For robotic navigation, where ground truth is given only as a reference rollout, we propose an embedding-level reference-anchored reward. Our experiments show that RL fine-tuning with verifiable rewards improves generalization. For example, on complex 3D mazes, our model improves exact match accuracy by 29.1% over the SFT baseline, and on trap-avoidance tasks by 51.4%. Our systematic reward analysis reveals that verifiable rewards are critical for stable training, while multimodal reward models could lead to degenerate solutions. These findings establish verifiable reward design as a key enabler for robust video reasoning.

Abstract:
Reinforcement learning (RL) has recently emerged as a promis-ing approach for aligning text-to-image generative models with humanpreferences. A key challenge, however, lies in designing effective and in-terpretable rewards. Existing methods often rely on either compositemetrics (e.g., CLIP, OCR, and realism scores) with fixed weights or asingle scalar reward distilled from human preference models, which canlimit interpretability and flexibility. We propose RubricRL, a simple andgeneral framework for rubric-based reward design that offers greater in-terpretability, composability, and user control. Instead of using a black-box scalar signal, RubricRL dynamically constructs a structured rubricfor each prompt—a decomposable checklist of fine-grained visual cri-teria such as object correctness, attribute accuracy, OCR fidelity, andrealism—tailored to the input text. Each criterion is independently eval-uated by a multimodal judge (e.g., o4-mini), and a prompt-adaptiveweighting mechanism emphasizes the most relevant dimensions. This de-sign not only produces interpretable and modular supervision signalsfor policy optimization (e.g., GRPO or DiffusionNFT), but also enablesusers to directly adjust which aspects to reward or penalize. Experimentswith autoregressive and diffusion text-to-image models demonstrate thatRubricRL improves prompt faithfulness, visual detail, and generalizabil-ity, while offering a flexible and extensible foundation for interpretableRL alignment across text-to-image architectures.

Abstract:
We introduce UEval, a benchmark to evaluate unified mod-els, i.e., models capable of generating both images and text. UEval com-prises 1,000 expert-curated questions that require both images and textin the model output, sourced from 8 real-world tasks. Our curated ques-tions cover a wide range of reasoning types, from step-by-step guides totextbook explanations. Evaluating open-ended multimodal generation isnon-trivial, as simple LLM-as-a-judge methods can miss the subtleties.Unlike prior methods relying on multimodal Large Language Models(MLLMs) to rate image quality or text accuracy, we design a rubric-basedscoring system in UEval. For each question, reference images and text an-swers are provided to an MLLM to generate an initial rubric, consisting ofmultiple evaluation criteria, and human experts then refine and validatethese rubrics. In total, UEval contains 10,417 validated rubric criteria,enabling fine-grained automatic scoring. UEval is challenging for currentunified models: GPT-5-Thinking scores only 66.4 out of 100, while thebest open-source model reaches merely 49.1. We observe that reasoningmodels often outperform non-reasoning ones, and transferring reasoningtraces from a reasoning model to a non-reasoning model narrows thegap. This suggests that reasoning may be important for tasks requiringcomplex multimodal understanding and generation. UEval is availableat https://huggingface.co/datasets/zlab-princeton/UEval

Abstract:
We present a framework for document-centric backgroundgeneration with multi-page editing and thematic continuity. To ensuretext regions remain readable, we employ a latent masking formulationthat softly attenuates updates in the diﬀusion space, inspired by smoothbarrier functions in physics and numerical optimization. In addition, weintroduce Automated Readability Optimization (ARO), which automat-ically places semi-transparent, rounded backing shapes behind text re-gions. ARO determines the minimal opacity needed to satisfy percep-tual contrast standards (WCAG 2.2) relative to the underlying back-ground, ensuring readability while maintaining aesthetic harmony with-out human intervention. Multi-page consistency is maintained througha summarization-and-instruction process, where each page is distilledinto a compact representation that recursively guides subsequent gen-erations. This design reflects how humans build continuity by retain-ing prior context, ensuring that visual motifs evolve coherently acrossan entire document. Our method further treats a document as a struc-tured composition in which text, figures, and backgrounds are preservedor regenerated as separate layers, allowing targeted background editingwithout compromising readability. Finally, user-provided prompts allowstylistic adjustments in color and texture, balancing automated consis-tency with flexible customization. Our training-free framework producesvisually coherent, text-preserving, and thematically aligned documents,bridging generative modeling with natural design workflows.

Abstract:
Recent advancements in large-scale generative models haveenabled the creation of high-quality images and videos, but have alsoraised significant safety concerns regarding the generation of unsafe con-tent. To mitigate this, concept erasure methods have been developed toremove undesirable concepts from pre-trained models. However, existingmethods remain vulnerable to adversarial attacks that can regeneratethe erased content. Moreover, achieving robust erasure often degradesthe model’s generative quality for safe, unrelated concepts, creating adifficult trade-off between safety and performance. To address this chal-lenge, we introduce Classifier-Guided Concept Erasure (CGCE), an effi-cient plug-and-play framework that provides robust concept erasure fordiverse generative models without altering their original weights. CGCEuses a lightweight classifier operating on text embeddings to first detectand then refine prompts containing undesired concepts. By modifyingonly unsafe embeddings at inference time, our method prevents harm-ful content generation while preserving the model’s original quality onbenign prompts. Extensive experiments show that CGCE achieves state-of-the-art robustness against a wide range of red-teaming attacks. Ourapproach also maintains high generative utility, demonstrating a superiorbalance between safety and performance. We showcase the versatility ofCGCE through its successful application to various modern T2I and T2Vmodels, establishing it as a practical and effective solution for safe gen-erative AI.

Abstract:
Personalized image retouching aims to adapt retouching stylesof individual users from reference examples, but existing methods oftenrequire user-specific fine-tuning or fail to generalize effectively. To addressthese challenges, we introduce RefRetouch, a general framework forpersonalized image retouching that instantly adapts to user retouchingstyles without any test-time fine-tuning. It employs an asymmetric auto-encoder to encode the retouching style from paired examples into a con-tent disentangled latent representation that enables faithful transfer ofthe retouching style to new images. To adaptively apply the encoded re-touching style to new images, we further propose retrieval-augmented re-touching (RAR), which retrieves and aggregates style latents from refer-ence pairs most similar in content to the query image. With these compo-nents, RefRetouch enables superior and generic content-aware retouch-ing personalization across diverse scenarios, including single-reference,multi-reference, and mixed-style settings, while also generalizing out ofthe box to photorealistic style transfer.

Abstract:
Recent advances in generative super-resolution (SR) havegreatly improved visual realism, yet existing evaluation and optimizationframeworks remain misaligned with human perception. Full-Referenceand No-Reference metrics often fail to reflect perceptual preference, ei-ther penalizing semantically plausible details due to pixel misalignmentor favoring visually sharp but inconsistent artifacts. Moreover, most SRmethods rely on ground-truth (GT)–dependent distribution matching,which does not necessarily correspond to human judgments. In this work,we propose RefReward-SR, a low-resolution (LR) reference-aware rewardmodel for preference-aligned SR. Instead of relying on GT supervisionor NR evaluation, RefReward-SR assesses high-resolution (HR) recon-structions conditioned on their LR inputs, treating the LR image asa semantic anchor. Leveraging the visual–linguistic priors of a Multi-modal Large Language Model (MLLM), it evaluates semantic consistencyand plausibility in a reasoning-aware manner. To support this paradigm,we construct RefSR-18K, the first large-scale LR-conditioned preferencedataset for SR, providing pairwise rankings based on LR–HR consistencyand HR naturalness. We fine-tune the MLLM with Group Relative Pol-icy Optimization (GRPO) using LR-conditioned ranking rewards, andfurther integrate GRPO into SR model training with RefReward-SR asthe core reward signal for preference-aligned generation. Extensive ex-periments show that our framework achieves substantially better align-ment with human judgments, producing reconstructions that preservesemantic consistency while enhancing perceptual plausibility and visualnaturalness.

Abstract:
Image-conditioned image editing (ICIE) guides edits with a reference image to convey visual attributes—such as style, tone, or object identity—that are difficult to specify through text alone. Despite growing practical demand, no large-scale unified ICIE dataset currently exists in the open-source ecosystem; existing efforts cover only a narrow subset of edit types at small scale, typically constructed via costly forward synthesis. We reformulate ICIE data construction as a reference-completion problem: high-quality text-conditioned image editing (TCIE) datasets already supply the input image, instruction, and edited target, and can be augmented with aligned reference images through type-specific synthesis and lightweight instruction adaptation. Building on this insight, we propose a scalable pipeline equipped with weak-instruction augmentation and five-dimensional VLM-based post-filtering, and use it to construct RCEdit-500K—the first large-scale unified open ICIE dataset comprising 477K quadruplets across six edit categories (add, remove, replace, background, style, alter) with both concrete and abstract reference types. Training on RCEdit-500K consistently improves reference-guided editing: LoRA adaptation on diffusion models yields up to +1.22 average gain, and an autoregressive model without native editing ability acquires competitive ICIE performance, demonstrating that data availability is the primary bottleneck for open-source ICIE.

Abstract:
We focus on the foundational task of Scene Staging: given a reference scene image and a text condition specifying an actor category to be generated in the scene and its spatial relation to the scene, the goal is to synthesize an output image that preserves the same scene identity as the reference image while correctly generating the actor according to the spatial relation described in the text. Existing methods struggle with this task, largely due to the scarcity of high-quality paired data and unconstrained generation objectives. To overcome the data bottleneck, we propose a novel data construction pipeline that combines real-world photographs, entity removal, and image-to-video diffusion models to generate training pairs with diverse scenes, viewpoints and correct entity-scene relationships. Furthermore, we introduce a novel correspondence-guided attention loss that leverages cross-view cues to enforce spatial alignment with the reference scene. Experiments on our scene-consistent benchmark show that our approach achieves better scene alignment and text–image alignment than state-of-the-art baselines, according to both automatic metrics and human preference studies. Our method generates images with diverse viewpoints and compositions while faithfully following the textual instructions and preserving the reference scene identity.

Abstract:
Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecified user prompts due to a distributional gap with their descriptive training captions. This frequently leads to suboptimal imagetext alignment, aesthetics, and overall visual quality. To bridge this gap, we propose POET (Preference Optimization for Enhanced Text-to-Image generation), an automated prompt rewriting framework that leverages large language models (LLMs) to refine user inputs before feeding them into frozen T2I backbones. POET introduces a carefully designed composite reward system and an iterative Direct Preference Optimization (DPO) training pipeline, enabling the rewriter to learn model-preferred prompt structures directly from multimodal feedback without requiring costly high-quality supervised fine-tuning (SFT) data. Extensive evaluations across diverse T2I models and benchmarks show that our prompt rewriter consistently improves image-text alignment, visual quality, and aesthetics, outperforming strong baselines. Furthermore, we demonstrate strong transferability by showing that a rewriter trained on one T2I backbone generalizes effectively to others without needing to be retrained. These findings highlight that POET is an effective, robust, and practical model-agnostic strategy for improving T2I systems.

Abstract:
Arbitrary-scale single image super-resolution (ASSR) has achieved tremendous success under synthetic degradations, but still faces performance deterioration in real-world scenarios where complex distortions are highly entangled with continuous zooming scales. While constructing paired real-world datasets with fractional scales attempts to alleviate this gap, such physical data is fundamentally constrained by discrete scale coverage and hardware-related biases. To overcome these limitations, we propose the Continuous Degradation Rectifier (CDR), a plug-and-play generative proxy that achieves zero-shot domain adaptation at inference time without requiring any real-world paired training data. Designed for any off-the-shelf ASSR model pre-trained on synthetic data, CDR leverages a conditional diffusion process to dynamically project scale-entangled real-world inputs into a canonical bicubic latent space, rectifying the continuous degradation manifold. Specifically, CDR achieves this through a progressive orthogonal decoupling mechanism: it first purifies scale-invariant content from real-world low-resolution images via a discrete codebook bottleneck, and subsequently extracts scaleindependent degradation and continuous scale modulation from unpaired bicubically downsampled reference image, which are orthogonally trained by scale-equivariant contrastive learning. Finally, these orthogonal representations are seamlessly integrated via scale-adaptive bandwidth modulation to guide the reverse diffusion process. Extensive experiments on real-world benchmarks demonstrate that equipping frozen, off-the-shelf ASSR models with CDR not only bypasses the need for domain-specific retraining but also establishes a new state-of-the-art.

Abstract:
Modern text-to-image models excel in visual fidelity andprompt adherence. However, this strict adherence comes at the cost ofdiversity: generated samples tend to collapse into a single visual inter-pretation. Existing methods to improve diversity produce outputs drivenby incidental variations rather than meaningful design choices. This mo-tivates a new variant of the diversity task where structure is enforced onthe generated samples.We introduce a method for controlled diversity that enables SemanticBrowsing, where users can navigate structured image galleries and expe-rience creative exploration through a systematic traversal of meaningful,interpretable axes of variation. Achieving this level of semantic controlrequires a deep understanding of the scene. We exploit the fact thatrecent text-to-image models are trained on elaborated captions, effec-tively decoupling semantic decision-making from pixel generation. Thisenables a paradigm shift: instead of relying on stochastic variation withinthe text-to-image model, we induce diversity directly at the text level.By leveraging rich textual representations, we allow a Vision LanguageModel (VLM) to operate on the full scene context. To overcome thegeneric outputs typical of standard VLMs, we employ an agentic work-flow that explicitly enforces structured variation attuned to the originalprompt. We demonstrate that our method produces diverse and naviga-ble design spaces where every variation corresponds to a specific, user-understandable semantic decision.

Abstract:
Flow-based generative methods offer a simple and effectiveframework for high-fidelity generation, yet pretrained flow models arerarely used in their vanilla conditional form: in image generation, sam-ples without guidance often appear diffuse and lack fine-grained detail.Existing guidance techniques such as classifier-free guidance (CFG) im-prove fidelity but reduce sample diversity. We introduce MomentumGuidance (MG), a guidance method that improves sample quality byextrapolating the current velocity away from an exponential moving av-erage of past velocities along the ODE trajectory, while preserving thestandard one-evaluation-per-step cost. MG provides gains beyond CFG,improving the precision–recall Pareto frontier. Experiments demonstratethe effectiveness of MG across benchmarks. On ImageNet-256, MG im-proves FID by 36.54% without CFG and 25.42% with CFG on averageacross sampling settings, attaining an FID of 1.553 at 16 sampling steps.Evaluations on large flow-based models, including Stable Diffusion 3 andFLUX.1-dev, further confirm improvements across standard metrics.

Abstract:
Incomplete Multi-View Clustering addresses the problem ofclustering multi-modal data when certain views are missing. Recent end-to-end generative approaches leverage diffusion models to recover miss-ing views via stochastic noise-to-data trajectories. While expressive, suchmechanisms are not explicitly designed for clustering, as they initializefrom cluster-agnostic noise and rely on stochastic denoising dynamics.In this work, we revisit probability path design in end-to-end generativeIMVC. We introduce a flow-matching framework with a linear interpo-lation path between paired view representations, that replaces diffusionwith probability flows between observed and missing views. We providea formal analysis showing that deterministic ODE flows are inherentlybetter aligned with clustering objectives than diffusion-based stochastictrajectories, especially in terms of transport mechanisms that respectclass-conditional data distributions and maintain cluster consistency infinite-step regimes. Building upon this insight, we develop an end-to-end IMVC architecture that integrates straight-path flow-matching viewcompletion with cluster-level and entropy-based alignment to enforcecross-view clustering consistency. Extensive experiments on standardIMVC benchmarks demonstrate that the proposed framework establishesnew state-of-the-art performance.

Abstract:
We propose D2PO (Dynamic Direct Preference Optimiza-tion), a principled framework for optimizing diffusion sampling policieswith respect to timestep schedules and classifier-free guidance (CFG)weights. Our work is motivated by a fundamental limitation of existingstudent-teacher regression frameworks; low-NFE student samplers aretrained to mimic high-NFE teachers, often sacrificing high-frequency tex-ture fidelity while preserving coarse global structures, thereby misalign-ing the sampler with perceptual quality. D2PO addresses this challengeby reformulating sampler optimization as a preference-based alignmentproblem, leveraging the Direct Preference Optimization (DPO) frame-work. To make DPO applicable to diffusion samplers, we model thesampling policy as an energy-based model (EBM), transforming pref-erence comparisons into tractable energy differences. We further intro-duce a novel energy formulation derived directly from the pretrainedscore network, enabling preference evaluation in perturbed spaces thatjointly capture structural consistency and fine-grained details. More-over, we introduce dynamic preferences, where the preferred samplesused for alignment progressively improve as the sampling policies arelearned. This self-improving mechanism replaces rigid static teacher su-pervision with an iterative, preference-guided refinement process, pro-viding progressively stronger alignment signals. Extensive experimentsdemonstrate that D2PO aligns diffusion samplers with perceptual qual-ity more faithfully, unlocking the full potential of high-quality teachersand consistently outperforming conventional regression-based schedulersunder low-NFE constraints.

Abstract:
We introduce Elastic Looped Transformers (ELT), a highlyparameter-efficient class of visual generative models based on a recurrenttransformer architecture. While conventional generative models rely ondeep stacks of unique transformer layers, our approach employs iterative,weight-shared transformer blocks to drastically reduce parameter countswhile maintaining high synthesis quality. To effectively train these modelsfor image and video generation, we propose the idea of Intra-Loop SelfDistillation (ILSD), where student configurations (intermediate loops)are distilled from the teacher configuration (maximum training loops)to ensure consistency across the model’s depth in a single training step.Our framework yields a family of elastic models from a single trainingrun, enabling Any-Time inference capability with dynamic trade-offs be-tween computational cost and generation quality, with the same parame-ter count. ELT significantly shifts the efficiency frontier for visual synthe-sis. With 4 × reduction in parameter count under iso-inference-computesettings, ELT achieves a competitive FID of 2.0 on class-conditional Im-ageNet 256 × 256 and FVD of 72.8 on class-conditional UCF-101.Vanilla Looped Transformers Elastic Looped TransformersXk 0 Xk 0ILSDTrainingXk intXk max Xk int Xk maxSolution Space Solution Space✗ Xk int away from solution space. ✓ Xk int moves towards solution space.✗ Fixed Depth Inference ✓ Any-Time Inference.Fig. 2: Latent Trajectories of Standard vs. Elastic Looped Transformers.Xk int & Xk max represent output of intermediate (Lint ) & final loops (Lmax ) respectivelyfor kth generation sampling step. Unlike standard recurrent models (left) where onlythe final iteration Xk max reaches the solution space, our ILSD training (right) guidesintermediate states Xk int also toward the target space. This transformation shifts themodel from a fixed-depth architecture to an Any-Time inference framework, supportingflexible computational budgets through early exits within a sampling step.

Abstract:
Training data for class-conditional image synthesis often ex-hibit a long-tailed distribution with limited amount of images for tailclasses. Such an imbalance causes mode collapse and reduces the diver-sity of synthesized images for tail classes. For class-conditional di!usionmodels trained with imbalanced data, we aim to improve the diversityand fidelity of tail class images without compromising the quality of headclass images. We propose contrastive conditional-unconditional align-ment (CCUA), which comprises two synergistic loss functions. Our firstloss is an Alignment Loss (AL) that aligns class-conditional generationwith unconditional generation at large timesteps. Alignment loss makesthe denoising process insensitive to class conditions for the initial steps,which enriches tail classes through knowledge sharing from head classes.Secondly, we diversify unconditional generation via an UnsupervisedContrastive Loss (UCL) to increase the distance/dissimilarity amongsynthetic images. We combine the two losses to implicitly diversify condi-tional generation. Our framework is easy to implement as demonstratedon both U-Net based architecture and Di!usion Transformer. Our methodoutperforms vanilla denoising di!usion probabilistic models, score-baseddi!usion model, and alternative contrastive methods for class-imbalancedimage generation across various datasets, in particular ImageNet-LT with256→256 resolution.

Abstract:
Test-time scaling (TTS) has emerged as a promising paradigm for im-proving the performance of large-scale models. However, existing vision TTSmethods largely rely on global exploration—e.g., resampling noise or rewritingprompts—and often struggle to efficiently search the vast compositional space.As a result, they can exhibit a “scaling plateau,” where additional computationyields diminishing returns in semantic alignment. In this paper, we propose Region-Aware Scaling (RAS), a framework that bridges region-aware generation andtest-time scaling. By treating regional decomposition as a powerful and previ-ously overlooked scaling axis, RAS converts complex compositional promptsinto coordinated regional sub-tasks, effectively increasing the density of validcandidates during inference. At its core, RAS builds on a training-free Region-Aware Generation (RAG). Unlike many layout-based methods that incur sub-stantial overhead, RAG injects regional guidance only during early denoising, en-abling precise attribute binding while preserving global structural coherence. Weevaluate RAS on the GenEval benchmark and observe consistent improvementsin scaling efficiency across diverse compositional challenges. RAS achieves anoverall score of 0.85 with only 2 samples, matching a 32-sample noise-scalingbaseline; with 4 samples, it reaches 0.88, surpassing the combination of 32-sample noise and prompt scaling. Overall, our results suggest that structuringthe search space via regional decomposition provides a principled and compu-tationally efficient direction for scaling compositional alignment.

Abstract:
Low-light image enhancement (LLIE) aims to restore visi-bility and structural details from severely degraded images under insuf-ficient illumination. However, real-world low-light images often exhibitspatially heterogeneous degradations, where mildly under-exposed areasare easy to recover while severely dark, noisy, or color-unstable regionsremain challenging. Existing LLIE methods typically apply a globallyuniform enhancement strategy, which often leads to suboptimal trade-offs such as over-smoothing textures in hard regions or over-enhancementin easier areas. To address this, we propose a Difficulty-ConditionedAttribute-Specific Restoration (DCASR) framework that explicitly mod-els restoration difficulty and allocates enhancement capacity accordingly.DCASR incorporates a difficulty-aware enhancement network with twoprior-driven modulators: a Global Intensity Modulator (GIM) for co-herent illumination correction and a Structural Refinement Modulator(SRM) for detail recovery, together with an adaptive weighting schemethat emphasizes hard regions during optimization. Since residual-deriveddifficulty priors are unavailable at test time, we adopt a teacher–studentscheme in which a privileged teacher derives difficulty priors from resid-ual cues only during training, while a latent conditional diffusion studentgenerates aligned priors conditioned on a single low-light input at infer-ence. Experiments demonstrate that DCASR consistently outperformsstate-of-the-art methods on multiple benchmarks. Code will be madepublicly available at github.com/mingzhuzhang1/DCASR-LLIE.

Abstract:
We propose the first compression approach for image-to-shape Diffusion Transformers (DiTs) that substantially reduces modelsize while preserving geometric fidelity. Despite remarkable progress in3D shape generation, large DiT-based models remain computationallyprohibitive in resource-constrained settings. Furthermore, it is difficult todirectly transfer existing diffusion model compression strategies developedfor different domains to 3D generation, and prior 3D efficiency approachesfocus primarily on inference speed rather than backbone compression. Toaddress this limitation, we build a geometry-aware compression frameworktailored to image-to-shape DiTs. Guided by the observation that 3DDiT layers exhibit non-uniform importance for geometry synthesis, weintroduce a vitality-guided framework integrating structured pruning,adaptive quantization, and targeted fine-tuning. Our method achieves upto 66% model-size reduction across state-of-the-art image-to-3D modelswhile maintaining synthesis fidelity comparable to full-sized counterparts.This highlights the potential of our framework as a plug-and-play solutionfor efficient 3D shape generation across diverse models.

Abstract:
005 Multimodal large language models (MLLMs) have recently 005006 shown promise for industrial anomaly understanding, where models are 006007 expected not only to determine whether an object is defective, but also to 007008 localize visual evidence and explain defect-related decisions. However, ex- 008009 isting MLLMs often rely on language priors or shortcut reasoning when 009010 facing fine-grained industrial defects, resulting in inconsistent outputs 010011 such as correct answers with invalid locations, hallucinated defect boxes 011012 on normal samples, or rationales that contradict the final prediction. To 012013 address these issues, we propose EGVLR, an evidence-grounded vision– 013014 language reinforcement framework for anomaly reasoning. EGVLR fol- 014015 lows a unified Evidence-Driven Diagnostic Protocol (EDDP), where each 015016 response explicitly contains visual evidence, diagnostic logic, spatial loca- 016017 tion, and the final answer. The framework consists of four stages: Progres- 017018 sive Visual-Evidential Fine-Tuning (PVE-FT), Knowledge-Grounded In- 018019 struction Tuning (KG-IT), Geometry-Semantic Decoupled Preference 019020 Optimization (GS-DPO), and Box-Guided Segmentation Rendering (BGSR). 020021 PVE-FT first aligns the model with localized visual evidence through 021022 synthetic anomalies, 3 × 3 grid grounding, local verification decoys, and 022023 null-hypothesis calibration. KG-IT then internalizes industrial-domain 023024 QA behavior while preserving the same output schema. GS-DPO fur- 024025 ther optimizes answer correctness, geometric localization, BGE-based 025026 rationale semantics, and answer–location–rationale consistency. Finally, 026027 BGSR converts predicted bounding boxes into dense masks using an off- 027028 the-shelf segmentation backend. Experiments on MMAD demonstrate 028029 that EGVLR improves evidence-grounded industrial anomaly reasoning, 029030 especially on defect localization, reasoning-dependent QA, and spatial 030031 false-positive control. 031

Abstract:
Knowledge-Intensive Visual Question Answering (KI-VQA)benchmarks evaluate Vision–Language Models (VLMs) as multimodalknowledge assistants by requiring external information beyond a pro-vided image to answer questions. KI-VQA involves multiple sub-problems—referring expression understanding, visual grounding, object recogni-tion, knowledge retrieval, and reasoning—yet existing benchmarks typ-ically report only end-task accuracy, obscuring where failures arise. Toanalyze the full KI-VQA pipeline, we introduce CRAG-MM-Diagnostics,a diagnostic benchmark with stage-wise data annotations that isolate1 language-based visual grounding, 2 object identification, and 3knowledge retrieval and reasoning. We evaluate fully parametric andretrieval-augmented VLMs, providing fine-grained analyses using newlycollected metadata, such as target ROIs, entity names, and visual com-plexity scores. Our results point to knowledge retrieval and reasoning asthe primary bottleneck, but also highlight issues in the other parts ofthe KI-VQA pipeline, such as the fact that VLMs struggle with targetobject identification or that image retrievers struggle to integrate textualcues. These findings expose fundamental limitations in current KI-VQAsystems and motivate stage-aware evaluation. We, lastly, leverage thesefindings to propose a grounded bimodal RAG pipeline that integrates avisual grounding module to crop targets before image retrieval, boostingGPT-5 and Qwen’s respective accuracies by 13.3 and 8.5 percentage points.

Abstract:
Workflow generation in visual creation systems such as Com-fyUI demands not only syntactic accuracy but also expert-level reason-ing over modular compositions. Existing large language model (LLM)approaches often treat this as a direct text-to-JSON generation task,struggling with structural brittleness and lacking the experiential knowl-edge required for effective design. We argue that successful workflowgeneration requires modeling knowledge itself, including its structure,hierarchy, and reasoning dynamics. To this end, we propose a knowledge-centric framework that learns to invert, inject, and infer with knowl-edge across multiple abstraction levels. We first perform knowledge in-version to distill hierarchical representations, ranging from full pseudo-codes and skeletons to high-level strategies, from large collections of real-world workflows. We then conduct knowledge injection through super-vised fine-tuning, teaching the model to reason from task descriptionsto strategies and from strategies to executable structures. During infer-ence, the model performs reversible reasoning to synthesize executableworkflows, augmented by self-refinement for structural coherence. Ex-tensive experiments demonstrate that our method produces workflowswith richer node diversity, more coherent structures, and higher execu-tion success rates than existing systems, establishing a new foundationfor knowledge-driven, agentic workflow generation.

Abstract:
Multimodal Large Language Models (MLLMs) remain vul-nerable to jailbreak attacks exploiting cross-modal interactions. Existingcross-modal attacks rely only on cross-entropy optimization, causingvisual and textual perturbations to act independently and push modelrepresentations toward misaligned vulnerability subregions, limiting theircombined effectiveness. To address this, we propose Co-Steer, a universalattack framework built on a key insight: visual and textual jailbreaks,despite appearing misaligned, are projections of a unified vulnerabilitydirection in the representation space. Co-Steer first extracts a universalsteering vector by aggregating representation shifts from successful vi-sual and textual jailbreaks via SVD. It then optimizes a single universalperturbation pair across queries using a steering alignment loss that coor-dinates both perturbations toward the extracted direction, transformingmisalignment into coordination. Analyzing such attack mechanisms helpsexpose gaps in current multimodal alignment and informs more robustdefense design. Experiments show our method achieves state-of-the-artattack success rates on MM-SafetyBench, HarmBench, and AdvBenchacross InternVL2-8B, Qwen2-VL-7B, and MiniGPT-4-13B, with notableimprovements in transferability to commercial models. Warning: Thispaper might contain harmful content.

Abstract:
We present HippoCamp, a new benchmark designed to eval-uate agents’ capabilities on multimodal file management. Unlike exist-ing agent benchmarks that focus on tasks like web interaction, tool-use, or software automation in generic settings, HippoCamp evaluatesagents in user-centric environments to model individual user profilesand search from massive personal files for context-aware reasoning. Ourbenchmark instantiates device-scale file systems over real-world profilesspanning diverse modalities, comprising 42.4 GB of data across over2K heterogeneous files. Building upon the raw files, we construct 581QA pairs to assess agents’ capabilities in search, evidence perception,and multi-step reasoning. To facilitate fine-grained analysis, we pro-vide 46.1K fine-grained trajectory annotations for step-wise failure di-agnosis. We evaluate a wide range of state-of-the-art multimodal large✉ Corresponding authors.language models (MLLMs) and agentic methods on HippoCamp. Ourcomprehensive experiments reveal a significant performance gap: eventhe most advanced commercial models achieve merely a 48.3% accu-racy in user profiling, struggling particularly with long-horizon retrievaland cross-modal reasoning within dense personal file systems. Further-more, our step-wise failure diagnosis identifies multimodal perceptionand evidence grounding as the primary bottlenecks. Ultimately, Hip-poCamp exposes the critical limitations of current agents in realistic,user-centric environments and provides a robust foundation for develop-ing next-generation personal AI assistants. Our dataset is publicly avail-able at: https://huggingface.co/datasets/MMMem-org/HippoCamp.

Abstract:
Video-text models adapted from image-text architectures(e.g., CLIP) frequently exhibit temporal blindness—the inability to per-ceive fundamental cues like order, direction, and motion dynamics. Stan-dard datasets mask this limitation by enabling models to exploit staticspatial shortcuts. To systematically evaluate this, we introduce XTE-Bench, a diagnostic probe revealing that even large-scale video-languagemodels struggle with basic temporal reasoning, indicating that param-eter scaling alone is insufficient to resolve this flaw. To address this,we propose Cross-Modal Temporal Edits (XTE), a self-supervisedframework that injects precise temporal supervision. By performing syn-chronized video-text transformations, XTE generates hard temporal neg-atives without manual annotation. We instantiate this with ViTAL-X,a lightweight model that equips frozen image-text backbones with tem-poral awareness while preserving their foundational spatial knowledge.Across six temporal benchmarks, ViTAL-X achieves state-of-the-art per-formance. Utilizing only 0.4B parameters and 1M training clips, ViTAL-X outperforms 7B-parameter models and surpasses baselines trained on600× more data. These results demonstrate that targeted, high-qualitytemporal alignment provides a highly efficient alternative to pure scaling.

Abstract:
Leveraging Multimodal Large Language Models (MLLMs) has become pivotal for advancing Universal Multimodal Embeddings (UME) in addressing diverse cross-modal tasks. Recent studies demonstrate that incorporating generative Chain-of-Thought (CoT) reasoning can substantially enhance task-specific representations compared to discriminative methods. However, the generated reasoning CoTs of existing generative embedding methods are limited to the textual analysis of queries and are irrelevant to the retrieval of the targets. To address these limitations, we propose a reasoning-driven UME framework that integrates Embedder-Guided Reinforcement Learning (EG-RL) to optimize the Reasoner to produce evidential Traceability CoT (T-CoT). Our key contributions are threefold: (1) We design an EG-RL framework where the Embedder provides explicit supervision to the Reasoner, ensuring the generated CoT traces are aligned with embedding tasks. (2) We introduce T-CoT, which extracts critical multimodal cues to focus on retrieval-relevant elements and provides multimodal inputs for the Embedder. (3) With limited computational resources, our framework outperforms the pioneering embedding model on both MMEB-V2 and UVRB benchmarks. The integration of multimodal evidence in structured reasoning, paired with retrieval-oriented alignment, effectively strengthens cross-modal semantic consistency and boosts the model’s fine-grained matching capability as well as its generalization across complex scenarios. Our work demonstrates that targeted reasoning optimization can significantly improve multimodal embedding quality, providing a practical and efficient solution for reasoning-driven UME development. Project page.

Abstract:
Cross-view geo-localization is challenging due to drastic viewpoint changes and large appearance discrepancies between street-level and satellite imagery. Although existing methods often use geometric warping to expose co-visible cues, such transformations rely on restrictive spatial assumptions and inevitably introduce severe visual distortions under view-dependent visibility, yielding noisy supervision and fragile correspondences. To overcome this, we propose a novel joint-view consensus-guided learning framework that entirely bypasses explicit geometric warping. Instead of forcing rigid spatial alignment, we dynamically mine and adaptively strengthen a semantic consensus directly within the feature space. Specifically, an auxiliary joint-view pathway during training enables direct cross-view interaction, allowing each view to selectively aggregate corroborative evidence into a unified consensus representation. To resolve feature heterogeneity among the singleand joint-view streams, we introduce global pattern probes acting as a semantic dictionary to project divergent modalities into a strictly aligned metric space. Guided by a consensus-mediated contrastive objective, single-view embeddings are explicitly pulled toward the joint-view anchor during training, distilling this consensus-mining capability into the single-view encoders for robust retrieval at inference. Extensive experiments demonstrate that our method achieves state-of-the-art performance across four standard benchmarks, underscoring the importance of discovering cross-view semantic consensus for reliable geo-localization.

Abstract:
Gaze target estimation aims to predict the semantic object an observer fixates upon within an image, a task deeply rooted in the object-oriented nature of human gaze. Observers tend to select a specific semantic entity as the attentional target, rather than responding randomly across arbitrary regions of the image. However, existing methods typically model this task as a direct mapping from global features to gaze heatmaps, essentially treating it as a pixel-level regression problem. This approach fails to explicitly represent the gazed object as a distinct entity, making it difficult to produce stable and semantically consistent predictions in complex scenes. To address this, we propose a two-stage gaze estimation framework guided by object semantics, reformulating gaze target estimation as a hierarchical reasoning process. Our method incorporates object-level representations during feature encoding to align image features with discrete semantic entities, then introduces multi-scale feature fusion and geometric constraints from head pose and gaze direction for fine-grained localization and object-level discrimination. Extensive experiments on GazeFollow, VideoAttentionTarget, ChildPlay, and GOO-Real demonstrate that our method achieves AUC of 0.961, 0.948, 0.987, and 0.977 respectively, delivering strong performance across all benchmarks while maintaining a compact parameter size of 7.1M.

Abstract:
Typical video object-centric learning (VOCL) approachesemploy slot-based frameworks that rely on reconstruction-driven en-coder–decoder architectures, where learning is mediated by two spatialmaps: attention maps from the encoder and object maps from the decoder.As these two distinct maps exhibit different properties, a recent densealignment strategy attempted to reconcile this discrepancy by enforcingagreement across all spatio-temporal patches via contrastive learning.However, this indiscriminate alignment inadvertently propagates the in-herent weaknesses of each module, such as noisy encoder predictionsand blurred decoder boundaries. Moreover, computing dense similaritiesacross all pairs incurs a computational cost quadratic in the total numberof spatio-temporal patches, severely limiting scalability. Motivated bythis, we propose Selective Synergistic Learning (SSync). Instead of ex-haustive patch-to-patch alignment, SSync prevents error propagation byselectively distilling only the most reliable cues: leveraging the encoderstrictly for boundary refinement and the decoder for interior denoising.This is realized via a pseudo-labeling with linear complexity, eliminatingthe need for quadratic spatial comparisons. Also, to prevent the rein-forcement of architectural biases like slot redundancy, we introduce atransitive pseudo-label merging that consolidates overlapping slots basedon spatio-temporal activation consistency. Extensive studies demonstratethat SSync improves decomposition quality and serves as a versatile,plug-and-play module while also exhibiting exceptional robustness to slotconfigurations. Code is available at github.com/wjun0830/SSync.

Abstract:
Accurate landslide segmentation from high-resolution remote sensing imagery remains challenging due to weak spectral contrast and strong dependence on terrain structure. Unlike appearance-defined objects, landslides follow geomorphological patterns governed by slope continuity and elevation discontinuities. However, most existing segmentation methods treat landslides as generic semantic regions and incorporate Digital Elevation Models (DEM) through simple feature fusion without explicitly enforcing terrain-consistent spatial reasoning. To address this limitation, we reformulate landslide segmentation as a terrainconditioned structured prediction problem and propose TerrainGraphNet, a terrain-aware graph reasoning framework that embeds geomorphological constraints into representation learning and spatial propagation. The proposed method introduces terrain-modulated feature interaction, where elevation structure adaptively regulates visual representations, and terrain-aware graph construction, where spatial connectivity is defined jointly by feature similarity and slope continuity. This formulation can be interpreted as learning a terrain-weighted smoothness prior that encourages predictions to respect geomorphological coherence. Extensive experiments on three benchmark datasets demonstrate consistent improvements over strong CNN and transformer baselines. In addition to higher IoU and F1 scores, TerrainGraphNet significantly improves boundary accuracy and topological consistency, highlighting the importance of terrain-constrained reasoning for reliable landslide delineation.

Abstract:
Diffusion models are primarily trained for image synthesis,yet their denoising trajectories encode rich, spatially aligned visual pri-ors. In this paper, we demonstrate that these priors can be utilized fortext-conditioned semantic and open-vocabulary segmentation, and thisapproach can be generalized to various downstream tasks to make ageneral-purpose diffusion segmentation framework. Concretely, we intro-duce DiGSeg (Diffusion Models as a Generalist Segmentation Learner),which repurposes a pretrained diffusion model into a unified segmenta-tion framework. Our approach encodes the input image and ground-truthmask into the latent space and concatenates them as conditioning sig-nals for the diffusion U-Net. A parallel CLIP-aligned text pathway injectslanguage features across multiple scales, enabling the model to align tex-tual queries with evolving visual representations. This design transformsan off-the-shelf diffusion backbone into a universal interface that pro-duces structured segmentation masks conditioned on both appearanceand arbitrary text prompts. Extensive experiments demonstrate state-of-the-art performance on standard semantic segmentation benchmarks, aswell as strong open-vocabulary generalization and cross-domaintransfer to medical, remote sensing, and agricultural scenarios—withoutdomain-specific architectural customization. These results indicate thatmodern diffusion backbones, can serve as generalist segmentation learn-ers rather than pure generators, narrowing the gap between visual gen-eration and visual understanding.

Abstract:
Continual learning for text-to-image retrieval is essential foradapting retrieval systems to evolving user interests and emerging con-cepts. While existing methods mitigate forgetting through image-textsimilarity distillation, they overlook a critical real-world scenario: newtextual queries often describe different semantic facets of images frompreviously seen visual domains, a phenomenon we term “unbalancedcross-modal shift”. This shift arises from the inherent polysemy of vi-sual content and is strongly associated with severe forgetting. To studythis phenomenon, we introduce two diagnostic metrics: image domainoverlap measured via principal subspace similarity, and textual querydivergence quantified through image-conditioned query probability. Ourempirical analysis shows that unbalanced cross-modal shift significantlyamplifies forgetting, with existing methods struggling to retain imagesemantics under such conditions. To address this challenge, we proposeCaPCL (Caption-Preserved Continual Learning), which regularizes mod-els to maintain consistent caption generation for previously learned con-tent, thereby preserving multi-faceted image semantics. By leveragingauxiliary captions as semantic anchors, CaPCL delivers more robusttraining signals than contrastive similarity alone. We construct a bench-mark integrating five heterogeneous datasets to enable comprehensiveevaluation. Extensive experiments demonstrate that CaPCL consistentlyoutperforms state-of-the-art methods in both knowledge retention andadaptation.

Abstract:
The incorporation of additional modalities into action recog-nition models increases their performance across a wide range of settings.However, how this additional information can contribute to making themodels more robust remains underexplored, particularly for the case ofmulti-modal out-of-distribution (OOD) detection. While methods existthat regularize the multi-modal training process with OOD detection inmind, they still apply off-the-shelf OOD detectors designed for the uni-modal case during inference, discarding important information. Basedon an interesting relationship we find between the multi-modal and uni-modal predictions, we propose to use this signal to build a post-hocdetector explicitly designed for the multi-modal scenario. We combinethis new source of information with a feature-space score, which detectsoff-manifold samples in the multi-modal space, and normalize them bythe multi-modal logits. In doing so, the proposed hybrid detector is com-patible with existing training-time approaches and consistently improvesperformance. Experiments on a wide range of established datasets fromthe MultiOOD benchmark show that, on average, our approach outper-forms the state of the art. Our results show the importance of explicitlyconsidering the different modalities at inference time for multi-modalOOD detection.

Abstract:
Prompt-conditioned foundation segmenters rely on explicit spatial prompts, such as points, boxes, or masks, to guide mask decoding. In prompt-absent deployment, however, no external/user prompts or detector-generated prompts are available at test time, creating a mismatch between the expected prompt-conditioned interface and automatic inference. We propose IP-SAM, a prompt-space adaptation framework that restores prompt-conditioned decoding without external prompts. Instead of bypassing SAM2’s prompt interface through direct feature-space adaptation, IP-SAM synthesizes complementary intrinsic foreground/background prompts and routes their dense logits through SAM2’s frozen prompt encoder, translating task-specific spatial cues into the native prompt embedding space. To reduce background leakage under severe camouflage, Prompt-Space Gating uses the intrinsic background prompt as an asymmetric suppressive constraint before decoding. Under a deterministic noexternal-prompt protocol, IP-SAM achieves state-of-the-art performance across four COD benchmarks, including MAE 0.017 on COD10K, with only 21.26M trainable parameters from SPG, PSG, a SAM2-initialized downstream mask decoder, and image-encoder LoRA, while keeping the prompt encoder frozen. Medical polyp experiments in the main paper and supplementary SOD results further show that the same prompt-space adaptation route transfers within foreground–background segmentation.

Abstract:
In-context segmentation (ICS) requires a model to segmenttarget regions in a query image using only a few reference images andtheir corresponding masks, without updating any parameters. Despiterecent progress, prior ICS studies have largely overlooked a critical as-pect: system robustness, i.e., whether the model can produce stable seg-mentation results for the same query under different references. In thiswork, we revisit ICS from the robustness perspective and introduce anovel paradigm, Concept-Guided In-Context Segmentation (CG-ICS),which performs segmentation by extracting high-level semantic conceptsfrom references rather than relying solely on low-level visual matching.Specifically, CG-ICS introduces a concept reasoning module that usesan MLLM to propose candidates and a SAM3-driven scoring functionwith tree-search refinement to select reliable textual concepts, togetherwith a parallel visual exemplar route that provides query-side spatialgrounding via a simple context construction. Both the textual conceptand the visual exemplar are then used to activate the segmentation ca-pability of a frozen SAM3 backbone. Extensive experiments on standardICS benchmarks demonstrate that CG-ICS not only achieves state-of-the-art accuracy but also substantially improves robustness, yielding amore reliable ICS system with significantly reduced variance across di-verse reference choices.

Abstract:
Cross-spectral image synthesis from visible to infrared facessigni x001C cant challenges due to the severe scarcity of infrared data in criti-cal scenarios. Existing methods primarily achieve style transfer throughpixel-level mappings, which often su x001B er from semantic misalignment andunrealistic spectral characteristics. To address this problem, we rethinkcross-spectral generation as a decoupling and reconstruction process ofshared-speci x001C c features between modalities and propose SHASP, a novelgenerative network. Speci x001C cally, an encoding-decoupling module is de-signed at the front of the generator, which consists of a structure contentencoder and a spectral feature encoder to decouple the shared-speci x001C cfeatures. A decoding-reconstruction module is designed at the backendof the generator to perform feature combination and reconstruction. Wefurther impose semantic consistency constraints on shared features toensure precise cross-modal alignment. Extensive experiments on aerial,driving, and monitoring scenarios demonstrate that our method outper-forms state-of-the-art approaches. This work reveals that there are com-mon representations that can be mined at the semantic structure levelin cross-spectral data, and modality gaps can be accurately modeled viaa decoupling-reconstruction mechanism.

Abstract:
Taxonomic classification of ecological families, genera, andspecies underpins biodiversity monitoring and conservation. Existing com-puter vision methods typically address fine-grained recognition and long-tailed learning in isolation. However, additional challenges such as spa-tiotemporal domain shift, hierarchical taxonomic structure, and previ-ously unseen taxa often co-occur in real-world deployment, leading tobrittle performance under open-world conditions. We propose TaxoNet,an embedding learning framework with a theoretically grounded dual-margin objective that reshapes class decision boundaries under classimbalance to improve fine-grained discrimination while strengtheningrare-class representation geometry. We evaluate TaxoNet in open-worldsettings that capture co-occurring recognition challenges. Leveraging di-verse plant datasets, including Google Auto-Arborist (urban tree im-agery), iNaturalist (Plantae observations across heterogeneous ecosys-tems), and NAFlora-Mini (herbarium collections), we demonstrate thatTaxoNet consistently outperforms strong baselines, including multimodalfoundation models.

Abstract:
Open-Vocabulary object Detection (OVD) typically relies onVision–Language Models (VLMs) to associate visual regions with arbi-trary textual concepts. To compensate for the limited localization capa-bility of VLMs, existing approaches often incorporate large pre-trainedvisual features that inherently encode object-centric spatial cues. How-ever, these features are commonly introduced through direct fusion orshallow adaptation, which fails to explicitly align semantic representa-tions with object-level spatial information, resulting in imprecise local-ization. In this paper, we propose a novel framework for explicit seman-tic–spatial alignment in open-vocabulary object detection. Rather thandirectly merging pre-trained object-centric visual features, our methodprogressively aligns semantic representations with object-level spatialcues through a staged alignment process. Specifically, we introduce alightweight spatial adapter that spatially recalibrates auxiliary object-centric visual features to suppress background responses and emphasizesalient object regions. Building upon this, we design a frequency-awarefusion mechanism that decomposes the adapted object-centric visual fea-tures into spectral components and adaptively injects object-sensitivespatial cues into semantic features at multiple levels. This targeted fu-sion strategy enables effective semantic alignment while preserving theoriginal semantic embedding space. Extensive experiments on the OV-COCO and OV-LVIS benchmarks demonstrate the effectiveness of ourapproach against state-of-the-art methods.

Abstract:
Multimodal object tracking has received extensive attentiondue to its strong robustness and complementary collaboration. However,most existing methods only evaluate the cross-modal fusion at specificmulti-modal layers, and overlook the interaction among different fea-ture layers from single modal, which limits the utilization and propa-gation of cross-modal complementary information and consequently de-grades tracking performance. To alleviate this issue, we propose a pro-gressive multimodal tracker based on State Space Model (SSM) namedPSMTrack, which consists of three key modules. First, Difference-guidedBidirectional State-space Mamba Enhancement (DBME) is developedto leverage difference-aware cues and bidirectional SSM modeling toprogressively enhance modality representations for subsequent tracking,while suppressing background and similar distractions. Second, we adoptan expert routing mechanism to adaptive select a subset of layers andperform cross-layer interaction to alleviate the information loss causedby relying solely on the last layer. Finally, we design a Hybrid SpiralMamba Fusion (HSMF) module that performs dynamic spatial modelingwith spiral Mamba from both local and global scales for final cross-modalfusion, thereby capturing long-range dependencies with near-linear com-plexity and improving bounding-box regression stability. Extensive ex-periments on multiple mainstream RGB-X benchmarks validate that ourmodel consistently improves both accuracy and robustness.

Abstract:
Unpaired image dehazing models cast the restoration process as unsupervised domain translation, effectively avoiding the rigid physical assumptions of traditional methods. However, these methods frequently suffer from severe semantic hallucinations and content distortion. This stems from the significant semantic gap between unpaired clean and hazy domains, forcing the discriminator to overfit to high-level semantics rather than low-level clarity. In this paper, we propose Degradation-Agnostic Clarity Learning (DCL), a novel paradigm that steers the network to learn genuine low-level clarity. We introduce two core insights: (1) Explicitly injecting low-level degradations provides strong surface statistical shortcuts that effectively suppress high-level semantic interference; (2) Clarity is a relative concept, and various degraded variants of a clean image share a common intrinsic clarity direction. To realize this, we innovatively formulate discriminator training as a Multi-Task Learning (MTL) problem. First, we employ Adversarial AutoAugment to dynamically mine unlearned, diverse degraded clean samples by maximizing the discriminator’s loss. Second, we utilize Pareto Optimization to extract the shared common descent direction across degraded variants, thereby preventing the model from overfitting to any specific degradation type. Furthermore, we extend our core hypothesis to an Enhance-to-Resist self-supervised texture enhancement paradigm that benefits both dehazing and lowlight enhancement tasks. Extensive experiments on real-world datasets demonstrate that DCL significantly suppresses semantic hallucinations and achieves state-of-the-art performance in unpaired dehazing.

Abstract:
Scene Change Detection (SCD) evaluates changes using pre-defined query-reference (i.e., present-past) image pairs. However, thisformulation overlooks a critical dependency: the corresponding query-reference pair is assumed to be prepared in advance. In real-world appli-cations, such as mobile robots, future query views cannot be known inadvance, and thus their corresponding reference images cannot be pre-defined. To remove this dependency and push change detection towardmore practical applications, we introduce Environmental Change Detec-tion (ECD). A key aspect of ECD is to avoid unrealistically predefinedand aligned query-reference pairs and instead retrieve environmental cuesfrom an uncurated image database of reference scenes. To tackle thisnew challenging task, we additionally introduce an initial solution thatenables change detection under unknown and imperfect query-referenceconditions. The main idea of our solution is to retrieve multiple referencecandidates and aggregate semantically rich representations for changedetection. We further construct ECD benchmark sets by reformulatingthree standard change detection datasets. Extensive experimental resultsdemonstrate the efficacy of our solution in both ECD and SCD.

Abstract:
Visible-infrared person re-identification aims to match pedes-trian images of the same identity captured by visible and infrared cam-eras, and is hindered by severe domain gaps due to the absence of colorin the infrared modality. Some methods employ text to supplement colorcues in infrared modality. However, they rely on global textual guidance,which hinders fine-grained alignment between textual color informationand local infrared semantics. To this end, this paper proposes a GranularSemantic Cognition (GSC) method, which leverages cross-modal sharedsemantics to facilitate the transfer of color features. Specifically, a Hierar-chical Refinement Module (HRM) progressively refines shared semanticprototypes via multi-stage interactions with pixel-wise features, yieldingfine-grained semantics. Based on these refined semantics, a Semantics-guided Adjustment Module (SAM) generates semantic-conditioned fu-sion weights to selectively aggregate textual color information in se-mantically relevant regions. In addition, a fusion-weighted identity con-trastive loss is proposed to alleviate semantic ambiguity across identi-ties by regularizing the semantics-guided fusion process. Extensive ex-perimental results conducted on the SYSU-MM01, RegDB and LLCMdatasets demonstrate the favorable performance.

Abstract:
We present a deployed system for on-orbit wildfire detec-tion aboard OroraTech’s thermal infrared Constellation Plane 1 (OTC-P1), operating under a demanding set of joint constraints: sub-megabytemodel footprint, sub-150 ms per-batch TensorRT FP16 inference on anNVIDIA Jetson Xavier NX, and an end-to-end alert pipeline targetingunder 10 minutes from satellite overpass to fire event communication.The system operates on uncalibrated mid-wave infrared (MWIR) single-band imagery at 200 m ground sampling distance, where fires frequentlyappear as sub-pixel or single-pixel thermal anomalies under extremeclass imbalance—challenges not addressed by the contextual thermal-thresholding pipelines (MODIS, VIIRS) that currently dominate opera-tional fire monitoring.We present an empirical study of lightweight dense representation learn-ing for this regime using a proprietary nine-satellite MWIR dataset. Wecompare dense masked autoencoding (DenseMAE) and a hybrid Dense-MAE+EMA (exponential moving average) distillation variant, and eval-uate representations via linear probing and full-distribution pixel-levelaverage precision (AP) under extreme class imbalance. DenseMAE pre-training enables compact downstream models that sit on the latency–accuracy Pareto frontier: our fastest SSL-pretrained model achieves 0.640test AP and 0.69 event-level Fire-F1 with 65.34 ms TensorRT FP16 la-tency per batch (224 × 224, B=8) and a 0.52 MB engine, achieved with-out pruning or compression. The best-performing configuration reaches0.699 test AP and 0.744 Fire-F1 while remaining below 1 MB. Bothoutperform a supervised baseline (up to 0.650 AP) under comparableconstraints.

Abstract:
Appearance-based gaze estimation typically relies on facenormalization to reduce appearance variability, but this requires costlyand error-prone landmark detection and head pose estimation. Whilecrop-based alternatives have been explored, their geometric propertiesand performance trade-offs relative to normalization remain underex-plored. In this work, we provide the first systematic comparison of nor-malization versus crop-based gaze estimation. To enable fair compari-son, we formalize the crop-based approach through Constrained Rota-tion Optimization (CROp), making its geometric transformation explicitand comparable to normalization. We further adopt multi-task learningto recover head pose information lost in cropping. Through extensiveexperiments across various datasets, head pose distributions, and pre-processing conditions, we identify the conditions under which each ap-proach excels. CROp shows advantages under extreme poses and noisydetection, while normalization benefits from landmark-based refinementin moderate conditions. Our analysis provides practical guidelines forchoosing preprocessing strategies in real-world gaze estimation systems.

Abstract:
Video anomaly detection is critical for public safety and security, yet remains highly challenging despite extensive research due to large variations in appearance, viewpoint, and scene dynamics. Among existing approaches, human pose-based methods have emerged as a major line of research, showing strong performance since many anomalies in public datasets involve humans and pose representations are robust to appearance changes while providing compact motion descriptions. However, these methods often overlook bounding-box trajectories, although such information is inherently available in pose-based pipelines. In this paper, we explicitly leverage these trajectories as a primary anomaly cue. We present TrajVAD, a framework that models multi-class boundingbox trajectories using normalizing flows to learn normal kinematic patterns. Its trajectory-only variant, TrajVAD-T, eliminates pose estimation, reaches 87.7 AP on ShanghaiTech, and achieves the best results on MSAD among compared methods. TrajVAD-P adds a reliability-gated pose branch and improves performance to 88.6 AUROC and 90.9 AP on ShanghaiTech, establishing bounding-box trajectories as an effective modality for video anomaly detection.

Abstract:
The performance of visual anomaly inspection in industrial quality control is often constrained by the scarcity of real anomalous samples. Consequently, anomaly synthesis techniques have been developed to enlarge training sets and enhance downstream inspection. However, existing methods either suffer from poor integration caused by inpainting or fail to provide accurate masks. To address these limitations, we propose GroundingAnomaly, a novel few-shot anomaly image generation framework. Our framework introduces a Spatial Conditioning Module that leverages per-pixel semantic maps to enable precise spatial control over the synthesized anomalies. Furthermore, a Gated Self-Attention Module is designed to inject conditioning tokens into a frozen U-Net via gated attention layers. This carefully preserves pretrained priors while ensuring stable few-shot adaptation. Extensive evaluations on the MVTec AD and VisA datasets demonstrate that GroundingAnomaly generates high-quality anomalies and achieves state-of-the-art performance across multiple downstream tasks, including anomaly detection, segmentation, and instance-level detection. Code will be made publicly available from this URL.

Abstract:
Implicit Neural Representations (INRs) encode signals as the weights of a coordinate-based neural network and have recently been proposed as an alternative domain for downstream learning. While promising, classification directly in weight space remains challenging due to the high dimensionality and complex structure of INR parameters. Furthermore, the way discriminative information is distributed across INR weights remains poorly understood. We propose a hierarchical Mixture-of-Experts (HMoE) Transformer that processes INR weights using conditional computation aligned with the structure of the underlying implicit network. Coupled with a meta-learning framework that shapes INR parameters for downstream tasks, our model achieves state-of-the-art accuracy across standard benchmarks, ranging from low-resolution datasets to high-resolution ImageNet-1K. To gain insight into how INRs encode discriminative information, we develop weight-space attribution and pruning methods that identify parameters most relevant for classification. These analyses reveal how class-specific structure emerges within INR layers and support the suitability of MoE architectures for weight-space learning. Our approach advances both the performance and interpretability of weight-space classifiers.

Abstract:
Text-to-image (T2I) models face significant safety risks fromadversarial induction, yet current concept erasure methods often causecollateral damage to benign attributes when suppressing selected neu-rons entirely. This occurs because sensitive and benign semantics ex-hibit non-orthogonal superposition, sharing activation subspaces wheretheir respective vectors are inherently entangled. To address this issue,we propose OrthoEraser, which leverages sparse autoencoders (SAE) toachieve high-resolution feature disentanglement and subsequently rede-fines erasure as an analytical orthogonalization projection that preservesthe benign manifold’s invariance. OrthoEraser first employs SAE to de-compose dense activations and segregate sensitive neurons. It then usescoupled neuron detection to identify non-sensitive features vulnerableto intervention. The key novelty lies in an analytical gradient orthog-onalization strategy that projects erasure vectors onto the null spaceof the coupled neurons. This orthogonally decouples the sensitive con-cepts from the identified critical benign subspace, effectively preservingnon-sensitive semantics. Experimental results on safety demonstrate thatOrthoEraser achieves high erasure precision, effectively removing harm-ful content while preserving the integrity of the generative manifold, andsignificantly outperforming SOTA baselines. WARNING: This papercontains results of unsafe models.

Abstract:
Vector Neuron Networks (VNNs) are widely used in 3D tasksfor their data efficiency and strong generalization from equivariance.However, their rigid equivariance constraints hinder handling symmetry-breaking, where low-symmetry outputs must be inferred from highlysymmetric inputs. In this paper, we reveal the representation collapse is-sue in VNNs and propose Ada-VNNs, a vector-neuron architecture thatcan adaptively relax equivariance constraints according to the symmetrylevel of the data. Ada-VNNs introduce an Adaptive Gating Unit (AGU)to control equivariance relaxation in linear layers, and design a resid-ual pathway that transforms hard structural constraints into adjustablesoft priors. We further provide a theoretical characterization that linksthe learned equivariance behavior to intrinsic symmetry breaking in thedata. Experiments on pose estimation across 26 categories with differ-ent degrees of symmetry breaking demonstrate that Ada-VNNs enabledata-driven adaptive equivariance adjustment, alleviate representationcollapse, and significantly improve symmetry-breaking capability overVNN and VN-Transformer, achieving higher performance with almostno additional overhead.

Abstract:
Retrieval-based image geolocalization has emerged as a powerful technique for de-termining the location of a query image by matching it against a large, geotagged database.The success of deep learning based approaches has raised concerns regarding privacy andsafety. A way to protect users from geolocalization is to design adversarial attacks for suchmethods. In this paper, we introduce RoadTrip Attack (RTA), a novel and highly effectivetargeted adversarial attack for geolocalization. RTA conceptualizes the adversarial process asfinding an optimal “distractor” journey to a specific, attacker-chosen location. It employs abeam search algorithm to iteratively construct a sequence of incorrect geographic locationsthat form a path to the target. At each step, the attack generates subtle perturbations tothe query image, guiding the geolocalization model toward the next location in this decep-tive path. We show that our method is also strong in black-box settings, obtaining highlytransferable attacks with less perceptible image artifacts.

Abstract:
Analytic Continual Learning (ACL) offers a computationallyefficient alternative to gradient-based approaches. Recent ACL methodsare based on Recursive Least Squares (RLS) and have achieved the state-of-the-art results compared to other alternatives. However, they faltersignificantly in Class-Incremental Learning scenarios characterized byLong-Tailed distributions. While the ill-conditioning of the autocorrela-tion (Gram) matrix is a known limitation of RLS, we demonstrate thatclass imbalance exacerbates this issue into a distinct spectral pathology:"tail" classes suffer from severe spectral collapse, rendering their sub-spaces numerically indistinguishable from noise. Standard Ridge Regres-sion (L2 ) fails to address this effectively as it applies isotropic regular-ization - a uniform penalty that is insufficient to stabilize the tail with-out over-shrinking the head. To address this, we propose Geometry-Spectral Rectification (GSR), a theoretically grounded frameworkthat treats long-tailed learning as a spectral regularization problem. Un-like standard isotropic regularization (Ridge) which uniformly penalizesall eigenvalues, GSR acts as an anisotropic spectral filter, selectively in-flating the collapsed eigenvalues of tail classes. We construct a structured,data-dependent spectral perturbation matrix ∆ that selectively inflatescollapsed tail eigen-directions of the Gram matrix. Theoretical analysisproves that GSR guarantees an improved stable rank for the Gram ma-trix, ensuring numerical stability. Extensive experiments show that GSRestablishes a new state-of-the-art for analytic CIL, offering a superiortrade-off between computational efficiency and robust generalization inlong-tailed settings.

Abstract:
Recovering dynamic environment maps from a single in-the-wild video is crucial for photorealistic rendering, yet remains a challenge.Recent video generation models can produce photorealistic scenes withcomplex lighting, possessing an inherent understanding of lighting. Inthis paper, we introduce V-LITE (Video generation models are inherentlighting estimators), a framework that unlocks this internal knowledge byreframing lighting estimation as a guided video inpainting task. Inspiredby visual effects (VFX) industry practices, we insert a synthetic chromeball into the scene to compel the model to generate physically plausiblereflections from the surrounding spatio-temporal context. To bridge thegap from LDR-native models to the HDR domain, we design an HDR-aware VAE and employ an efficient LoRA-based fine-tuning strategy. Wethen construct a mixed dataset comprising high-fidelity HDR images toprovide realistic HDR priors, and in-the-wild HDR videos to provide dy-namic spatio-temporal context. Extensive experiments demonstrate thatV-LITE produces temporally coherent HDR environment maps, reveal-ing that modern video diffusion models are not merely synthesizers butalso powerful, inherently capable estimators of physical scene lighting.

Abstract:
This paper presents SOMO, a scalable framework for crossembodiment grasp synthesis that transfers to novel robot hands using only their hand description (i.e., a Unified Robot Description Format (URDF) file), without requiring any hand–object interaction annotations. Unlike prior approaches that rely on hand-specific models or annotated grasp data for each embodiment, SOMO introduces a shared Morphology-Prior Diffusion model applicable across heterogeneous hands through three key designs. First, grasping is formulated as a 3D assembly problem by predicting per-link SE(3) poses, enabling geometry-driven generation independent of hand-specific kinematics, with feasibility enforced through a post joint optimization stage. Second, a classifier-free training strategy learns a morphology prior from physically valid hand configurations generated through forward-kinematic exploration without object–grasp annotations, enabling grasp synthesis for unseen robot hands given only their URDF files. Third, a 3D shape-aware VAE encodes link and object geometry into a shared embedding, enabling consistent reasoning about hand–object complementarity across embodiments. Experiments show that SOMO achieves state-of-the-art grasp synthesis across six robot hands and demonstrates strong annotation-free generalization to previously unseen hands. Code and models will be released soon.

Abstract:
Retargeting human–object interaction demonstrations tophysics-based simulation requires reproducing not only body motion butalso the object motion and contacts that make manipulation succeed.However, position-only hand trajectories do not specify the contact forcesneeded to manipulate objects, and directly tracking them can overcon-strain contact-rich finger behavior. We introduce WristMimic, a wrist-guided whole-body control framework that explicitly separates contact-free body motion from contact-rich hand manipulation. The contact-freebody and wrist are guided by kinematic pose targets, whereas the fin-gers are not directly supervised by human hand pose. Instead, they learngrasping and manipulation behaviors from object tracking and contactoutcomes. Our key insight is that the wrist is the natural gate betweenthese two regimes. It is largely free from contact and can be tracked kine-matically, yet it determines the global hand configuration and places thefingers within reachable grasp affordances. To ensure reliable wrist place-ment during interaction, we introduce wrist-specific reset constraintsand reward prioritization. Experiments show that WristMimic matchesor surpasses methods using full finger pose supervision while enablingfinger-agnostic retargeting across diverse hand embodiments.

Abstract:
Camera motion control is essential for directing viewpointchanges in generative systems. However, existing methods typically condi-tion the generation process on a single specific modality, such as explicitpose trajectories or reference videos, limiting their ability to support het-erogeneous user inputs. To address this limitation, we present TriMotion,a modality-agnostic framework for camera-controlled video generationthat maps video, pose, and text inputs, describing the same cameratrajectory into a shared motion embedding space. Learning such a spacerequires synchronized supervision across modalities. Therefore, we buildthe Motion Triplet Dataset by extending a Multi-Cam Video Dataset withgeometry-grounded motion descriptions derived from camera extrinsics.We further introduce a latent motion consistency objective that lever-ages the motion embedding space to encourage the generated video tofollow the target camera trajectory directly in latent space, avoiding thecost of pixel-space decoding. Extensive experiments show that TriMotiongenerates high-quality videos that accurately follow the target cameratrajectories across all three modalities. Beyond standard generation, theshared motion embedding space also enables flexible applications such assequential motion composition and cross-modal motion interpolation.

Abstract:
Streaming video editing has made rapid progress, yet practi-cal deployment is still limited by two core issues: maintaining stable back-grounds and non-edited regions over time, and achieving the low latencyrequired for real-time interactive scenarios. Meanwhile, recent streamingvideo generation methods are mostly developed for synthesis and cannotbe directly applied to editing due to the strict preservation requirementand region-specific control. In this work, we present a novel streamingvideo editing framework that performs causal, frame-by-frame editingwith strong content preservation and real-time responsiveness. Our keydesign is a three-stage distillation pipeline that progressively transfersediting capability from a powerful bidirectional foundation model to anefficient unidirectional streaming editor, enabling stable long-horizon ed-its without sacrificing visual fidelity. To further support real-time deploy-ment, we introduce an AR-oriented mask cache that reuses region-relatedcomputation across frames, substantially reducing redundant processingand accelerating inference. Finally, we establish a dedicated benchmarkfor streaming video editing. Extensive evaluations demonstrate that ourmethod achieves state-of-the-art visual quality among streaming base-lines while drastically boosting inference speed to 12.66 FPS, making itsuitable for interactive and augmented reality applications.

Abstract:
We present BlenderFusion, a generative visual compositing framework that recomposes objects, camera, and background to synthesize new scenes. It follows a layering-editing-compositing pipeline that (i) segments and lifts up visual inputs into editable 3D entities (layering), (ii) edits them in Blender with 3D-grounded control (editing), and (iii) fuses them into a coherent scene using a generative compositor (compositing). The generative compositor extends a pre-trained diffusion model to a novel dual-stream architecture, processing both the original (source) and edited (target) scenes in parallel, and is fine-tuned on video frames with two important training strategies: (i) source masking, enabling flexible image modifications like background replacement or object replacement; (ii) simulated object jittering, facilitating robust disentangled control over objects and the camera. Extensive experiments on synthetic and real-world datasets show that BlenderFusion significantly outperforms prior methods in precise 3D-aware control and complex compositional scene editing. The framework also generalizes to unseen data and fine-grained editing operations beyond the training distribution. See the project page for demos and more results: blenderfusion.github.io.

Abstract:
Interactive traffic simulation is a vital world model for au-tonomous driving. A central challenge in long-horizon simulation is mod-eling sustained multi-agent interactions, which is further exacerbatedby dynamic token cardinality as agents continuously enter and exit thescene. In this work, we propose that the solution lies in the synergybetween the architectural inductive biases and statistical priors of large-scale sequence models, e.g., Large Language Models (LLMs). Our prob-ing experiments reveal that the transferability of attention mechanismsand the distributional consistency between motion tokens and naturallanguage enable small-scale, heavily frozen LLMs to rapidly adapt to traf-fic modeling. Building on this insight, we introduce RosettaSim, a unifiedframework that projects scene topology, agent states, and spawning in-tents into a structured autoregressive stream with variable length, achiev-ing both strong short-term accuracy and stable long-horizon simulationfidelity. Furthermore, evaluating extended rollouts presents yet anotherhurdle, as one-to-one agent correspondence inevitably fades over time.To address this, we introduce Retrieval-based Traffic Evaluation (RTE),which retrieves semantically similar real-world scenarios as context-awarereference anchors. Experiments on the Waymo Open Sim Agent Chal-lenge (WOSAC) demonstrate that RosettaSim achieves state-of-the-artperformance in both short- and long-term simulation. Furthermore, RTEexhibits a stronger correlation with standard metrics (r = 0.83) than ex-isting approaches (r = 0.74), indicating improved alignment with long-horizon simulation fidelity.

Abstract:
Driven by next-token prediction, NLP shifted from taskspecific models into powerful generalist foundation models. What, then, is the equivalent catalyst needed to achieve a general-purpose model in computer vision? In this paper, we contend that large-scale text-tovideo generation serves as a strong pre-training paradigm for computer vision, providing the necessary spatiotemporal priors, vision-language alignment, and scalability required for general visual intelligence. We introduce GenCeption, which leverages a pre-trained video generative diffusion backbone to define a feed-forward perception model, capable of performing various vision tasks steered by text instructions. Empirical results demonstrate that GenCeption achieves state-of-the-art performance across a diverse suite of tasks, including depth, surface normal, and camera pose estimation, expression-referring segmentation, and 3D keypoint prediction, often matching or surpassing specialized models (e.g., DepthAnything V3, SegmentAnything V3). Furthermore, the video generative pretrained backbone outperforms alternative pretraining paradigms (e.g., V-JEPA, and Video MAE) under comparable settings. Importantly, GenCeption exhibits preliminary data and model scaling properties along with exceptional data efficiency where it achieves comparable performance with leading models like D4RT and VGGT-Ω with 7× to 500× less training data. Finally, GenCeption also exhibits intriguing emergent behaviors: a model trained exclusively on synthetic human videos generalizes to real-world footage and out-of-distribution object categories (e.g., animals and robots). These findings suggest that video generation is not merely a synthesis tool, but a foundational path toward generalist vision intelligence for the physical world.

Abstract:
Understanding human emotions in spoken conversations is a key challenge in affective computing, with applications in empathetic AI, human computer interaction, and mental health monitoring. However, existing datasets vary in scale, emotion distribution, modality alignment, and data partitioning strategies, which can influence reliable crossdataset generalization and minority-emotion modeling. We introduce SpEmoC a Speaking segment Emotion for Conversations comprising 306,544 raw clips from 3,100 English language movies and TV series. From these, 30,000 high quality, class balanced clips are curated, featuring synchronized visual, audio, and textual modalities annotated for seven emotions through a hybrid pipeline that integrates pretrained models with human validation. SpEmoC uses strict movie and series-level splits to prevent content overlap between split sets, allowing more reliable evaluation of model generalization. The dataset also maintains a nearbalanced distribution across seven emotions, including minority classes such as Fear and Disgust, which supports more balanced learning across categories. Extensive experiments, including in-domain benchmarking, cross-dataset transfer, low-data training, class-imbalance analysis, and modality transfer show that balanced data and careful splitting lead to more stable performance across emotions when models are evaluated on other datasets. These results highlight the importance of dataset design for robust and transferable multimodal emotion recognition.

Abstract:
We propose CAT, a trajectory-level continuous action representation framework for robotic manipulation. Existing visuomotor systems often entangle action representation with control frequency or rely on fixed temporal parameterizations. This leads to representational redundancy at high sampling rates and limits the modeling of critical motion. CAT instead encodes action trajectories within a fixed realtime interval into a set of continuous latent tokens. To ensure temporal consistency across varying control frequencies, we further incorporate a frequency-aware positional encoding that establishs a shared temporal coordinate system. Trajectory-level regularization further stabilizes the latent representation. This approach prevents representation growth with timestep density and avoids reliance on predefined temporal parameterizations. Extensive system-level evaluations on LIBERO, MimicGen, and real-world long-horizon manipulation tasks demonstrate that CATbased policies consistently outperform both competitive VQ-based and continuous visuomotor baselines under matched training settings. Across various model backbones and control frequencies, CAT consistently improves success rates. These results highlight the advantages of trajectorylevel continuous action modeling for scalable robotic manipulation across varying control rates.

Abstract:
Recent diffusion models enable high-quality video generation, but suffer from slow runtimes. The large transformer-based backbones used in these models are bottlenecked by spatiotemporal attention. In this paper, we identify that a significant fraction of token-totoken connections consistently yield negligible scores across various inputs, and their patterns often repeat across queries. Thus, the attention computation in these cases can be skipped with little to no effect on the result. This observation continues to hold for connections among local token blocks. Motivated by this, we introduce CalibAtt, a training-free method that accelerates video generation via calibrated sparse attention. CalibAtt performs an offline calibration pass that identifies block-level sparsity and repetition patterns that are stable across inputs, and compiles these patterns into optimized attention operations for each layer, head, and diffusion timestep. At inference time, we compute the selected input-dependent connections densely, and skip the unselected ones in a hardware-efficient manner. Extensive experiments on Wan 2.1 14B, Mochi 1, and few-step distilled models at various resolutions show that CalibAtt achieves up to 1.58× end-to-end speedup, outperforming existing training-free methods while maintaining video generation quality and text-video alignment.

Abstract:
Existing video personalization methods preserve visual likeness but treat video and audio separately. Without access to the visual scene, audio models cannot synchronize sounds with on-screen actions; and because classical voice-cloning models condition only on a reference recording, a text prompt cannot redirect speaking style or acoustic environment. Although prompt-conditioned audio models could offer such control, they lack access to the visual scene. We propose ID-LoRA (Identity-Driven In-Context LoRA), which jointly generates a subject’s appearance and voice in a single model, letting a text prompt, a reference image, and a short audio clip govern both modalities together. ID-LoRA adapts the LTX-2 joint audio-video diffusion backbone via parameter-efficient In-Context LoRA and, to our knowledge, is the first method to personalize visual appearance and voice within a single generative pass. Two challenges arise from this formulation. Reference and generation tokens share the same positional-encoding space, making them hard to distinguish; we address this with negative temporal positions, which place reference tokens in a disjoint region of the RoPE space while preserving their internal temporal structure. Furthermore, speaker characteristics tend to be diluted during denoising; we introduce identity guidance, a classifier-free guidance variant that amplifies speaker-specific features by contrasting predictions with and without the reference signal. In human preference studies ID-LoRA is preferred over Kling 2.6 Pro, the leading commercial unified model with voice personalization capabilities, by 73% of annotators for voice similarity and 65% for speaking style. Automatic metrics confirm these gains: on crossenvironment settings, speaker similarity improves by 24% over Kling, with the gap widening as reference and target conditions diverge. A preliminary user study further suggests that joint generation provides a useful inductive bias for physically grounded sound synthesis. ID-LoRA achieves these results with only ∼3K training pairs on a single GPU. Code, models, and data are available at https://id-lora.github.io.

Abstract:
Video large language models have demonstrated strong video understanding capabilities, but suffer from high inference costs due to the massive number of tokens in long videos. Inspired by efficient biological vision systems, we propose EventSTU, a training-free framework guided by bio-inspired event cameras to significantly reduce redundant tokens, thereby enabling efficient spatio-temporal understanding. In the temporal domain, we design a coarse-to-fine keyframe sampling algorithm that exploits the change-triggered property of event cameras to eliminate redundant frames. In the spatial domain, we design an adaptive token pruning algorithm that leverages the visual saliency of events as a zero-cost prior to guide token reduction. From a holistic spatio-temporal perspective, we further integrate question relevance from keyframe sampling to adaptively allocate token retention budgets. To facilitate evaluation, we construct EventBench, the first event-inclusive, human-annotated multimodal benchmark that covers diverse real-world scenarios. Beyond physical event cameras, EventSTU also supports general video understanding through simulated events. Comprehensive experiments show that EventSTU achieves 2.87× FLOPs reduction and 3.10× prefilling speedup over the strongest baseline while still improving performance.

Abstract:
Visually-guided acoustic highlighting seeks to rebalance au-dio in alignment with the accompanying video, creating a coherent au-dio–visual experience. While visual saliency and enhancement have beenwidely studied, acoustic highlighting remains underexplored, often leadingto misalignment between visual and auditory focus. Existing approachesuse discriminative models, which struggle with the inherent ambiguityin audio remixing, where no natural one-to-one mapping exists betweenpoorly-balanced and well-balanced audio mixes. To address this limitation,we reframe this task as a generative problem and introduce a ConditionalFlow Matching (CFM) framework. A key challenge in iterative flow-basedgeneration is that early prediction errors — in selecting the correct sourceto enhance — compound over steps and push trajectories off-manifold. Toaddress this, we introduce a rollout loss that penalizes drift at the finalstep, encouraging self-correcting trajectories and stabilizing long-rangeflow integration. We further propose a conditioning module that fusesaudio and visual cues before vector field regression, enabling explicit cross-modal source selection. Extensive quantitative and qualitative evaluationsshow that our method consistently surpasses the previous state-of-the-artdiscriminative approach, establishing that visually-guided audio remixingis best addressed through generative modeling. Qualitative samples areavailable at the project page.

Abstract:
This paper presents DeRA, a novel 1D video tokenizer thatdecouples the spatial-temporal representation learning in video tokeniza-tion to achieve better training efficiency and performance. Specifically,DeRA maintains a compact 1D latent space while factorizing video en-coding into appearance and motion streams, which are aligned withpretrained vision foundation models to capture the spatial semanticsand temporal dynamics in videos separately. To address the gradientconflicts introduced by the heterogeneous supervision, we further pro-pose the Symmetric Alignment-Conflict Projection (SACP) module thatproactively reformulates gradients by suppressing the components alongconflicting directions. Extensive experiments demonstrate that DeRAoutperforms LARP, the previous state-of-the-art video tokenizer by 25%on UCF-101 in terms of rFVD. Moreover, using DeRA for autoregressivevideo generation, we also achieve new state-of-the-art results on bothUCF-101 class-conditional generation and K600 frame prediction.

Abstract:
Long-form narrative-to-film generation requires shot-level con-trollability and cross-clip consistency in both visual identity and charac-ter behavior—requirements that remain difficult to satisfy with currentprompt-based workflows. A core reason existing workflows remain brit-tle is the lack of a structured intermediate layer between scripts andvideo models, especially when screenplays are underspecified at key cin-ematic decision points. We introduce a structured orchestration layer forfilm-oriented script-to-video generation, implemented as a multi-agentframework that operates between scripts and off-the-shelf video genera-tors. The layer is centered on FilmDSL, a film-oriented domain-specificlanguage that makes cinematic constraints explicit, including shot andcamera directives, asset and continuity requirements, and persona cues,so that agents coordinate through a shared structured specification forplanning, generation, critique, and repair. Specifically, a generation agentconstructs asset packs and storyboard keyframes that anchor composi-tion before clip-by-clip synthesis, while a critic agent produces structuredQA signals and triggers targeted refinement without retraining the basemodel. Experiments on TV-style segments show improved controllabilityand consistency over text-only and reference-only baselines.

Abstract:
The intrinsic link between facial motion and speech is oftenoverlooked in generative modeling, where talking head synthesis andtext-to-speech (TTS) are typically addressed as separate tasks. Thispaper introduces JAM-Flow, a unified framework to simultaneously syn-thesize and condition on both facial motion and speech. Our approachleverages flow matching and a novel Multi-Modal Diffusion Transformerarchitecture, integrating specialized Motion-DiT and Audio-DiT modules.These are coupled via selective joint attention layers and incorporate keyarchitectural choices, such as temporally aligned positional embeddingsand localized joint attention masking, to enable effective cross-modalinteraction while preserving modality-specific strengths. By analyzing andleveraging pretrained representation embeddings, JAM-Flow is designedfor efficient, near real-time sampling. Trained with an inpainting-styleobjective, JAM-Flow supports a wide array of conditioning inputs (in-cluding text, reference audio, and reference motion) facilitating taskssuch as synchronized talking head generation from text, audio-drivenanimation, and much more, within a single, coherent model. JAM-Flowsignificantly advances multi-modal generative modeling by providing apractical solution for holistic audio-visual synthesis. Project website

Abstract:
Latent diffusion models have emerged as a dominant frame-work for high-fidelity image and video synthesis, operating in compactlatent spaces with variational autoencoders (VAEs) to enhance compu-tational efficiency without compromising visual quality. However, con-ventional VAEs are suboptimal for video data as they employ fixed com-pression ratios that cannot adapt to the varying complexity of spatio-temporal content. We present KATok (Keep-or-Drop? Adaptive Tok-enizer for Compact Video Representation), a transformer-based VAEthat incorporates an adaptive token selector which is jointly learned withlatent tokens. By evaluating each token’s content-richness as keep-or-dropprobability, the token selector effectively discards uninformative tokens,naturally allowing data-dependent compression. Applying adaptive tok-enization to diffusion models may cause spatial misalignment, as tokendropping can disturb the original spatio-temporal structure. To allevi-ate this issue, we propose two position-prediction strategies: cascadedand joint generation, to ensure spatial consistency. We empirically showthat our model achieves strong reconstruction and generation quality at astate-of-the-art compression ratio. Further analysis on video data revealsthat this improvement is primarily achieved by reducing spatio-temporalredundancy and removing uninformative tokens, as supported by bothquantitative and qualitative results.

Abstract:
We investigate the token mixer in vision backbones by revisit-ing clustering, one of the most classic approaches in machine learning. Aneffective token mixer is a fundamental component of modern vision back-bones like vision Transformers, facilitating information exchange betweenimage patches. Mainstream token mixers, which rely on convolution, at-tention, MLP, or their hybrids, primarily focus on navigating the trade-offbetween accuracy and computational cost. However, a significant draw-back of these methods is their black-box nature; their encoding processis opaque and lacks interpretability. Diverging from these opaque designs,we introduce ClusterMixer, a transparent token mixer that is grounded ina clustering paradigm and interpretable by design. ClusterMixer explicitlyformulates the token mixing process through a hierarchical clusteringmechanism. To model the natural, tree-like relationships inherent in visualdata, the clustering is performed in hyperbolic space, which is well-suitedfor embedding hierarchies with low distortion. Building on this innovation,we present HCFormer, a new backbone architecture that integratesClusterMixer with a series of meticulously designed clustering strategiesto ensure robust performance across tasks. Extensive experiments demon-strate that HCFormer consistently outperforms its counterparts acrossdiverse tasks, including image classification, object detection, instancesegmentation, and semantic segmentation. Considering its transparencyand efficacy, we hope HCFormer can facilitate a paradigm shift towardinterpretable backbones.

Abstract:
Vision foundation models trained with self-supervised objec-tives achieve strong performance across diverse tasks and exhibit emer-gent object segmentation properties. However, their alignment with hu-man object perception remains poorly understood. Here, we introducea behavioral benchmark in which participants make same/different ob-ject judgments for dot pairs on naturalistic scenes, scaling up a classicalpsychophysics paradigm to over 1000 trials. We test a diverse set ofvision models using a simple readout from their representations to pre-dict subjects’ reaction times. We observe a steady improvement acrossmodel generations, with both architecture and training objective con-tributing to alignment, and transformer-based models trained with theDINO self-supervised objective showing the strongest performance. Toinvestigate the source of this improvement, we propose a metric to quan-tify the object-centric component of representations by measuring patchsimilarity within and between objects. Across models, stronger object-centric structure predicts human segmentation behavior more accurately.We further show that matching the Gram matrix of supervised trans-former models, capturing similarity structure across image patches, withthat of a self-supervised model through distillation improves their align-ment with human behavior, converging with the prior finding that Gramanchoring improves DINOv3’s feature quality. Together, these resultsdemonstrate that self-supervised vision models capture object structurein a behaviorally human-like manner, and that Gram matrix structureplays a role in driving perceptual alignment. Code and data are availablehere.

Abstract:
A striking feature of the human visual system is that itingests image information through a sequence of local glimpses, ratherthan a single global computation. This makes the human vision distinctlydifferent from most popular computer vision models in use today, whichingest images globally and in a single shot. A natural question thereforeis whether local, sequential vision models may provide any fundamentalcomputational benefits, in addition to being biologically more plausiblethan global models. In this work, we propose an answer to this questionfrom the perspective of state tracking and length generalization. Inspiredby recent studies of length generalization in language models, we studythe behavior of vision models trained on simple vision tasks that requirethe aggregation of local information across an image. Our experimentsreveal that, similar to language models, vision models can learn to ex-ploit global shortcuts and thereby fail to generalize over task length orcomplexity. We also show that recurrent vision policies based on strictlylocal perception can mitigate these failures, thereby allowing models togeneralize on these tasks. Our results show that local attention may be anessential overlooked requirement for robust compositional generalizationin vision tasks.

Abstract:
Test-time adaptation (TTA) methods are typically evaluated by task accuracy, leaving open the question of whether adaptation also changes which features the model relies on. We examine this question by tracking concept sensitivity throughout adaptation using TCAV directional derivatives. Across five TTA methods and five benchmarks spanning natural image and medical imaging domains, we observe recurring shifts in concept sensitivity: adaptation can reduce sensitivity to task-relevant or pathology-related concepts while increasing sensitivity to nuisance concepts such as backgrounds or institutional artifacts—a phenomenon we term Concept Sensitivity Drift, even when accuracy improves. On Waterbirds, where the causal/spurious split is defined by construction, this drift coincides with degraded worst-group accuracy; on cross-hospital medical adaptation, it coincides with increased reliance on hospital-specific artifacts rather than pathology. Motivated by this observation, we propose CS-TTA, a source-concept-supervised but target-label-free plug-in regularizer that mitigates excessive conceptsensitivity drift during adaptation. CS-TTA requires no architectural changes and can be added to any existing TTA objective. Experiments show that CS-TTA provides consistent worst-group accuracy gains on spurious-correlation benchmarks, improves AUC by 1.4–1.8 points on cross-hospital medical adaptation, and gives consistent overall accuracy gains on CIFAR-10-C and ImageNet-C.1234

Abstract:
Despite the remarkable success of large-scale pre-trained im-age representation models (i.e., vision encoders) across various visiontasks, they are predominantly trained on 2D image data and thereforeoften fail to capture 3D spatial relationships between objects and back-grounds in the real world, constraining their effectiveness in many down-stream applications. To address this, we propose SpatialBoost, a scalableframework that enhances the spatial awareness of existing pre-trained vi-sion encoders by injecting 3D spatial knowledge expressed in linguisticdescriptions. The core idea involves converting dense 3D spatial infor-mation from 2D images into linguistic expressions, which is then usedto inject such spatial knowledge into vision encoders through a LargeLanguage Model (LLM). To this end, we adopt a multi-turn Chain-of-Thought (CoT) reasoning process that progressively incorporates densespatial knowledge and builds hierarchical spatial understanding. To val-idate effectiveness, we adapt SpatialBoost to state-of-the-art vision en-coders such as DINOv3, and evaluate its performance gains on a widerange of benchmarks requiring both 3D perception and general visionabilities. For instance, SpatialBoost improves DINOv3 performance from55.9 to 59.7 mIoU on ADE20K, achieving state-of-the-art performancewith 3.8%p gain over the pre-trained DINOv3. Project page.

Abstract:
Diffusion transformers have demonstrated remarkable generative capabilities, yet the rich perceptual representations computed across their denoising trajectory are discarded once the content is rendered. We present MMDiff, a framework that transforms a frozen diffusion transformer into a multi-modal generative system that jointly produces images alongside any combination of dense perceptual modalities using lightweight decoder heads. Our central finding is that perceptual information is temporally distributed along the denoising trajectory, and that multi-timestep feature fusion with spatially varying aggregation weights is essential, improving semantic segmentation results by up to 28.7% mIoU over single-timestep extraction. We further adopt conceptdriven attention extraction for interpretable spatial guidance, and show that frozen diffusion features are competitive with and complementary to state-of-the-art encoders such as DINOv3. By training only lightweight decoder heads on a frozen backbone, we achieve strong performance in semantic segmentation, salient object detection, and depth estimation, and demonstrate that this framework enables effective synthetic data generation at scale. Code, models, and results are available here.

Abstract:
Reasoning about objects in the physical world requires understanding both the spatial structure of a scene and the properties of objects within it. To perform reasoning tasks, a model must identify candidate objects from the scene and then reason over their properties, such as material, physical attributes, affordance, and function to satisfy the task constraints. We formalize this reasoning process as Perceptual Taxonomy, a hierarchical framework that organizes visual understanding into structured scene–object–property representations. Existing benchmarks, however, address only parts of this framework: some focus on spatial understanding alone, while others evaluate individual property families—material, affordance, or physical attributes—each under a separate schema. They fail to cover a challenging yet practically important capability: cross-property reasoning, where a model must simultaneously integrate reasoning across all property families to answer questions about physical environments. To evaluate this capability, we introduce PercepTax, a benchmark for perceptual taxonomy reasoning. We annotate 3,173 object classes with 54 fine-grained attributes across four property families: material, physical attributes, affordance, and function. The benchmark contains 5,802 images from both synthetic and real domains and 28,033 questions spanning object description, singleproperty recognition, 3D spatial reasoning, and cross-property reasoning. Experiments show that state-of-the-art vision language models (VLMs) perform strongly on object description and single-property tasks, but accuracy drops substantially on cross-property reasoning that requires integrating multiple attributes simultaneously. Oracle experiments with ground-truth objects’ properties with 3D positions and taxonomy-guided in-context learning both improve performance substantially yet remain well below human levels, confirming that both visual perception of individual properties and their cross-property integration are bottlenecks for VLMs.

Abstract:
Advances in generative AI have made image falsi(cid:28)cation highly realistic, demanding trustworthy authentication systems. Existing forensic detectors can target certain forgery types but lack interpretability, while vision-language models (VLMs) provide explanations but cannot exploit forensic traces for reliable detection. We propose Forensic Knowledge Graphs (FKGs), a uni(cid:28)ed framework that integrates forensic evidence extraction, structured reasoning, and human-interpretable explanation. Our FKG structure encodes forensic traces along with their causal dependencies and links to scene content. To generate accurate FKGs, we introduce a novel forensic authentication network and an Iterative Context Re(cid:28)nement strategy that guides VLMs to produce faithful, grounded explanations. We also present FKG-50K, a dataset of 50,000 realistic forgeries with ground-truth FKGs. Experiments demonstrate that FKG outperforms both forensic detectors and VLMs in detection, forgery identi(cid:28)cation and localization, and forensic justi(cid:28)cation. • •

Abstract:
Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception. Previous studies have primarily relied on weakly supervised learning with action labels from exocentric images. However, these methods often struggle with visually ambiguous exocentric images containing co-occurring actions; moreover, they fail to distinguish semantically similar actions because existing methods typically rely on brief action phrases that lack rich semantic details for action-specific localization. Although large vision-language models (LVLMs) encode rich action semantics and their action-conditioned textual outputs implicitly contain spatial cues, they do not directly provide action-specific spatial localization. To address these problems, we propose TokAG, a zero-shot affordance grounding framework that exploits the token-level semanticspatial signals in LVLMs to localize action-relevant regions without external supervision. We observe that attention maps associated with different LVLM output tokens vary significantly, with many attending to irrelevant regions such as the background. Thus, we introduce a spatialaware token-selection mechanism to systematically evaluate each output token and select the one whose attention maps exhibit dominant activation over the target object, instead of relying on arbitrary attention maps. By extracting these object-focused attention maps, we transform the LVLM’s implicit semantic signals into zero-shot affordance heatmaps. Our zero-shot framework consistently outperforms prior weakly supervised approaches across multiple benchmarks, improving NSS by 10.7% on the unseen split of AGD20K and by 29.7% on HICO-IIF. The code and models will be made publicly available.

Abstract:
Spatial reasoning from a single RGB image, including the inference of distances, sizes, and inter-object relationships without depth sensors or multiple views, remains a critical blind spot for current Vision Language Models (VLMs). Existing benchmarks either rely on multiview video sequences that expose explicit geometric cues, or are confined to indoor environments too small for model training. To close this gap, we introduce MonoSR, a large-scale dataset for open-world monocular spatial reasoning comprising over 1M QA pairs from 230K images spanning indoor, outdoor, and object-centric domains across 98 semantic categories. Every QA pair is guaranteed answerable from a single RGB image via a four-stage observability filter validated by human audit. Tasks are organized into three cognitive levels: foundational perception, perspective-aware imagination, and situational reasoning, supporting both large-scale training and fine-grained evaluation. Comprehensive benchmarking of state-of-the-art openand closed-source VLMs reveals consistent limitations across all three domains, with object-centric metric estimation emerging as the most challenging frontier. We further conduct a systematic auxiliary information study by injecting scene context, 2D visual prompts, and 3D bounding boxes, allowing us to quantify the geometric gap that future monocular perception modules must close and provide actionable design guidelines. Our project page is available at this link.

Abstract:
Large Language Models (LLMs) and Vision Language Mod-els (VLMs) have shown impressive reasoning abilities, yet they strugglewith spatial understanding and layout consistency when performing fine-grained visual editing. We introduce a Structured Reasoning frameworkthat performs text-conditioned spatial layout editing via scene-graph rea-soning. Given an input scene graph and a natural-language instruction,the model reasons over the graph to generate an updated scene graphthat satisfies the text condition while maintaining spatial coherence. Byexplicitly guiding the reasoning process through structured relationalrepresentations, our approach improves both interpretability and controlover spatial relationships. We evaluate our method on a new text-guidedlayout editing benchmark encompassing sorting, spatial alignment, androom-editing tasks. Our training paradigm yields an average 15% im-provement in IoU and 25% reduction in center-distance error comparedto Chain of thought Fine-tuning (CoT-SFT) and vanilla GRPO base-lines. Compared to SOTA zero-shot LLMs, our best models achieve upto 20% higher mIoU, demonstrating markedly improved spatial precision.

Abstract:
We introduce ScenarioControl, the first vision-language con-trol mechanism for learned driving scenario generation. Given a textprompt or an input image, ScenarioControl synthesizes diverse, realistic3D scenario rollouts – including map, 3D boxes of reactive actors overtime, pedestrians, driving infrastructure, and ego camera observations.The method generates scenes in a vectorized latent space that representsroad structure and dynamic agents jointly. To connect multimodal con-trol with sparse vectorized scene elements, we propose a cross-global con-trol mechanism that integrates cross-attention with a lightweight global-context branch, enabling fine-grained control over road layout and traf-fic conditions while preserving realism. The method produces tempo-rally consistent scenario rollouts from the perspectives different actorsin the scene, supporting long-horizon continuation of driving scenarios.Extensive experiments validate that the control adherence and fidelityof ScenarioControl compare favorable to all tested methods across allexperiments. To facilitate training and evaluation, we release a datasetwith text annotations aligned to vectorized map structures at the projectwebpage: https://light.princeton.edu/ScenarioControl

Abstract:
Anatomical understanding, which is the ability to identify,localize, or segment anatomical structures, is critical in medical imageanalysis; however, its progress is constrained by the scarcity of expert-labeled data. A promising remedy is to leverage an annotated referenceimage to guide the interpretation of an unlabeled target. Although re-cent vision–language models (VLMs) exhibit non-trivial visual reasoning,their reference-based understanding and fine-grained localization remainlimited. We introduce RAU, a framework for reference-based anatomicalunderstanding with VLMs. We first show that a VLM learns to identifyanatomical regions through relative spatial reasoning between referenceand target images, trained on a moderately sized dataset. We validatethis capability through visual question answering (VQA) and boundingbox prediction. Next, we demonstrate that the VLM-derived spatial cuescan be seamlessly integrated with the fine-grained segmentation capabil-ity of SAM2, enabling localization and pixel-level segmentation of smallanatomical regions, such as vessel segments. Across two in-distributionand two out-of-distribution datasets, RAU consistently outperforms aSAM2 fine-tuning baseline using the same memory setup, yielding moreaccurate segmentations and more reliable localization. More importantly,its generalization ability to unseen modalities makes it scalable to unseendatasets, a property crucial for medical image applications. To the bestof our knowledge, RAU is the first to explore the capability of VLMs forreference-based identification, localization, and segmentation of anatom-ical structures in medical images. Its promising performance highlightsthe potential of VLM-driven approaches for anatomical understandingin automated clinical workflows.

Abstract:
Prompt learning adapts vision-language models, such as CLIP,by adjusting a small set of context tokens. However, under few-shot su-pervision, even moderate label noise can disrupt prompt optimization. Toaddress this issue, we propose TecoPrompt, a closed-loop robust prompt-learning framework that revisits optimal transport (OT) pseudo-labelingfrom a temporal perspective. TecoPrompt employs an entropic OT planin the CLIP semantic space to obtain globally consistent label candidates.It verifies the reliability of these candidates by examining trajectorystability: a noisy label is only rewritten if the OT candidate remainsunchanged within a K-epoch temporal stability window and passes a con-fidence gate based on Exponential Moving Average (EMA). This approachhelps reduce confirmation bias. The rewritten labels are then integratedback into prompt training using a tri-group objective that includes threeloss functions aligned with clean, mid, and noisy subsets. Experimentson seven datasets with synthetic symmetric and asymmetric noise, aswell as Food101N, demonstrate significant performance improvements.For example, on the OxfordPets dataset, with 50% asymmetric noise,TecoPrompt achieves an accuracy of 0.843, up from 0.775.

Abstract:
Reasoning-induced vision-language models (VLMs) advanceimage quality assessment (IQA) with textual reasoning, yet their scalarscores often lack sensitivity and collapse to a few values, so-called discretecollapse. We introduce ME-IQA, a plug-and-play, test-time memory-enhanced re-ranking framework. It (i) builds a memory bank and re-trieves semantically and perceptually aligned neighbors using reason-ing summaries, (ii) reframes the VLM as a probabilistic comparator toobtain pairwise preference probabilities and fuse this ordinal evidencewith the initial score under Thurstone’s Case V model, and (iii) per-forms gated reflection and consolidates memory to improve future deci-sions. This yields denser, distortion-sensitive predictions and mitigatesdiscrete collapse. Experiments across multiple IQA benchmarks showconsistent gains over strong reasoning-induced VLM baselines, existingnon-reasoning IQA methods, and test-time scaling alternatives.

Abstract:
Vision–language–action (VLA) policies often become brit-tle under deployment distribution shift when only limited target-domaindemonstrations are available. Subgoal-image planning addresses this is-sue by providing an explicit visual target. However, the target imagespecifies what future scene to reach, not how the robot should transitiontoward it. We propose TDSR-VLA, a VLA framework that reuses condi-tioning states from a diffusion-based Vision Planner to guide action gen-eration. The Vision Planner predicts a subgoal image and provides tworepresentations: a Sequence Representation (SR) grounded in the currentobservation and an Updated Sequence Representation (USR) refined dur-ing denoising steps. A flow-matching Action Expert generates continuousaction chunks by injecting these representations through layer-wise pre-fix key–value conditioning. On real-world tasks, TDSR-VLA achieves anaverage OpenArm score of 0.83, outperforming GR00T N1.5 (0.74), π0(0.71), and SmolVLA (0.58), with the largest gains on object-cooperativetasks (0.88 vs. 0.70). On LIBERO, TDSR-VLA reaches 82.0% on Goaland 56.0% on Long without robot-demonstration pretraining, outper-forming OpenVLA on both suites and π0 on Long. Ablation studies showthat planner-side conditioning states provide action-relevant transitioncues, improving long-horizon manipulation under limited training data.

Abstract:
Multimodal large language models (MLLMs) are increas-ingly considered as a foundation for embodied agents, yet it remainsunclear whether they can reliably reason about the long-term phys-ical consequences of actions from an egocentric viewpoint. We studythis gap through a new task, Egocentric Scene Prediction with LOng-horizon REasoning: given an initial-scene image and a sequence of atomicaction descriptions, a model is asked to predict the final scene afterall actions are executed. To enable systematic evaluation, we introduceEXPLORE-Bench, a benchmark curated from real first-person videosspanning diverse scenarios. Each instance pairs long action sequenceswith structured final-scene annotations, including object categories, vi-sual attributes, and inter-object relations, which supports fine-grained,quantitative assessment. Experiments on a range of proprietary andopen-source MLLMs reveal a significant performance gap to humans,indicating that long-horizon egocentric reasoning remains a major chal-lenge. We further analyze test-time scaling via stepwise reasoning andshow that decomposing long action sequences can improve performanceto some extent, while incurring non-trivial computational overhead. Over-all, EXPLORE-Bench provides a principled testbed for measuring andadvancing long-horizon reasoning for egocentric embodied perception.

Abstract:
While many vision language models (VLMs) are developed to passively answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle in practice with complex open-ended tasks, which usually require multiple rounds of active exploration and reasoning in the visual space. Such visual thinking paths not only provide step-by-step exploration and verification as an AI detective but also produce better interpretations of the final answers. However, these paths are challenging to evaluate due to the large exploration space of intermediate steps. To bridge the gap, we develop an evaluation suite, “Visual Reasoning with multi-step EXploration (V-REX)”, which is composed of a benchmark of challenging visual reasoning tasks requiring native multi-step exploration and an evaluation protocol. V-REX casts the multi-step exploratory reasoning into a Chain-of-Questions (CoQ) and disentangles VLMs’ capability to (1) Planning: determining the exploratory direction to gather information at each step for solving the task; and (2) Following: answering curated CoQ sequentially to collect information for deriving the final answer. By curating finite options of questions and answers per step, V-REX achieves a reliable quantitative and finegrained analysis of the intermediate steps. By assessing SOTA proprietary and open-sourced VLMs, we reveal consistent scaling trends, significant differences between planning and following abilities, and substantial room for improvement in multi-step exploratory reasoning.

Abstract:
In this paper, we approach the machine unlearning (MU)problem from a novel perspective: Can we design a learning strategythat simplifies the later MU problem, i.e., leads to unlearning-compatiblemodels? Motivated by recent findings that the cost to reverse SGD (un-learning error) is proportional to the accumulated loss curvature and thatthe generalization error (a measure of acquired subject-specific knowl-edge) is bounded by loss sharpness, we focus on the loss sharpness as thekey factor to train unlearning-compatible models. By studying the theo-retical implications of defining the sharpness over different perturbationnorms, we propose a variation of the sharpness, quantified by the l1 normof the gradient, which minimizing it leads to a flatter learning trajectoryand, as a result, smaller unlearning and generalization errors. Consider-ing these results, to have unlearning-compatible models, we propose toregularize the training loss with l1 norm of the gradient, which encour-ages gradient sparsity during training. Our experiments show that onlyby using our regularizer in the training stage, the MU performance ofvarious baselines is considerably improved, suggesting the effectivenessof our approach in simplifying the MU problem by training unlearning-compatible models. Moreover, we demonstrate that the benefits of ourapproach become even more significant when dealing with poisoned datain backdoor attack scenarios. Our investigations show that in such ascenario, gradient sparsity not only simplifies model cleaning but alsodamps the gradient spikes, which are typically caused by poisonous dataand encourage the model to automatically learn less from such poisonousdata points.

Abstract:
Next-scale visual autoregressive (VAR) models offer stronggenerative fidelity but remain computationally prohibitive for resource-constrained edge devices. We introduce EVAR, a structured-pruningframework tailored to next-scale VAR models. We find that standardpruning paradigms are poorly matched to multi-scale VAR architec-tures: rapid token growth at later scales dominates Hessian accumu-lation, marginalizing critical coarse scales and amplifying autoregres-sive error cascading. To address this issue, EVAR introduces Pyramid-Aware Optimal Brain Surgeon. Through pyramid-aware Hessian accu-mulation, Pyramid-Aware OBS rebalances scale-wise activation statisticsand biases the solver toward preserving coarse-scale representations whilemaintaining an invertible Hessian estimate for closed-form weight com-pensation. Additionally, we propose Progressive Scale-Aware Distillation(PSAD) to counteract scale-wise gradient imbalance during fine-tuning.On ImageNet benchmarks, EVAR substantially reduces parameters andmodel footprint while retaining competitive quality. On an iOS deploy-ment, EVAR further cuts single-image latency from 494 ms to 277 ms(1.8× speedup), with FID changing only marginally.

Abstract:
Single-image reflection removal (SIRR) seeks to recover thetransmission layer from a mixture corrupted by reflections—a severelyill-posed problem. Existing methods operate in pixel space, where thenonlinear sRGB formation model entangles the two layers and limits gen-eralization. We observe that pretrained VAE latent spaces exhibit sub-stantially lower coherence between image layers compared to pixel space,providing a more favorable working space for decomposition. Buildingon this finding, we propose PRISM (Pretrained-latent Reflection ImageSeparation Model), which reinterprets SIRR as a latent linear separa-tion problem. Under an approximate additive formulation in latent space,PRISM learns a flow matching velocity field on a pretrained FLUX back-bone that recovers both transmission and reflection in a single forwardpass. To enforce robust disentanglement, we introduce a Latent Com-position Consistency (LCC) strategy that constructs synthetic mixturesby swapping reflection latents across samples and enforces consistent de-composition via a cycle loss. We further propose a Layer ContrastiveSeparation (LCS) loss that promotes semantic separation between layersthrough patch-level contrastive learning, without requiring explicit reflec-tion targets. Experiments on six benchmarks demonstrate that PRISMconsistently outperforms state-of-the-art methods by significant margins,with strong generalization to in-the-wild images.

Abstract:
While various works address reflective symmetry understand-ing in 3D data and images, pixel-level semantic left-right prediction ofin-the-wild images remains challenging, due to certain difficulties includ-ing the lack of 3D information, occlusion, object pose variation, partial-ity, etc. In this work, we propose an unsupervised learning framework totackle this challenge. Leveraging recent advances in vertex-wise semanticleft-right understanding of 3D data, our unsupervised learning methodjointly utilises 3D shape and image datasets to infer pixel-wise seman-tic left-right predictions in single-view images. In particular, we showthat a medium-scale 3D shape dataset comprising mainly of human- andquadruped animal-like shapes, combined with diverse in-the-wild imagedata, are sufficient to achieve high-quality semantic left-right predictionin images, even for entirely unseen 3D object categories, such as carsor trains. Overall, our approach achieves superior performance in densepixel-wise semantic left-right predictions on both rendered and in-the-wild image datasets when compared to existing state-of-the-art methods.

Abstract:
Computing exponential and logarithmic maps on raw geome-tries is a fundamental yet challenging task in geometry processing, oftenhindered by the lack of explicit connectivity and the presence of noise.We present a novel learning-based framework that computes these mapsefficiently by learning a continuous geometric embedding field directlyfrom discrete point sets. Our key insight is to construct an embeddingfield that encapsulates the intrinsic local geometry governing exponen-tial and logarithmic maps. Specifically, we encode the input into a high-dimensional feature volume using a sparse Octree-based CNN. For anyarbitrary query point, we retrieve its corresponding embedding to mod-ulate two specialized triplane-based neural networks, which then predictthe maps in a single forward pass. By formulating the problem as learninga query-able latent field, our method bypasses the need for mesh con-nectivity while ensuring robustness against irregular sampling and noise.Our results demonstrate that the proposed framework achieves compet-itive accuracy and runtime over previous methods, while maintainingrobustness on challenging geometric structures.

Abstract:
Classical mesh texturing techniques blend captured multi-view images directly, which inevitably suffer from baked-in shading andcasted shadows that compromise visual fidelity during relighting. To cir-cumvent this issue, we present a neural texturing framework, namelyDante-w, to enable high-fidelity diffuse albedo texture recovery fromunstructured image collections for large-scale, in-the-wild scenes, whichintegrates seamlessly with traditional 3D reconstruction pipelines. Givena reconstructed mesh and its surface parameterization, our method fusesview-space generative albedo priors into a coherent texture space viaan expressive neural representation, while substantially enhancing fine-grained textural details through physically principled neural rendering.To comprehensively evaluate our method, we curate a benchmark datasetfeaturing diverse, fine-grained textures, comprising both real-world in-the-wild scenes and synthetic objects. Extensive experiments verify theeffectiveness of our approach in reconstructing accurate albedo texturesand boosting relighting fidelity. Project page: dante-wild.github.io.

Abstract:
Compositional text-to-3D generation aims to create multiobject scenes from text descriptions, yet existing approaches often suffer from severe inter-object penetrations during joint optimization, where entangled object representations lead to physically implausible scenes. To address this, we propose CompSAG to enable penetration-free and complete compositional 3D generation. Our key insight is a Gaussian surface offset (GSO) mechanism that mimics real-world repulsive forces to effectively resolve inter-object penetrations. The proposed GSO calculates offset values by aggregating repulsive forces from an object proxy surface, pushing apart the Gaussians in collision regions. Furthermore, during appearance optimization, we design a Gaussian surface rectification module to suppress the spiky geometric artifacts introduced by SDS. These are built upon a layout-guided scene initialization driven by a fine-tuned LLM, which provides precise spatial grounding from the text. Extensive experiments across diverse multi-object scenarios demonstrate that CompSAG produces complete, spatially coherent, and penetrationfree 3D scenes, outperforming prior compositional generation baselines.

Abstract:
The representation of 3D clothed humans as standardized 2DUV texture and displacement maps over an underlying body model haslong been studied. This compact representation is enticing as it enablespretrained image networks to process, generate, and edit 3D avatars, butis only useful if scans are accurately aligned and brought into correspon-dence via high-fidelity registration. This prerequisite has never been met,which we argue explains the limited quality of prior UV-based methodsfor clothed humans. Despite its significance, no public method produceshigh-fidelity SMPL(−X)+D registrations with UV texture from arbitraryclothed scans. We present AvaImg, a multi-stage optimization pipeline, toclose this gap: it enforces body-inside-clothing constraint via signed wind-ing numbers, made viable by a three-level efficiency cascade (∼10× run-time reduced, ∼95% storage saved), and recovers fine surface detail usingcoarse-to-fine displacement optimization. AvaImg outperforms all base-lines in body fitting, shape estimation, and surface registration acrosssix datasets, yielding textured registrations near-indistinguishable fromscans (PSNR=34.48dB). For validation of AvaImg’s Avatar-as-Image rep-resentation as imminently compatible with image foundation models, weauto-encode our UV maps via the frozen FLUX VAE. This achieves only0.76mm added Chamfer error relative to scan and shows that the result-ing maps lie within natural-image distributions, supporting the use of 2Dgenerative priors for 3D avatar generation. Code, data, and Singularitycontainers will be publicly released.

Abstract:
Masked generative models have made significant advancesin text-to-motion synthesis. Previous approaches adopt a non-factorized,whole-body tokenization, limiting compositional and fine-grained con-trol. We present K-Mask, a kinematic-aware generative framework thatfactors motion into anatomically grounded groups and learns temporaland cross-group interactions through masked modeling. In the proposedapproach, a kinematic-group residual VQ-VAE (KG-RVQ) encodes mo-tion into disentangled, group-aligned latents. In particular, a latent-awarekinematic dropout (LAKD) loss is proposed to promote within-groupreconstruction and suppress inter-group leakage. Then, we introduce twobidirectional spatiotemporal transformers: (i) a base model that generatescoarse tokens over a time-group lattice with axis-factorized attention, and(ii) a residual model that predicts deeper quantizer layers for refinement.Finally, We introduce kinematic group masking, which randomly occludesentire group-specific subtrees, to enhance inter-group reasoning. On theHumanML3D and KIT-ML text-to-motion benchmarks, K-Mask obtainscompetitive FID scores of 0.041 and 0.154, respectively, while providingan anatomically factorized token interface for localized motion control.

Abstract:
Boundary representations (B-Reps) are fundamental to com-puter aided design (CAD), as they encode editable parametric surfacestogether with the precise topological structure required by downstreamengineering workflows. In contrast, modern 3D generative models pro-duce meshes or implicit fields, leaving a gap between high-quality geom-etry synthesis and CAD-native representations. Existing reconstructionpipelines treat B-Rep recovery as deterministic segmentation and fit-ting on discrete samples, despite the inherent ambiguity of CAD-styleface decompositions, often resulting in unstable topology recovery. Weintroduce BRepFacetGen, a generative framework that formulates CADstructure inference as a latent variable problem conditioned on geometryrepresenting continuous surfaces. Our representation couples a pretrainedgeometry latent set with a geometry-conditioned label latent space thatmodels multiple plausible dense surface segmentations, naturally han-dling permutation ambiguity and structural non-uniqueness. Joint de-coding yields a faceted mesh where each segment maps to a B-Rep facethat can be recovered by surface fitting and topology reconstruction. Ex-periments show improved segmentation and more reliable downstreamB-Rep recovery, as well as strong performance in image-conditioned andpoint-conditioned B-Rep generation.

Abstract:
Generating functional neural-network weights from trained model collections is a central problem in weight-space learning. We introduce TiltDiff, a performance-tilted latent diffusion framework for neural-network generation. TiltDiff tokenizes network weights, encodes them into a compact latent space with a Transformer autoencoder, and uses a U-Net-based diffusion model to synthesize latent representations that decode into functional parameters. To favor stronger models, we weight the denoising loss by validation accuracy, biasing the learned distribution toward higher-performing regions of weight space. Experiments show that TiltDiff improves predictive performance, robustness to random parameter masking, and representational diversity over prior weight-generation methods. We further combine diffusion U-Net connectivity with attribution analysis to identify class-specific decision pathways. These pathways exhibit emergent correspondence across independently generated models despite differing raw parameters, and pathwaylevel masking verifies their importance for target-class prediction. Our results show that performance-tilted diffusion generates accurate, robust, diverse, and structurally interpretable neural-network weights.

Abstract:
Differentiable vector graphics have enabled powerful gradientbased optimization of vector primitives directly from raster images. However, existing frameworks formulate this as a flat optimization problem, forcing hundreds to thousands of randomly initialized curves to blindly compete for pixel-level error reduction. This disordered optimization leads to topology collapse, where macroscopic structures are distorted by internal high-frequency noise, resulting in a redundant and uneditable “polygon soup” that limits practical editability. To address this limitation, we propose Vector Scaffolding, a novel hierarchical optimization framework that shifts from flat pixel-matching to structured topological construction tailored for vector graphics. By identifying a key cause of topology collapse as the mathematical imbalance between area and boundary gradients, we introduce Interior Gradient Aggregation to stabilize the learning dynamics of multi-scale curve mixtures. Upon this stabilized landscape, we employ Progressive Stratification and Rapid Inflation Scheduling to progressively densify vector primitives with extremely high learning rates (×50). Experiments demonstrate that our approach accelerates optimization by 2.5× while simultaneously improving PSNR by up to 1.4dB over the previous state of the art.

Abstract:
Reconstructing articulated objects with multiple movable parts is essential for understanding object structure and enabling physical interaction. However, this reconstruction task poses significant challenges due to the entanglement of geometry, appearance, and motion parameters during optimization. Existing methods rely primarily on photometric supervision, which commonly fails to disentangle these interdependent components, resulting in poor part decomposition with blurred boundaries and geometric artifacts. To address this limitation, we introduce StructureGS, a reconstruction framework for articulated objects that integrates structure-aware guidance into 3D Gaussian Splatting. Our approach leverages oriented bounding boxes of object parts to enforce two key structural properties: spatial coherence, which constrains each part’s geometry to remain compact and spatially coherent within its designated region, and structural connectivity, which enforces physically plausible contact relationships between adjacent parts. These properties are realized through structure-aware losses that inject explicit structural constraints into the optimization process. Extensive experiments demonstrate that our method achieves state-of-the-art performance in articulated object reconstruction, producing high-quality results with welldefined part geometries.

Abstract:
Local editing of 3D objects remains a long-standing challenge. When interacting with 3D content, humans naturally tend to specify a coarse region of interest for modification rather than defining precise editing boundaries. However, previous methods rely on fully edited 2D images, precise 3D masks, or redundant pipelines, which present a gap. To bridge this gap, we propose EditVerse3D, a novel 3D editing framework that enables high-quality object editing under such coarse guidance. Our approach takes as input a 3D object to be edited, a coarse 3D bounding box indicating the target region, and a reference 2D image describing the desired modification. It produces a coherent, high-fidelity edited 3D object. To facilitate this editing, we introduce a novel regionaware adaptive loss that emphasizes hard-to-learn regions and balances the objective between target and preserved areas. Complementing our loss function, we enhance model robustness and generalization through targeted data augmentations, such as training with scaled 3D masks and filtering out unrealistic editing pairs. We construct a large-scale 3D editing dataset derived from parts information. Extensive experiments demonstrate that EditVerse3D achieves superior visual quality and quantitative performance compared to existing 3D editing approaches. Please visit our project page at https://editverse3d.github.io.

Abstract:
We tackle a new problem: generating geometrically consistent multi-view scenes from a single freehand sketch. Freehand sketches are the most geometrically impoverished input one could offer a multiview generator. They convey scene intent through abstract strokes while introducing spatial distortions that actively conflict with any consistent 3D interpretation. No prior method attempts this; existing multi-view approaches require photographs or text, while sketch-to-3D methods need multiple views or costly per-scene optimisation. We address three compounding challenges; absent training data, the need for geometric reasoning from distorted 2D input, and cross-view consistency, through three mutually reinforcing contributions: (i) a curated dataset of ∼9k sketch-to-multiview samples, constructed via an automated generation and filtering pipeline; (ii) Parallel Camera-Aware Attention Adapters (CA3) that inject geometric inductive biases into the video transformer; and (iii) a Sparse Correspondence Supervision Loss (CSL) derived from Structure-from-Motion reconstructions. Our framework synthesizes all views in a single denoising process without requiring reference images, iterative refinement, or per-scene optimization. Our approach significantly outperforms state-of-the-art two-stage baselines, improving realism (FID) by over 60% and geometric consistency (Corr-Acc) by 23%, while providing up to a 3.7× inference speedup.

Abstract:
Single-image-to-3D generative models can now produce highquality geometry, yet conditioning on a single view inevitably introduces ambiguity about unseen regions. Multi-view conditioning can reduce this ambiguity, but existing methods either require fixed canonical viewpoints or rely on external reconstruction modules that impose heavy training costs and limit generation quality. We observe that pretrained singleview models already possess strong 2D-to-3D grounding that can be reused for multi-view conditioning. However, a closer analysis reveals that their conditioning mechanism entangles orientation control with geometry transfer, two functions that conflict when images from different viewpoints are naively combined. Based on this analysis, we propose ROAR-3D, a lightweight method that upgrades a pretrained singleview model to accept an arbitrary number of unposed images. A tokenwise view router assigns each 3D latent token to its most relevant view, implicitly establishing 2D-to-3D correspondences without explicit pose input. A dual-stream attention design preserves the pretrained primaryview behavior while routing auxiliary views through a separate path dedicated to geometric enrichment. An orientation perturbation strategy ensures the auxiliary path learns orientation-independent geometry transfer. These components introduce lightweight architectural additions and incur only low additional inference overhead relative to the singleview baseline. ROAR-3D achieves state-of-the-art multi-view 3D generation quality and supports test-time view scaling from 1 to 12+ views with consistent improvements.

Abstract:
We present SyncFix, a framework that enforces cross-view consistency during the diffusion-based refinement of reconstructed scenes. SyncFix formulates refinement as a joint latent bridge matching problem, synchronizing distorted and clean representations across multiple views to fix the semantic and geometric inconsistencies. This means SyncFix learns a joint conditional over multiple views to enforce consistency throughout the denoising trajectory. Our training is done only on image pairs, but it generalizes naturally to an arbitrary number of views during inference. Moreover, reconstruction quality improves with additional views, with diminishing returns at higher view counts. Qualitative and quantitative results demonstrate that SyncFix consistently generates high-quality reconstructions and surpasses current state-of-the-art baselines, even in the absence of clean reference images. SyncFix achieves even higher fidelity when sparse references are available.

Abstract:
Implicit Neural Representations (INRs) are widely used formodeling continuous 2D images, enabling high-fidelity reconstruction,super-resolution, and compression. Architectures such as SIREN, WIRE,and FINER demonstrate their ability to capture fine image details. How-ever, conventional INRs lack explicit geometric structure, limiting localediting, and integration with physical simulation. To address these lim-itations, we propose GaINeR (Geometry-Aware Implicit Network Rep-resentation), a novel framework for 2D images that combines trainableGaussian distributions with a neural network-based INR. For a givenimage coordinate, the model retrieves the K nearest Gaussians, aggre-gates distance-weighted embeddings, and predicts the RGB value viaa neural network. This design enables continuous image representation,interpretable geometric structure, and flexible local editing, providing afoundation for physically aware and interactive image manipulation. Ourmethod supports geometry-consistent transformations, seamless super-resolution, and integration with physics-based simulations. Moreover, theGaussian representation allows lifting a single 2D image into a geometry-aware 3D representation, enabling depth-guided editing. Experimentsdemonstrate that GaINeR achieves state-of-the-art reconstruction qual-ity while maintaining flexible and physically consistent image editing.

Abstract:
Intrinsic image decomposition aims to estimate physicallybased rendering (PBR) parameters such as albedo, roughness, and metal-licity from images. While recent methods achieve strong single-view pre-dictions, applying them independently to multiple views of the samescene often yields inconsistent estimates, limiting their use in down-stream applications such as editable neural scenes and 3D reconstruc-tion. Video-based models can improve cross-frame consistency but re-quire dense, ordered sequences and substantial compute, limiting theirapplicability to sparse, unordered image collections. We propose Geo-ID, a novel test-time framework that repurposes pretrained single-viewintrinsic predictors to produce cross-view consistent decompositions bycoupling independent per-view predictions through sparse geometric cor-respondences that form uncertainty-aware consensus targets. Geo-ID ismodel-agnostic, requires no retraining or inverse rendering, and appliesdirectly to off-the-shelf intrinsic predictors. Experiments on syntheticbenchmarks and real-world scenes demonstrate substantial improvementsin cross-view intrinsic consistency as the number of views increases, whilemaintaining comparable single-view decomposition performance. We fur-ther show that the resulting consistent intrinsics enable coherent appear-ance editing and relighting in downstream neural scene representations.

Abstract:
Pose-Free Feed-forward 3D Gaussian Splatting (3DGS) hasrecently emerged as a powerful paradigm for fast scene reconstruction.However, its performance degrades significantly in long image sequencesdue to cumulative camera pose estimation drift, which propagates er-rors into geometric modeling and severely limits rendering fidelity. Inthis work, we revisit the long-sequence bottleneck and identify posedrift as the primary factor restricting reconstruction quality. Further-more, while SfM-based pseudo ground-truth poses introduce sensor noise,purely rendering-based supervision often leads to optimization instabil-ity and local minima due to the entangled optimization of geometryand pose. To address the challenges, we propose a synergistic pose-free framework that explicitly couples geometry and appearance via aRaymap-Guided Coupling Module (RGC). Concretely, we anchor Gaus-sian centers to raymap-induced geometry and jointly optimize RGB re-construction, raymap consistency, and camera regularization under a uni-fied objective, yielding a bidirectional feedback loop: stronger geometryimproves rendering, and appearance supervision in turn refines geometryand pose. To further stabilize learning across wide temporal ranges, weintroduce a Dual-Frequency Viewpoint Scheduling strategy that com-bines easy-to-hard interval expansion with replay of short-interval pairs.Extensive experiments across in-domain and cross-domain datasets showconsistent gains in both rendering and pose estimation, with notably im-proved robustness on long sequences. Ablation studies validate our cen-tral insight: explicitly designed geometry-appearance synergy is the keyto scalable and drift-robust pose-free feed-forward 3D reconstruction.

Abstract:
Pose-free feed-forward 3D Gaussian Splatting enables novelview synthesis from uncalibrated multi-view images. Although more viewsshould improve performance, existing methods often degrade with dense-view inputs because global aggregation spreads attention over many to-kens, and naive voxel fusion averages many Gaussians into overly smoothrepresentations. We present AVSplat, a framework that turns additionalviews into reliable signals for both aggregation and representation. Be-fore global attention, each view performs a single lightweight interactionwith a small set of Assist Views chosen for relevance and diversity, andthe cached features provide a focused scene context that stabilizes corre-spondence. For representation, we use adaptive temperature-aware voxelfusion that sharpens attribution under high occupancy, guided by oc-cupancy and point confidence. Crucially, AVSplat restores positive viewscaling where performance remains stable or improves as more inputviews are added, instead of degrading in the dense-view regime. Abla-tions show that Assist View Preconditioning is primarily responsible forpreventing dense-view degradation, while Occupancy-guided Voxel Fu-sion contributes most of the single-point image-quality gains.

Abstract:
Existing 3D understanding models are typically built on afixed geometric representation and often fail when the representationformat changes. Unlike domain generalization, which studies distribu-tion shift under a fixed input structure, we focus on representation shift,where identical 3D semantics are expressed through heterogeneous geo-metric encodings such as points, voxels, meshes, and 3D Gaussian prim-itives. As 3D representations continue to evolve beyond a predefinedset, this challenge is becoming increasingly important. We formalize thisproblem as Representation Generalization (RG), a new setting for unified3D learning. RG requires a model to share knowledge across representa-tions during training, remain effective when only a single representationis available at test time, and extend efficiently to unseen or newly emerg-ing representations. To this end, we propose ReGen3D, a unified frame-work with three modules: Unified Representation Tokenization (URT),which maps heterogeneous representations into a shared token space;Shared Memory Interaction (SMI), which enables cross-representationlearning during training while supporting inference from any single avail-able representation; and Efficient Representation Adapter (ERA), whichsupports lightweight adaptation to unseen representations without modi-fying the shared backbone. We further establish RepShift-3D for system-atic evaluation under the RG setting. Extensive experiments on classi-fication, segmentation, and reconstruction show that ReGen3D achievesstrong and robust performance across diverse representation scenarios.

Abstract:
Active 3D Gaussian Splatting methods select views usingfactorised per-pixel metrics, which cannot represent relationships be-tween surfaces. Incompleteness that arises from unverified geometric re-lationships therefore goes undetected. We present ActiveStructure,which lifts information gain from pixels to scene-level relational topol-ogy through three innovations: (1) a Plane Scene Graph whose topolog-ical health, captured through node connectivity, edge completeness, andangular consistency, quantifies a form of structural completeness thatno pixel-level criterion can reach; (2) a gain decomposition that splitsthe value of a viewpoint into a rendering-fidelity term and a structural-integrity term that can be optimised independently; (3) informationalpolarity reversal, which lets scaffold planes switch from exploration at-tractors to suppressors and provides state-driven phase control tied tostructural objectives. Experiments on Replica and Matterport3D demon-strate state-of-the-art reconstruction accuracy and rendering quality.

Abstract:
Novel-view synthesis of dynamic scenes, crucial for AR/VRapplications, remains a challenging problem. Recent methods adapt rep-resentations like 3D Gaussian Splatting (3DGS) and Neural RadianceFields (NeRF) for dynamic scenes by incorporating time as the fourth di-mension (4D representations). These 4D representations still suffer fromaliasing artifacts, especially when generating novel views from divergentviewpoints (zoom-in/zoom-out operations). While using 3D smoothingfilters like those proposed in Mip-Splatting might seem like a possible so-lution, they fail to account for local motion and also exhibit aliasing. Toaddress this, we propose a motion-aware 3D smoothing filter specificallydesigned for 4D representations. Our approach adapts the filter strengthbased on local motion information, effectively mitigating aliasing withoutcompromising rendering quality. This is achieved by estimating the jointdensity function of time and focal-to-depth ratio using a non-parametricestimation method. During inference, we sample from this joint distribu-tion to determine the appropriate smoothing filter. This flexible strategycan be integrated with various 4D representations. Our evaluations onstandard datasets demonstrate superior performance compared to state-of-the-art methods.

Abstract:
3D scene inpainting is essential for reconstructing areas cor-rupted by occlusions or limited viewpoints. While recent methods lever-age Gaussian Splatting (GS) for efficient 3D editing, they often de-pend on precise multi-view segmentation masks and are inherently con-strained to object removal tasks. We propose CoIn, a novel frameworkthat bridges 2D inpainting models and 3DGS through a multi-stage con-sistency pipeline. Our approach first generates initial inpainted imagesusing a diffusion model, enabling the use of arbitrary-shaped masks anddiverse tasks like object insertion. We then introduce Reference Adap-tive GS with Feature Attention to reconstruct a coarse 3D scene byadaptively weighing towards a reference view (2D → 3D). This 3D rep-resentation provides geometric guidance to the diffusion process via GS-based Reference Feature Warping, ensuring multi-view consistency (3D→ 2D). Finally, a Texture-Enhancing Discriminator refines the 3D sceneto achieve high photometric realism (2D → 3D). Experiments show thatCoIn, effectively leveraging bidirectional information flow, achieves state-of-the-art performance and effectively handles both object removal andobject insertion with flexible mask input.

Abstract:
3D Gaussian Splatting (3DGS) has emerged as a powerfultechnique for real-time, high-fidelity novel view synthesis. Despite theseadvantages, its multi-million point representation leads to large stor-age footprints that hinder asset distribution and on-device deployment.Existing compression approaches typically rely on access to training im-ages, camera poses, or the rendering pipeline for retraining or iterativefine-tuning. However, in realistic post-training and archival scenarios,often only the optimized model parameters are available. To addressthis limitation, we propose a render-agnostic, post-training compressionpipeline for 3DGS operating strictly under a model-only constraint. Wefirst introduce a camera-independent, multi-criteria importance score toprune redundant Gaussians based on a combination of geometric, spa-tial, and appearance-related cues. Following this pruning, a lightweight,training-free refinement step conservatively restores local coverage. Tofurther reduce the storage footprint, we apply degree-wise vector quanti-zation to the spherical harmonic coefficients and compress the remainingattributes via entropy coding. The compression performance is signifi-cantly enhanced by a custom spatial reordering to maximize local dataredundancy. Experiments on standard benchmarks show that our ap-proach achieves an average of 30× compression rate while maintainingcompetitive visual quality against existing methods.

Abstract:
Despite modeling temporal motion, dynamic 3D GaussianSplatting (3DGS) methods still inherit a static densification strategy ill-suited for dynamic scenes. This neglect of temporal behavior leads tounder-reconstructed and blurry dynamic regions, as short-lived Gaus-sians receive sparse supervision and fail to densify effectively. We pro-pose a Visibility-Aware Densification (VAD) framework that integratestemporal visibility into the densification process, ensuring that Gaus-sians are refined based on their actual temporal presence. A Temporally-Adaptive Thresholding (TAT) mechanism further adjusts each Gaussian’sdensification threshold according to its temporal lifespan, promoting bal-anced refinement of both static and dynamic regions. Finally, a TemporalOffset Warping (TOW) design enhances deformation capacity aroundtemporal centers, extending the lifespan of highly dynamic Gaussiansand facilitating more effective densification. Our approach achieves sub-stantial improvements in the visual quality of dynamic regions, outper-forming existing methods across three dynamic multi-view benchmarkdatasets. Moreover, the proposed VAD module generalizes across diversedynamic 3DGS methods, consistently improving dynamic reconstructionas a plug-and-play component. The project page is available here.

Abstract:
Video diffusion models lack explicit geometric supervisionduring training, leading to inconsistency artifacts such as object defor-mation, spatial drift, and depth violations in generated videos. To addressthis limitation, we propose a geometry-based reward model that leveragespretrained geometric foundation models to evaluate multi-view consis-tency through cross-frame reprojection error. Unlike previous geometricmetrics that measure inconsistency in pixel space, where pixel intensitymay introduce additional noise, our approach conducts error computa-tion in a pointwise fashion, yielding a more physically grounded and ro-bust error metric. Furthermore, we introduce a geometry-aware samplingstrategy that filters out low-texture and non-semantic regions, focusingevaluation on geometrically meaningful areas with reliable correspon-dences to improve robustness. We apply this reward model to align videodiffusion models through two complementary pathways: post-training ofa bidirectional model via SFT or Reinforcement Learning and inference-time optimization of a Causal Video Model (e.g., Streaming video genera-tor) via test-time scaling with our reward as a path verifier. Experimentalresults validate the effectiveness of our design, demonstrating that ourgeometry-based reward provides superior robustness compared to othervariants. By enabling efficient inference-time scaling, our method offersa practical solution for enhancing open-source video models without re-quiring extensive computational resources for retraining.

Abstract:
Incremental scene reconstruction is essential for real-worldapplications. Although 3D Gaussian Splatting shows strong potential,most existing approaches require offline conversion of the optimized Gaus-sians into an intermediate implicit field for explicit mesh extraction,which hinders seamless integration with downstream tasks. To addressthis limitation, we propose a novel online framework that incrementallyreconstructs and updates high-fidelity explicit meshes by directly trian-gulating a dense geometric Gaussian representation, which supports bothhigh-quality rendering and incremental surface reconstruction. More-over, we present a direct meshing algorithm that efficiently extractsand updates the mesh from the Gaussian set. To ensure mesh accu-racy, we enforce a plane-based pulling constraint that dynamically aligns3D Gaussian primitives to the approximated local surface. Furthermore,our framework significantly reduces memory and computational overheadduring long-sequence processing by dynamically freezing fully optimizedhistorical regions. Experiments on public datasets demonstrate that ourmethod outperforms conventional Gaussian-based methods on both ren-dering quality and reconstruction accuracy.

Abstract:
Large Chunk Test-Time Training (LaCT) has shown strongperformance on long-context 3D reconstruction, but its fully plastic inference-time updates remain vulnerable to catastrophic forgetting and overfit-ting. As a result, LaCT is typically instantiated with a single large chunkspanning the full input sequence, falling short of the broader goal ofhandling arbitrarily long sequences in a single pass. We propose ElasticTest-Time Training inspired by elastic weight consolidation, that sta-bilizes LaCT fast-weight updates with a Fisher-weighted elastic prioraround a maintained anchor state. The anchor evolves as an exponentialmoving average of past fast weights to balance stability and plasticity.Based on this updated architecture, we introduce Fast Spatial Memory(FSM), an efficient and scalable model for 4D reconstruction that learnsspatiotemporal representations from long observation sequences and ren-ders novel view-time combinations. We pre-trained FSM on large-scalecurated 3D/4D data to capture the dynamics and semantics of complexspatial environments. Extensive experiments show that FSM supportsfast adaptation over long sequences and delivers high-quality 3D/4D re-construction with smaller chunks and mitigates the camera-interpolationshortcut. Overall, we hope to advance LaCT beyond the bounded single-chunk setting toward robust multi-chunk adaptation, a necessary stepfor generalization to genuinely longer sequences, while substantially al-leviating the activation-memory bottleneck.

Abstract:
We present ViewSplat, a view-adaptive 3D Gaussian splat-ting network for novel view synthesis from unposed images. While recentfeed-forward 3D Gaussian splatting has significantly accelerated 3D scenereconstruction by bypassing per-scene optimization, a fundamental fidelitygap remains. We attribute this gap to the limited capacity of single-stepfeed-forward networks to regress static Gaussian primitives that sat-isfy all viewpoints. To address this limitation, we shift the paradigmfrom static primitive regression to view-adaptive splatting. Instead of arigid Gaussian representation, our pipeline learns a view-adaptive latentrepresentation. Specifically, ViewSplat initially predicts base Gaussianprimitives alongside the weights of scene-conditioned View MLPs. Duringrendering, these MLPs take target-view coordinates as input and predictview-dependent residual updates for each Gaussian attribute (i.e., 3Dposition, scale, rotation, opacity, and color). This mechanism, whichwe term view-adaptive splatting, allows each primitive to rectify initialestimation errors, effectively capturing high-fidelity appearances. Exten-sive experiments demonstrate that ViewSplat achieves state-of-the-artfidelity while maintaining fast inference and real-time rendering; our largebackbone variant runs at 15 FPS during inference and 90 FPS duringrendering.

Abstract:
Extracting structured, parametric 3D representations fromraw images remains a fundamental challenge in computer vision andgraphics. While recent advancements in the 3D Gaussian Splatting (3DGS)pipeline integrate planar primitives to yield compact and editable geom-etry, these approaches typically treat planes as isolated, discrete sets.This lack of topological connectivity hinders robust geometric reasoning,leading to fragmented reconstructions and misaligned boundaries thatfall short of the precision for rigorous spatial analysis and professionaldesign workflows. To address this, we introduce TopoGS, the first 3DGSframework to explicitly integrate both planar and topological constraintsfor coherent 3D reconstruction. Specifically, we extract global 2D topo-logical relationships from multi-view image segmentations and anchorGaussian primitives to these structural elements. This formulation en-ables the joint optimization of plane parameters, rendering fidelity, andtopological adjacency. By enforcing strict multi-view consistency along-side these topological constraints, our method significantly mitigates ge-ometric misalignments and produces connected, structured 3D models.Extensive evaluations on the ScanNet++ dataset demonstrate that To-poGS achieves state-of-the-art performance, providing a highly robustsolution for generating accurate, topologically sound, and visually faith-ful scene representations.

Abstract:
Adapting 2D foundation models for 3D spatial understanding faces a critical dilemma. Fine-tuning 2D models to learn 3D geometry causes the catastrophic forgetting of native 2D knowledge. Conversely, freezing the 2D model restricts 3D spatial perception. To resolve this issue, we propose DINOv3D, a joint optimization framework built upon DINOv3 that employs a homologous teacher-student architecture to establish a regularized integration between the 2D and 3D understanding. To preserve original 2D priors, DINOv3D distills 2D knowledge into a 3D Gaussian regularization field, which aligns the rendered features with the reference visual features from a frozen teacher model. Meanwhile, a student model processes long-context multi-view inputs through parameter-efficient fine-tuning. This step injects 3D spatial consistency priors to 2D model while using the regularization field to mitigate the forgetting of 2D knowledge. To enrich the hierarchical representation of 3D spatial understanding, the student model predicts additional semantic and instance Gaussian features. We then apply a ray-depth-semantic alignment mechanism, which uses 3D depth-ray priors to enforce multiview consistency across 2D semantic rendering. Extensive experiments demonstrate that, despite updating only 10% of the parameters of a 1B-parameter foundation model VGGT, DINOv3D achieves state-of-theart results in comprehensive 3D scene understanding across challenging novel view synthesis, depth estimation, and open-vocabulary semantic segmentation. Furthermore, DINOv3D consistently enhances the generalizability of the DINOv3 backbone on 2D linear probing benchmarks.

Abstract:
Semantic matching aims to establish pixel-level correspon-dences between instances of the same category, and previous approachesbased on 2D foundation models have achieved promising results on thistask. However, they fail to achieve geometry-grounded dense semanticmatching, which encompasses geometric awareness, manifold preserva-tion, and cross-image invisibility reasoning. This new matching task si-multaneously needs geometry-aware descriptors and holistic dense match-ing mechanisms, but existing approaches rarely provide both. To bridgethis gap, we leverage VGGT, a 3D geometric foundation model that in-herently provides strong geometry-grounded priors to support both capa-bilities in a unified framework. However, directly transferring faces twochallenges: task heterogeneity, where VGGT originally matches cross-view images of the same instance, not cross-instance variations in shapeor appearance; and scarce dense matching annotations for task adapta-tion. To address these challenges, we propose an approach that (i) retainsVGGT’s intrinsic capabilities by reusing early feature stages, fine-tuninglater ones, and adding a semantic head for bidirectional correspondences;and (ii) adapts VGGT for semantic matching under data scarcity throughcycle-consistent training strategy, synthetic data augmentation, and pro-gressive training recipe. Extensive experiments demonstrate that our ap-proach achieves superior geometric awareness, manifold preservation, andmatching reliability, outperforming previous baselines.

Abstract:
Interactive segmentation of 3D Gaussians offers a compellingopportunity for real-time manipulation of 3D scenes, thanks to the real-time rendering capability of 3D Gaussian Splatting (3DGS). However, ex-isting methods require a time-consuming per-scene setup—typically tensof seconds or even minutes—before interactive segmentation can beginon a raw 3DGS scene. This setup involves multi-view mask preparation,mask lifting, and feature distillation, creating a major bottleneck for on-line applications. To address this limitation, we aim to completely elimi-nate the setup stage for interactive 3DGS segmentation while keeping thesegmentation time practical (under 1 second). In this work, we presentSAGO (Segment Any Gaussians Online), a novel setup-free frameworkfor interactive 3DGS segmentation. By introducing virtual drones, ourmethod reframes the 3D segmentation problem as an online Next-Best-View (NBV) planning task formulated within a Markov process. Exten-sive experiments demonstrate that SAGO can extract clean 3D assetsdirectly from 3D Gaussians with sub-second latency, thereby enablinga broad range of downstream applications such as object manipulationand scene editing. Moreover, our method achieves over a 50× speedupcompared to the previous setup-free 3DGS segmentation frameworks.

Abstract:
While feedforward 3D reconstruction excels at efficient novelview synthesis, it typically falters when faced with scenes under varyingillumination. To this end, we introduce WildSplat, the first feedforward3D Gaussian Splatting framework capable of appearance-conditionednovel-view synthesis for unposed in-the-wild images. To handle inconsis-tent photometric conditions, we propose a dual-branch architecture thatexplicitly decouples geometry from appearance. The geometry branchextracts an appearance-invariant 3D structure and jointly predicts cam-era poses. To govern the rendering appearance, the appearance branchinjects target appearance cues into the content features via a globallypre-modulated cross-attention mechanism. To further prevent featureentanglement, we introduce a joint multi-reference training strategy thatstabilizes the training process. Extensive experiments show that Wild-Splat surpasses existing optimization-based and feedforward methods,achieving state-of-the-art performance in in-the-wild novel view synthesisand appearance editing from sparse inputs in a single forward pass.

Abstract:
Rendering views using 3D scene representations such as Gaus-sian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or evenpoint clouds produces artifacts when input views are sparse or targetviews lie far from the input. Recent work mitigates these artifacts usingdiffusion-based generative priors, but is specialized to individual repre-sentations and require custom architectures or extensive retraining. Wepresent FixAnything, a single model for fixing a wide range of render-ing artifacts. It does so by repurposing a pretrained video generativemodel, leveraging its implicit multi-view priors with only minimal mod-ification and lightweight finetuning. Our key insight is that even noisily-rendered sequences preserve camera motion and coarse scene structure,allowing cleanup to be formulated as video-to-video translation. To con-trol what scene structure should be preserved, we introduce a binarymask denoting the clean pixels, enabling the model to anchor its outputto high-quality inputs (e.g. training views) while refining the rest. To en-courage FixAnything to produce 3D-consistent renderings that supportdownstream reconstruction, we use camera pose accuracy (recovered viastructure-from-motion) as a reward signal for direct preference optimiza-tion (DPO). Across four distinct 3D representations, FixAnything con-sistently improves rendering quality with lightweight finetuning, demon-strating that a single generalist video prior can replace multiple specialistrefinement pipelines. The simplicity of the framework enables immediateadoption of stronger future video models without architectural redesign.

Abstract:
Learning-based image-to-point-cloud (I2P) registration hasgarnered increasing attention in recent years. Nevertheless, existing meth-ods still struggle with severe outliers under challenging scenarios withunseen, low-inlier, or distorted cases. A fast and robust 2D-3D correspon-dence pruning method is therefore highly desirable. Recently, a promis-ing scheme lifts 2D-3D correspondences to 3D-3D correspondences usingdepth priors, casting correspondence pruning as a Sim(3) registrationproblem. However, depth priors estimated from monocular images areinherently noisy, which undermines the reliability of this scheme. In thispaper, to explicitly model non-negligible depth noise, we reformulatecorrespondence pruning as an extended Sim(3) registration problem andpropose a simple yet effective pruning algorithm termed Ex-Sim(3)-Reg.We further provide a theoretical analysis to justify the effectiveness ofour method. Extensive experiments on the 7-Scenes, RGBD-V2, Scan-Net, and TUM datasets demonstrate that Ex-Sim(3)-Reg achieves up to24.7% improvement in registration recall over state-of-the-art baselinemethods. Code is released at github.com/anpei96/ex-sim3-demo.

Abstract:
Unsupervised registration of large-scale LiDAR point cloudsremains challenging due to the geometric ambiguity inherent in out-door scenes, which degrades pseudo-label quality and leads to subop-timal convergence, particularly for sparse, low-resolution scans such asthose from nuScenes. We reveal that registration models intrinsicallyencode semantic awareness that strongly correlates with registration ac-curacy, albeit without explicit semantic supervision. However, this nativeawareness is fragile: noisy supervision arising from geometric ambigu-ity in unsupervised settings rapidly erodes the learned semantic struc-ture, causing performance collapse. To this end, we propose CAESAR,a teacher-student framework guided by an off-the-shelf 3D segmenta-tion model exclusively during training. We observe that potential in-lier matches are often buried just beneath a few spurious neighbors inthe noisy feature space, motivating Dual-Cue Guided Re-Matching torecover them through reselection rather than simply rejecting. Build-ing on this, a train-only Semantic-Geometric Label Mining performsCorresponding author.lightweight, batch-specific teacher refinement and mines reliable pseudo-labels under semantic guidance. We further introduce Semantic Predic-tive Distillation to consolidate the student’s semantic awareness in thefeature space. Extensive experiments on KITTI and nuScenes demon-strate state-of-the-art performance, with pronounced gains on the chal-lenging nuScenes benchmark. Crucially, CAESAR incurs zero inferenceoverhead and requires no semantic annotations on the registra-tion data.

Abstract:
3D point cloud semantic segmentation remains a fundamental challenge for autonomous driving and robotic perception. While Transformerbased architectures have achieved significant progress by capturing longrange dependencies, existing methods rely on deterministic point-wise predictions that fail to maintain semantic consistency across geometric voids such as occlusions and sparse boundaries. This can be attributed to the isotropic nature of conventional discrete feature aggregation, which lacks the directional awareness necessary to propagate reliable information across these discrete gaps. To address this, we propose Uncertainty-driven Gaussian Sphere Propagation (UGSP), a framework that transitions from discrete point processing to continuous geometric field reconstruction. By leveraging approximate Bayesian inference to identify reliable semantic anchors and high-uncertainty voids, a Spherical Harmonics (SH)-based aggregation mechanism is introduced that models the local scene as a collection of anisotropic Gaussian spheres. This approach enables the interpolation of semantic information along continuous spatial paths, allowing for direction-aware semantic propagation that effectively recovers structural integrity in high-uncertainty regions. Extensive experiments on indoor and outdoor benchmarks demonstrate the superiority of UGSP in semantically ambiguous and geometrically complex scenarios. The source code is publicly available at https: //github.com/LENGYI1221/UGSP.

Abstract:
Image-to-point cloud registration aims to estimate the 6-DoFcamera pose of a query image with respect to a 3D point cloud, whichremains challenging due to the heterogeneous nature of dense visual ap-pearance and sparse geometric structure. Most existing learning-basedmethods follow an overlap–then–registration paradigm, where pose esti-mation is restricted to a predicted overlapping region. Such a sequentialdecomposition is inherently fragile, as inaccurate overlap prediction maydiscard geometrically consistent regions and irreversibly constrain poseoptimization. In this work, we reformulate image-to-point cloud registra-tion as a pose–geometry coupled reasoning problem, where camera poseand scene geometry are treated as interdependent variables. Based onthis formulation, we propose PGCR, a unified framework that estimatescamera pose by explicitly reasoning over dense cross-modal geometry.PGCR jointly predicts camera pose together with dense geometric rep-resentations and grounds pose inference in global geometric consistency.To enable effective pose–geometry coupling, we introduce a Geometry-aware Pose Refinement mechanism to enforce prediction-level consistencybetween pose and geometry, and a Pose-guided Progressive Refinementstrategy to adapt cross-modal interaction according to intermediate poseestimates. Extensive experiments on two widely used outdoor and twoindoor benchmarks demonstrate that PGCR consistently outperformsprior state-of-the-art methods across all evaluation metrics while remain-ing computationally efficient.

Abstract:
We present Scale3D, an autoregressive model for generatinghigh-quality outdoor 3D scenes at scale. Scaling 3D generation to vastenvironments like city blocks usually degrades local geometric detailsas the layout expands. Scale3D decouples this problem into a two-stageprocess. A causal autoregressive model x001C rst plans the global layout bypredicting scene chunks over a 2D spatial grid. A masked autoregressivemodel then synthesizes the actual 3D geometry within each chunk. Wereconstruct these local structures using a 3D chunk-based VAE designedto preserve sharp edges and high- x001C delity surfaces. We also introduce apipeline that extracts spatial relationships directly from geometry, al-lowing users to control complex scene layouts via simple text prompts.Unlike patch-based di x001B usion models that struggle with boundary arti-facts, Scale3D maintains long-range spatial coherence across expansiveoutdoor datasets while rendering detailed 3D structures.

Abstract:
High-fidelity 3D generative modeling increasingly relies onthe latent diffusion paradigm, where the reconstruction quality of theunderlying 3D VAE becomes a primary bottleneck. Existing approacheslargely follow two paradigms: sparse voxel-based representations achievestrong reconstruction quality but incur significant memory and compu-tational overhead, while set-based representations are compact and con-tinuous yet typically lag in fidelity due to latent sparsity and excessiveglobal smoothness. We propose MSVS-VAE, a hierarchical set-based VAEthat closes this fidelity gap without sacrificing compactness. Our keyidea is to progressively densify anchored VecSet latents via hierarchicalpoint-shuffle upsampling, increasing spatial capacity for fine-grained ge-ometry modeling. To efficiently decode from the densified hierarchy, wereplace global cross-attention with AVS-Conv, a geometry-aware localaggregation operator operating within local neighborhoods rather thanthe exhaustive latent set. We further introduce multi-scale query decod-ing to fuse coarse-to-fine latent features, where coarse scales providestable global context, and fine scales refine localized geometry, reducingartifacts from overly local receptive fields. Extensive experiments on Ob-javerse, ABO, and in-the-wild benchmarks demonstrate that MSVS-VAEconsistently outperforms prior set-based and voxel-based VAEs, deliver-ing ∼10× faster decoding than prior set-based methods and ∼10× highercompactness than voxel-based baselines.

Abstract:
We propose Event-LiDAR , a 3D eventification frameworkthat converts conventional LiDAR scans into temporally sparse repre-sentations for efficient point cloud processing. Unlike dense multi-scanprocessing, naive frame differencing, or correspondence-based residuals,3D event extraction is formulated as a temporal estimation problemunder sparse, viewpoint-dependent observations. Short-term geometricevolution across consecutive scans is approximated with a first-ordergeometric model, and deviations unexplained by this model are treatedas events. This yields compact, information-preserving representationsthat suppress redundancy while retaining changes unpredictable by thefirst-order model. Event-LiDAR is applied to LiDAR-based 3D objectdetection with existing backbones, and an event-aware network designis further introduced to reallocate modeling capacity toward the inputstage for sparse inputs. On nuScenes, under the 1F+9T setting witha 72% point reduction, Event-LiDAR maintains accuracy comparableto full-scan baselines while achieving 23% faster end-to-end inferenceon PTv3, driven in part by a 32% speedup in its feature extractionbackbone. The gains generalize across backbones, with a 9% end-to-endspeedup on CenterPoint under the same setting. Training is likewiseaccelerated by 17% under the same setting. Event-LiDAR thus serves asa low-latency, architecture-agnostic geometric preprocessor inspired byevent-based sensing, providing a practical front-end for efficient 3D pointcloud perception.

Abstract:
Category-Agnostic Pose Estimation (CAPE) aims to local-ize category-specific keypoints on a query image from only a few anno-tated support examples. Existing CAPE methods are largely based ondirect coordinate regression or local heatmap matching, which becomeunreliable under severe occlusion because missing keypoints cannot berecovered from local evidence alone. We present FlowCape, a condi-tional flow-matching framework for occlusion-resilient CAPE. FlowCapefirst extracts query-image features and combines them with keypoint-description embeddings in a Heatmap-Guided Initialization module, pro-ducing a stable initial pose for transport. A shared Riemannian PoseHead then predicts the conditional velocity field, while its internal GraphFlow Encoder fuses query image, textual keypoint semantics, and skele-ton topology to enforce structured pose evolution. The final predictionis obtained by probability-flow ODE rollout, and training is driven byhybrid flow-matching supervision together with initialization and roll-out consistency constraints. To better evaluate robustness under severeocclusion, we further introduce Occ80, an occlusion-focused benchmarkspanning 80 categories. Experiments on MP-100 and Occ80 show thatFlowCape consistently outperforms strong CAPE baselines, reach thestate-of-the-art under occluded scenario.

Abstract:
Point cloud segmentation is critical for 3D scene understand-ing. However, sparse and irregular point distributions provide limitedappearance evidence, making geometry-only features insufficient to dis-tinguish objects with similar shapes but distinct appearances (e.g., color,texture, and material). We propose Gaussian-to-Point (G2P), which trans-fers Gaussian attributes from 3D Gaussian Splatting to point clouds formore discriminative and appearance-consistent segmentation. Our G2Paddresses the misalignment between optimized Gaussians and originalpoint geometry by establishing point-wise correspondences. By distillingopacity-derived visibility cues, we mitigate the geometric ambiguity thatlimits existing models. Additionally, Gaussian scale attributes enableprecise boundary localization in complex 3D scenes. Extensive experi-ments demonstrate that our approach achieves competitive performanceon standard benchmarks and shows notable improvements on geomet-rically challenging classes, without pretrained 2D features or languagesupervision in our segmentation pipeline.

Abstract:
Skeleton-based motion data leak personally identifiable information through both static skeletal structure and dynamic motion patterns, enabling re-identification even without facial features. We present DisentangledTMR, a Transformer Motion Retargeting (TMR) architecture that achieves privacy through explicit architectural disentanglement. Two encoders with complementary inductive biases, temporal convolutions for action and spatial graph convolutions for identity, feed a factorized decoder that fuses their representations through separate crossattention streams and adaptive gating. A three-stage training curriculum progressively establishes disentanglement, reconstruction, and endto-end refinement, and a tunable partial-retargeting ratio trades privacy for compatibility with pre-trained downstream models. On three benchmarks, DisentangledTMR substantially reduces re-identification while preserving action recognition, outperforming single-encoder baselines.

Abstract:
Metric feed-forward 3D reconstruction for panoramic data remains under-explored due to the lack of large-scale panoramic RGBD training data. We present Realsee3D, a hybrid dataset of 10K indoor scenes (1K real, 9K synthetic) with 299K panoramic viewpoints and precise metric annotations, and Argus, a feed-forward network trained on it for metric panoramic 3D reconstruction. In the sparse unordered capture setting of Realsee3D, a poorly chosen coordinate anchor can cause global pose drift. Argus addresses this with a learned covisibility module that selects the geometrically optimal reference view to anchor the metric world frame. To further improve multi-task learning, we decompose the bidirectional pixel-to-world mapping into interpretable sub-steps with per-step supervision and cross-coordinate joint constraints, reinforcing geometric consistency across prediction branches. On the Realsee3D benchmark, Argus achieves state-of-the-art metric performance in camera pose estimation, depth estimation, and point cloud reconstruction. Project page: https://argus-paper.realsee.ai.

Abstract:
Computational pathology needs whole-slide image (WSI) foun-dation models that transfer across diverse clinical tasks, yet current ap-proaches remain largely slide-centric, often depend on private data andexpensive paired-report supervision, and do not explicitly model relation-ships among multiple slides from the same patient. We present MOOZY,a patient-first pathology foundation model in which the patient case, notthe individual slide, is the core unit of representation. MOOZY explicitlymodels dependencies across all slides from the same patient via a casetransformer during pretraining, combining multi-stage self-supervisionwith scaled low-cost task supervision. In Stage 1, we pretrain a vision-only slide encoder on 77,134 public slide feature grids using masked self-distillation. In Stage 2, we align these representations with clinical se-mantics using a case transformer and multi-task supervision over 333tasks from 56 public datasets, including 205 classification and 128 sur-vival tasks across four endpoints. Across sixteen held-out tasks, MOOZYimproves macro weighted F1, balanced accuracy, and macro weightedROC-AUC relative to PRISM by +4.19%, +7.93%, and +6.95%, re-spectively. MOOZY is also parameter efficient with 85.77M parameters,14× smaller than GigaPath. These results suggest that patient-level pre-training yields transferable embeddings, providing a path toward scalablepatient-first histopathology foundation models.

Abstract:
Pansharpening aims to fuse a high-resolution panchromatic(PAN) image and a low-resolution multispectral (LRMS) image to pro-duce a high-resolution multispectral (HRMS) image. Recent deep modelshave achieved strong performance, yet they typically rely on large-scalepretraining and often generalize poorly to unseen real-world image pairs.Prior zero-shot approaches improve real-scene generalization but requireper-image optimization, hindering weight reuse, and the above methodsare usually limited to a fixed scale. To address this issue, we proposeG-ZAP, a generalizable zero-shot framework for arbitrary-scale pansharp-ening, designed to handle cross-resolution, cross-scene, and cross-sensorgeneralization. G-ZAP adopts a feature-based implicit neural representa-tion (INR) fusion network as the backbone and introduces a multi-scale,semi-supervised training scheme to enable robust generalization. Extensiveexperiments on multiple real-world datasets show that G-ZAP achievesstate-of-the-art results under PAN-scale fusion in both visual qualityand quantitative metrics. Notably, G-ZAP supports weight reuse acrossimage pairs while maintaining competitiveness with per-pair retraining,demonstrating strong potential for efficient real-world deployment.

Abstract:
We introduce iMED, the first synchronized multi-endoscopedataset for robot-assisted minimally invasive surgery (RAMIS), designedto address a fundamental limitation in surgical vision benchmarking: theabsence of independent held-out viewpoints. Existing surgical datasetscapture single-trajectory sequences from a single endoscope, making itimpossible to distinguish geometric generalization from photometric in-terpolation along a narrow forward-facing path. iMED provides 340 se-quences (≈170K synchronized timepoints with 4 views per timepoint)recorded simultaneously from two independent stereo endoscopes acrossex vivo, postmortem, and live surgical settings, spanning 14 specimenswith diverse anatomical regions and motion regimes. The dataset in-cludes calibrated camera intrinsics, ArUco-based frame-wise pose esti-mates with uncertainty quantification, instrument segmentation masks,and rich clinical metadata. Using a train-on-one-endoscope, test-on-anotherevaluation protocol, we benchmark 23 state-of-the-art methods acrossrigid and deformable novel view synthesis, pose estimation, feature match-ing, and monocular depth estimation. Our experiments show that meth-ods relying primarily on photometric supervision degrade substantiallyunder this held-out-endoscope setting, while methods with explicit ge-ometric regularization are more robust across views in our benchmark.Notably, large-scale foundation models transfer surprisingly well to sur-gical imagery, often outperforming domain-specific models, suggestingpitfalls in current fine-tuning protocols. iMED establishes a new evalua-tion protocol for geometric generalization in surgical vision. Dataloaderand dataset links can be found at github.com/surgical-vision/imed.

Abstract:
As neuromorphic sensors, event cameras asynchronously recordchanges in brightness as streams of sparse events with the advantages ofhigh temporal resolution and high dynamic range. Reconstructing inten-sity images from events is a highly ill-posed task due to the inherent am-biguity of absolute brightness. Early methods generally follow an end-to-end regression paradigm, directly mapping events to intensity frames ina deterministic manner. While effective to some extent, these approachesoften yield perceptually inferior results and struggle to scale up in modelcapacity and training data. In this work, we propose EvDiff, an event-based diffusion model that follows a surrogate training framework to pro-duce high-quality videos. To reduce the high computational cost of high-frame-rate video generation, we design an event-based diffusion modelthat performs only a single forward diffusion step, equipped with a tem-porally consistent EvEncoder. Furthermore, our novel Surrogate TrainingFramework eliminates the dependence on paired event–image datasets,allowing the model to leverage large-scale image datasets for higher ca-pacity. The proposed EvDiff is capable of generating high-quality col-orful videos solely from monochromatic event streams. Experiments onreal-world datasets demonstrate that our method strikes a sweet spotbetween fidelity and realism, outperforming existing approaches on bothpixel-level and perceptual metrics. The code will be released publicly.

Abstract:
In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abnormalities, predicting likelihoods of abnormality, or, with downstream adaptation, generating radiological reports. While the methodology holds promise, three challenges limit the capabilities of current 3D VLEs: data scarcity due to privacy concerns, high computational costs resulting from the volumetric nature of the images, and a domain shift between the long reports used for training and the short prompts used during inference for, e.g., zeroshot classification. As a consequence, natural-image VLE recipes do not directly transfer to 3D medical imaging. In this paper, we overcome these challenges by injecting additional supervision via a report generation objective and combining vision-language with vision-only pre-training, allowing us to leverage both image-only and paired image-text 3D datasets. Further, we propose a novel loss that addresses the domain shift between long reports and short textual prompts. Through these additional objectives, paired with best practices of the 3D medical imaging domain, we develop the Comprehensive Language–Image Pre-training (COLIPRI) encoder family. Our COLIPRI encoders achieve state-of-the-art performance in report generation, semantic segmentation, classification probing, and zero-shot classification. The model weights and inference code are freely available at https: //huggingface.co/microsoft/colipri.

Abstract:
The Mueller matrix encodes rich structural and physical information, but its rapid and accurate estimation remains challenging. While physics-based polarimetric Bidirectional Reflectance Distribution Function (pBRDF) models provide useful formulation constraints, their application is limited by inaccessible parameters and model mismatch. To address this challenge, we propose a physics-guided two-stage framework for single-shot linear Mueller matrix recovery. Given one set of four polarization images and an object mask under a fixed known incident polarization state, the first stage estimates pBRDF-related parameters and uses an analytical pBRDF model to construct a structured Mueller initialization. A second network then refines this initialization through residual correction. Evaluated on a newly constructed hybrid real-synthetic dataset, our method improves matrix reconstruction, observed-state rendering, held-out forward Stokes prediction, and physical-plausibility diagnostics over adapted pBRDF fitting baselines. Downstream validation through shape from polarization (SfP) and material classification further provides secondary evidence that the recovered matrices preserve useful polarimetric cues for geometric and material analysis.

Abstract:
Medical report understanding from real-world document im-ages is essential for generating patient-facing explanations and enablingstructured information exchange in clinical systems.Existing VLMs and LLMs have shown strong performance on documentunderstanding, but structured understanding of medical reports remainsinsufficiently benchmarked. Therefore, we introduce MedRepBench, abenchmark with 1,925 de-identified Chinese medical report images span-ning diverse departments, patient demographics, and acquisition formats.In MedRepBench, we mainly focus on report-grounded interpretationrather than evaluating diagnostic reasoning, treatment recommendation,or the integration of patient history. The interpretation is defined asstructured extraction of report fields (e.g., item, value, unit, referencerange, abnormal flag) plus a patient-facing explanation grounded strictlyin the report content.The benchmark primarily evaluates end-to-end VLMs, and also includesa controlled text-only setting (high-quality OCR + LLM) to approximatean upper bound when character recognition errors are minimized. Ourevaluation framework provides two complementary protocols: (1) an ob-jective protocol measuring field-level recall of structured items, and (2)an automated subjective protocol that uses an LLM-based judge to scorefactuality, interpretability, and reasoning quality under a fixed prompt.Using the objective metric as a reward signal, we also provide a lightweightGRPO-based alignment baseline for a mid-sized VLM, which improvesfield-level recall by up to 6%.Finally, we analyze practical limitations of OCR+LLM pipelines, includ-ing layout-related errors and additional system latency, showing the needfor robust end-to-end vision-based medical report understanding. Thedataset and evaluation resources are publicly available on HuggingFace.

Abstract:
Multi-label classification models are commonly trained byassuming conditional independence among labels, typically through theuse of binary cross-entropy objectives. While this assumption simpli-fies optimization, it introduces a structural bias that ignores dependen-cies among labels and can adversely affect the calibration of predictedprobabilities. In this work, we theoretically analyze the impact of thisassumption and show that neglecting label dependencies leads to a mis-calibration effect that is linked to the conditional dependency of thelabels. Motivated by this observation, we propose Pairwise CorrelationDifference (PCD), a novel auxiliary loss designed to incorporate labeldependency information during training. PCD aligns the pairwise cor-relations of model logits with those of the ground-truth labels, therebyencouraging the network to capture conditional dependencies among la-bels. We combine PCD with the standard binary cross-entropy loss toform the Correlated Multi-Label Loss (CMLL), which serves as a objec-tive for dependency-aware training. We provide theoretical justificationfor the functional form of the proposed loss and its connection to thedependency structure of the label distribution. Extensive experimentson three benchmark multi-label datasets demonstrate that CMLL con-sistently produces better-calibrated predictions while maintaining com-petitive classification accuracy compared to existing multi-label learningobjectives. Additionally, we show that the proposed approach exhibitsrobustness to the choice of binning schemes used in calibration evalua-tion.

Abstract:
Digital cameras digitize scene light into linear raw representations,which the image signal processor (ISP) converts into display-ready outputs. Whileraw data preserves full sensor information—valuable for editing and vision tasks—formats such as Digital Negative (DNG) require large storage, making them im-practical in constrained scenarios. In contrast, JPEG is a widely supported format,offering high compression efficiency and broad compatibility, but it is not well-suited for raw storage. This paper presents Raw-JPEG Adapter, a lightweight,learnable, and invertible pre-processing pipeline that adapts raw images for stan-dard JPEG compression. Our method applies spatial and optional frequency-domain transforms, with compact parameters stored in the JPEG comment field,enabling accurate raw reconstruction. Experiments across multiple datasets showthat our method achieves higher fidelity than direct JPEG storage, supports othercodecs, and provides a favorable trade-off between compression ratio and recon-struction accuracy.

Abstract:
Offline feature extraction with pretrained encoders followedby multiple instance learning (MIL) aggregation is still the dominantparadigm for whole slide image (WSI) analysis, but the domain gap be-tween natural and histopathology images limits representation quality.End-to-end optimization can reduce this gap by training the encoder withslide-level objectives, but it often suffers from (i) cost-driven fine-grainedand morphological information loss caused by low-magnification small in-puts and random patch sampling, and (ii) sparse supervision from slidelabels alone. We propose PyraE2E, an end-to-end framework that turnsthe intrinsic multi-resolution WSI pyramid into a dense self-supervisionsignal via super-resolution (SR). For each sampled low-resolution (LR)patch at low magnification, we use its spatially aligned high-resolution(HR) patch at higher magnification as a reconstruction target, providingcross-scale pixel-level supervision while keeping computation boundedby operating on LR inputs. PyraE2E combines an embedded Cluster-Score Sampling module to select informative LR regions with a sharedGlobal–Local Partitioned (GLP) encoder whose HR-informed featuresare jointly optimized by an SR reconstruction head and a slide-level pre-diction head. Joint training couples dense cross-scale supervision withslide-level objectives, improving end-to-end representations and down-stream WSI prediction under controlled computational cost.

Abstract:
Snapshot hyperspectral imaging avoids sequential scanning,but systems that jointly achieve stable reconstruction, low cost, andcompact optics remain limited. We present a snapshot hyperspectralimaging system based on angular-to-spectral diversity conversion. A ta-pered kaleidoscope creates replicated views with distinct incidence di-rections, and a directly attached birefringent filter converts them intoview-channel-dependent spectral transmittances, yielding complemen-tary measurements that better condition the inverse problem for morestable single-shot spectral reconstruction. The system preserves a sim-ple pixel-wise linear model for fast non-learning-based reconstruction anduses only off-the-shelf components without relay optics or cascaded mod-ules. We select the birefringent filter configuration using a condition-number-based criterion and validate the system on both synthetic andreal data.

Abstract:
Achieving reliable single-shot high dynamic range (HDR)imaging under extreme illumination conditions remains a long-standingchallenge, yet no comprehensive benchmark exist for evaluating HDRperception in multi-sensor robotic systems. To fill this gap, we intro-duce a large-scale dataset collected via a custom robotic vision platformand an iPhone 13 Pro: 121 real-world scenes spanning modest and ultra-high dynamic range conditions, alongside 20 synthetic video sequencesfrom the CARLA simulator. As a reference pipeline for this dataset,we propose Depth-guided Multi-view Exposure Bracketing (DMEB), asingle-shot HDR method that distributes drastically different exposuresacross multi-view low-bit-depth cameras and fuses them via depth-guidedconfidence-aware fusion. Evaluations on our dataset show that DMEBestablishes a strong reference point and highlight the promise of thissensor configuration for robust HDR perception in diverse multi-cameraand depth sensor system.

Abstract:
This work introduces and demonstrates the first system capable of imaging fast-moving extended non-rigid objects through strong atmospheric turbulence at high frame rate. Event cameras are a novel sensing architecture capable of estimating high-speed imagery at thousands of frames per second. However, on their own, event cameras are unable to disambiguate scene motion from turbulence. In this work, we overcome this limitation using event-based light field cameras: By simultaneously capturing multiple views of a scene, event-based light field cameras and machine learning-based reconstruction algorithms are able to disambiguate motion-induced dynamics, which produce events that are strongly correlated across views, from turbulence-induced dynamics, which produce events that are weakly correlated across view. Tabletop experiments demonstrate event-based light field can overcome strong turbulence while imaging high-speed objects traveling at up to 16,000 pixels per second.

Abstract:
This paper introduces Tricam-rPPG, a multimodal datasetdesigned to support systematic studies of remote photoplethysmography(rPPG) using multispectral imaging. Remote (noncontact) monitoringoffers the potential for unobtrusive measurement of physiological signalsrelated to health, cognitive load, and affect. However, most existing rPPGdatasets rely primarily on RGB imaging, limiting the study of spectraleffects, illumination variability, and sensing biases associated with differ-ences in optical properties of the skin. Tricam-rPPG provides synchro-nized recordings of 31 human subjects from three co-located cameras: astandard RGB camera and two near-infrared (NIR) cameras operatingat 850 nm and 940 nm, all captured under controlled illumination con-ditions. For fourteen participants, simultaneous recordings from MetaAria glasses are also included. All cases are supplemented by referencewaveforms of blood volume pulses (BVP) measured using a fingertipPPG sensor. In addition to presenting the dataset, we establish base-line benchmarks with widely used rPPG algorithms to evaluate heart-rate estimation performance across different combinations of spectralchannels. By providing synchronized RGB and NIR video together withphysiological ground truth, Tricam-rPPG enables new research directionsin multispectral physiological sensing, fairness-aware rPPG algorithms,and robust remote cardiovascular monitoring. The dataset is availableunder a data use license and mutual agreement (check https://tricam-rppg.github.io).

Abstract:
We consider the problem of reconstructing a H × W × 31 hy-perspectral image from a H × W grayscale snapshot measurement thatis captured using only a single diffractive lens and a filterless panchro-matic photosensor. This problem is severely ill-posed, but we presenta model that produces high-quality results in simulation and experi-ment. We make efficient use of limited training data by creating a con-ditional denoising diffusion model that operates on small patches in ashift-invariant manner. During inference, we synchronize per-patch hy-perspectral predictions using guidance by physical consistency with thesystem’s optical point spread function. Our experiments reveal that thepatch size can be as small as the point spread function, with local opticalcues being the main source of information about complete spectra. Also,by drawing multiple samples, our model provides per-pixel uncertaintyestimates that strongly correlate with reconstruction error.

Abstract:
Images composed of 2D pixel arrays are the standard inputto computer vision algorithms, yet many underlying computations can bedistributed across pixels. Transmitting raw, redundant, and noisy pixeldata off the sensor remains inefficient, motivating a shift toward focal-plane sensor-processors that perform a significant part of the computa-tion directly within each pixel. We envision pixels synthesizing higher-level signals locally, reducing downstream load, and providing richer in-puts for higher-level vision tasks.We propose a fully parallelizable form of visual odometry and depthestimation across pixels, where sensor-processors exchange informationthrough Gaussian Belief Propagation (GBP) to achieve consensus aboutcamera motion and infer depth from per-pixel photometric observationsand a surface normal prior. To maintain geometric stability during op-timization, we introduce a keyframe-like anchoring mechanism that reg-ulates the effective baseline between frames, enabling consistent mo-tion and depth updates. Our method is evaluated on realistic datasets,demonstrating the feasibility of GBP-based pixel-level distributed odom-etry and depth estimation with keyframe anchoring on-sensor. ProjectPage: https://www.shinjeongkim.com/pixvod/

Abstract:
Depth foundation models (DFMs) offer strong learned priors for 3D perception from single RGB images but lack physical depth cues, leading to ambiguities in metric scale. We introduce metalenses, an emerging class of ultrathin planar optical elements, as a solution to physically encode missing metric depth cues via nanophotonics. In this paper, we bridge the gap between metalens and DFMs to achieve accurate metric monocular depth sensing. In a single monocular shot, our metalens embeds depth-dependent positional shifts into two polarized optical wavefronts. With an input adaptation strategty, we enable direct fine-tuning that aligns a pretrained DFM with the optical signals. To scale the training data, we further develop a comprehensive simulation pipeline that synthesizes metalens responses from RGB-D datasets, incorporating physical factors to minimize the sim-to-real gap. Experiments demonstrate that this approach outperforms both monocular metric depth estimation and depth-from-defocus baselines, showing an effective pathway for accurate monocular metric depth sensing.

Abstract:
Accurate Remaining Useful Life (RUL) prediction is funda-mental to Prognostics and Health Management, yet remains challeng-ing due to stochastic degradation and non-stationary vibration patterns.Existing image-based approaches predominantly rely on time–frequencyrepresentations (e.g., spectrograms or wavelet transforms), which cap-ture spectral energy variations but overlook the intrinsic geometric struc-ture of system dynamics. In this work, we reformulate RUL predictionthrough a phase-space-inspired visual representation. We introduce thePhase Space Density Image (PSDI), a novel representation that encodesthe spatial density of time-delay embedded trajectories. Unlike spectralheatmaps, PSDI characterizes degradation as a progressive geometric dis-persion of the system attractor, revealing structural transitions invisibleto conventional representations. To ensure that these geometric changesreflect true degradation rather than coordinate drift, we further proposea Globally Anchored Reconstruction strategy that enforces consistentphase-space alignment across samples and time. We evaluate the effec-tiveness of the proposed representation across multiple vision architec-tures and integrate it with a compact knowledge distillation frameworkthat transfers useful visual priors while reducing prediction jitter throughtemporal aggregation. Experiments on benchmark datasets demonstratethat PSDI achieves competitive and robust performance compared withclassical signal representations and state-of-the-art time-series models,supporting phase-space density maps as an effective visual representa-tion for vibration-based degradation tracking.

Abstract:
World models have emerged as a promising paradigm for scaling autonomous driving (AD) data, yet existing video generative models fall short as interactive simulators. Layout-conditioned renderers rely on "oracle" future trajectories of all background agents, rendering them strictly non-reactive. Conversely, pure action-conditioned predictors lack semantic control over complex interactions and suffer from prohibitive diffusion latencies, hindering closed-loop policy learning. To bridge this gap, we present CausalDrive, a controllable, real-time foundation driving world renderer. CausalDrive operates solely on the initial front-view frame, the ego-vehicle’s trajectory, and a macroscopic text prompt. By excluding future NPC layouts, we compel the model to intrinsically predict causal interactions, enabling text-driven control over Driving Sociology—allowing users to dynamically orchestrate diverse counterfactual reactions to identical ego-actions. To overcome the efficiency bottleneck and address the covariate shift in autoregressive generation, we propose a novel Context-Forced DMD architecture. This combines continuous flow-matching with a self-correcting distillation objective, achieving interactive speeds of 12 FPS. This breakthrough transforms the passive video generator into a playable neural simulator. We demonstrate its versatility across three downstream applications: (1) generative closed-loop evaluation with significantly mitigated collision artifacts, (2) large-scale Reinforcement Learning (RL) post-training driven by a Video2Reward module, and (3) real-time human-in-the-loop simulation. Extensive experiments validate that policies trained within CausalDrive’s reactive scenarios exhibit superior interaction capabilities in the real world.

Abstract:
Robotic imitation learning faces a fundamental trade-off be-tween modeling long-horizon dependencies and enabling fine-grained closed-loop control. Existing fixed-frequency action chunking approaches strug-gle to achieve both. Building on this insight, we propose HiPolicy, ahierarchical multi-frequency action chunking framework that jointly pre-dicts action sequences at different frequencies to capture both coarsehigh-level plans and precise reactive motions. We extract and fuse hi-erarchical features from history observations aligned to each frequencyfor multi-frequency chunk generation, and introduce an entropy-guidedexecution mechanism that adaptively balances long-horizon planningwith fine-grained control based on action uncertainty. Experiments ondiverse simulated benchmarks and real-world manipulation tasks showthat HiPolicy can be seamlessly integrated into existing 2D and 3D gen-erative policies, delivering consistent improvements in performance whilesignificantly enhancing execution efficiency.

Abstract:
Automatically generating pedestrian pathways from aerialimages requires producing a connected network suitable for routing, notjust detecting where sidewalks appear. Sidewalks and crossings, in con-trast to roads, may be partially occluded, implicitly defined, and exhibitcomplex connectivity patterns. Existing segmentation-based approachesfocus on labeling pixels to infer segments, but often produce disconnectedor fragmentary graphs that are unreliable for navigation. We introduceTraversRL, a vision-conditioned model that iteratively grows a path-way network from an aerial image, simulating a traveler navigating thebuilt environment. TraversRL uses an action space of short and long di-rection–distance segments designed to adapt to complex patterns andspan occlusions, and uses a combination of graph-level and step-wise re-wards to balance overall connectivity with precise edge placement. Acrossthree visual backbones and three intersection datasets, TraversRL sub-stantially improves buffered IoU with the ground-truth graph relative toa state-of-the-art segmentation baseline, and more than doubles metricsof connectivity. Moreover, combining global and local rewards producescleaner graphs with fewer spurious branches while further improvingoverall performance. These results demonstrate that modeling pathwayextraction as a sequential decision process from the perspective of a trav-eler, while optimizing for final graph quality with reinforcement learning,produces significantly more reliable pedestrian networks.

Abstract:
World models of embodied agents predict future observationsconditioned on an action taken by the agent. For complex embodiments,action spaces are high-dimensional and difficult to specify: for example,precisely controlling a human agent requires specifying the motion ofeach joint. This makes the world model hard to control and expensiveto plan with as search-based methods like CEM scale poorly with actiondimensionality. To address this issue, we train a lightweight policy thatmaps high-level actions to sequences of low-level joint actions. Composingthis policy with the frozen world model produces a lifted world model thatpredicts a sequence of future observations from a single high-level action.We instantiate this framework for a human-like embodiment, defining thehigh-level action space as a small set of 2D waypoints annotated on thecurrent observation frame, each specifying a near-term goal position for aleaf joint (pelvis, head, hands). Waypoints are low-dimensional, visuallyinterpretable, and easy to specify manually or to search over. We showthat the lifted world model substantially outperforms searching directlyin low-level joint space (3.8× lower mean joint error to the goal pose),while remaining more compute-efficient and generalizing to environmentsunseen by the policy.

Abstract:
Large-scale language models for autonomous driving enableenhanced global understanding and long-horizon planning. However, whendeployed in isolated vehicles, limited sensing range and occlusions re-strict reliable decision-making, and the substantial computational andlatency overhead makes on-board deployment impractical. Cooperativedriving provides a potential solution by leveraging external agents forinformation exchange, but existing methods remain limited in semanticreasoning capability under practical constraints. To address these chal-lenges, we propose DH-VLM, a dual-horizon cooperative latent reasoningframework that enables asymmetric semantic cooperation between theinfrastructure and ego vehicle. The infrastructure aggregates multi-layerhidden states to form a global-reasoning horizon latent guidance, whichis integrated into the ego model through an Infrastructure-Driven LatentEvolution mechanism for conditional latent refinement. This enables theego vehicle to leverage long-range contextual understanding while pre-serving autonomous decision-making within its local planning horizon.Furthermore, we construct a cooperation-oriented question–answer (QA)dataset covering fundamental scene understanding and ego-personalizedcomprehension to support counterfactual and safety-aware reasoning.Extensive experiments demonstrate that DH-VLM achieves state-of-the-art planning performance, outperforming the previous state of the art by14.6% in L2 error and 26.9% in collision rate. Compared with query-basedend-to-end cooperative driving methods, our approach reduces the com-munication cost by 57.3% and GPU memory usage by 25.5%, while main-taining strong robustness against infrastructure guidance errors, provid-ing a practical and robust paradigm for cooperative autonomous driving.

Abstract:
Digital twins replicate the appearance and physical behav-ior of real-world objects for interactive simulation in XR, robotics, andgaming. While neural representations such as 3D Gaussians enable high-fidelity twins reconstructed from images, existing systems simulate onlyone instance at a time and barely reach real-time performance, makinglarge-scale rollout evaluation for robot learning, model predictive control,and multi-user XR impractical. We present Boba, the first batched simula-tor for physics-based Gaussian digital twins. Our key idea is to separate thestatic twin template from the dynamic simulation state and co-design thephysics, deformation, and rendering/visualization pipelines for batchedexecution. This structure enables shared template computation acrossinstances while minimizing memory traffic and synchronization overhead.Guided by this principle, we introduce compact surrogate spring–massmodels and a set of system-level optimizations—including mixed-precisionGaussian skinning, memory-efficient execution, and shared-memory-awarebatching—that significantly reduce compute and memory costs. On anXR-configured Jetson Orin, Boba’s local configuration achieves over 10×single-instance speedup over the prior state-of-the-art PhysTwin pipeline.Its distributed configuration, split between the Orin and a server, reaches25.1 ms end-to-end latency, 13.2× lower than the same PhysTwin base-line on Orin, while lowering incremental power by 22.2% relative toBoba’s local configuration. For batched execution, Boba scales to 3,310FPS on RTX 4090, a 16.2× system-level aggregate-throughput improve-ment over Boba’s optimized single-instance configuration on the sameGPU, and accelerates predictive control for deformable object manipula-tion, achieving speedups from 26× to an estimated > 2,000× relative toPhysTwin-based planning. These results show that Boba can supportpower-constrained XR applications and scalable robot planning.

Abstract:
We present RadarGen, a diffusion model for synthesizing re-alistic automotive radar point clouds from multi-view camera imagery.RadarGen adapts efficient image-latent diffusion to the radar domain byrepresenting radar measurements in bird’s-eye-view form that encodesspatial structure together with radar cross section (RCS) and Dopplerattributes. A lightweight recovery step reconstructs point clouds fromthe generated maps. To better align generation with the visual scene,RadarGen incorporates BEV-aligned depth, semantic, and motion cuesextracted from pretrained foundation models, which guide the stochasticgeneration process toward physically plausible radar patterns. Condition-ing on images makes the approach broadly compatible, in principle, withexisting visual datasets and simulation frameworks, offering a scalable di-rection for multimodal generative simulation. Evaluations on large-scaledriving data show that RadarGen captures characteristic radar measure-ment distributions and reduces the gap to perception models trained onreal data, marking a step toward unified generative simulation acrosssensing modalities.

Abstract:
Vision-Language-Action Models (VLAs), which leverage theadvanced reasoning capabilities of Vision-Language Models (VLMs), showpromising generalization in complex autonomous driving scenarios. Ex-isting VLAs typically predict and optimize 3D trajectories from 2D im-ages. While intuitive, this 2D-to-3D prediction is inherently entangledwith camera parameters, leading to limited data scalability across het-erogeneous driving datasets. Moreover, directly optimizing in 3D spaceinduces severe convergence to trivial solutions, where VLAs rely on ego-status rather than visual scene understanding. To address these issues,we propose PixelPilot, a novel VLA featuring a decoupled planning andlifting paradigm. In the planning phase, PixelPilot reformulates scene un-derstanding and trajectory prediction as sensor-agnostic 2D-to-2D tasksin the image plane, thereby facilitating scalable training across diversedatasets. The planned 2D trajectories are then deterministically lifted to3D only during inference, ensuring the full exploitation of visual cues andgeneralization across different vehicles. To realize this paradigm, we pro-pose a knowledge-instilled policy learning strategy that applies dense, in-termediate rewards via Group Relative Policy Optimization (GRPO) toenforce a rigorous causal chain from visual perception to spatial planning.Extensive experiments demonstrate that PixelPilot achieves state-of-the-art performance in both open-loop and closed-loop settings, validatingits superior scalability and visual reasoning capabilities.

Abstract:
Achieving general-purpose robotic assembly is a longstand-ing goal in AI. Early data-driven methods, particularly those based onReinforcement Learning (RL), made progress but are fundamentally lim-ited by poor sample efficiency and struggle to generalize to unseen ob-jects in zero-shot scenarios. To overcome these issues, more structuredapproaches have emerged. Classical planners like ASAP explicitly enforcegeometric feasibility but require precise, pre-existing geometric models,which can hinder deployment under real-world perception noise. Con-versely, recent Vision-Language Model (VLM)-based systems like Man-ual2Skill excel at semantic understanding but are critically dependenton pre-authored manuals, restricting their autonomy in unstructuredenvironments. To address both planning-time model dependency andmanual dependency, we introduce ZAP, a novel framework for zero-shotVLM-guided assembly planning with geometry-based simulation verifi-cation. ZAP constructs an internal structural representation from per-ception without relying on task-specific manuals, pre-authored assemblysequences, or hard-coded assembly logic. Our framework features twocore modules: a VLM-Parser that analyzes segmentation-based multi-view RGB-D observations to infer an “implicit manual” and compile itinto structured part dossiers, and a VLM-Planner that performs Chain-of-Thought (CoT) reasoning over these dossiers to produce physicallyplausible, robot-executable assembly sequences. We validate candidateplans using a geometry-based digital-twin verifier and further exam-ine representative executions on a robotic arm. Our experiments showthat ZAP remains effective in manual-free scenarios where methods likeManual2Skill are less directly applicable. Furthermore, by reasoning di-rectly from visual perception, ZAP can be more flexible than geometry-dependent classical planners like ASAP in the evaluated settings. Theframework generates and executes plans for complex items such as multi-part LEGO models and household furniture in our evaluation setting,providing evidence toward more autonomous assembly without task-specific manuals.

Abstract:
Robot navigation typically assumes an obstacle-free path exists between start and goal. In real environments, however, clutter may block all routes. We introduce Lifelong Interactive Navigation, where a mobile robot with manipulation capabilities must move objects to forge paths and complete sequential object-placement tasks. Because environment modifications persist, decisions impact future navigability and task difficulty. We propose CoReLIN, an LLM-driven constraintbased reasoning framework with active perception. CoReLIN reasons over a structured scene graph to decide which objects to relocate, where to place them, and where to explore next. A standard motion planner executes reliable navigation and manipulation primitives. To evaluate longhorizon behavior, we introduce 2 new metrics - Long-term Efficiency Score (LES), a unified metric capturing success, execution efficiency, environment optimality, captured by Price of Clutter. In ProcTHOR- 10k, CoReLIN outperforms best baseline by 16% under standard metrics and LES, and transfers to real-world hardware5.

Abstract:
Non-prehensile manipulation is often used as a preparatory step for robotic grasping, yet existing approaches typically require a predefined target object pose. In practice, however, objects admit multiple graspable configurations and the desired pose is not known in advance. We reformulate non-prehensile manipulation for grasping as optimizing an object centric graspability objective rather than reaching a specific pose. We construct a graspable set from synthesized grasps and define a graspability field that measures how suitable an object configuration is for successful grasp execution. The scalar measure provides a dense learning signal for reinforcement learning and determines when to terminate manipulation. This yields a closed-loop manipulation-to-grasp pipeline driven by a single policy. Experiments in simulation and on a real robot show that the policy reliably reconfigures objects into graspable states and transitions to grasping without external planners or manually specified stopping conditions. The predicted graspability distance correlates with real world grasp success, which indicates that the learned representation captures grasp feasibility of object configurations.

Abstract:
A deep understanding of the physical world is essential forrobotic manipulation and physically realistic simulation. While currentmethods, including VLM-based and other learning-based approaches,have shown promise in physical property inference, their evaluation isoften hindered by the lack of physically grounded reference data. Toaddress this gap, we introduce XDen-1K, the first large-scale multi-modal dataset that provides physically grounded density field for real-world objects. XDen-1K comprises 1,000 real-world objects spanning137 categories, with comprehensive data for each object, including ahigh-resolution, carefully curated 3D geometric model with part-levelannotations and paired real-world biplanar X-ray scans. In addition,XDen-1K includes high-fidelity volumetric density field reconstructedfrom sparse biplanar X-ray views via a novel optimization framework.XDen-1K also provides a benchmark for density estimation and enablesX-ray-conditioned volumetric segmentation. Experiments further demon-strate that the center-of-mass prior derived by XDen-1K can improverobotic manipulation performance. By providing real-world X-ray scansand physics-consistent density field, XDen-1K establishes a foundationfor advancing physical property inference and embodied AI. Additionaldetails and dataset resources are available at https://xden-1k.github.io/ and https://huggingface.co/datasets/zhangjxx/XDen-1K.

Abstract:
Federated parameter-efficient fine-tuning (PEFT) enables scalable adaptation of large pre-trained backbones under communication constraints. In heterogeneous federated settings, personalization is commonly realized through gated residual mixing between shared and clientspecific branches. However, this parameterization is inherently scale nonidentifiable: only the product of residual magnitude and mixing weight determines the functional contribution. When private residuals are optimized locally, their scales may drift across clients and communication rounds, rendering mixing coefficients an unreliable measure of personalization strength. We address this limitation with FedSDG, a structuredecoupled gating framework for federated PEFT. FedSDG maintains separate shared and private LoRA branches and introduces projection-level scalar gates for depth-aware personalization. Crucially, we propose Dynamic Alignment, a backbone-anchored calibration mechanism that stabilizes private residual scale prior to gated mixing, thereby improving gate identifiability without additional communication. Extensive experiments demonstrate consistent personalization improvements and stable depth-wise adaptation across diverse non-IID regimes.

Abstract:
Despite the exceptional reasoning capabilities of MultimodalLarge Language Models (MLLMs), their adaptation into universal em-bedding models is significantly impeded by task conflict. To addressthis, we propose TSEmbed, a universal multimodal embedding frameworkthat synergizes Mixture-of-Experts (MoE) with Low-Rank Adaptation(LoRA) to explicitly disentangle conflicting task objectives. Moreover,we introduce Expert-Aware Negative Sampling (EANS), a novel strategythat leverages expert routing distributions as an intrinsic proxy for se-mantic similarity. By dynamically prioritizing informative hard negativesthat share expert activation patterns with the query, EANS effectivelysharpens the model’s discriminative power and refines embedding bound-aries. To ensure training stability, we further devise a two-stage learningparadigm that solidifies expert specialization before optimizing repre-sentations via EANS. TSEmbed achieves state-of-the-art performance onboth the Massive Multimodal Embedding Benchmark (MMEB) and real-world industrial production datasets, laying a foundation for task-levelscaling in universal multimodal embeddings.

Abstract:
Continual visual question answering (VQA) requires learningnew question types while retaining previously acquired visual ground-ing and reasoning. Existing methods mainly reduce parameter interfer-ence, but do not explicitly control how sequential adaptation changesthe coupling between a question and its visual evidence. We proposeQ-STEER, a task-agnostic framework that separates plasticity from re-tention through a shared question-conditioned controller. For plasticity,probe-based MoE-LoRA self-expansion activates new expert slots onlywhen the current capacity is insufficient. For retention, a KL-based late-layer attention-drift signal conditions a low-rank correction to attentionlogits; the drift is diagnostic rather than an attention-matching loss. Oncontinual VQA v2, Q-STEER obtains 53.68 average performance and4.51 average forgetting, and reaches 51.00/51.48 on Novel/Seen compo-sitional splits. Experiments on a heterogeneous visual-instruction stream,together with seed, ablation, grounding, sensitivity, and overhead anal-yses, support the effectiveness of drift-aware steering and on-demandexpansion.

Abstract:
Continual vision–language learning is increasingly required in real deploymentswhere data, skills, and objectives drift, yet models still struggle to learn new tasks withouterasing old ones and to act reliably in task-agnostic settings with limited memory. We ad-dress this gap by turning cross-task conflict into an instance-level signal that drives how themodel adapts over time. We propose an interference-aware adapter routing framework thatestimates, for each sample, a metric-consistent projection energy to quantify potential inter-ference, routes to the least interfering experts with capacity balancing, enlarges inter-expertseparation via principal-angle packing, and grows LoRA rank only along the residual princi-pal direction when current experts are insufficient. Across classification, structured conceptmatching, generative VQA, and retrieval, our approach improves over strong baselines. Anal-yses show that the conflict signal reliably predicts downstream forgetting, packing widensprincipal angles over time, and rank growth is sparse yet beneficial—especially in mid–highlayers.

Abstract:
Continual fine-tuning of large vision–language models (VLMs)such as CLIP is crucial for adapting foundation models to evolving real-world distributions. However, such adaptation often disrupts pretrainedrepresentations and severely harms zero-shot generalization. Despite re-cent progress, existing methods overlook the underlying optimization ge-ometry, failing to anchor convergence within regions robust to distribu-tion shift. While recent studies suggest that flatter loss minima improvegeneralization, we empirically demonstrate that seeking local flatnesson a single task is insufficient to bridge the distributional gap betweenpre-trained and task-specific representations. Motivated by this obser-vation, we propose Dual-Generalization-aware Minimization (DGM), aplug-and-play optimization framework for continual adaptation of VLMs.DGM extends flatness-aware training by redefining the perturbation ob-jective to jointly promote pre-trained knowledge preservation and task-specific robustness. By incorporating these objectives into the inner max-imization step, DGM steers optimization toward solutions that remainstable under both zero-shot evaluation and task adaptation. Extensiveexperiments demonstrate that DGM effectively maintains zero-shot gen-eralization while achieving strong performance on sequential tasks.

Abstract:
Deep neural networks remain vulnerable to backdoor at-tacks, in which adversaries embed hidden triggers during training tocause misclassification of any trigger-carrying input into an attacker-chosen target class, while maintaining normal behavior on clean inputs.Though recent fine-tuning-based defenses have shown promise in miti-gating backdoor attacks and are particularly practical in continual learn-ing and MLaaS scenarios, where backdoors are removed from pretrainedmodels using small clean datasets, we demonstrate that this defensiveparadigm remains fundamentally exploitable by an adaptive adversary.We study a threat model in which attackers anticipate fine-tuning-baseddefenses and craft backdoors whose resulting optimization signals areentangled with those of the clean task. Our approach leverages FisherInformation to identify task-critical neurons and optimizes a trigger gen-erator such that backdoor gradients align with benign gradients on theseneurons, ensuring that any attempt to suppress the backdoor inevitablyinterferes with clean task preservation. As a result, fine-tuning defensesfails to effectively reduce attack success rate without incurring substan-tial degradation in clean accuracy. Through extensive evaluation againstseven attacks and eight defenses, we show our method consistently showsa stronger effect than existing backdoor attacks across all settings. Ourwork reveals a critical vulnerability in fine-tuning-based defenses andhighlights the need for more robust defense mechanisms against adap-tive adversaries.

Abstract:
Dataset distillation (DD) condenses large corpora into com-pact, information-rich subsets for efficient training and reuse. However,under noisy supervision, DD risks condensing corrupted associations to-gether with useful signals, degrading robustness. Conventional noisy-label remedies (sample selection, loss weighting, label correction) tightlycouple noise estimation with model optimization, often require clean an-chors, and can amplify confirmation bias—assumptions that are mis-aligned with DD’s goal of compact, plug-and-play supervision. We there-fore propose a trajectory-based DD framework that jointly suppressesnoise and preserves transferable knowledge without relabeling or cleansubsets. It comprises two complementary components: Selective Guid-ance Reweighting (SGR), which fuses global forgetting patterns (second-split forgetting) with local neighborhood consistency into a progressivereweighting scheme that prioritizes clean supervision along the teachertrajectory; and Teacher-Inspired Auxiliary Targets (TIAT), which injectauxiliary residual guidance distilled from intermediate teacher dynamicsto reinforce informative signals while remaining internally consistent. To-gether, SGR and TIAT produce distilled datasets with cleaner and richerrepresentations under noisy supervision. The framework is robust, label-preserving, computationally lightweight, and broadly applicable, yield-ing consistent gains over state-of-the-art DD baselines across symmetric,asymmetric, and real-world noise.

Abstract:
A unified representation for text and vision is a natural pur-suit, as it enables simpler multimodal modeling and more efficient training.However, representing images as discrete signals in the same way as textinevitably introduces severe information loss. Existing work strugglesto balance low-level details and high-level semantics in discrete repre-sentations: reconstruction-oriented representations often lack semanticinformation, whereas semantically stronger features typically suffer fromsevere loss of detail. We present ViQ, a Visual Quantized Representationsframework, which is designed to balance semantics and details in discreterepresentations while supporting inputs at native resolutions, therebyenabling it to serve as a unified and general discrete representation forarbitrary visual inputs. Our approach structures quantization learninginto two stages: text-aligned pre-training and feature discretization. Withtext-aligned pre-training, we enhance the visual encoder semantic-richsupervision from the pretrained language model and enable it to pro-cess native-resolution visual inputs. During discretization, we proposea proximal representation learning strategy to progressively compactthe feature space, along with a position-aware head-wise quantizationmechanism that enables flexible processing of arbitrary resolutions. Ex-tensive experiments on multimodal tasks demonstrate that ViQ achievescompetitive performance compared to state-of-the-art multimodal visionencoders with continuous and high-dimensional visual features, whilemaintaining high precision in low-level reconstruction. We also showthat multimodal training with visual quantized representations largelyimproves efficiency, yielding up to 20%-70% acceleration with differentbase LLMs and training recipes.

Abstract:
Dataset distillation (DD) aims to create a smaller datasetthat encapsulates the essential knowledge of a larger dataset, therebyreducing storage demands and accelerating downstream training. Forlarge-scale dataset distillation, state-of-the-art methods achieve satisfac-tory performance by using soft labels generated by well-trained teachermodels during downstream training. However, it will cause some issues:(1) a substantial amount of additional storage is required to retain theteacher models, often significantly exceeding the storage needed for thesynthetic images; (2) generating labels through these teacher modelsslows down the downstream training process, counteracting the efficiencygoals of dataset distillation; and (3) downstream training guided by theseteacher models, according to our studies, yields suboptimal performance.Focusing on these drawbacks, in this paper, we propose plug-and-playparameter-efficient label generation techniques for dataset distillation,which maximizes the benefits of limited model parameters and can begeneralized to different DD methods, datasets, and settings. Specifically,we propose a DD-oriented model parameter reduction method that au-tomatically determines the optimal capacity of teacher models and elim-inates redundant parameters for dataset distillation tasks. Furthermore,for additional parameter space, we turn to model ensemble strategiesand propose guidelines to optimize the utilization efficiency of the ad-ditional space. Compared to the state-of-the-art methods, Flash-DD re-quires only 0.03% of the additional storage and significantly acceleratesdownstream label generation by 843.81× while maintaining compara-ble performance. Alternatively, with a mere 1.8% storage budget, it canboost accuracy by up to 13.4% over previous leading methods.

Abstract:
Training deep neural networks (DNNs) on classi(cid:28)cation tasks can be performed with a number of di(cid:27)erent losses, but cross-entropy (CE) loss is the de-facto standard. Here, we propose an alternative loss, high error margin (HEM), which is a margin based loss modi(cid:28)ed to improve the training dynamics of neural networks. HEM loss is evaluated extensively using a wide range of DNN architectures and benchmark datasets with all experimental settings and training hyper-parameters taken from the literature, and hence, optimised for CE loss. HEM is found to be more e(cid:27)ective than CE loss across a range of image-based tasks: unknown class rejection, adversarial robustness, learning with imbalanced data, continual learning, and semantic segmentation (a pixel-wise classi- (cid:28)cation task). HEM is inferior to CE only in terms of clean and corrupt image classi(cid:28)cation with balanced training data, and this di(cid:27)erence is small. We also compare HEM to specialised losses that have previously been proposed to improve performance for speci(cid:28)c vision tasks. LogitNorm, a loss achieving state-of-the-art performance on unknown class rejection, produces similar performance to HEM for this task, but is much poorer for continual learning and semantic segmentation. Logit-adjusted loss, designed for imbalanced data, has superior results to HEM for that task, but performs worse on unknown class rejection and semantic segmentation. DICE, a popular loss for semantic segmentation, is inferior to HEM loss on all tasks, including semantic segmentation. Overall, HEM is competitive with the best alternative loss for all the tasks we have used and performs better than all other tested losses in terms of rejecting out-of-distribution examples, for continual learning, and by a substan- 1 tial margin for semantic segmentation . • • •

Abstract:
Adapting large pretrained models to new tasks e!ciently andcontinually is crucial for real-world deployment but remains challengingdue to catastrophic forgetting and the high cost of retraining. Whileparameter-e!cient tuning methods like low-rank adaptation (LoRA) re-duce computational demands, they lack mechanisms for strict contin-ual learning and knowledge integration, without data replay or multi-adapters or MoE. We propose Share, a novel approach to parameter-e!cient continual finetuning that learns and dynamically updates a sin-gle, shared low-rank subspace, enabling seamless adaptation across multi-ple tasks and modalities. Share constructs a foundational subspace thatextracts core knowledge from past tasks and incrementally integratesnew information by identifying essential subspace directions. Knowledgefrom each new task is incorporated into this evolving subspace, enablingforward knowledge transfer while minimizing catastrophic interference.This approach achieves up to 100× reduction in trainable parametersand 281× memory savings over traditional LoRA methods, maintain-ing performance comparable to jointly trained models. A single Sharemodel can replace hundreds of task-specific LoRA adapters, support-ing scalable, asynchronous continual learning. Experiments across imageclassification, natural language understanding, 3D pose estimation, andtext-to-image generation validate its e"ectiveness, making Share a prac-tical and scalable solution for lifelong learning in large-scale AI systems.

Abstract:
Vision and vision–language models rely on high-level visualrepresentations that are increasingly used across recognition, retrieval,and multimodal reasoning pipelines. However, recent advances in gen-erative modeling have shown that such features can often be inverted,enabling realistic reconstructions of the underlying image and raisingsignificant privacy risks. We revisit this problem through the lens ofreconstruction and propose TrustCLIP, a reconstruction-driven frame-work that treats a feature-conditioned generator as an explicit privacyadversary. TrustCLIP learns a projection between encoder features anddownstream modules that is explicitly optimized to degrade the recon-structions produced by generative attackers while retaining the necessarysignals for downstream tasks. Unlike prior defenses that rely on discrimi-native privacy metrics, TrustCLIP directly optimizes against a generativereconstruction attacker, targeting a threat not captured by standard eval-uation protocols. We demonstrate its effectiveness in both conventionalclassification and multimodal large language model pipelines. Acrossthese settings, TrustCLIP consistently reduces the fidelity of genera-tive inversions while maintaining downstream task performance. Projectpage: atnikos.github.io/trustclip.

Abstract:
Federated Learning (FL) allows multiple devices to collaboratively train machine learning models while keeping their raw data private. However, in real-world scenarios, FL often struggles with unstable optimization and slow convergence. This is largely caused by heterogeneous client data, where both the data distributions and dataset sizes vary significantly across devices. To address these challenges, we propose FedDO, an adaptive federated optimization framework guided by reinforcement learning. Instead of using predefined rules or binary selection, FedDO formulates client coordination as a continuous control problem. Specifically, we employ a sample-efficient Distributional Soft Actor-Critic with Three Refinements (DSAC-T) agent to dynamically allocate a continuous data-usage ratio for each client per round. This fine-grained adjustment effectively mitigates gradient drift. Furthermore, we introduce a low-rank parameterization technique to compress the agent’s action space, ensuring scalability to thousands of clients. Extensive experiments on standard image classification benchmarks show that FedDO consistently achieves higher accuracy and much faster convergence than state-of-the-art FL baselines, especially under highly unbalanced and heterogeneous data settings. Code is available at https: //github.com/leafuan/FedDO_code.

Abstract:
Vision–Language Models (VLMs) are increasingly deployedin real-world applications. To ensure efficient inference, these deploy-ments typically rely on specialized system kernels (e.g., CUDA or cuDNN)for acceleration. However, existing system-level research on VLMs has fo-cused primarily on improving kernel efficiency and performance, whilelargely overlooking their potential impact on model security and ro-bustness. In particular, the security implications of inconsistencies be-tween training and inference kernels remain underexplored. To addressthis gap, we present the first systematic study revealing a new classof vulnerabilities: system-level backdoor attacks that exploit floating-point inconsistencies across training and inference kernels. We introduceVLMSysTrojan, a red-teaming framework that constructs models behav-ing benignly under standard training kernels but exhibiting backdooredbehavior when executed on specific target inference kernels. Specifically,VLMSysTrojan produces models whose backdoor triggers remain inactiveunder standard kernels and successfully evade three state-of-the-art back-door detection methods. Yet, when deployed on a target kernel, kernel-specific floating-point behavior activates the backdoor. Empirically, thecompromised models achieve a 99% attack success rate on triggered in-puts while maintaining normal accuracy on clean inputs. Moreover, theattack generalizes across multiple kernel configurations, exposing a previ-ously unrecognized risk in VLM deployment and highlighting the urgentneed for kernel-aware robustness analysis techniques.

Abstract:
Empowering Large Multimodal Models (LMMs) with imagegeneration often leads to catastrophic forgetting in understanding tasksdue to severe gradient conflicts. While existing paradigms like Mixture-of-Transformers (MoT) mitigate this conflict through structural isola-tion, they fundamentally sever cross-modal synergy and suffer from ca-pacity fragmentation. In this work, we present Symbiotic-MoE, a unifiedpre-training framework that resolves task interference within a nativemultimodal Mixture-of-Experts (MoE) Transformers architecture withzero-parameter overhead. We first identify that standard MoE tuningleads to routing collapse, where generative gradients dominate expertutilization. To address this, we introduce Modality-Aware Expert Dis-entanglement, which partitions experts into task-specific groups whileutilizing shared experts as a multimodal semantic bridge. Crucially, thisdesign allows shared experts to absorb fine-grained visual semantics fromgenerative tasks to enrich textual representations. To optimize this, wepropose a Progressive Training Strategy featuring differential learningrates and early-stage gradient shielding. This mechanism not only shieldspre-trained knowledge from early volatility but eventually transformsgenerative signals into constructive feedback for understanding. Exten-sive experiments demonstrate that Symbiotic-MoE achieves rapid gener-ative convergence while unlocking cross-modal synergy, boosting inherentunderstanding with remarkable gains on MMLU and OCRBench.

Abstract:
Semantic segmentation models remain vulnerable to adversarial perturbations, which can degrade pixel-wise predictions despite being imperceptible. Existing adversarial training methods primarily rely on iterative prediction-driven attacks that push features across decision boundaries between in-distribution (ID) classes while remaining close to the ID manifold. This limits their ability to expose more severe representation failures. In this paper, we propose SegOOD, a novel adversarial attack framework that explicitly encourages out-of-distribution (OOD)- like deviations in latent feature space. SegOOD introduces a Feature Wasserstein Separation objective to increase distributional discrepancy between adversarial features and ID semantic prototypes, along with a Weighted K-Nearest-Neighbor Separation objective to enforce local feature-level deviation. These objectives are efficiently implemented using prototype aggregation and feature-aware superpixel clustering. Furthermore, we integrate SegOOD with conventional prediction-driven attacks during adversarial training, improving robustness against both existing ID-style and our OOD-like perturbations. Extensive experiments demonstrate state-of-the-art robustness while maintaining competitive clean performance and no additional inference cost.

Abstract:
Federated learning (FL) enables collaborative model trainingwithout sharing raw data, but practical deployments must balance sta-tistical heterogeneity, resource-constrained clients, and communicationefficiency. While conventional FL requires clients to train full modelslocally, split federated learning (SFL) reduces client-side computationby partitioning models across tiers, at the cost of transmitting high-dimensional activations and gradients during training. We propose Hier-archical Split-Federated Prototyping (H-SFP), a communication-efficientframework for hierarchical cloud–edge–client environments. Instead ofexchanging sample-level activations or gradients, H-SFP communicatescompact class-wise feature statistics and uses them to synthesize repre-sentative feature distributions at upper tiers. This statistical prototyp-ing mechanism enables independent training of model segments whilepreserving the computational advantages of split-model execution andsubstantially reducing synchronization and communication overhead. Akey finding of this work is that lightweight first- and second-order fea-ture statistics are sufficient to support hierarchical split-model train-ing, providing an effective communication interface without requiringsample-level feature exchange. We analyze the communication proper-ties and stability of the proposed statistical interface under hierarchi-cal aggregation. Extensive experiments on CIFAR-10/100, HAM10000,ImageNet-1K, and ISIC-2018 demonstrate that H-SFP remains robustunder heterogeneous data distributions while reducing communicationoverhead by up to two orders of magnitude compared with federatedand split-learning baselines.

Abstract:
Federated Learning (FL) emerged as a promising distributedmachine learning paradigm. However, extending FL to the class incre-mental learning scenarios introduces unique challenges: 1) Capacity con-flict and catastrophic forgetting from the shared model overloading, 2)Heterogeneity from Non-Independent and Identically Distributed (Non-IID) data, and 3) Synchronized class misalignment. In this paper, we pro-pose Fisher-Routed MiXture of Experts for Federated Class-IncrementalLearning (FedFMX), a novel framework to address these challenges viaadaptive expert specialization across clients. The crucial insight is toroute each sample to an expert subset that jointly optimizes knowledgeacquisition and retention. Specifically, we introduce a Fisher-Routed Ex-pert Scoring (FRES) module to estimate expert importance via Fisher-based stability cost and gradient-based plasticity gain. Then, we designan Adaptive Expert Selection (AES) module by quantifying marginalcontributions for adaptive expert subset determination. Finally, by therouting-aware regularization (RAR), we achieve load balance and effi-cient FL training. We theoretically prove the O(T −1 ) convergence rate.Extensive experiments on multiple benchmarks compared with state-of-the-art methods demonstrate the superiority of FedFMX.

Abstract:
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, but remains challenging when client data are highly heterogeneous. These challenges are further amplified in multi-label scenarios, where inter-label dependencies and mismatches between local and global label relationships introduce additional optimization conflicts. While most FL studies focus on single-label classification, many real-world applications are inherently multi-label and often exhibit severe label skew across clients. To address this important yet underexplored problem, we propose FedNCA-ML, a novel FL framework that aligns client representations and learns discriminative, well-clustered features inspired by Neural Collapse (NC) theory. NC describes an ideal latent geometry where each class’s features collapse to their mean, forming a maximally separated simplex. FedNCAML further introduces an attention-based module to extract class-specific representations, enabling more balanced learning under heavy label imbalance. These class-wise representations are then aligned via a shared NC-inspired structure, mitigating inter-client conflicts induced by heterogeneous local data and inconsistent label dependencies. In addition, we design regularisation losses to encourage compact and consistent feature clustering in the latent space. Experiments on five benchmark datasets under nine FL settings demonstrate the effectiveness of the proposed method, achieving improvements of up to 3.92% in class-wise AUC and 4.93% in class-wise F1 score. Code is available at https: //github.com/CanPeng123/multi label fl_fednca.

Abstract:
Microscaling floating-point (MXFP) formats have emerged as a promising standard for deploying Multi-modal Large Language Models (MLLMs) and Large Language Models (LLMs) on modern accelerator architectures. However, existing Post-Training Quantization (PTQ) methods, particularly rotation-based techniques designed for integer formats, suffer from severe performance collapse when applied to MXFP4. Recent studies attribute this failure to a fundamental format mismatch: global orthogonal rotations inadvertently transfer outlier energy across quantization blocks, inducing new outliers that disrupt local block-wise scaling, while often creating bimodal activation distributions that underutilize the limited quantization range. To address these issues, we propose BATQuant (Block-wise Affine Transformation), which restricts transformations to align with MXFP granularity to prevent cross-block outlier propagation, while relaxing orthogonality constraints to optimize distribution shaping. To ensure parameter efficiency, we introduce Global and Private Kronecker (GPK) decomposition to effectively reduces storage and runtime overhead and incorporate Block-wise Learnable Clipping to suppress residual outliers. Extensive experiments on both MLLMs and LLMs demonstrate that BATQuant establishes new state-of-theart results under aggressive W4A4KV16 configurations, recovering up to 96.43% of full-precision performance on multimodal benchmarks and clearly outperforming existing methods across diverse tasks.

Abstract:
Humans can effortlessly anticipate how objects might moveor change through interaction—imagining a cup being lifted, a knifeslicing, or a lid being closed. We aim to endow computational systemswith a similar ability to predict plausible future object motions directlyfrom passive visual observation. We introduce ObjectForesight, a 3Dobject-centric dynamics model that predicts future 6-DoF poses andtrajectories of rigid objects from short egocentric video sequences. Un-like conventional world/dynamics models that operate in pixel or latentspace, ObjectForesight represents the world explicitly in 3D at the objectlevel, enabling geometrically grounded and temporally coherent predic-tions that capture object affordances and trajectories. To train such amodel at scale, we leverage recent advances in segmentation, mesh re-construction, and 3D pose estimation to curate a dataset of 2+ millionshort clips with pseudo-ground-truth 3D object trajectories. Through ex-tensive experiments, we show that ObjectForesight achieves significantgains in accuracy, geometric consistency, and generalization to unseenobjects and scenes—establishing a scalable framework for learning phys-ically grounded, object-centric dynamics models directly from observa-tion. Project page: https://objectforesight.github.io

Abstract:
Establishing accurate point-to-point correspondences betweennon-rigid 3D shapes remains a critical challenge, particularly under non-isometric deformations and topological noise. Existing functional mappipelines suﬀer from ambiguities that geometric descriptors alone can-not resolve, and spatial inconsistencies inherent in the projection of trun-cated spectral bases to dense pointwise correspondences. In this paper,we introduce SGMatch, a learning-based framework that couples 3D-lifted semantic cues with trajectory-level feature transport regulariza-tion. Speciﬁcally, we design a Semantic-Guided Local Cross-Attentionmodule that integrates semantic features from vision foundation modelsinto geometric descriptors while preserving local structural continuity.Furthermore, we adapt conditional ﬂow matching as a time-conditionedfeature transport regularizer that promotes spatially coherent point-wiserecovery. Experimental results on multiple benchmarks demonstrate thatSGMatch achieves competitive performance across near-isometric set-tings and consistent improvements under non-isometric deformations andtopological noise.

Abstract:
Reliable Radar-inertial fusion hinges on accurate extrinsic calibration, yet mmWave Radar poses unique calibration ambiguities: sparse and noisy returns, limited angular resolution, and a fundamental lack of stable geometric correspondences. Prior approaches typically rely on dedicated calibration targets, restrictive motion patterns, or strong scene assumptions, limiting their practicality for real-world deployment. In this paper, we present RadarCalib, a targetless, plug-and-play calibration framework that uses rigorous steering on the SO(3) manifold to establish reliable Radar-inertial constraints. Our key insight is that 3D rotational motion naturally induces a temporal synthetic aperture (TSA), which enables recovery of a fine-grained, high-resolution azimuthal spectrum. This TSA-enhanced representation reveals a highly stable rotationdependent azimuthal shift, which we use as a calibration signal to tightly couple Radar observations with IMU preintegration on the SO(3) manifold. Unlike point-based or motion-prior paradigms, RadarCalib establishes motion-consistent alignment in the spectral domain, regularizing SO(3) optimization and avoiding collapse induced by sparse Radar returns. Extensive real-world experiments show that RadarCalib achieves sub-degree rotational accuracy and outperforms state-of-the-art RadarIMU baselines in accuracy, robustness, and computational efficiency. Our self-collected dataset and code is released at https://github.com/ MetaIoT-WHU/RadarCalib.

Abstract:
We introduce LivingWorld, an interactive framework forgenerating 4D worlds with environmental dynamics from a single image.While recent advances in 3D scene generation enable large-scale environ-ment creation, most approaches focus primarily on reconstructing staticgeometry, leaving scene-scale environmental dynamics such as clouds,water, or smoke largely unexplored. Modeling such dynamics is challeng-ing because motion must remain coherent across an expanding scenewhile supporting low-latency user feedback. LivingWorld addresses thischallenge by progressively constructing a globally coherent motion field asthe scene expands. To maintain global consistency during expansion, weintroduce a geometry-aware alignment module that resolves directionaland scale ambiguities across views. We further represent motion using acompact hash-based motion field, enabling efficient querying and stablepropagation of dynamics throughout the scene. This representation alsosupports bidirectional motion propagation during rendering, producinglong and temporally coherent 4D sequences without relying on expen-sive video-based refinement. On a single RTX 5090 GPU, generatingeach new scene expansion step requires 9 seconds, followed by 3 secondsfor motion alignment and motion field updates, enabling interactive 4Dworld generation with globally coherent environmental dynamics. Videodemonstrations are available at paper.pnu-cvsp.com/LivingWorld.

Abstract:
Real-time co-speech gesture generation requires producing 3D motion clip by clip as speech streams in. Existing methods are fundamentally open-loop: each clip is synthesized conditioned only on past context, with no mechanism to verify or correct trajectory plausibility. Small per-clip errors therefore compound silently, causing the well-known drift problem--motion that gradually deviates from natural distributions over minute-scale horizons. We make a key observation: drift stems not from poor local motion quality--modern diusion models already produce convincing short clipsbut from the absence of forward constraints that tell each clip where it should arrive. Supplying even a single plausible key pose at a clip's tail as a destination anchor is sufficient to suppress drift dramatically. Building on this insight, we propose StreamTalk, a closed-loop streaming framework that introduces a periodic generate-retrieve-refine feedback cycle. At inference, Streaming Pose-Guided Generation (SPG) first produces a coarse clip, retrieves a plausible tail key pose from a speaker-specic motion database, and refines the clip with this anchor before passing it to the next window. To enable the model to exploit such sparse anchors effectively, we introduce Stochastic Anchor Masking (SAM) during training, which independently masks random pose and translation frames so the model learns to inpaint complete motion from partial boundary conditions. A part-aware DiT architecture further disentangles hand, body, and translation streams to prevent global displacement from interfering with local articulation. Extensive experiments on the BEAT2 benchmark demonstrate that StreamTalk achieves state-of-the-art motion quality (FGD), signicantly suppresses long-horizon drift compared to open-loop baselines, and runs in real time at 76 FPSenabling practical minute-scale streaming co-speech gesture generation. Code will be released upon acceptance.

Abstract:
Recent research in geospatial machine learning demonstratesthat models pretrained with self-supervised learning on Earth observa-tion data can perform well on downstream tasks with limited labeleddata. However, most benchmark datasets have few data modalities andpoor global representation, limiting the ability to evaluate multimodalpretrained models at global scales. In order to fill this gap, we introduceMMEarth-Bench, a collection of five new environmental tasks with 12modalities, globally distributed data, and both random and geographictest splits. We benchmark a diverse set of pretrained models and findthat while (multimodal) pretraining tends to improve model robustnessin limited data settings, geographic generalization abilities remain poor.Moreover, a simple randomly initialized multimodal model is competi-tive given enough labeled data. Although data is abundant, models cancurrently only make use of the modalities on which they were pretrained.To solve this problem, we propose using all the modalities available attest time as auxiliary tasks for test-time adaptation. Our model-agnosticmethod for test-time training with multimodal reconstruction (TTT-MMR) can improve performance across all models and tasks on bothtest splits. Furthermore, geographic batching leads to a good trade-off be-tween regularization and specialization during TTT, which is especiallybeneficial for long-tail distributions. Our dataset, code, and visualizationtool are linked on the project page: lgordon99.github.io/mmearth-bench.

Abstract:
Feedforward reconstruction is crucial for autonomous drivingapplications, where rapid scene reconstruction enables efficient utiliza-tion of large-scale driving datasets in closed-loop simulation and otherdownstream tasks, eliminating the need for time-consuming per-scene op-timization. We present StreetForward, a pose-free and tracker-free feed-forward framework for dynamic street reconstruction. Building uponthe alternating attention mechanism from Visual Geometry GroundedTransformer (VGGT), we propose a simple yet effective temporal maskattention module that captures dynamic motion information from im-age sequences and produces motion-aware latent representations. Staticcontent and dynamic instances are represented uniformly with 3D Gaus-sian Splatting, and are optimized jointly by cross-frame rendering withspatio-temporal consistency, allowing the model to infer per-pixel ve-locities and produce high-fidelity novel views at new poses and times.We train and evaluate our model on the Waymo Open Dataset, demon-strating superior performance on novel view synthesis and depth estima-tion compared to existing methods. Furthermore, zero-shot inference onCARLA validates the generalization capability of our approach.

Abstract:
Generating human motion synchronized with speech is es-sential for creating realistic virtual avatars. While recent work has madestrides in generating gestures from speech, these approaches often fallshort in enabling avatars to perform meaningful, context-specific actions.This paper presents CtrlCoMo, a motion generation framework that usestextual input alongside speech audio to provide explicit control over ac-tions in co-speech scenarios. To address the interference between ges-tures and actions in co-speech motions, we introduce Pyramid-VQ, anautoencoder that separates gestures from actions through hierarchicalquantization, with shallow layers capturing global semantics and deeplayers encoding localized gestures, guided by layer-wise CLIP-based se-mantic regularization. Additionally, the model provides explicit controlover gesture intensity via an AdaLN mechanism. For evaluation, we intro-duce CoHuMo, a large-scale co-speech motion dataset of 370 hours withrich human actions. This dataset encompasses a wide range of scene typesand languages, providing a robust benchmark for future research. Exten-sive evaluations are conducted on CoHuMo which validate our model’seffectiveness.

Abstract:
3D object classification is a crucial problem due to its significant practical relevance in many fields, including computer vision, robotics, and autonomous driving. Although deep learning methods applied to point clouds sampled on CAD models of objects and/or captured by LiDAR or RGBD cameras have achieved remarkable success in recent years, achieving high classification accuracy remains a challenging problem due to the unordered point clouds and their irregularity and noise. To this end, we propose a novel state-of-the-art (SOTA) 3D object classification technique that combines topological data analysis with various image filtration techniques to classify objects when they are represented using point clouds. We transform every point cloud into a voxelized binary 3D image to extract distinguishing topological features. Next, we train a lightweight one-dimensional Convolutional Neural Network (1D CNN) using the extracted feature set from the training dataset. Our framework, TACO-Net, sets a new state-of-the-art by achieving 99.05% and 99.52% accuracy on the widely used synthetic benchmarks ModelNet40 and ModelNet10. It also achieved one of the highest accuracies among all the from-scratch supervised learning techniques when tested on the hardest variant of the ScanObjectNN dataset. When tested with ten different kinds of corrupted ModelNet40 inputs, the proposed TACO-Net demonstrates strong resiliency overall. The code can be found in the supplementary material.

Abstract:
We present E(3)-Pose, a novel fast pose estimation methodthat jointly and explicitly models rotation equivariance and object sym-metry. Our work is motivated by the challenging problem of accountingfor fetal head motion during a diagnostic MRI scan. We aim to enableautomatic adaptive prescription of diagnostic 2D MRI slices with 6-DoFhead pose estimation, supported by rapid low-resolution 3D MRI vol-umes acquired before each 2D slice. Existing pose estimation methodsstruggle to generalize to clinical volumes due to pose ambiguities inducedby inherent anatomical symmetries, as well as low resolution, noise, andartifacts. In contrast, E(3)-Pose captures anatomical symmetries andrigid pose equivariance by construction, and yields robust estimates ofthe fetal head pose. Our experiments on publicly available and represen-tative clinical fetal MRI datasets demonstrate the superior robustnessand generalization of our method across domains. Crucially, E(3)-Poseachieves state-of-the-art accuracy on clinical MRI volumes, supportingfuture clinical translation. Our implementation is publicly available1 .

Abstract:
Online reinforcement learning (RL) has become an effectivetool for aligning text-to-image models with human preferences, but itsefficiency is often limited by group-relative variance reduction. Exist-ing methods generate multiple trajectories per prompt to estimate localbaselines, which introduces synchronization overhead and can weaken thelearning signal when group rewards become homogeneous. We proposeMonolithic Policy Optimization (MPO), a group-free online RL frame-work based on a single-stream training principle: one prompt, one trajec-tory, and one policy update. MPO retains stochastic differential equation(SDE) exploration within each trajectory, while replacing group-wisebaselines with a persistent Bayesian value tracker. The tracker main-tains a history-aware reward estimate for each prompt and adapts itsuncertainty using a Girsanov-inspired policy-drift proxy, providing stableglobal advantage estimation under non-stationary policy updates. Acrosscompositional generation, visual text rendering, and human-preferencealignment benchmarks, MPO consistently improves alignment qualityover group-based baselines. It also delivers a 26× wall-clock speedupand a 5× sample-efficiency gain under matched training settings.

Abstract:
The widespread adoption of generative AI models has inten-sified concerns about misuse, including the creation of unsafe or disturb-ing imagery. To mitigate such issues, several concept erasure approacheshave been proposed to remove harmful content from multimodal gen-erative models. Yet concept erasure for autoregressive image generationremains largely unexplored, despite the growing relevance of these modelsin recent trends toward unified multimodal architectures. In this work,we fill this gap by introducing Obliviate, a guidance-based concepterasure method for autoregressive image generation. Our method buildson three key design choices: KL-based supervision over visual tokendistributions, trajectory-level updates over full autoregressive rollouts,and aligned visual prefixes for stable target construction. We evaluateObliviate on three state-of-the-art autoregressive text-to-image mod-els, Liquid, Emu3-Gen, and Janus-Pro, covering the erasure of explicitcontent, graphic violence, and branded imagery. Obliviate consistentlyoutperforms current alternatives, reducing nudity on the defensive RABbenchmark from 91.58 to 3.15 while preserving overall model utility.

Abstract:
Reliable reward system is essential for reinforcement learn-ing in multi-modal reasoning, yet existing methods face key limitations:preference-based models often misjudge correctness on complex tasks,while rule-based approaches struggle with semantically equivalent an-swers and therefore restrict the scale of usable RL data. To address thesechallenges, we propose Consistency Reward Models (CRM), which assesshow well a candidate response aligns semantically and mathematicallywith ground truth. Our model produces a continuous reward derived fromthe logits associated with “Consistent” vs. “Inconsistent”, enabling moreinformative and stable optimization signals than binary rewards duringRL. By design, CRM compares answers in text space, which our ablationshows is both sufficient and more accurate for consistency judgment. Wealso find that incorporating Chain-of-Thought (CoT) reasoning improvesthe reliability of these consistency assessments. As a judging model, itachieves an 77.8% win rate over VLMEvalKit, demonstrating strongerjudging accuracy. When used for RL, the approach enables a 7B pol-icy model to attain state-of-the-art performance on MathVista (75.2%)and MathVision (30.6%), outperforming rule-based systems, while sub-stantially increasing the scale of RL-usable data. The method furthergeneralizes to OCR, MMMU and open tasks and supports stable RLtraining up to 72B model. These results show consistency reward modelsoffer a more scalable and robust foundation for multi-modal reasoningRL.

Abstract:
While recent diffusion models have achieved remarkable suc-cess in visual content editing, extending them to stereo image editingremains highly challenging due to the lack of explicit stereo geomet-ric consistency alignment, which leads to cross-view inconsistencies andgeometric distortions. We propose StereoEdit, a training-free diffusionframework designed to achieve geometrically consistent and structurallycoherent stereo editing. StereoEdit introduces two complementary mod-ules that explicitly enforce geometric reliability during the denoising pro-cess. The Geometrically-Gated Attention Reference (GGAR) adaptivelyfilters cross-view attention based on disparity-aware confidence, ensur-ing stable correspondences across epipolar regions, while the Geometry-Aware Latent Alignment (GALA) performs confidence-weighted, bidi-rectional latent corrections to maintain parallax alignment throughoutthe diffusion process. Together, these modules enable robust and consis-tent stereo edits. Extensive experiments on diverse stereo benchmarksdemonstrate that StereoEdit significantly improves cross-view coherenceand visual realism, establishing a new foundation for controllable andimmersive stereoscopic content editing.

Abstract:
Makeup-transfer applies the reference makeup to the sourceface while preserving the source identity. Despite advances in full-faceediting by diffusion-based methods, strong regional controllability, makeupfidelity, and identity preservation remain challenging. The reasons are(i) pixel-to-attention misalignment that causes spillover into non-targetareas and weakens regional control; (ii) unclear transfer/preservationconcept separation under two-image conditioning, leading to couplingbetween makeup attributes and identity; and (iii) the lack of a high-resolution dataset that is identity-consistent and region-labeled for fine-grained supervision. In this paper, we propose MagicMakeup, a dif-fusion transformer-based framework for region-controllable and high-fidelity makeup transfer, built on spatial constraints and concept dis-entanglement. To enable precise region-specific editing while preservingidentity, we propose Token-Aligned Region Gating, which aligns pixelmasks with attention and applies region-specific logit gating. To clarifythe concepts of transfer and preservation, we further introduce Cross-Modal Perception Guidance, which aligns text and image features to en-hance cross-modal concept perception. We also design a pipeline for thegeneration of 1024 × 1024 data pairs through region-specific makeup re-moval and establish a unified benchmark in synthetic and real settings.Extensive quantitative and qualitative experiments show that Magic-Makeup improves regional controllability, makeup fidelity, and identitypreservation, with strong robustness across styles, races, and poses.

Abstract:
This paper proposes InstaEdit, an acceleration frameworkfor image editing models that reduces the inference steps while preserv-ing, or even improving, generation quality. Unlike existing approaches,the proposed method employed a noise prediction strategy that incorpo-rates both textual instructions and visual conditions into the noise initial-ization process, producing latent variables that are closer to intermediatestates along the generative trajectory. These optimized latent variableswere then used as the starting point for the sampling process. The keycomponent of the framework is a deep noise predictor that estimates amore suitable initial noise state for the generation procedure, therebyreducing redundant denoising steps and improving inference efficiency.InstaEdit can be seamlessly integrated into existing mainstream FlowMatching inference pipelines. We applied InstaEdit to several state-of-the-art image editing models, including Step1X-Edit, FLUX.1-Kontext,and FLUX.2-klein-base-9B. Experimental results demonstrated that In-staEdit achieved acceleration factors of 4.13×, 3.88×, and 4.22×, respec-tively, while maintaining stable and high-quality generative performanceacross multiple standard image editing benchmarks.

Abstract:
While synthetic data has proven effective for improving sci-entific reasoning in the text domain, multimodal reasoning remains con-strained by the difficulty of synthesizing scientifically rigorous images.Existing Text-to-Image (T2I) models often produce outputs that arevisually plausible yet scientifically incorrect, resulting in a persistentvisual–logic divergence that limits their value for downstream reason-ing. Motivated by recent advances in next-generation T2I models, weconduct a systematic study of scientific image synthesis across genera-tion paradigms, evaluation, and downstream use. We examine both di-rect pixel-based generation and programmatic synthesis, and instantiateImgCoder as a structured implementation of the code-driven workflow,following an explicit “understand → plan → code” prompting strategyto encourage clearer structural specification. To rigorously assess scien-tific correctness, we introduce SciGenBench, which evaluates generatedimages based on information utility and logical validity. Our evaluationreveals systematic failure modes in pixel-based models and highlightsa fundamental expressiveness–precision trade-off. Finally, we show thatfine-tuning Large Multimodal Models (LMMs) on rigorously verified syn-thetic scientific images yields consistent reasoning gains, with potentialscaling trends analogous to the text domain, validating high-fidelity sci-entific synthesis as a viable path to unlocking massive multimodal rea-soning capabilities.

Abstract:
Building virtual cells with generative models to simulate cel-lular behavior in silico is emerging as a promising paradigm for acceler-ating drug discovery. However, prior image-based generative approachescan produce implausible cell images that violate basic physical and bio-logical constraints. To address this, we propose to post-train virtual cellmodels with reinforcement learning (RL), leveraging biologically mean-ingful evaluators as reward functions. We design seven rewards spanningthree categories—biological function, structural validity, and morpho-logical correctness—and optimize the state-of-the-art CellFlux modelto yield CellFluxRL. CellFluxRL consistently improves over CellFluxacross all rewards, with further performance boosts from test-time scal-ing. Overall, our results present a virtual cell modeling framework thatenforces physically-based constraints through RL, advancing beyond “vi-sually realistic” generations towards “biologically meaningful” ones.

Abstract:
We introduce the Noise-Tilted Reverse Kernel (NTRK), areward-guided diffusion sampler that injects reward gradients through thenoise term, leaving the pretrained reverse kernel unchanged and requiringonly a single sample per step. Reward-guided sampling at inference timehas greatly expanded the versatility of pretrained diffusion models. Yet ex-isting methods face a trade-off. Gradient-based guidance shifts the reversemean, steering generation but pushing intermediate states outside theregion that the model was trained on and degrading quality. Search-basedmethods preserve quality but gain no gradient signal. No prior methodachieves both. NTRK resolves this by keeping the reverse mean fixed andbiasing the noise term toward high reward. This is enabled by a whiteningoperator, the central mechanism behind NTRK, which converts rewardgradients into noise-compatible perturbations without losing their guidingsignal. Across various reward alignment tasks, NTRK outperforms recentstate-of-the-art baselines without losing sample quality. Remarkably, onaesthetic generation, NTRK surpasses the reward of the best baseline at500 NFEs using only 25 NFEs, a 20× reduction in compute.

Abstract:
Multi-tool agents for instruction-guided image editing re-main brittle under open-loop pipelines, where early routing errors propa-gate unchecked through the entire workflow. We propose VLTR (Vision-Language Tool Reasoning), a training-free framework that reformulatesediting as closed-loop tool reasoning over a directed acyclic graph (DAG)of atomic primitives. A generative decomposer first converts each natural-language instruction into executable primitives. A Bayes-UCB routerthen adaptively selects tools by combining contextual statistics withsemantic priors, balancing exploration and exploitation online. Aftereach execution, a heteroscedastic verifier produces a calibrated quality–uncertainty signal (q, σ 2 ) through inverse-variance fusion of three com-plementary assessment tiers. A verifier-guided scheduler uses this jointsignal to retry, reroute, or replan only the failed subgraph while preserv-ing validated ancestors, turning full-pipeline restarts into efficient localrepair. On PIE-Bench++, VLTR achieves the best overall VLM rankingamong all evaluated baselines. On MagicBrush, it delivers a 4.8× run-time speedup over the strongest multi-tool competitor with no qualityloss. Pilot evaluations on GEdit-Bench and RISEBench further confirmgeneralization to challenging edits.

Abstract:
Although GRPO substantially improves flow-matching models for human preference alignment in vision generation, mainstream methods such as DanceGRPO rely on Global-Stochastic Di!erential Equations (SDE) sampling across full timesteps in the Markov Decision Process (MDP), which remains computationally ine"cient. In this paper, we propose MixGRPO, a novel framework that leverages the flexibility of mixed sampling strategies through the integration of SDE and Ordinary Di!erential Equations (ODE). This redesign streamlines optimization within the MDP, delivering gains in both generation performance and training e!ciency. Specifically, MixGRPO introduces a sliding window mechanism, using SDE sampling and GRPO-guided optimization only within the window, while applying ODE sampling outside. This design confines sampling randomness to the time-steps within the window, thereby reducing the optimization overhead, and allowing for more focused gradient updates to accelerate convergence. Additionally, as time-steps beyond the sliding window are not involved in optimization, higher-order solvers are supported for faster sampling. So we present a faster variant, termed MixGRPO-Flash, which further improves training e"ciency while achieving comparable performance. MixGRPO exhibits substantial gains across multiple dimensions of human preference alignment, outperforming DanceGRPO in both e!ectiveness and e"- ciency, with nearly 50% lower training time. Notably, MixGRPOFlash further reduces training time by 71%.5

Abstract:
Text-guided image editing aims to modify visual content ac-cording to a target prompt while preserving the background. Recentinversion-free image editing frameworks such as FlowEdit have demon-strated strong editing capability without requiring inversion. Empirically,FlowEdit can achieve substantial semantic changes under appropriatehyperparameter settings. However, we observe that under certain globalattribute shifts, the editing trajectory may not effectively move awayfrom the source distribution in the early timesteps. Our analysis sug-gests that in the high-noise regime, the dominant manifold-seeking flowtoward the data manifold can reduce the influence of the text-conditioneddirection, leading to limited global modification while background struc-tures remain only moderately preserved. Inspired by this observation,we propose an inversion-free, frequency-aware semantic compensationstrategy that strengthens the effective signal in the early stage of gener-ation, while maintaining structural consistency in the background. Theproposed method improves global editing capacity without sacrificingbackground fidelity.

Abstract:
Multimodal Diffusion Large Language Models (MDLLMs)achieve high-concurrency generation through parallel masked decoding,yet the architectures remain prone to multimodal hallucinations. Thisstructural vulnerability stems from an algorithmic flaw: the decoderranks candidate tokens based on textual likelihood without verifyinglocalized visual support. We establish that this language-only rankinginduces an objective mismatch, where language probability mass acts asa misspecified proxy for the intended multimodal task. Consequently, wereinterpret hallucination as a localized optimization error, a phenomenonwhere the decoder exploits language shortcuts to maximize a proxy scoreat the expense of visual grounding. To address this objective mismatch,we introduce VISAGE, a training-free decoding framework that cali-brates the objective at inference time. VISAGE estimates the proxydiscrepancy by quantifying the spatial entropy of cross-attention distri-butions. By enforcing a localization consensus across attention heads, themethod penalizes spatially uniform distributions and re-ranks token com-mitments to favor visually grounded outcomes. We provide an analyti-cal stability guarantee establishing that VISAGE maintains a boundedobjective loss under estimation error. Evaluations across hallucination-sensitive and general-purpose benchmarks demonstrate the robustnessof the framework, yielding relative gains of 8.59% on MMMU-val and7.75% on HallusionBench.

Abstract:
Editing images with pre-trained text-to-image flow models typically requires carefully balancing target alignment with the desired prompt and source consistency with the original image. Existing approaches either rely on inversion-based pipelines or heuristic sourceto-target trajectory constructions, which often depend on architecturespecific designs or are sensitive to hyperparameters. In this paper, we propose h-flow, a training-free and theoretically grounded flow-based editing framework. Inspired by Doob’s h-Transform, we reformulate image editing as conditional generation under multiple terminal events corresponding to source consistency and target alignment. We first extend the classical h-Transform from SDE-based models to the deterministic RF framework by constructing an equivalent SDE with identical marginals. Within this formulation, we design dedicated h-functions for source consistency and target alignment, yielding closed-form reconstruction guidance and velocity-based semantic editing signals. We further introduce a velocity orthogonal decomposition to decouple reconstruction and editing directions, enabling a controllable trade-off between the two objectives. Extensive experiments demonstrate that h-flow achieves effective, robust, and flexible editing across diverse scenarios.

Abstract:
Generating high-resolution images with latent diffusion models is limited by the cost of high-resolution denoising, while post-hoc super-resolution introduces artifacts and additional latency by operating after decoding. We present the Latent Upscaling Adapter (LUA), a lightweight module that performs super-resolution directly on the generator’s latent code before the final VAE decoding step. LUA integrates as a drop-in component requiring no modifications to the base model or additional diffusion stages, enabling high-resolution synthesis through a single feed-forward pass in latent space. A shared Swin-style backbone with scale-specific pixel-shuffle heads supports ×2 and ×4 factors with nearly 3× lower decoding and upscaling time. Because LUA operates directly on latents, it transfers across diffusion systems–including SDXL, SD3, and FLUX–through architecture-level reuse, adapting only the input convolution with brief fine-tuning rather than retraining from scratch. Extensive experiments demonstrate that LUA closely matches the fidelity of native high-resolution generation at 2K and 4K while offering a practical and efficient path to scalable image synthesis.

Abstract:
Real-time autoregressive (AR) video diffusion has progressedrapidly. Existing approaches typically distill pretrained bidirectional videofoundation models into few-step causal students; however, naive distilla-tion often collapses due to architectural mismatch. To obtain stable ini-tialization, prior methods rely on ordinary differential equation (ODE)trajectory matching, which incurs substantial computation and can in-troduce errors by forcing the student to imitate global trajectories. Inthis paper, we bypass trajectory matching stage by pretraining a robustcausal AR model that equips the student for direct few-step distillation.Moreover, to bridge the gap between pretraining and distillation, we pro-pose Error Forcing, an error-aware and parallelizable AR video diffu-sion framework. During training, Error Forcing injects controlled residualerrors into the conditioning context, approximating the inference-timehistory distribution. This enables the causal model to learn from de-graded yet clean contexts, improving robustness to accumulated errorswithout sacrificing parallel training throughput. With this initialization,we can perform direct distillation, and the student and teacher/criticnaturally operate under consistent conditioning distributions. Extensiveexperiments show that our method outperforms existing baselines in gen-eration quality during both the pre-training and distillation stages.

Abstract:
With the growing demand for immersive visual experiences, high-quality omnidirectional images (ODIs) have become increasingly important. However, limitations in imaging devices and transmission bandwidth often lead to low-resolution ODIs, hindering the rendering of fine-grained 360◦ details, especially in the presence of real-world degradations and geometric distortions. Existing real-world super-resolution (Real-SR) methods are inadequate for ODIs, as their degradation models fail to account for the complex imaging pipeline involving fisheye capture and Equirectangular Projection (ERP), introducing severe aliasing and projection-specific distortions. To address these challenges, we propose D2R2OSR, a Degradation-Disentangled Representation framework for Real-world Omnidirectional image Super-Resolution. D2R2OSR explicitly models degradations arising from both fisheye imaging and ERP projection, guided by two key insights: (1) projection priors play a critical role in shaping real-world degradations, and (2) human perception in immersive environments is inherently viewpoint-centric. Accordingly, we introduce a Perspective Projection Representation (PPR) operating alongside the ERP branch to capture viewpoint-aware features, together with a Degradation-Specific Module (DSM) that jointly models ERPinduced geometric distortions and PPR-specific real-world degradations. Extensive experiments demonstrate that D2R2OSR achieves state-of-theart performance and produces visually compelling, high-fidelity omnidirectional Real-SR results while maintaining favorable computational efficiency for low-resource deployment.

Abstract:
Diffusion and flow-based generative models produce strong images, yet their controllability remains largely endpoint-centric: users specify conditions and receive final outputs, while the intermediate generative dynamics remain hidden. Recent methods have begun to exploit generation order and process decomposition to improve sample quality, but still treat intermediate states as internal computation rather than objects for interaction. We propose Trajectory Forcing (TF), a trajectory-centric framework that makes the generation path explicit, semantic, and editable. TF organizes synthesis as a sequence of semantically structured stages, progressing from global layout to object-, part-, and detail-level representations. Each stage produces a decodable latent state that can be inspected, evaluated, and locally edited before the next stage begins. To instantiate this path, we derive coarse-to-fine teacher hierarchies by clustering pretrained visual representations such as DINOv2, and train a hierarchy-conditioned one-step flow-matching model at each level. We further introduce trajectory-aware metrics that measure structural consistency and local controllability beyond endpoint quality metrics such as FID. Experiments show that TF achieves competitive sample quality while exposing coherent intermediate states and supporting localized edits across semantic levels. By shifting the focus from final images to the generative path itself, TF opens a route toward controllable, trajectory-aware image synthesis.

Abstract:
Diffusion-based generative models have transformed visualcontent synthesis, yet they remain vulnerable to unauthorized usage andlack reliable attribution methods. Existing watermarking techniques of-ten treat latent tensors as static spatial feature maps or depend on pixel-domain modification, and most do not explicitly leverage the internalfrequency structure of the latent space for dual-band redundant embed-ding, leaving them susceptible to the stochastic nature of diffusion andregeneration attacks. We introduce BiSLW, a trainable bi-spectral la-tent watermarking framework that jointly embeds aligned identity signalsacross complementary spectral bands of the decoded diffusion latent us-ing learned encoders and decoders, going beyond fixed-pattern frequencyapproaches. We leverage the inherent frequency structure of diffusionlatents to design a dual-band watermarking framework. Low-frequencycomponents encode global semantics, while high-frequency componentscapture fine texture. We exploit this structure to embed watermarksacross complementary spectral bands. The watermark is independentlyinjected into both bands via learned encoders and recombined before de-coding, ensuring it becomes intrinsic to the generative trajectory. Dualspectral decoders recover the watermark from each band, while a cross-band consistency constraint enforces alignment between semantic andtextural embeddings. Experiments show that BiSLW achieves a strongbalance between perceptual fidelity and robustness, improving PSNR byover 3 dB compared to prior latent diffusion watermarking methods whilepreserving near-perfect bit accuracy under aggressive regeneration andcommon distortions, all with negligible computational overhead.

Abstract:
Recent advances in video diffusion models have delivered re-markable generation quality, yet their generation cost remains high. Thisinefficiency is largely driven by the iterative denoising process, with com-putationally expensive per-step architectures. Step distillation enablesfew-step generation, but per-step latency remains high because a large-scale backbone is invoked at each denoising step. Several works seek toreduce per-step latency via block pruning using importance scores aver-aged across denoising steps. However, such averaged scores can obscurestage-critical blocks and bias the pruning process, removing blocks essen-tial for certain stages while leaving redundancy in others. In this paper,we propose a novel pruning paradigm, Stage-Specialized Block Pruning,that partitions the denoising process into multiple stages and assigns atailored pruned variant to each stage. Each variant consists of sharedblocks across multiple stages and stage-specific blocks for its assignedstage. With parameter sharing, the overall parameter budget is smallerthan the original model, while the per-step computational footprint is re-duced for lower latency. To derive stage-specialized variants, we introducea hierarchical selection mechanism consisting of an Inter-Stage VarianceSelector (IVS) and an Intra-Stage Quality Selector (IQS). IVS comparesthe variance of stage-wise block-importance distributions and prioritizesthe stage with higher variance for pruning, indicating greater removableredundancy. IQS mitigates noisy importance estimation by generatingmultiple low-importance pruning candidates within the selected stageand choosing the one with the highest video generation quality. IVS andIQS are applied iteratively until reaching a target latency budget. Fur-thermore, we introduce a two-phase training pipeline to recover the per-formance of these variants and facilitate sequential composition for few-step generation. Extensive experiments on the representative video diffu-sion model Wan2.1-1.3B demonstrate that our method reduces per-stepinference cost while maintaining competitive performance on VBench.

Abstract:
Inverse problems—inferring an underlying signal or statefrom incomplete or noisy measurements—are fundamental to science andengineering. Recent approaches employ diffusion models as powerful im-plicit priors for such problems, owing to their ability to capture com-plex data distributions. However, existing diffusion-based methods forinverse problems often rely on strong approximations of the posteriordistribution, require computationally expensive gradient backpropaga-tion through the score network, or are restricted to linear measurementmodels.In this work, we propose Restart for Posterior Sampling (RePS), a gen-eral and efficient framework for solving both linear and non-linear in-verse problems using pre-trained diffusion models. RePS builds on theidea of restart-based sampling, previously shown to improve sample qual-ity in unconditional diffusion, and extends it to posterior inference. Ourmethod employs a conditioned ODE applicable to any differentiable mea-surement model and introduces a simplified restart strategy that con-tracts accumulated approximation errors during sampling. Unlike someof the prior approaches, RePS avoids backpropagation through the scorenetwork, substantially reducing computational cost. We demonstrate thatRePS achieves faster convergence and superior reconstruction qualitycompared to existing diffusion-based baselines across a range of inverseproblems, including both linear and non-linear settings.

Abstract:
Resting-state functional connectivity (FC) is widely used tomodel brain organization and predict phenotypes, yet short fMRI acqui-sitions often yield unreliable estimates. We propose a Structure-AwareLatent Diffusion framework, SALD, to synthesize long-scan-like FC fromlimited observations. Our model conditions the generation process onshort-scan FC, structural connectivity (SC) as a lightweight anatomi-cal prior, and subject-level covariates. Crucially, in the Adolescent BrainCognitive Development Study (ABCD) cohort, our results suggest a fi-delity–utility tension in FC synthesis: likelihood-based diffusion trainingmay improve distributional fidelity while attenuating subtle inter-subjectvariation relevant to phenotype prediction. To address this, we introducea reward-guided Low-Rank Adaptation (LoRA) strategy that distills aguidance signal isolating the FC-congruent portion of phenotype vari-ance, steering generation toward preserving this signal while maintainingsample realism. Experiments on the ABCD cohort show that our methodbetter preserves phenotype-relevant signal in short-to-long FC synthesis,with the most pronounced improvements over short-scan baselines occur-ring in the low-scan-time regime across the evaluated phenotypes. Thesefindings suggest a promising direction for phenotype-aware short-to-longFC synthesis.

Abstract:
Existing latent di!usion models typically couple scale withcontent complexity, using more latent tokens to represent higher-resolutionimages or higher–frame rate videos. However, the latent capacity re-quired to represent visual data primarily depends on content complex-ity, with scale serving only as an upper bound. Motivated by this obser-vation, we propose DCS-LDM, a novel paradigm for visual generationthat decouples information complexity from scale. DCS-LDM constructsa hierarchical, scale-independent latent space that models sample com-plexity through multi-level tokens and supports decoding to arbitraryresolutions and frame rates within a fixed latent representation. This la-tent space enables DCS-LDM to achieve a flexible computation–qualitytradeo!. Furthermore, by decomposing structural and detailed informa-tion across levels, DCS-LDM supports a progressive coarse-to-fine gen-eration paradigm. Experimental results show that DCS-LDM deliversperformance comparable to state-of-the-art methods while o!ering flex-ible generation across diverse scales and visual qualities.

Abstract:
Visual autoregressive (VAR) models synthesize images in acoarse-to-fine manner by predicting multi-scale token maps, achievingcompetitive text-to-image quality with fewer decoding iterations thanraster-scan autoregression. However, late-scale refinement dominates in-ference time because computation grows quadratically with resolutionand attention becomes increasingly costly. Prior accelerations either fil-ter tokens heuristically, risking severe artifacts, or rely on extra deci-sion models that require per-model fine-tuning with reduced flexibility.We present the first in-depth study of attention behavior across VARgeneration steps and uncover two key properties: (1) attention patternscorresponding layers remain consistent across steps; (2) conditional andunconditional branches progressively converge at later scales. Leveragingthese insights, we introduce a novel training-free, flashattention-friendlyacceleration recipe ARVAR with two primary components: (1) Atten-tion Retrospect (AR) trims tokens at high resolution phase by to-ken saliency from early stage; (2) Branch Halving (BH) drops oneof the conditional/unconditional branches in the late steps. Combined,AR and BH deliver faster and higher-quality autoregressive image gen-eration, achieving 1.56× and 3.01× speedups on HART and Infinityrespectively with negligible performance drop. Compared with existingacceleration methods, ARVAR substantially improves image fidelity.

Abstract:
Diffusion Transformers (DiTs) achieve superior image gener-ation quality but suffer from quadratic computational complexity rela-tive to token count. While various token reduction (TR) methods havebeen proposed to mitigate this cost, they overlook the primary objectiveof generative models: minimizing recovery error, which requires reflect-ing output token similarity. Specifically, they rely solely on input tokensimilarity inherited from reduction-only ViT paradigms, leading to a fun-damental misalignment with this objective.To bridge this gap, we propose DiTo, a novel TR paradigm that shiftsthe focus toward output-centric token reduction. Based on the observa-tion that output token similarity is consistently preserved across adjacenttimesteps, DiTo utilizes prior-step similarities as an effective proxy toestablish token correspondences at a Matching timestep, and reuses theresulting correspondences across multiple subsequent Reduction steps.To optimize this interleaved scheduling, we propose Pair Match Ra-tio (PMR)-guided Interval Scheduling to determine the optimal match-ing frequency. Furthermore, to mitigate localized approximation errorsand resulting blocking artifacts caused by repeated reuse, we proposeFrequency-aware Token Matching by incorporating a selection-frequencypenalty. Extensive experiments demonstrate that DiTo consistently out-performs existing TR methods with 1.6–3.9 dB higher PSNR at compa-rable speedups, achieving a superior Pareto frontier.

Abstract:
In this work, we propose Image-to-Image Rectified Flow Re-formulation (I2I-RFR), a practical plug-in reformulation that recastsstandard I2I regression networks as continuous-time transport models.While pixel-wise I2I regression is simple, stable, and easy to adapt acrosstasks, it often over-smooths ill-posed and multi-modal targets, whereasgenerative alternatives often require additional components, task-specifictuning, and more complex training and inference pipelines. Our methodaugments the backbone input by channel-wise concatenation with a noise-corrupted version of the ground-truth target and optimizes a simplet-reweighted pixel loss. This objective admits a rectified-flow interpre-tation via an induced velocity field, enabling ODE-based progressiverefinement at inference time while largely preserving the standard su-pervised training pipeline. In most cases, adopting I2I-RFR requiresonly expanding the input channels, and inference can be performed witha few explicit solver steps (e.g., 3 steps) without distillation. Exten-sive experiments across multiple image-to-image translation and videorestoration tasks show broad perceptual improvements, with possibleperception–distortion trade-offs in highly pixel-aligned settings such assuper-resolution.

Abstract:
Native 3D generative models have achieved remarkable x001C -delity and speed, yet they su x001B er from a critical limitation: inability toprescribe precise structural articulations, where precise structural con-trol within the native 3D space remains underexplored. This paper pro-poses SK-Adapter, a simple yet e x001E cient and e x001B ective framework thatunlocks precise skeletal manipulation for native 3D generation. Mov-ing beyond text or image prompts, which can be ambiguous for precisestructure, we treat the 3D skeleton as a x001C rst-class control signal. SK-Adapter is a lightweight structural adapter network that encodes jointcoordinates and topology into learnable tokens, which are injected intothe frozen 3D generation backbone via cross-attention. This design al-lows the model to not only e x001B ectively x0010 attend x0011 to speci x001C c 3D structuralconstraints but also preserve its original generative priors. To bridgethe data gap, we contribute the Objaverse-TMS dataset, a large-scale⋆ Equal contribution. Equal advising.dataset of 24k text-mesh-skeleton pairs. Extensive experiments con x001C rmthat our method achieves robust structural control while preserving thegeometry and texture quality of the foundation model, signi x001C cantly out-performing existing baselines. Furthermore, we extend this capability tolocal 3D editing, enabling region-speci x001C c editing of existing assets withskeletal guidance, which is unattainable by previous methods. Projectpage: https://sk-adapter.github.io/

Abstract:
Diffusion models have achieved state-of-the-art performanceacross 2D, video, and 3D generation, but their sampling quality remainsheavily dependent on guidance methods. Existing training-free guidancetechniques construct a weakened auxiliary branch through static per-turbations, yet these methods fundamentally disregard the stage-wisestructure of the denoising process: early timesteps govern magnitude-driven global formation, whereas later timesteps refine directional ge-ometry. We propose a principled training-free diffusion guidance withStage-Aware Noise Decomposition (SAND) that considers this temporalasymmetry by decomposing Gaussian noise into orthogonal magnitude-and direction-oriented components and modulating them with a time-adaptive weighting schedule aligned to diffusion dynamics. The decom-posed perturbations are injected directly into the self-attention queries,keys, and values, forming a dynamically degraded branch that requires noretraining while faithfully mirroring the evolving geometry of the sample.This representation-level perturbation provides a temporally coherentguidance signal that better balances global structure and fine detail. Ex-tensive experiments on Stable Diffusion XL (SDXL) and Stable Diffusion3 (SD3) demonstrate that our method consistently improves global co-herence, sharpens fine-grained texture, and yields superior sample qual-ity in both unconditional and conditional image generation. We furthershow that SAND can be extended to the guidance-distilled image gener-ation model FLUX.1 and the text-to-video model Wan2.2, highlightingits broad applicability.

Abstract:
Generative flow and diffusion models provide the continu-ous, multimodal action distributions needed for high-precision roboticpolicies. However, their reliance on iterative sampling introduces severeinference latency, degrading control frequency and harming performancein time-sensitive manipulation. To address this problem, we proposethe One-Step Flow Policy (OFP), a from-scratch self-distillation frame-work for high-fidelity, single-step action generation without a pre-trainedteacher. OFP unifies a self-consistency loss to enforce coherent transportacross time intervals, and a self-guided regularization to sharpen predic-tions toward high-density expert modes. In addition, a warm-start mech-anism leverages temporal action correlations to minimize the generativetransport distance. Evaluations across 56 diverse simulated manipulationtasks demonstrate that a one-step OFP achieves state-of-the-art results,outperforming 100-step diffusion and flow policies while accelerating ac-tion generation by over 100×. We further integrate OFP into the π0.5model on RoboTwin 2.0, where one-step OFP surpasses the original 10-step policy. These results establish OFP as a practical, scalable solutionfor highly accurate and low-latency robot control.

Abstract:
In recent years, face super-resolution (FSR) methods haveachieved remarkable progress, generally maintaining high image fidelityand identity (ID) consistency under standard settings. However, underextreme degradation scenarios (e.g., scale ¿ > 8×), critical facial at-tributes and ID information are often severely lost in the input image,making it difficult for conventional models to reconstruct realistic andID-consistent faces. Existing methods tend to generate hallucinatedfaces under such conditions, producing restored images that lack faith-ful ID constraints. To address this challenge, we propose a novel FSRmethod with Identity Decoupling and Fitting (IDFSR), designed to en-hance ID restoration under large scaling factors while mitigating hallu-cination effects. Our approach involves three key designs: (1) maskingthe facial region in the low-resolution (LR) image to eliminate unre-liable ID cues; (2) warping a reference image to align with the LRinput, providing style guidance; and (3) leveraging ID embeddingsextracted from ground-truth (GT) images for fine-grained ID modelingand personalized adaptation. We first pretrain a diffusion-based modelto explicitly decouple style and ID by forcing it to reconstruct maskedLR facial regions using both style and ID embeddings. Subsequently, wefreeze most network parameters and perform lightweight fine-tuning ofthe ID embeddings using a small set of target ID images. This embeddingencodes fine-grained facial attributes and precise ID information, signifi-cantly improving both ID consistency and perceptual quality. Extensivequantitative evaluations and visual comparisons demonstrate that theproposed IDFSR substantially outperforms existing approaches underextreme degradation, particularly in terms of ID consistency.

Abstract:
Tokenizers are a key component of state-of-the-art generativeimage models, extracting the most important features from the signalwhile reducing data dimension and redundancy. Most current imagetokenizers are based on KL-regularized variational autoencoders (KL-VAE), trained with reconstruction, perceptual and adversarial losses.Diffusion decoders have been proposed as a more principled alternativeto model the distribution over images conditioned on the latent. However,matching the performance of KL-VAE still required adversarial losses,as well as a higher decoding time due to iterative sampling. To addressthese limitations, we introduce a new pixel diffusion decoder architecturefor improved scaling and training stability, benefiting from transformercomponents and GAN-free training. We use distillation to replicate theperformance of the diffusion decoder in an efficient single-step decoder.This makes SSDD the first GAN-free single-step diffusion decoder toreach state-of-the-art continuous tokenization, with higher reconstructionquality and faster sampling than KL-VAE. In particular, SSDD improvesreconstruction FID from 0.87 to 0.46 with 1.4× higher throughput andpreserves generation quality of DiTs with 3.8× faster sampling. As such,SSDD can be used as a drop-in replacement for KL-VAE, and for buildinghigher-quality and faster generative models.

Abstract:
Personalization models generate images guided by a few subject references, while style transfer methods aim to produce images aligned with a global style derived from a reference image. Recent approaches perform well when the reference image contains a single object, effectively capturing a global style that encompasses all implicit attributes. However, when applied to complex real-world scenes containing multiple objects with distinct attributes, existing methods struggle to selectively control generation using attributes from specific local regions of the reference image. Due to their primarily global conditioning strategies, these methods cannot reliably guide generation using attributes from specific local elements in the reference image. Moreover, existing methods typically employ a single identifier token to capture all details from the reference, resulting in a lack of individual, attribute-level control. Motivated by these limitations, we propose RefDiT, a novel framework for reference-guided image generation. RefDiT takes as input a reference image, a text prompt, and an optional user-provided guidance control. RefDiT employs local region guidance using the attributes of local elements. It constructs an attribute-aware conditioning signal from the reference image by performing attribute-level decomposition of the identifier tokens to train low-rank adapter (LoRA) blocks of a diffusion transformer (DiT)-based generative model. RefDiT leverages multi-modal joint attention to learn the correspondence between identifier tokens and local regions in the reference image, enabling more effective local guidance. RefDiT achieves a local attribute matching score of 0.88, outperforming state-of-the-art methods such as UnZipLoRA (0.54), K-LoRA (0.58), and B-LoRA (0.42). Compared to commercial models like Gemini-Banana (0.74) and GPT-5 (0.84), user studies indicate that participants find RefDiT’s outputs to be of similar quality.

Abstract:
Text-driven inversion of generative models is a core paradigmfor manipulating 2D or 3D content, unlocking numerous applicationssuch as text-based editing, style transfer, or inverse problems. How-ever, it relies on the assumption that generative models remain sen-sitive to natural language prompts. We demonstrate that for state-of-the-art native text-to-3D generative models, this assumption often col-lapses. We identify a critical failure mode where generation trajectoriesare drawn into latent “sink traps”: regions where the model becomesinsensitive to prompt modifications. In these regimes, changes to theinput text fail to alter internal representations in a way that altersthe output geometry. Crucially, we observe that this is not a limita-tion of the model’s geometric expressivity; the same generative mod-els possess the ability to produce a vast diversity of shapes but, as wedemonstrate, become insensitive to out-of-distribution text guidance. Weinvestigate this behavior by analyzing the sampling trajectories of thegenerative model, and find that complex geometries can still be repre-sented and produced by leveraging the model’s unconditional generativeprior. This leads to a more robust framework for text-based 3D shapeediting that bypasses latent sinks by decoupling a model’s geometricrepresentation power from its linguistic sensitivity. Our approach ad-dresses the limitations of current 3D pipelines and enables high-fidelitysemantic manipulation of out-of-distribution 3D shapes. Project web-page: https://daidedou.sorpi.fr/publication/beyondprompts

Abstract:
Generative models like Di x001B usion Models and Flow Matchinghave demonstrated remarkable capabilities in synthesizing high- x001C delitydriving videos, but are severely constrained by high inference latencydue to the requirement of extensive sampling steps. We argue that thisine x001E ciency stems from the prevailing reliance on a standard Gaussiansource distribution, where consecutive frames are initialized as indepen-dent Gaussian noise. This paradigm disregards the rich spatiotemporalcorrelations inherent in driving videos, compelling the model to regener-ate deterministic scene structures existing in previous frames from noise,which is both computationally redundant and prone to geometric incon-sistency. To address this problem, we propose GeoFlow, a novel frame-work designed to achieve e x001E cient driving video generation by harnessingexplicit geometric priors. Instead of sampling from standard Gaussiannoise, we leverage multi-view geometry and spatially-adaptive noise in-jection to construct a Geometry-Aligned Prior (GAP) distribution asstarting point. This initialization bridges the gap between source dis-tribution and data distribution, yielding a signi x001C cantly straighter andshorter sampling trajectory. Extensive experiments demonstrate that Ge-oFlow can achieve remarkable e x001E ciency of both training and inference:merely several hours of x001C ne-tuning on baseline models can signi x001C cantlyboost few-step generation quality, while fully converged training drasti-cally reduces number of inference steps required for state-of-the-art videogeneration.

Abstract:
Diffusion models for multi-agent trajectory prediction arelimited by iterative denoising, which causes inference latency that hinderstheir use in time-critical settings like autonomous driving. Fast-samplingvariants using DDIM and informed initial noise distribution partiallyalleviate this issue, but they either fail to achieve true single-step gen-eration or are constrained by the chosen noise distribution. ConsistencyModels (CMs) offer high-quality one-step generation by mapping noisedirectly to data, but are difficult to train from scratch. We propose EC-Traj , an enhanced CM pipeline with improved training and conditionalgeneration for trajectory prediction. Our framework extends the student-teacher consistency training scheme: the student produces standard out-puts, while the teacher explicitly fuses its predictions with parts of theground truth to give stronger supervision. We also exploit CMs’ directdenoising for top-K multi-shot generation during training. Combiningconditional generation with this enhanced consistency objective yieldsfaster inference and improved prediction accuracy, establishing compet-itive new benchmarks on the large-scale Argoverse 2 dataset.3

Abstract:
Recent real-world image super-resolution (SR) methods often adapt text-to-image (T2I) backbones with ControlNet-style branches or spatial conditioning tokens, which increases memory and computes with resolution and often constrains training to a fixed scale. We propose Fill2SR, which repurposes a masked-inpainting Diffusion Transformer for SR without extra spatial branches. Our Inpainting-Interface Evidence Adapter (IIEA) writes the low-quality (LQ) observation into the native masked-image slot under a full-image mask, turning inpainting into a reverse-degradation conditional rectified flow trained with LoRA-only tuning. We further introduce RCDT, an offline pipeline that distills degradation descriptors from unpaired real images and transfers them onto clean targets using frozen open-source models. Fill2SR supports mixed-resolution training up to QHD and yields stable performance across 512/1024/2048 outputs. On synthetic benchmarks, our base model with IIEA achieves the best LPIPS on DIV2K and LSDIR; adding RCDT trades a small LPIPS drop for consistently stronger no-reference quality on RealLQ250 and RealPhoto60. Fill2SR remains memory-predictable, running 15362 inference on a single 32GB GPU and extending to multimegapixel outputs via tiled restoration.

Abstract:
Magnetic resonance imaging (MRI) inherently suffers fromnoise, which limits downstream medical analyses. In MRI, noise-freeimages are unobtainable; therefore, existing denoising approaches for-mulate surrogate training objectives, compromising between preservingdetail and concealing noise, causing domain shifts or incomplete de-noising. To enable denoiser training directly on unmodified, noisy im-ages, we exploit repeated acquisitions. This naturally constitutes a phys-ical Noise2Noise (pN2N) setting. For unrepeated data, we introducea diffusion-based re-noiser that synthesizes noisy image pairs, extend-ing pN2N to Renoise2Noise (ReN2N). Furthermore, we demonstratethat ReN2N improves generalization to unseen datasets. Additionally,we propose to combine pN2N or ReN2N with optional guidance fromco-acquired contrast, yielding four versions of our novel denoising frame-work: YADO (You Accurately Denoise real Observations). Across 14 testconditions, YADO consistently outperforms 17 state-of-the-art baselines,matching the quality of physically noise-suppressed images obtained viabrute-force averaging of independent acquisitions. YADO thus estab-lishes practical denoising for real-world acquisition settings.

Abstract:
While personalizing Image-to-Video (I2V) diffusion modelswith specific visual effects is increasingly demanded for high-end genera-tion, current practice requires training a separate Low-Rank Adaptation(LoRA) module for each effect, incurring substantial data curation anditerative optimization costs that hinder interactive control. We presentPrompt2Effect, a weight-driven hypernetwork that amortizes per-effecttraining by directly synthesizing effect-specific LoRA weights in a singleforward pass. Unlike prior hypernetworks that regress adapter weightspurely from semantics, Prompt2Effect is explicitly conditioned on thefrozen base model weights, grounding prediction in the structural geom-etry of each layer. Furthermore, instead of predicting raw LoRA matri-ces, we introduce an SVD-canonicalized parameterization that resolvesfactorization ambiguity and stabilizes large-scale synthesis. Extensive ex-periments demonstrate that Prompt2Effect achieves on-par or superiorvideo quality and effect alignment compared to conventional LoRA fine-tuning, while reducing the computational cost from 56 GPU traininghours to 3.3 seconds of hypernetwork inference. When used as initializa-tion for subsequent fine-tuning, our predicted weights further improvefinal performance and accelerate optimization by approximately 10×.

Abstract:
Synthetic data generation has emerged as a powerful tool for improving data scalability in computer vision. Recent diffusion-based pipelines have demonstrated strong photorealism. However, enforcing precise 3D structure and pose consistency in generated images remains challenging. Existing methods leverage visual prompts such as edge maps to guide diffusion models, but often suffer from over-conditioning artifacts that degrade image realism and limit dataset quality. In this paper, we present a diffusion-based image generation framework that enforces 3D structural alignment while preserving photorealism through adaptive conditioning. Our framework, Adaptive Conditioning for 3D-Aware Synthetic Data Generation (AC3S), introduces a self-supervised visual prompt modulator that dynamically adjusts the strength of ControlNet conditioning, preventing over-conditioning and enabling the diffusion model to retain its generative expressiveness. To further enhance diversity and semantic consistency, we develop a multi-agent vision language model framework that composes detailed and 3D-aware prompts aligned with the underlying geometric structure. Together, these components enable the scalable generation of high-quality synthetic datasets with accurate 2D and 3D annotations. Extensive experiments demonstrate that our method significantly improves image quality and downstream utility.1

Abstract:
The frontier of visual reasoning is shifting toward models likeOpenAI o3, which can intelligently create and operate tools to trans-form images for problem-solving, also known as thinking-with-images inchain-of-thought. Yet existing benchmarks fail to fully capture this ad-vanced capability. Even Visual Search, the most common benchmark forcurrent thinking-with-images methods, tests only basic operations suchas localization and cropping, offering little insight into more complex,dynamic, and tool-dependent reasoning. We introduce TIR-Bench, acomprehensive benchmark for evaluating agentic thinking-with-imagesacross 13 diverse tasks, each requiring novel tool use for image process-ing and manipulation in chain-of-thought. We evaluate 22 multimodallarge language models (MLLMs), from leading open-sourced and propri-etary models to those with explicit tool-use augmentation. Results showthat TIR-Bench is universally challenging, and strong performance re-quires genuine thinking-with-images capabilities. Finally, we present apilot study comparing direct versus agentic fine-tuning.

Abstract:
Recent Vision-Language Models (VLMs) have demonstrated significant progress in video understanding. However, we notice that VLMs perform coarse perception and reasoning when answering finegrained motion questions. We investigate the reasons for this phenomenon and discover that motions in video change rapidly, so fixed frame rate sampling easily misses inter-frame motion details. Moreover, when answering motion-centric questions, VLMs tend to introduce motion-irrelevant contextual information and lack a structured and precise representation of motions, making it difficult to distinguish what motion the subject performs and when. To address these problems, we propose MotionChain, a training-free method that decouples motion question answering into a sequence of ⟨time range, subject, motion, environment⟩ tuples. Concretely, MotionChain leverages optical flow variations to calibrate when motions occur and extracts motion-relevant key frames as supplementary input, effectively alleviating the problem of missing critical motion information. For fine-grained motion question answering (QA), each tuple element in MotionChain can be converted into a visually grounded claim, providing the model with visual evidence. Experiments on motion-centric video QA benchmarks demonstrate the superiority of our MotionChain.

Abstract:
Video Question Grounding (VideoQG) requires models toanswer questions and localize supporting temporal evidence. Despiteprogress in video-language models, they remain susceptible to variousbiases. Prior debiasing methods primarily focus on dataset-level correla-tions, while easily overlooking intra-video temporal bias, where visuallysimilar segments differ substantially in their causal relevance to answer-ing the question. To address this challenge, we present Wavelet-basedIntra-video Causal Intervention (WICI), a framework designed to dis-entangle causal temporal evidence from redundant visual contexts. OurWICI comprises two key components: (1) an Intra-video Causal Interven-tion module, which utilizes counterfactual reasoning to suppress irrele-vant intra-video biases, and (2) Wavelet-based Dynamic Modeling, whichdecomposes contextual variations between visually similar frames to cap-ture fine-grained temporal cues. Extensive experiments on two VideoQGbenchmarks demonstrate that the proposed WICI significantly improvesquestion grounding accuracy and yields more robust, faithful questionreasoning.

Abstract:
Chain-of-Thought (CoT) prompting has proven remarkablyeffective for eliciting complex reasoning in large language models (LLMs).Yet, its potential in multimodal large language models (MLLMs) re-mains largely untapped, hindered by the absence of large-scale datasetsthat capture the rich, spatially grounded reasoning intrinsic to visual un-derstanding. Existing visual-CoT resources are typically small, domain-specific, or lack the structured stepwise supervision necessary for com-positional visual reasoning. In this paper, we introduce VisReason, alarge-scale dataset designed to advance visual Chain-of-Thought rea-soning. VisReason comprises 489K annotated examples spanning fourdiverse domains, each featuring multi-round, RoI-grounded rationalesthat guide MLLMs through interpretable visual reasoning steps. Build-ing upon this, we curate VisReason-Pro, a 165K subset produced witha stronger GPT annotator, enriched with detailed reasoning traces anddepth-augmented spatial annotations derived from monocular depth andsegmentation cues. Fine-tuning strong MLLM backbones on VisRea-son and VisReason-Pro yields substantial improvements in step-by-stepvisual reasoning accuracy, RoI localization, interpretability, and fine-grained/spatial reasoning performance. These results demonstrate thatVisReason equips MLLMs with more systematic and verifiable visualreasoning capabilities. We envision VisReason as a cornerstone for cul-tivating human-like visual reasoning, paving the way toward the nextgeneration of multimodal intelligence.

Abstract:
Understanding fine-grained eating behaviors in unconstraineddaily-life videos is essential for dietary monitoring and behavioral healthassessment, yet existing video-language benchmarks lack the temporalgranularity and domain-specific grounding required to rigorously evalu-ate models in this health-critical domain. We introduce EatVid-Bench,a large-scale, multi-dimensional benchmark designed to advance fine-grained eating behavior understanding. Our dataset comprises nearly700 real-world eating sessions (around 3,000 minutes) annotated througha novel three-tier automated pipeline that integrates 12 complementarysignals, including body pose, food detection, bite events, and facial ex-pressions, with each annotation traceable to its source signal for fullinterpretability and verifiability. Building on this foundation, we con-struct a question-answering benchmark spanning seven capability di-mensions, three difficulty levels, and five question types, with rigorousprovenance tracking throughout. Comprehensive evaluation of state-of-the-art Video-LLMs exposes a substantial perception–reasoning gap, par-ticularly in sub-second temporal grounding and cross-frame counting. Tofurther validate the effectiveness of EatVid-Bench, we propose a domain-adapted fine-tuning strategy to provide a strong open-source baselineby leveraging structured training annotations as explicit reasoning su-pervision. Code and benchmark are publicly available at GitHub andHuggingFace.

Abstract:
High-resolution (HR) image perception remains a key chal-lenge in multimodal large language models (MLLMs), as fine-graineddetails are often lost when the image is processed as a whole. Existingmethods either require training to teach models where to look or heuris-tically divide the image into fixed regions, both of which struggle togeneralize in complex HR scenes. In this work, we propose HierarchicalEntity Exploration (HEE), a training-free and model-agnostic frameworkthat transforms static image understanding into dynamic, query-guidedentity exploration. HEE first evaluates each region using a dual scoringmechanism to determine whether it already contains sufficient evidenceto answer the question. If not, it applies object detection within themost promising region to extract fine-grained entities, clusters them intocoherent subregions, and organizes them into a multi-level semantic hi-erarchy for deeper exploration. When deeper regions still fail to yieldconfident answers, a confidence-guided backtracking mechanism revisitsalternative paths to ensure adaptive perception. Extensive results showthat HEE outperforms training-free methods like ZoomEye and RAPin both accuracy and efficiency on two complex HR benchmarks (VisualProbe and HR-Bench), across different MLLMs such as Qwen2.5-VL andLLaVA-OneVision. Moreover, HEE demonstrates generalization on theMME-RealWorld benchmark.

Abstract:
Embodied Question Answering (EQA) requires an agent toexplore unseen environments, gather visual evidence, and answer natural-language questions. Efficient exploration requires both global planningand task-aware prioritization of informative regions. However, existingfrontier-based methods typically rely on greedy, step-wise decisions, whichoften cause redundant motion and frequent Vision-Language Model (VLM)queries. We propose SAFE-EQA, a semantic-aware framework that im-proves exploration efficiency through globally coherent path planning andreduced VLM interaction. SAFE-EQA incrementally builds a landmarkgraph to capture scene topology and a query-conditioned semantic mapto estimate task relevance of different regions. To support comprehen-sive, question-oriented exploration, we formulate planning as a Semantic-Aware Traveling Salesman Problem (TSP), which optimizes the visita-tion order over discovered landmarks. We further introduce an adaptivereplanning mechanism that selectively interrupts local navigation andre-optimizes trajectories when higher-priority frontiers emerge. Exten-sive experiments on OpenEQA, EXPRESS-Bench and HM-EQA showthat SAFE-EQA matches or outperforms state-of-the-art EQA methodswhile reducing VLM token consumption by 27.8–45.6%.

Abstract:
Composition is a high-level visual intent that governs wheresubjects are placed and how a scene is organized, yet current unifiedmultimodal models remain unreliable at fine-grained composition recog-nition and struggle to turn such intent into controllable generation. Wepresent COMPASS, the first unified multimodal framework that groundscomposition-intent control in a single system spanning both compositionperception and composition-guided generation, with a shared expert to-ken τc as the central intent anchor. On the perception side, COMPASSinjects composition expertise into an MoE backbone in a minimally in-vasive manner and distills the inferred intent into τc . On the generationside, COMPASS reuses τc as a global conditioning signal that steers thedenoising trajectory, effectively converting passive composition analysisinto explicit layout control. To support systematic instruction-followingcomposition learning and evaluation at scale, we construct Comp-11, alarge-scale dataset with an 11-class taxonomy and reasoning-augmentedannotations. Extensive experiments show that COMPASS substantiallyimproves category-level composition understanding and delivers morecomposition-consistent, prompt-faithful generation than strong baselines.The code and dataset for this work will be released here.

Abstract:
Multimodal Large Language Models (MLLMs) have shownimmense promise in universal multimodal retrieval, which aims to findrelevant items of various modalities for a given query. However, theirpractical application is often hindered by the substantial computationalcost incurred from processing a large number of tokens from visual inputs.In this paper, we propose Magic-MM-Embedding, a series of novel modelsthat achieve both high efficiency and state-of-the-art performance inuniversal multimodal embedding. Our approach is built on two synergisticpillars: (1) a highly efficient MLLM architecture incorporating visualtoken compression to drastically reduce inference latency and trainingtime, and (2) a multi-stage progressive training strategy designed tonot only recover but significantly boost performance. This coarse-to-finetraining paradigm begins with extensive continued training to restoremultimodal understanding and generation capabilities, progresses tolarge-scale contrastive pretraining and hard negative mining to enhancediscriminative power, and culminates in a task-aware fine-tuning stageguided by an MLLM-as-a-Judge for precise data curation. Comprehensiveexperiments show that our model outperforms existing methods by alarge margin while being more inference-efficient.

Abstract:
Scientific research follows multi-turn, multi-step workflows that require proactively searching the literature, consulting figures and tables, and integrating evidence across papers to align experimental settings and support reproducible conclusions. This joint capability is not systematically assessed in existing benchmarks, which largely underevaluate proactive search, multi-evidence integration and sustained evidence use over time. In this work, we introduce EpiBench, an episodic multi-turn multimodal benchmark that instantiates short research workflows. Given a research task, agents must navigate across papers over multiple turns, align evidence from figures and tables, and use the accumulated evidence in the memory to answer objective questions that require cross-paper comparisons and multi-figure integration. EpiBench introduces a process-level evaluation framework for fine-grained testing and diagnosis of research agents. Our experiments show that even the leading model achieves an accuracy of only 29.23% on the hard split, indicating substantial room for improvement in multi-turn, multievidence research workflows. Code and data are available at https: //github.com/RetroDnix/EPIBench.

Abstract:
What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks? The strongest vision-language models (VLMs) suggest that broad visual reasoning is within reach, yet their closed data and reinforcement learning (RL) pipelines make their gains difficult to study, reproduce, or extend. We introduce Vero, a family of fully open VLMs that match or exceed existing open-weight models across diverse visual reasoning tasks. We scale RL data and rewards across six broad task categories, constructing Vero-600K, a 600K-sample dataset from 59 datasets, and designing task-routed rewards that handle heterogeneous answers. Across VeroEval, our 30-benchmark suite, Vero-600K outperforms existing RL datasets under controlled comparisons. Applied to five starting models, Vero variants gain 2.9–5.4 points on average over their initial models. Notably, Vero-Qwen3I-8B, trained on the Instruct model, surpasses Qwen3-VL-8B-Thinking by 3.8 points on average without additional distillation. Systematic ablations reveal that different task categories elicit distinct reasoning patterns and that broad gains depend on learning them jointly rather than in isolation. All data, code, and models are released.

Abstract:
Reducing visual token redundancy is critical for acceleratingMultimodal Large Language Models (MLLMs) without degrading cross-modal reasoning performance. Existing token pruning methods typicallyrely on single-layer signals, such as attention scores or token similari-ties, which overlook the cross-layer transformation of visual representa-tions and may exhibit positional bias in multimodal token sequences.To address this limitation, we propose a training-free token pruningframework based on Cross-Layer Spectral Evolution (CLSE). Instead ofmeasuring token importance from single-layer feature magnitudes, CLSEquantifies how token representations evolve across Transformer layers inthe frequency domain. This evolution reflects the transition from high-frequency structural details to low-frequency semantic abstractions. Weobserve that tokens with stronger spectral redistribution across layers aremore likely to be semantically active and should therefore be preserved.By modeling cross-layer token dynamics, CLSE provides a stable im-portance criterion that mitigates positional bias. Extensive experimentson both image and video benchmarks demonstrate that CLSE achievesa superior trade-off between efficiency and accuracy under aggressivetoken reduction. Across multiple MLLMs, CLSE reduces FLOPs, KVcache memory, and latency while maintaining competitive or improvedperformance.

Abstract:
Region-level retrieval aims to align user-specified image regions with relevantregions or textual descriptions, playing a crucial role in realworld applications such as e-commerce product search and RAG. Although recent Large Multimodal Models (LMMs)have made significant strides in multimodal retrieval, they primarily focus on global-level tasksand struggle to capture effective region-level representations. To bridge this gap, we presentRegRet, an LMM-based Region-level Retrieval framework that enhances the regional rep-resentations without compromising overall global retrieval performance. At its core, RegRetintegrates a Region-Aware Encoder to capture detailed regional features while balancing themwith the global background context. To further enhance the fine-grained understanding anddiscriminability of representations, we design a multi-stage training pipeline that includesdetailed localized captioning and regional contrastive learning tasks. In addition, consideringthe absence of region-level contrastive training data and the limited diversity of evaluationtasks in current benchmarks, we introduce the REGMB benchmark. It comprises 225k con-trastive pairs, covering four multimodal retrieval tasks. Extensive experiments validate theeffectiveness of our approach. RegRet outperforms strong baselines in the zero-shot setting.Further training with contrastive learning leads to an average improvement of more than 20%on both REGMB and public benchmarks, while achieving comparable or better results onglobal-level retrieval tasks. The code and data will be released for future research.

Abstract:
Human decision-making interprets the world through highlevel concepts, such as recognizing a bird by its belly color. To bridge the gap between opaque deep learning representations and human understanding, Post-Hoc Concept Bottleneck Models (post-hoc CBMs) project latent features onto interpretable concept spaces using auxiliary datasets or vision-language models. However, relying on target task accuracy as the primary measure of post-hoc CBM success obscures whether the learned concepts are semantically meaningful or merely predictive artifacts. For example, random concept projections can achieve competitive accuracy despite being semantically meaningless. In this work, we analyze the learned projections directly and identify two failure cases: First, for concept projections learned from auxiliary data, covariate shifts can lead to unfaithful concept representations for the target task. In particular, we provide an upper bound on the error introduced by this shift. Second, systematic label noise in surrogate concept labels generated by visionlanguage models leads to unfaithful projections. After formalizing these failure modes, we introduce novel metrics that decouple concept faithfulness from predictive accuracy. Our empirical results across real-world and synthetic benchmarks confirm that these metrics identify unfaithful behaviors that standard accuracy-based evaluation fails to detect4.

Abstract:
Weakly supervised object localization (WSOL) aims to train an object locator using only image-level annotations. Recent progress in WSOL has been predominantly driven by visual transformer architectures, which effectively model long-range feature dependencies through self-attention mechanisms and multilayer perceptrons. However, existing transformer-based approaches typically depend solely on location tokens for localization, neglecting valuable semantic insights provided by class tokens, resulting in diffuse activations and imprecise localization. In this paper, we propose a novel framework named Cross-token Guidance TRansformer (CGTR), which enhances semantic coherence and localization precision by facilitating effective interaction between class and location tokens. Specifically, we introduce an Attention Regulation Module (ARM) to globally refine spatial activations using semantic guidance from class tokens, mitigating contextual biases. Additionally, we develop a Filter Regulation Module (FRM) that applies local structural refinement guided by semantic information, enriching the semantic representation of localization maps. Extensive experimental evaluations demonstrate the effectiveness of the proposed CGTR, achieving substantial and consistent performance gains compared with previous approaches on both the CUB-200-2011 and ILSVRC datasets.

Abstract:
Open-set action recognition (OSAR) requires a model toclassify known actions while rejecting unseen ones. Existing methodstypically use predictive uncertainty only at inference time, treating it asa rejection score after representation learning is complete. This leaves theunderlying representation unchanged and allows spurious background orcontext cues to remain embedded in the model, which weakens known–unknown separation. We propose a staged uncertainty-guided fine-tuningframework that uses ensemble-derived epistemic uncertainty not only forrejection, but also as a training signal for representation refinement. Theframework proceeds in three stages: motion-guided ensemble training tobias learning toward action-relevant evidence, in-distribution epistemicregularization to improve uncertainty reliability on known samples, anduncertainty attribution mask-based input attention (UAM-IA), whichconverts uncertainty attribution maps into input attention masks for re-finement. To improve deployment efficiency, we further distill the refinedensemble into a single student model trained to approximate both theensemble predictive distribution and the ensemble-derived epistemic un-certainty. Under a controlled OSAR protocol with UCF101 as the knownset and HMDB51 and MiT-v2 as unknown sources after removing over-lapping classes, the proposed method improves known–unknown separa-tion across multiple backbones. The distilled student preserves much ofthis benefit while enabling efficient single-model inference.

Abstract:
Images tell us what a scene looks like, but rarely what itwould feel like to be there. While recent datasets pair visual sceneswith electronic-nose measurements, aligning smell signals with imagesremains challenging because many olfactory cues arise from contextualenvironmental factors that are not directly visible in pixels. We intro-duce SCENT, a multimodal framework that uses language guidance asa semantic bridge between vision and olfaction. Our approach leveragesVision-Language Models (VLMs) to generate scene descriptors captur-ing objects, environmental context, and plausible ambient smell cuessuggested by the visual scene. These descriptors provide semantic guid-ance for learning olfactory representations. We train a smell encoderthat maps electronic-nose signals into a shared embedding space alignedwith both visual and textual representations, and introduce a language-guided latent decomposition that separates object-specific odors fromcontextual environmental contributions. Experiments on the New YorkSmells dataset demonstrate that SCENT significantly improves cross-modal retrieval compared to vision-only baselines, achieving state-of-the-art performance on smell-to-image and smell-to-text retrieval tasks. Inaddition, our framework produces interpretable olfactory representationsthat enable the disentanglement of complex smell mixtures. Our resultsreveal the importance of contextual semantic information for groundingolfactory perception in multimodal learning and pave the way for futureresearch in this area.

Abstract:
Video understanding has largely relied on deep spatiotem-poral architectures, including 3D convolutional networks and opticalflow (OF) based models. While effective, these methods are often com-putationally expensive and depend on heuristic motion representationsthat are sensitive to illumination, scale, and structural changes. To ad-dress these limitations, we propose ReynoldsFlow, a physics-inspiredrepresentation grounded in the Reynolds transport theorem (RTT) andHelmholtz-Hodge decomposition (HHD). ReynoldsFlow decomposes mo-tion into curl-free (CF) and divergence-free (DF) components, providinga principled and interpretable characterization of scene dynamics. Bycoupling intensity information with decomposed motion cues, it producesdynamics-aware, texture-preserving features that boost downstream taskssuch as pose estimation, action recognition, and tiny object detection.Lightweight and modular, ReynoldsFlow can be readily integrated intoexisting architectures. Experiments across diverse benchmarks show thatReynoldsFlow consistently matches or surpasses existing approaches, of-fering improved generalizability and computational efficiency.

Abstract:
Establishing reliable correspondences across diverse objectinstances is fundamental to robust 3D understanding and generalizablemanipulation. However, existing methods are often inconsistent acrossgeometries or vulnerable to non-canonicalized poses, making correspon-dence transfer unreliable. To address this, we propose XSemanticFlow,which learns correspondences from pure semantic features. Given per-object semantic feature x001C elds, XSemanticFlow applies hierarchical intra-object self-attention and inter-object cross-attention to predict soft cor-respondence maps. By restricting cross-attention strictly to semantic to-kens, XSemanticFlow avoids geometric over x001C tting, enabling it to predictgeneralizable correspondences across di x001B erent topologies and producealigned feature x001C elds. This enables XSemanticFlow to learn e x001B ectivelythrough self-supervised cross-pose alignment on large-scale unlabeledshapes, followed by supervised cross-instance x001C netuning on a cosegmen-tation dataset containing labeled object pairs from PartNet. We eval-uate our method on both 3D understanding and manipulation tasks.For 3D understanding, XSemanticFlow improves self-segmentation withSE(3) augmentations by +19.1 mIoU and +20.6 accuracy, and cross-instance cosegmentation by +10.9 mIoU and +12.5 accuracy over base-lines, demonstrating stronger transform consistency and semantic align-ment. For manipulation, XSemanticFlow provides a reliable alternativeto recent video-generation-based pipelines for zero-shot manipulation.Rather than relying on synthesized visual plans, we directly transfer con-tact regions and end-e x001B ector trajectories from a single reference demon-stration to novel instances, bypassing the brittle video generation failurescommon in contact-critical tasks.

Abstract:
The nonparametric identification of causal mediation effectsfrom heterogeneous multimodal observational data remains an opentheoretical problem. Existing single-modal methods cannot handlecross-modal causal relationships, while multimodal learning methodslack causal inference capabilities, preventing reliable mediation pathwayidentification and effect quantification under structural uncertainty.We propose the Multimodal Structure-Informed Guided MediationAnalysis (MM-SIGMA) framework, which achieves automated cross-modal mediation pathway identification and end-to-end uncertaintypropagation through probabilistic causal structure discovery. Weestablish nonparametric identification conditions for the Cross-ModalNatural Direct Effect and Cross-Modal Natural Indirect Effect fromheterogeneous multimodal observational data, and build MM-SIGMAupon this theoretical foundation. Specifically, it employs multimodalvariational autoencoders for causality-preserving latent representation,differentiable Flow-Structural Equation Models for asymptoticallyconsistent latent structure learning, Cross-Modal Path Stability Scoringfor high-confidence pathway identification, and Efficient InfluenceFunctions with Bayesian Model Averaging for end-to-end uncertaintypropagation. Experiments on synthetic data demonstrate state-of-the-art performance under structural uncertainty, nonlinearity, andcross-modal heterogeneity. On the HPP dataset, MM-SIGMA identifiescross-modal mediation pathways connecting sleep, fundus imaging, andcardiovascular health, revealing undiscovered cross-system mechanisms.

Abstract:
Video polyp segmentation plays a pivotal role in early col-orectal cancer diagnosis, yet its clinical deployment is heavily bottle-necked by the prohibitive cost of dense pixel-level annotations. To addressthis challenge, we propose a foundation-guided spatiotemporal distilla-tion framework named FSD-Net for sparsely annotated video polyp seg-mentation. Our approach introduces a Semantic Flow Distillation (SFD)module that leverages a frozen foundation model as a teacher to extractrobust semantic features and generate continuous supervision signals fora lightweight student network. To tackle the frequent target disappear-ance caused by complex intestinal folds, we design an Occlusion-AwareCycle Consistency (OACC) mechanism that dynamically blocks erro-neous gradient propagation based on visibility estimation. Furthermore,we introduce a Prototype Re-identification Module (PRM) to maintainlong-term memory and successfully recapture target identities upon reap-pearance. Extensive experiments on three standardized datasets, includ-ing SUN-SEG, CVC-612, and CVC-300, demonstrate that our frame-work significantly outperforms existing weakly supervised methods andeven surpasses state-of-the-art fully supervised models. Notably, on thehighly challenging SUN-SEG-Hard dataset, our method achieves a mDice87.04%, mIoU of 79.20%, and mHD of 20.64 mm, proving its exceptionalrobustness and label efficiency in complex endoscopic environments.

Abstract:
The reliance on extensively annotated 3D data confines cur-rent autonomous driving systems to a closed-set detection paradigm, lim-iting their robustness in real-world open-world scenarios where novel ob-jects frequently emerge. To bridge this gap, we propose BEVOpen3D,a novel distillation framework that facilitates open-world 3D object de-tection by effectively transferring knowledge from 2D vision-languagemodels into the 3D domain via the Bird’s-Eye-View (BEV) space undera partial-label setting. Our approach first generates initial 3D proposalsfor both seen and unseen categories using an open-vocabulary 2D detec-tor combined with a greedy spatial search strategy. We then introducea Triple-Source Label Refinement mechanism, which fuses original, lo-cally rectified (via a Seen-Guided Local Query), and globally re-examinedproposals to produce high-quality pseudo labels for unseen-category. Fi-nally, we propose a Heatmap Proposal Distillation strategy, where animage-based BEV teacher transfers its vision-grounded semantic priorsto a LiDAR-based student through aligned BEV heatmaps, enabling thestudent to detect both seen and unseen objects without requiring addi-tional 3D annotations. Extensive experiments validate the effectivenessof BEVOpen3D in open-world 3D detection.

Abstract:
Zero-shot anomaly detection (ZSAD) requires detecting andlocalizing diverse defects in unseen categories. Recent vision-languagemodel (VLM) based approaches improve ZSAD via prompt learning, yettheir prompts are often weakly grounded to transferable visual evidence,making them prone to semantic drift under domain shifts and limit-ing the reuse of normal/abnormal primitives. We propose a VariationalPrototype-conditioned prompting framework (VarProtoAD) that ex-plicitly conditions prompt learning on visual prototypes discovered in thefeature space. Specifically, multi-layer patch tokens are ℓ2 -normalized andmodeled with a variational von Mises–Fisher mixture to learn layer-wisenormal and abnormal prototype banks. These prototypes form multi-cluster semantic anchors on the hypersphere and are naturally alignedwith cosine-based VLM matching, where anomalies correspond to direc-tional deviations. Conditioned on these anchors, learnable prompt con-text tokens interact with the prototype banks via confidence-gated cross-attention, producing explicit prototype-conditioned prompts as well asimplicit deviation prompts derived from normal prototypes to bettercover unseen anomaly semantics. Finally, multi-layer image–text simi-larities are fused within a unified framework to jointly produce image-level anomaly scores and pixel-level anomaly maps. Experiments on 15industrial and medical datasets demonstrate strong cross-domain per-formance, indicating that VarProtoAD yields more stable and robustprompt learning.

Abstract:
Bas-relief sculptures are vital historical records. However,many are inaccessible for modern 3D scanning due to damage or occlu-sion, leaving archival monocular photographs as the only available visualdata. Semantic segmentation, such as isolating human figures in theseimages, is essential for digital documentation and subsequent archaeo-logical analysis, yet remains highly challenging for foundation modelslike the Segment Anything Model (SAM). The intrinsic properties ofbas-reliefs, including extreme material homogeneity, subtle depth varia-tions, and indistinct soft edges, result in a severe lack of both chromaticand geometric contrast. Consequently, models pretrained on natural im-agery struggle to perceive the shallow 2.5D structural cues embeddedin relief surfaces, leading to imprecise boundary delineation. To addressthis limitation, we propose ReliefSAM, a prompt-free and parameter-efficient framework that explicitly incorporates image-aligned depth andedge priors derived from a single monocular RGB photograph. These ge-ometric cues are encoded via a lightweight Multi-Prior Feature Encoder(MFE) and injected into a frozen SAM backbone through adapter-basedinteraction blocks. For high-resolution archival imagery, we further adoptoverlapped sliding-window inference with Gaussian-weighted merging toensure spatial consistency. Extensive experiments demonstrate that whileadapting the frozen SAM image encoder with lightweight adapters es-tablishes a highly competitive pure-RGB baseline that outperforms stan-dard decoder-only fine-tuning, naively injecting a single geometric priorcan induce modality interference. Crucially, ReliefSAM’s joint integra-tion of depth and soft-edge priors achieves a geometric consensus inwhich the two priors counterbalance and neutralize prior-specific biases.This synergistic 2.5D guidance breaks the RGB-only performance ceil-ing, effectively bridging the gap between natural RGB appearance andheritage-specific structures without requiring large-scale retraining of thefoundation model.

Abstract:
Accurate 3D tumor segmentation is vital for clinical decision-making, but pixel-wise annotation is expensive. Mixed-supervised learn-ing combines a small set of mask labels with abundant box labels, yetreconstruction-kernel heterogeneity creates large domain gaps. We pro-pose WCC4MS, a jointly optimized lesion-centered framework that mod-els kernel-related frequency shifts via a 3D discrete wavelet transform.The input volume is decomposed into low- and high-frequency com-ponents, and the scaled high-frequency coefficients generate syntheticsmooth and sharp variants. A dual-branch architecture learns from maskand box annotations with task-specific decoders, while a cross-branchKL-divergence constraint enforces semantic consistency. An auxiliarydomain-aware head encourages quality-sensitive features and improvesrobustness to domain shift. At inference, only the segmentation branch isused on candidate-centered patches; the box branch is used only duringtraining to exploit weak annotations. Experiments across three mask-annotated CT datasets and one box-only dataset show consistent gainsin segmentation accuracy and cross-domain generalization compared toprior mixed- and weakly supervised methods.

Abstract:
Robust object detection under adverse visual conditions re-mains a long-standing challenge for multi-modal perception systems. Ex-isting fusion-based methods typically require both RGB and infrared(IR) inputs, and treat them equally during both training and inference,which compromises their robustness when the RGB modality becomesunreliable or unavailable. In this case, we propose InfraNet, an IR-centric quality-aware framework that regulates RGB guidance duringtraining and supports flexible RGB–IR or IR-only deployment. InfraNetemploys an asymmetric architecture where the primary IR pathway ex-tracts multi-scale infrared features for predictions, while the auxiliaryRGB pathway provides reliability-controlled supervisory signals. Thecore of InfraNet is QualGate, a quality-aware fusion module that learnsa task-oriented control signal to suppress unreliable RGB guidance andcompensate IR features during cross-modal training. Built upon InfraNet,we design two architectural variants: a lightweight IR-only architectureInfraNet-IR and an RGB–IR architecture InfraNet-RGB-IR. Our methodis evaluated through extensive experiments on four benchmark datasets(LLVIP, FLIR-Aligned, M3 FD, and DroneVehicle), showing strong orcompetitive accuracy in challenging low-light and adverse weather con-ditions. Notably, InfraNet maintains high efficiency in IR-only inference,making it both accurate and computationally efficient.

Abstract:
Cross-scene hyperspectral image (HSI) classification facespersistent challenges due to domain shifts in spectral signatures, spatialstructures, and semantic compositions. Existing domain generalizationmethods typically align either features or predictions, but they overlookhow local context (e.g., boundary mixing and class co-occurrence pat-terns) changes across scenes. As a result, one-sided alignment often yieldspartial adaptation: feature-invariant models may still produce unstableoutputs, while output-aligned models can rely on domain-specific short-cuts. We propose Context-Aware Joint Alignment (CAJA), a pixel-faithful framework that preserves the single-label objective while inject-ing explicit context into training. CAJA combines Context-Aware Su-pervision (CAS) to improve boundary-aware supervision using neigh-borhood context and homogeneity, and Context-Conditioned JointAlignment (CCJA) to enforce context-conditioned alignment in bothfeature and prediction spaces across domains. By coupling supervisionand joint alignment under shared context, CAJA mitigates the partial-alignment limitation of one-sided methods. Extensive experiments onthree cross-scene HSI benchmarks show that CAJA consistently improvesgeneralization, especially at class boundaries where context shift is mostsevere, establishing a simple, principled, and deployment-friendly solu-tion for cross-scene HSI domain generalization.

Abstract:
Generalized 3D hand-object pose estimation from a singleRGB image remains challenging due to the large variations in objectappearances and interaction patterns, especially under heavy occlusion.We propose GenHOI, a framework for generalized hand-object pose esti-mation with occlusion awareness. GenHOI integrates hierarchical seman-tic knowledge with hand priors to enhance model generalization underchallenging occlusion conditions. Specifically, we introduce a hierarchicalsemantic prompt that encodes object states, hand configurations, andinteraction patterns via textual descriptions. This enables the model tolearn abstract high-level representations of hand-object interactions forgeneralization to unseen objects and novel interactions while compen-sating for missing or ambiguous visual cues. To enable robust occlu-sion reasoning, we adopt a multi-modal masked modeling strategy overRGB images, predicted point clouds, and textual descriptions. Moreover,we leverage hand priors as stable spatial references to extract implicitinteraction constraints. This allows reliable pose inference even undersignificant variations in object shapes and interaction patterns. Exten-sive experiments on the challenging DexYCB and HO3Dv2 benchmarksdemonstrate that our method achieves state-of-the-art performance inhand-object pose estimation.

Abstract:
Camouflaged Object Detection (COD) remains challengingdue to the deliberate alignment of foreground statistics with surround-ing background patterns, which induces strong scene-dependent ambi-guity. Existing approaches typically adopt object-centric modeling orreference-based augmentation. They often treat object appearance andbackground statistics independently, without explicitly modeling theirconditional relationship. This work treats COD as a scene-conditionedpattern-deviation reasoning problem and captures how a camouflagedobject deviates from its background scene. In this direction, we proposea pattern-aware scene-conditioned reasoning (PASR) method with twostages. The first stage constructs a scene-aligned background anchor viabackground-first retrieval, while the second stage performs a conditionedpatch-level deviation reasoning. A reference prototype library is con-structed in an offline and annotation-free manner, where foreground pro-totypes are derived from self-inferred rough masks rather than manuallycurated annotations. Unlike retraining-based paradigms, PASR achievesgeneralization through prototype-level expansion rather than parameteradaptation, enabling scalable inference across diverse background dis-tributions. The resulting deviation maps can serve as priors for genericsegmentation models. Extensive experiments on challenging COD bench-marks demonstrate that PASR outperforms existing unsupervised andweakly supervised methods and significantly narrows the gap with fullysupervised models.

Abstract:
Vision-language models (VLMs) have recently been adoptedfor open-vocabulary segmentation by aligning visual features with textprototypes. However, this alignment often degrades under domain shift,as text prototypes encode language priors learned from natural imagesthat may not hold in the target domain. To address this challenge, wepropose VPTA, a framework that adapts text prototypes to the tar-get domain to mitigate language prior bias using only unlabeled target-domain images. VPTA gathers reliable visual evidence from confidentpixels, estimates per-class evidence reliability to drive class-dependentprototype updates, and explicitly preserves prototype separation to pre-vent collapse between similar categories. This lightweight adaptationproceeds through a small number of iterations with negligible computa-tional overhead. We conduct extensive evaluations under diverse domainshifts across three representative domains, including remote sensing, au-tonomous driving, and natural scene segmentation. VPTA achieves state-of-the-art performance on ten remote sensing datasets and consistentlyimproves segmentation performance on autonomous driving and naturalscene benchmarks. These results demonstrate the strong generalizationability of VPTA and highlight its effectiveness as a practical solution forlabel-free prototype adaptation in vision-language models.

Abstract:
Few-shot anomaly detection remains challenging due to lim-ited training data. Multi-modal anomaly detection (MAD) offers a viablesolution, leveraging 3D geometric cues to enrich 2D RGB representationsand compensate for this scarcity. However, existing MAD methods applyspatially uniform feature processing, conflating stable macroscopic struc-tures with high-frequency localized defect signals, exacerbating cross-modal misalignment and inflating false-positive rates. To overcome this,we present CMDS-AD, a Cross-Modal Dual-Stream Anomaly Detectionframework. A LoRA-guided diffusion model generates diverse RGB sam-ples to mitigate extreme data scarcity. For 3D normal augmentation, weemploy a pre-trained diffusion model as a normal estimator. Crucially,this estimator inherently acts as a non-linear low-pass filter, directly ex-tracting low-frequency normal representations from RGB inputs. Thisestablishes an auxiliary estimated stream of purely low-frequency infor-mation, anchoring robust structural templates and assisting the uncom-pressed real stream, containing coupled high- and low-frequency compo-nents, to precisely isolate micro-defects. A Coordinate-Aware Hierarchi-cal Feature Mapper adaptively aligns cross-modal semantics, while a mul-tiplicative scoring mechanism filters modality-specific noise. Under theextreme 1-shot setting, CMDS-AD achieves absolute performance gainsof 5.7% (I-AUROC) and 2.0% (AUPRO) on MVTec 3D-AD, along-side 7.7% and 5.6% improvements on EyeCandies, establishing a newstate-of-the-art. Code is available at Junhaocai27/CMDS-AD.

Abstract:
Drone image–text retrieval (DITR) aims to associate aerialimages with textual descriptions in complex low-altitude environments.However, intricate spatial layouts and long-range structural dependen-cies in drone imagery challenge conventional Euclidean embedding mod-els, often leading to cross-modal misalignment. To address this issue, wepropose SARA, a structure-aware Riemannian-guided alignment frame-work that incorporates Riemannian manifold modeling to complementsemantic embeddings. Specifically, the Manifold Structural Feature Ex-traction (MSFE) module captures second-order structural dependenciesby encoding local feature correlations as symmetric positive definite rep-resentations. The Log-Euclidean Structural Alignment (LESA) moduleperforms geometry-consistent alignment in the tangent space, integratingsemantic correspondence with structural coherence through distributionalignment and feature fusion. We further provide theoretical analysisdemonstrating convergence and minimization of an upper bound on theexpected alignment error. Experiments on ERA and UDV benchmarksdemonstrate that SARA consistently improves retrieval performance overstate-of-the-art methods, particularly in structurally complex or seman-tically ambiguous drone scenarios.

Abstract:
In person re-identi x001C cation, neighbor-based methods haveachieved signi x001C cant success by interacting with neighbor samples to ob-tain more robust representations. However, existing methods rely onlyon a x001E nity relations, causing their success to depend heavily on the re-liability of selected neighbors. We x001C nd that a x001E nity-only interaction of-ten fails in challenging scenarios due to the inevitable presence of noisyneighbors. To enable e x001B ective interactions under noisy neighborhoods, werevisit neighbor-based methods under distinct reliability conditions andpropose a novel Adaptive Neighbor Feature Interaction (ANFI)method. The core idea of ANFI is to account for negative e x001B ects fromnoisy neighbors, allowing samples to remain distinguishable from falsepositive neighbors. Unlike existing methods, ANFI models not only a x001E n-ity relations but also discrepancy relations, and employs sample-wiseadaptive weighting for these two types of relations. Given that capturingnegative e x001B ects from noisy neighbors di x001B ers signi x001C cantly from traditionalrelation learning, we derive discrepancy relations from a new neighbor-hood similarity, which provides more information than pairwise sim-ilarity. In addition, we propose Noisy Relation Supervision (NRS)to train ANFI, gradually injecting robustness to noisy relations into themodel. Extensive experiments conducted under standard, cross-modal,and cross-domain settings, including comparisons with neighbor-basedmethods and re-ranking methods, demonstrate the superiority of ourmethod across various neighbor distributions.

Abstract:
Cycle self-training (CST) breaks the shared classifier assumption of the standard self-training framework, which is effective for unsupervised domain adaptation and exploits unlabeled target data by training with target pseudo-labels. CST introduces a target classifier and employs an inner-outer loop updating strategy, addressing the issue of unreliable pseudo-labels and enabling pseudo-labels to generalize across domains. Despite its success in image classification, extending CST to object detection faces three main challenges. First, the upper bound of CST in object detection is constrained by three types of unreliable pseudo-labels, such as classification error alone, localization error alone, and their combination. Second, since object detection involves detecting multiple target objects, directly applying CST leads to training instability. Third, a wider numerical range of regression coordinates leads to exploding losses. To this end, we apply CST to both classification and regression and propose the Dual-Stream Bilevel-Cycle Optimization framework. Specifically, we construct CST upon Mean Teacher to prevent training instability and use extra normalization to map the regression bounding box into a standardized space, effectively addressing exploding losses. Also, we provide a theoretical derivation of the regression bound. Extensive experiments across four cross domain standard scenarios demonstrate that our framework achieves considerable results.

Abstract:
Unified unsupervised anomaly detection aims to train a sin-gle model to detect and localize diverse anomalies across multi-class sam-ples, representing one of the most challenging tasks in anomaly detec-tion. Existing methods typically rely on highly compressing inputs intoa low-dimensional latent space to subsequently reconstruct anomalousfeatures. However, low-dimensional latent spaces critically limit infor-mation capacity, leading to unavoidable feature loss during compressionand significantly degrading the quality of reconstruction and represen-tational power of the model. Therefore, we indicate that dimensionalitycompression in latent space is not a necessary requirement for anomalydetection, and the editability introduced by compression is not inher-ently essential for models reconstructing anomalies. Thus, we propose anovel anomaly detection framework without latent space compression,called HLRAD (High-dimensional Latent Representation for UnifiedAnomaly Detection). Unlike prior methods, HLRAD innovatively en-ables dimension expansion rather than compression in the latent space,and the method effectively avoids feature loss from compression to ensurehigh-quality reconstruction while constructing a semantically enrichedhigh-dimensional latent representation space. Furthermore, by embed-ding the expanded high-dimensional semantic features into the latentrepresentation, HLRAD enables the model to more fully capture the se-mantic feature distribution of normal samples, significantly enhancingcomplex anomaly detection performance. We conducted extensive ex-periments on major anomaly detection benchmark datasets, includingMVTec-AD, VisA, and Real-IAD. In unified settings, HLRAD outper-forms state-of-the-art methods.

Abstract:
Controllable image generation methods, such as ControlNet,have demonstrated a remarkable capacity to introduce visual conditions(e.g., depth maps) to guide image generation. However, these methodsoften struggle with complex multi-instance scenes, frequently leading toattribute confusion among instances. While recent approaches attemptto mitigate this via manual instance labeling, such requirements arelabor-intensive. In this paper, we propose InstanceControl, a novel multi-instance controllable generation method that eliminates the need for in-stance labeling. We identify the primary bottleneck in existing meth-ods as the inability to accurately associate instance descriptions withtheir corresponding regions within visual conditions. To address this, weleverage the Vision-Language Model (VLM) to establish instance-levelcorrespondences between text prompts and visual conditions. Specifi-cally, the VLM automatically parses instance descriptions from the textprompts and simultaneously predicts instance masks based on the visualconditions. Furthermore, since the predicted masks may contain noise,we introduce an adaptive mask refinement strategy that dynamically re-fines these instance masks during the generation process. Extensive ex-periments demonstrate that our approach outperforms state-of-the-artmethods, achieving superior fidelity and precise instance-level control.The code and dataset will be made publicly available.

Abstract:
Self-supervised learning methods learn high-quality visualrepresentations, yet recent studies show that these representations of-ten capture demographic biases present in the training data. Existingfairness-aware methods address this by redesigning the self-supervisedobjective itself, limiting portability across the rapidly evolving land-scape of self-supervised learning (SSL) frameworks. We propose Proto-Fair, a fairness-aware contrastive loss designed to work alongside ex-isting SSL objectives without modifying them. ProtoFair leverages un-supervised prototype clustering to identify pseudo-counterfactual pairs:samples sharing the same cluster assignment but belonging to differentsensitive groups. By pulling these content-matched, cross-group samplestogether in the embedding space, ProtoFair encourages the encoder tolearn representations that are invariant to the sensitive attribute. Themethod requires only sensitive attribute annotations, no target labels,and integrates seamlessly with both SimCLR and SupCon. Experimentson CelebA and UTKFace demonstrate consistent fairness improvementswhile maintaining competitive accuracy.

Abstract:
Event cameras provide sequential visual data with spatialsparsity and high temporal resolution, making them attractive for low-latency object detection. Existing asynchronous event-based neural net-works exploit this low-latency advantage by updating predictions eventby event, but still suffer from two bottlenecks: recurrent architecturesare difficult to train efficiently on long sequences, and improving accu-racy often increases per-event computation and latency. Linear atten-tion is appealing because it enables parallel training and recurrent infer-ence. However, its dense state updates make per-event computation scalewith the state size, yielding a poor accuracy-efficiency trade-off for ob-ject detection, where accurate localization requires fine-grained spatialstates. The key challenge is therefore to introduce sparse state activa-tion that exploits the spatial sparsity of events while preserving efficientparallel training. We propose Spatially-Sparse Linear Attention (SSLA),which introduces a mixture-of-spaces state decomposition and a scatter-compute-gather training procedure, enabling state-level sparsity as wellas training parallelism. Building on SSLA, we develop an end-to-endasynchronous linear attention model, SSLA-Det, for low-latency event-based object detection. On Gen1 and N-Caltech101, SSLA-Det achievesstate-of-the-art accuracy among asynchronous methods, reaching 0.375mAP and 0.515 mAP, respectively, while reducing per-event computationby over 20

Abstract:
RGB-T detectors leverage the complementary strengths ofvisible and thermal infrared modalities, achieving robust performanceunder challenging conditions. Many of them resort to heavy dual back-bones and exhaustive cross-modality fusion across the entire image, lead-ing to impractically high computational costs. We observe that most im-age regions are smooth backgrounds (e.g., sky, ground) that can be easilyhandled by lightweight single-modality models. In light of this observa-tion, we propose a sparse fusion mechanism for efficient RGB-T detec-tion: first rapidly scanning the image to identify the proposals and thencarefully examining the remaining sparse proposals via feature fusion.We propose a two-stage framework to instantiate this mechanism, whichperforms detection in two stages: 1) a lightweight and modality-specificdetection stage that produces high-recall RoIs, and 2) a fusion-driven ex-amination and refinement stage that filters out the false positives and re-fines the bounding boxes. This design enables the detector to adaptivelyallocate more computational resources to the potential foregrounds, im-proving the efficiency while ensuring detection accuracy. Extensive ex-periments show that our method achieves competitive performance withsubstantially fewer parameters and lower cost, while maintaining strongscalability to high-resolution images.

Abstract:
The core challenge of Aerial-Ground Person Re-Identification (AG-ReID) is the drastic viewpoint discrepancy, which induces a significant cross-view feature gap that hinders aerial-ground matching. However, existing AG-ReID methods overlook this gap and attempt to learn direct global semantic correspondences through auxiliary components (e.g., attributes, tokens, or prompts), which limits training effectiveness and increases computational overhead. To address these issues, we propose a novel AG-ReID framework, MCVL, which effectively bridges the cross-view gap through multi-space cross-view learning. Our method aligns both spatial and global feature spaces, learning view-invariant yet identity-discriminative representations to effectively handle viewpoint discrepancies. Specifically, we introduce a Deformable Homography Transformation (DHT) module that projects features across aerial and ground view planes, creating an intermediate cross-view spatial feature space. Based on this, we propose a Cross-View Learning (CVL) strategy that leverages both original and projected features to reduce the spatial feature gap and enhance identity discrimination while preserving perspective invariance. Additionally, we design a View Decorrelation Contrastive Loss (VDCL) to mitigate view-specific biases by pushing global features away from their corresponding view-specific prototypes while pulling together global features of the same identity, thereby suppressing view-related cues and strengthening identity-related representations. Extensive experiments on five AG-ReID datasets, including CARGO, AG-ReIDv1, AG-ReIDv2, LAGPeR, and G2APS-ReID, demonstrate the effectiveness of our proposed MCVL method.

Abstract:
Multiple Instance Learning (MIL) is widely used for weaklysupervised learning, particularly in digital pathology, where fine-grainedannotations are costly. Most MIL methods aggregate instance featuresvia attention mechanisms. However, attention weights do not alwaysfaithfully reflect instance importance and may focus on spuriously cor-related regions. In this work, we propose CAR-MIL, a framework thatexplicitly guides attention learning through a counterfactual attentionregularization objective inspired by counterfactual explanations. Built ona standard attention-based MIL architecture, our approach introduces alightweight counterfactual attention branch trained to produce an alter-native prediction while remaining close to the factual attention distribu-tion. This encourages prediction changes to arise from minimal, struc-tured redistributions of attention, leading to more informative evidenceallocation. The resulting factual and counterfactual attention maps cap-ture complementary evidence: the former highlights regions supportingthe prediction, while the latter reveals regions whose reweighting wouldchallenge it. We evaluate our method on synthetic MIL benchmarks withinstance-level ground truth enabling controlled analysis of attention be-havior and on five digital pathology datasets across four tasks. CAR-MILmaintains competitive classification performance, with the largest gainsobserved on more challenging tasks, while improving attention reliability,demonstrating the benefits of integrating counterfactual explainabilityreasoning into attention learning.

Abstract:
Customized Portrait Generation (CPG) technologies havebeen widely used to generate high-fidelity person images given an inputimage indicating the identity and a text prompt indicating the requirededits. Yet these methods pose significant privacy risks by spreading fakevisual information. Against such risks, each public generator should beable to suppress its generation ability for a particular person when re-quested. Therefore, in this work we investigate the identity unlearningproblem for CPG. Since there are no previous methods in this field, wepropose a simple baseline that updates the image encoder by minimizingidentity similarity between generated and input images for target iden-tities to be unlearned, while maximizing it for identities to be retained.However, we find such a global perturbation in the feature space harmsthe fidelity of generated images for other identities to be retained. Tosolve this problem, we propose a novel method IREU, which first locatesidentity-related features in an offline manner and then only performsfeature perturbations on them. The experimental results show that ourproposed method IREU achieves better identity unlearning performancefor target identities to be unlearned, and also keeps high fidelity for otheridentities to be retained. In addition, our unlearned image encoder isgeneralizable across different generators with the same encoder withoutfine-tuning, which is friendly for deployment in practice.

Abstract:
Visibility in underwater environments degrades rapidly under turbid conditions, yet the effects on computer-vision models remain unclear. This issue is compounded by reliance on synthetic turbidity datasets, which may misrepresent real-world information loss. To address this gap, we introduce the Turbid Underwater Baseline (TUB) dataset, comprising 1,320 images captured under extreme turbidity and over 16,000 high-confidence ground-truth segmentation masks. We additionally propose PCD, a metric derived from phase congruency maps that is invariant to contrast and aims to capture the loss of structural information in real turbidity. We show that PCD correlates strongly with the performance of instance segmentation models on both real and synthetic turbid images, whereas common metrics in the field show weak to no correlation at all. The dataset and relevant code can be found on the project page: https://vap.aau.dk/pcd

Abstract:
Most privacy evaluations in computer vision treat datasets and models as isolated, static disclosures, failing to account for how risk accumulates in practice. We formalize Compositional Non-Face ReIdentification Risk, where identities remain linkable through non-face cues, such as clothing, gait, and scene context that aggregate across sequential releases of data expansions, model checkpoints, and metadata. We introduce the Vision Re-Identification Pressure Index (vRPI ), a foundational, information-theoretic measure derived from α Arimoto conditional Rényi entropy that quantifies the systemic erosion of anonymity. We prove that vRPI is monotone under cu- α mulative releases, establish an exact bridge to Bayes-optimal guessing probability, and derive additive decompositions under conditionalindependence benchmarks. Through the NFLeak protocol, we provide illustrative experiments on Market-1501-Tau and a CUHK03- Tau closed-world replication using a structured Attacker Ladder, together with masking-level, dependence-overlap, temperature-calibration, and non-uniform-prior diagnostics, demonstrating that vRPI accurately α tracks realized re-identification success even after explicit face removal. Our framework enables principled auditing of the “leaky bucket” effect in vision benchmarks, shifting the focus from single-release compliance to ecosystem-level privacy hygiene.

Abstract:
Diffusion-based generators have made synthetic images ubiquitous, but detectors often fail under simultaneous shifts in generator, prompt/style, and source-domain. We study AI-generated image detection as a transfer system described by training prior, frozen representation, and decision rule, and ask when classifier head training adds value beyond what is already separable in modern features. As a controlled diagnostic, we fit a prior-conditioned Gaussian discriminant ladder: closed-form heads built from firstand second-order feature statistics under nested covariance assumptions. On Percept-Lens, a unified protocol over 39 public datasets (7.1 million images), the best rung is frequently competitive with, and sometimes exceeds, released detector heads when matched on both prior and encoder. We further quantify strong sensitivity to the training prior, data-efficiency of moment-based heads, and representation dependence of Gaussian shift metrics, motivating (prior, encoder, head)-level reporting and stronger analytical baselines for AIGI transfer.

Abstract:
Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input complexity, and available platform resources. Current networks struggle with such fluctuations – adaptive networks cannot adhere to a strict compute budget, controller-based networks neglect to consider input complexity, and statically provisioned networks fail at all the above. Consequently, they do not extract maximum utility from the expended computational resources. We present SWAN (Sample and World-Aware Multimodal Network), the first adaptive multimodal network that accomplishes all three goals. SWAN employs a quality-aware controller to assign resources among modalities according to a variable user-specified maximum budget. Within this budget, an adaptive gating module further optimizes e!ciency by scaling layer utilization according to sample complexity. For further gains, SWAN also employs a token dropping module that masks semantically irrelevant multimodal features before performing detections. We evaluate SWAN in the domain of autonomous driving with complex multi-object 3D detection, reducing FLOPs by up to 49% with minimal degradation.

Abstract:
One of the pivotal recent challenges in neural network interpretability is polysemanticity, where a single neuron is activated by multiple, often unrelated concepts, hindering clear functional understanding. Although prior work has explored this phenomenon, existing approaches remain architecture-specific and depend on manual heuristics such as a fixed number of concept clusters (K), limiting their generality and scalability—especially for modern Transformer-based models. To address these limitations, we introduce SPICE (Simple Polysemantic Feature Interpretation via Clustering-based Explanation), a generalizable framework for analyzing polysemanticity in deep vision architectures. SPICE avoids architecture-dependent propagation rules, enabling the first systematic comparison of polysemanticity across both CNNs and Transformers, and automatically determines the number of concept clusters per neuron, eliminating reliance on a preset K and supporting scalable analysis for large models. Using SPICE, we conduct a comprehensive investigation into how polysemanticity emerges, varies across depth and architecture, and forms through distinct computational pathways.

Abstract:
Adversarial training is the most effective defense against adversarial attacks, but it often suffers from robust overfitting, whose causes remain unclear. In this paper, we observe a counterintuitive phenomenon: the model robust stability consistently deteriorates during training. To quantify this behavior, we introduce the Robust Stability Rate (RSR) and define robust instability. Our analysis demonstrates that improving the Robust Stability Rate can effectively reduce robust instability, thereby mitigating robust overfitting. Building on this insight, we propose a novel framework called Relearning Adversarial Training (RAT). Our min-max optimization establishes a new adversarial dynamic where generated adversarial examples induce robust instability, while model training enforces robust stability, aiming to enhance the model Robust Stability Rate of the current learning model by leveraging knowledge from historical model. Experimental results demonstrate that RAT can be integrated with various methods to significantly enhance their performance. It effectively reduces robust instability and mitigates robust overfitting.

Abstract:
Learning with noisy labels in large-scale classification re-mains challenging. Existing forward-correction methods estimate class-dependent label noise via a transition matrix, but this matrix is typ-ically assumed to be full-rank and unconstrained. However, real-worldlabel noise is typically structured and exhibits low-rank properties dueto semantic clustering, especially when the number of classes is large.This mismatch between modeling assumptions and the intrinsic struc-ture of label noise leads to unstable optimization and degraded general-ization performance. Motivated by this observation, we propose a novelLow-Effective-Rank Noisy-Label Learning (LENL) framework, whichexplicitly constrains the transition matrix via nuclear-norm regulariza-tion. This low-rank inductive bias captures structured confusion pat-terns while suppressing spurious noise modes, yielding both statisticalefficiency and improved generalization. We further provide theoreticalanalysis establishing the benefits of rank-constrained estimation understructured noise assumptions. To enable stable and scalable optimiza-tion, we introduce a Newton–Schulz optimizer tailored for label-noiselearning, which approximates the nuclear norm through decomposition-free iterative updates, avoiding the numerical instability and computa-tional overhead of explicit singular value decomposition. Extensive exper-iments on CIFAR-100, CIFAR-100N, ImageNet-1K, and Noisy Ostracodsdemonstrate consistent and significant improvements over state-of-the-art label-noise learning methods.

Abstract:
Deep neural networks remain vulnerable to imperceptible ad-versarial perturbations, yet efficient robustness certification for seman-tic segmentation is largely unexplored. We propose the first real-timecompatible certifiably robust semantic segmentation framework basedon networks with built-in Lipschitz constraints. Our method achievescompetitive pixel accuracy on Cityscapes while providing determinis-tic robustness guarantees, without the computational overhead of ran-domized smoothing or formal verification. We introduce a generalizedcertification framework for segmentation that computes worst-case per-formance under ℓ2 attacks of radius ϵ across diverse evaluation metrics.Crucially, our approach is approximately 600× faster than randomizedsmoothing at inference on an NVIDIA A100 GPU, delivering comparablecertificates. We validate our worst-case bounds against state-of-the-artadversarial attacks, demonstrating that Lipschitz-constrained architec-tures offer a practical and scalable path toward trustworthy dense pre-diction in safety-critical applications.

Abstract:
Adversarial attacks pose a challenge to the reliability of deeplearning models, motivating effective detection methods. Existing tech-niques often rely on attack-specific assumptions, access to adversarialsamples, or knowledge of the underlying classifier (white-box). We pro-pose A4 D (Attack- and Architecture-Agnostic Adversarial Detector), acompletely black-box, zero-shot adversarial attack detection frameworkthat utilizes prompt-based similarity scores derived from CLIP. To thebest of our knowledge this is the first attempt to utilize CLIP for sucha task. The method is based on two key observations: (i) CLIP is sen-sitive even to small imperceptible non-semantic perturbations; (ii) Theshift in CLIP embedding space is not arbitrary and can be used as arobust attack indicator. Experiments across multiple attacks, datasetsand classifiers validate that A4 D achieves SOTA detection results in theattack-agnostic and classifier-agnostic setting.

Abstract:
Modern convolutional neural networks (CNNs) organize com-putation as a discrete stack of layers whose parameters are independentlystored and learned, with the number of layers fixed as an architecturalhyperparameter. In this work, we explore an alternative perspective: cannetwork parameterization itself be modeled as a continuous dynamicalsystem? We introduce Puppet-CNN, a framework that represents con-volutional layer parameters as states evolving along a learned parameterflow governed by a neural ordinary differential equation (ODE). Underthis formulation, layer parameters are generated through continuousevolution in parameter space, and the effective number of generatedlayers is determined by the discretization resolution used to sample thelearned parameter trajectory, which can be modulated by input com-plexity to enable input-adaptive computation. We validate its feasibilityon standard image classification benchmarks and show that continuousparameter dynamics can maintain competitive predictive performancewhile substantially reducing stored trainable parameters. These resultssuggest that viewing neural network parameterization through the lensof dynamical systems provides a structured and flexible design space foradaptive convolutional models.

Abstract:
Out-of-distribution (OOD) detection is crucial for the safedeployment of deep neural models in applications such as autonomousdriving. With the emerging multimodal nature of modern applications,recent attention has shifted toward OOD detection in multimodal set-tings. However, current multimodal OOD detection methods fail to fullyexploit the synergy among modalities: they treat all modalities equally,disregarding their varying detection performance, and they are unableto capture the diverse uncertainty information encoded at the logit level.In this paper, we propose to exploit the dark knowledge within unimodalexperts as the key to revealing their synergy. To this end, we introducea self multimodal OOD distillation framework, which leverages logits asuncertainty-aware soft targets to train a holistic model that operates inthe joint embedding space of all modalities. Specifically, the proposedframework accounts for the negative effects of underperforming modal-ities and effectively fuses both the rich feature-level knowledge and thelogit-level knowledge of modalities. As a result, our method improvesthe performance of current state-of-the-art multimodal OOD detectionmethods, achieving gains of up to 30% across diverse OOD detectionbenchmarks, spanning two tasks and five multimodal OOD datasets.

Abstract:
True video intelligence demands more than recognizing whatis visible: it requires reasoning about why events unfold, predicting whatwould change under different conditions, and deciding what to do next.We refer to this full progression—from perception through causal reason-ing and simulation to strategic planning—as Strategic Video Intelligence(SVI). No existing benchmark evaluates this capability stack: in-the-wildvideos lack verifiable ground truth for causal and strategic questions,while synthetic environments sacrifice the complexity of real multi-agentsystems. To bridge this gap, we introduce SVI-Bench, a large-scale bench-mark that leverages team sports as a dynamic microworld, a domain thatuniquely combines the complexity of real-world multi-agent interactionwith the verifiability of explicit rules and definitive outcomes. SVI-Benchcomprises ∼35K hours of broadcast video, ∼15M annotated actions,∼15K hours of expert commentary, ∼23K game reports, and ∼103Kstructured statistical records across basketball, soccer, and hockey, allconstructed via a data engine that transforms raw game data into adense, cross-referenced corpus. We organize evaluation into 9 tasks span-ning a progressive four-pillar hierarchy: Dynamic Scene Understanding,Causal Reasoning, Strategic Simulation, and Agentic Synthesis. Evaluat-ing strong multimodal and agentic baselines, we find a capability cliff :models perform competently on perceptual tasks (achieving ∼74% onfine-grained action QA) but degrade sharply at higher levels of the stack.Agentic tasks prove hardest of all: the strongest model achieves only 5%accuracy when required to autonomously gather and integrate evidenceacross a corpus of 1.8M clips. We release the full benchmark to catalyzeprogress toward AI systems capable of strategic intelligence in complex,dynamic multi-agent environments.§ Code github.com/texaser/svi-benchõ Data huggingface.co/mvp-group/svi-bench Website svi-bench.github.ioA Extended Paper svi-bench.github.io/svi bench extended.pdfPillar 1: Perceptionparsing who is where, doing what and whenPillar 2: Reasoningexplaining why something happenedPillar 3: Simulationgenerating plausible alternative futuresPillar 4: Agencyautonomous analysis over multimodal evidenceThe Strategic Video Intelligence StackFig. 1: Overview of SVI-Bench, illustrated through a single play from the 2022NCAA Final Four. SVI-Bench is the first large-scale video benchmark evaluatingthe full SVI stack: Perception (describing what happens), Reasoning (explaining why),Simulation (generating plausible alternatives), and Agency (autonomous analysis).

Abstract:
Recent advances in online reconstruction of dynamic scenesdemonstrate impressive visual quality, showing potential for real-worldapplications such as VR content streaming. Yet, existing online recon-struction methods still require a large number of input views and time-consuming iterative optimization. Moreover, reliable pose estimation,a prerequisite for NVS, introduces additional delay. In this paper, wepresent REON-NVS, a feedforward online NVS framework for sparse-view input video streams, which goes from pose estimation to novel-viewreconstruction in real time. REON-NVS comprises two main compo-nents. First, our method leverages a feedforward pose estimator andmapper that replace conventional camera pose optimization pipelines.Second, we design a scene reconstructor built upon a state-space model(SSM), which efficiently synthesizes temporally consistent novel-view im-ages by exploiting information from previous frames. To train and eval-uate our approach in realistic in-the-wild streaming scenarios, we intro-duce a new multi-view dynamic scene dataset of 150 dynamic scenescaptured with moving cameras. Extensive experiments demonstrate thatREON-NVS achieves high visual quality while operating in real time (32FPS), validating its applicability in real-world scenarios.

Abstract:
Fabric moiré is a sampling-induced aliasing artifact caused by the interaction between fine textile patterns and camera sensor grids, producing structured interference that severely degrades image quality. Unlike screen-induced moiré, which stems from strictly periodic display lattices, fabric moiré is intrinsically more challenging due to the broadband and semi-periodic nature of textile weaves. The heavy spectral overlap between intrinsic texture and aliasing components renders fabric demoiréing substantially more ill-posed. Consequently, existing models trained on screen moiré datasets generalize poorly to these complex textile patterns. Despite its practical importance, fabric image demoiréing remains underexplored and lacks standardized benchmarks. We present the first comprehensive benchmark for fabric image demoiréing. To address the difficulty of acquiring pixel-aligned real-world pairs, we develop a physically motivated synthesis framework and construct a large-scale dataset comprising 16,050 paired multi-resolution fabric images with controllable aliasing severity. Furthermore, we customize a baseline model, which establishes promising performance on the proposed benchmark dataset with strong generalization ability. Our benchmark provides a standardized platform for advancing research in fabric image demoiréing.

Abstract:
Video generation models achieve high visual quality but of-ten struggle to generate physics-aware videos. Unlike rigid-body motion,which can be described by explicit trajectories or formulas, complex de-formation dynamics remain challenging to synthesize. We observe that alack of physical reasoning for localizing dynamic areas allows irrelevantregions to dilute the model’s attention, leading to generation failure.In this paper, we propose DeforM, a reasoning-guided image-to-videogeneration framework that directs the model’s focus toward physics-critical regions. To reason and localize these critical regions, we introducea VLM-guided physical reasoning module, DeforM-Reason, to identifytarget objects and generate spatial-temporal masks. For physical guid-ance, we develop two alternative strategies: DeforM-Free for training-freemechanism analysis, and DeforM-Injection as a powerful training-basedgenerator. Experimental results demonstrate that DeforM improves therealism of generated deformation scenarios, outperforming baseline mod-els in both visual quality and physical consistency.

Abstract:
High-quality 2D assets have become increasingly abundantand easy to edit, providing a rich foundation for creative content acrossart, design, and virtual environments. However, while diffusion-based 3Dgeneration has achieved remarkable progress in single-object synthesis,leveraging such 2D assets for controllable 3D composition remains a chal-lenging problem. To address this, we introduce CAST3D, a training-freeframework that enables Customized Composition in 3D: transformingarbitrary 2D assets into a coherent 3D object or scene under textualguidance. CAST3D consists of two stages: 3D Layout Hinting and Com-positional Generation. To maintain structural consistency and eliminateartifacts, we further design stochastic trajectory manipulation (STM)for structure-preserving modification and a connectivity-based pruningstrategy for clean geometry integration. Extensive experiments demon-strate that CAST3D produces semantically consistent and visually faith-ful 3D compositions, bridging 2D asset creation and 3D world synthesis.

Abstract:
Reconstructing 4D scenes from casually captured monocularvideo is vital for applications in virtual reality (VR) and embodied AI.Recent advances in 4D reconstruction and novel view synthesis havesubstantially propelled this capability. However, existing reconstructionmethods generally cannot recover regions beyond visible camera limits.Consequently, we introduce a new paradigm that achieves 4D scene syn-thesis by combining visible-region reconstruction from monocular inputwith invisible-region generation beyond observable camera boundaries.A straightforward approach is to leverage video generation models as“generative priors” for invisible-region exploration. However, their inherentstochasticity and large solution space prevent stable and consistent viewsynthesis. As a result, naively incorporating generative content into thereconstruction process often causes artifacts, especially when camera tra-jectories deviate significantly from the original video. To overcome thesechallenges, we present Panoramic Alignment for Strategic Exploitation ofGenerative Priors (PASTEL). Specifically, PASTEL proposes panoramicscene alignment, a novel representation that reformulates the intractable3D "invisible region” exploration into a tractable 2D directional trajectoryplanning. This is achieved by reducing the viewpoint planning from 6-DoFsearch to a 2D directional search with explicit visibility boundaries. Byoperating within this panoramic space, our method strategically iden-tifies camera trajectories that maximize exploration beyond observableboundaries while minimizing viewpoint deviation. Experimental resultsshow that PASTEL can not only extrapolate plausible scene contentbeyond the observable boundaries of input monocular videos, but alsosubstantially boost monocular 4D reconstruction performance. PASTELoutperforms the previous state-of-the-art method by 0.9dB in full-imagePSNR on the DyCheck IPhone dataset.

Abstract:
Recent advances in diffusion models have enabled impressivevideo editing capabilities, yet production-grade Video Object Insertion(VOI) remains challenging due to inadequate 4D scene understandingand a lack of proper optical interactions, such as shadows and reflec-tions. To address these limitations, we present InsertAnywhere, a com-prehensive VOI framework that achieves geometrically grounded objectplacement and optics-aware video synthesis. Our approach first lever-ages a 4D-aware mask generation module that allows users to anchor anobject’s 3D pose in a single frame. The framework automatically prop-agates this placement across the video, accurately handling local scenedynamics and occlusions. To synthesize realistic physical lighting inter-actions, we introduce Optics-Aware Representation Alignment, a novelstrategy that utilizes an extended mask to guide feature extraction, en-abling optical effects to seamlessly extend beyond the inserted object’sboundary. Finally, to overcome the lack of training data for such phenom-ena, we construct and open-source ROSE++, a specialized quadrupletdataset tailored for the supervised learning of optical effects. Extensiveexperiments demonstrate that InsertAnywhere produces geometricallyplausible and photometrically realistic insertions in complex real-worldscenarios, significantly outperforming existing research and commercialgenerative tools.

Abstract:
We introduce EgoSim, a closed-loop egocentric world sim-ulator that generates spatially consistent interaction videos and per-sistently updates the underlying 3D scene state for continuous simu-lation. Existing egocentric simulators either lack explicit 3D ground-ing, causing structural drift under viewpoint changes, or treat the sceneas static, failing to update world states across multi-stage interactions.EgoSim addresses both limitations by modeling 3D scenes as updatableworld states. We generate embodiment interactions via a Geometry-action-aware Observation Simulation model, with spatial consistencyfrom an Interaction-aware State Updating module. To overcome the crit-ical data bottleneck posed by the difficulty in acquiring densely alignedscene–interaction training pairs, we design a scalable pipeline that ex-tracts static point clouds, camera trajectories, and embodiment actionsfrom in-the-wild large-scale monocular egocentric videos. Extensive ex-periments demonstrate that EgoSim significantly outperforms existingmethods in terms of visual quality, spatial consistency, and generalizationto complex scenes and in-the-wild dexterous interactions, while support-ing cross-embodiment transfer to robotic manipulation. Project page isat: egosimulator.github.io.

Abstract:
While recent video generation models have achieved significant visual fidelity, they often suffer from the lack of explicit physical controllability and plausibility. To address this, some recent studies attempted to guide video generation with physics-based rendering. However, these methods face inherent challenges in accurately modeling complex physical properties and effectively controlling the resulting physical behavior over extended temporal sequences. In this work, we introduce PhysChoreo, a framework that can generate videos with diverse controllability and physical realism from a single image. Our method consists of two stages: first, it estimates the static initial physical properties of all objects in the image through part-aware physical property reconstruction. Then, through temporally instructed and physically editable simulation, it synthesizes high-quality videos with rich dynamic behaviors and physical realism. Experimental results show that PhysChoreo can generate videos with rich behaviors and physical realism, outperforming state-of-the-art methods on multiple evaluation metrics.

Abstract:
Designing functional and aesthetically coherent floor plansrequires exploring a vast space of possible room arrangements, a taskthat quickly becomes overwhelming for human designers. In this pa-per, we propose GRE-Diff, a controllable and interactive diffusion-basedframework that automates the creation and editing of apartment floorplans under user-specified constraints. By combining AI-generated sug-gestions with real-time, human-in-the-loop editing, the system enablesusers to specify room types, room counts, boundary shapes, and editingoperations through LLM-parsed instructions or GUI-based interaction.It then generates a diverse set of plausible and well-structured designs forrefinement. At the core of our approach is Gaussian Room Embedding(GRE), a continuous latent representation that models each room as aspatial Gaussian distribution capturing its location and extent. Exten-sive experiments on the RPLAN dataset show that GRE-Diff produceshigh-quality, constraint-aware, and editable polygonal layouts, offeringa practical step toward bridging AI-driven automation and human cre-ativity in spatial design.

Abstract:
Predicting human gaze in video is fundamental to advanc-ing scene understanding and multimodal interaction. While traditionalsaliency maps provide spatial probability distributions and scanpaths of-fer ordered fixations, both abstractions often collapse the fine-grainedtemporal dynamics of raw gaze. Furthermore, existing models are typi-cally constrained to short-term windows (≈ 3–5s), failing to capture thelong-range behavioral dependencies inherent in real-world content. Wepresent a generative framework for infinite-horizon raw gaze predictionin videos of arbitrary length. By leveraging an autoregressive diffusionmodel, we synthesize gaze trajectories characterized by continuous spa-tial coordinates and high-resolution timestamps. Our model is condi-tioned on a saliency-aware visual latent space. Quantitative and qualita-tive evaluations demonstrate that our approach significantly outperformsexisting approaches in long-range spatio-temporal accuracy and trajec-tory realism. Project website: https://www.immersivecomputinglab.org/publication/infinite-gaze/.

Abstract:
Although existing 3D dance generation methods performwell in controlled scenarios, they often struggle to generalize in the wild.When conditioned on unseen music, existing methods often produce un-structured or physically implausible dance, largely due to limited music-to-dance data and restricted model capacity. This work aims to pushthe frontier of generalizable 3D dance generation by scaling up bothdata and model design. 1) On the data side, we develop a fully auto-mated pipeline that reconstructs high-fidelity 3D dance motions frommonocular videos. To eliminate the physical artifacts prevalent in exist-ing reconstruction methods, we introduce a Foot Restoration DiffusionModel (FRDM) guided by foot-contact and geometric constraints thatenforce physical plausibility while preserving kinematic smoothness andexpressiveness, resulting in a diverse, high-quality multimodal 3D dancedataset totaling 100.69 hours. 2) On model design, we propose Choreo-graphic LLaMA (ChoreoLLaMA), a scalable LLaMA-based architecture.To enhance robustness under unfamiliar music conditions, we integratea retrieval-augmented generation (RAG) module that injects referencedance as a prompt. Additionally, we design a slow/fast-cadence Mixture-of-Experts (MoE) module that enables ChoreoLLaMA to smoothly adaptmotion rhythms across varying music tempos. Extensive experimentsacross diverse dance genres show that our approach surpasses existingmethods in both qualitative and quantitative evaluations, marking a steptoward scalable, real-world 3D dance generation.

Abstract:
Multi-view visual reasoning is essential for intelligent systems that must understand complex environments from sparse and discrete viewpoints, yet existing research has largely focused on single-image or temporally dense video settings. In real-world scenarios, reasoning across views requires integrating partial observations without explicit guidance, while collecting large-scale multi-view data with accurate geometric and semantic annotations remains challenging. To address this gap, we leverage physically grounded simulation to construct diverse, high-fidelity 3D scenes with precise per-view metadata, enabling scalable data generation that remains transferable to real-world settings. Based on this engine, we introduce VIEW2SPACE, a multi-dimensional benchmark for sparse multi-view reasoning, together with a scalable, disjoint training split supporting millions of grounded question–answer pairs. Using this benchmark, a comprehensive evaluation of state-of-theart vision–language and spatial models reveals that multi-view reasoning remains largely unsolved, with most models performing only marginally above random guessing. We further investigate whether training can bridge this gap. Our proposed Grounded Chain-of-Thought with Visual Evidence substantially improves performance under moderate difficulty, and generalizes to real-world data, outperforming existing approaches in cross-dataset evaluation. We further conduct difficulty-aware scaling analyses across model size, data scale, reasoning depth, and visibility constraints, indicating that while geometric perception can benefit from scaling under sufficient visibility, deep compositional reasoning across sparse views remains a fundamental challenge. Code and benchmark are available online3.

Abstract:
Recent advances in video diffusion have enabled the development of “world models” capable of simulating interactive environments. However, these models are largely restricted to single-agent settings, failing to control multiple agents simultaneously in a scene. In this work, we tackle a fundamental issue of action binding in existing video diffusion models, which struggle to associate specific actions with their corresponding subjects. For this purpose, we propose ActionParty, an action controllable multi-subject world model for generative video games. It introduces subject state tokens, i.e. latent variables that persistently capture the state of each subject in the scene. By jointly modeling state tokens and video latents with a spatial biasing mechanism, we disentangle global video frame rendering from individual action-controlled subject updates. We evaluate ActionParty on the Melting Pot benchmark, demonstrating the first video world model capable of controlling up to seven players simultaneously across 46 diverse 2D game environments. Our results show significant improvements in action-following accuracy and identity consistency, while enabling robust autoregressive tracking of subjects through complex interactions.

Abstract:
Recent co-speech gesture generation methods often overlookcultural differences, limiting their effectiveness in human–agent inter-action. Moreover, culture-conditioned models are rarely evaluated un-der speaker-disjoint splits, so apparent “cultural” behavior may be con-founded with speaker-specific gesturing style. We introduce SICAGE, amodular framework for culture-aware co-speech gesture generation thatconditions motion synthesis models on speaker-independent cultural rep-resentations. SICAGE learns these representations from audio and textby treating each speaker as a separate domain while imposing invari-ance across speakers. This encourages representations to remain culture-discriminative while reducing dependence on speaker identity. The re-sulting cultural embeddings condition a multimodal generator to pro-duce culturally appropriate gestures. We instantiate this idea with twodomain generalization approaches: adversarial learning and Fishr reg-ularization. We further introduce ALaDiT, a real-time diffusion-basedgesture generator designed to efficiently incorporate the learned culturalembeddings. To validate our method, we built TED4C-L, a 106-hourmultimodal dataset of 764 TED speakers from four cultural groups. Ex-periments show that SICAGE improves motion realism, diversity, beatsynchronization, semantic relevance, and cultural consistency.

Abstract:
Most digital videos are stored in 8-bit low dynamic range(LDR) formats, where much of the original high dynamic range (HDR)scene radiance is lost due to saturation and quantization. This loss ofhighlight and shadow detail precludes mapping accurate luminance toHDR displays and limits meaningful re-exposure in post-production work-flows. Although techniques have been proposed to convert LDR imagesto HDR through dynamic range expansion, they struggle to restore re-alistic detail in the over- and underexposed regions. To address this, wepresent DiffHDR, a framework that formulates LDR-to-HDR conversionas a generative radiance inpainting task within the latent space of a videodiffusion model. By operating in Log-Gamma color space, DiffHDR lever-ages spatio-temporal generative priors from a pretrained video diffusionmodel to synthesize plausible HDR radiance in over- and underexposedregions while recovering the continuous scene radiance of the quantizedpixels. Our framework further enables controllable LDR-to-HDR videoconversion guided by text prompts or reference images. To address thescarcity of paired HDR video data, we develop a pipeline that synthesizeshigh-quality HDR video training data from static HDRI maps. Extensiveexperiments demonstrate that DiffHDR significantly outperforms state-of-the-art approaches in radiance fidelity and temporal stability, produc-ing realistic HDR videos with considerable latitude for re-exposure.

Abstract:
4D volumetric video via Gaussian Splatting (GS) enables free viewpoint control of real-world scenes but does not enable motion editing. One challenge is achieving part-based control in multi-object scenes; prior works focus on single objects, assume rigid parts, or require manual annotations. MotionSplicer is a template-free and annotation-free method that automatically discovers 3D parts in GS. To bridge the semanticto-motion domain gap, it extracts initial parts from temporal difference images and resolves multi-view inconsistencies via an adaptive 3D unifyand-split process. For robust tracking, it optimizes a time-invariant feature grid with temporal refinement to learn spatially and temporally coherent skinning weights. Experiments show that MotionSplicer can be applied more generally to objects and scenes than state-of-the-art methods, and enables creative motion edits like isolation, ghosting, and echo.

Abstract:
We present D-Rex, a person-specific framework for photore-alistic, relightable, expressive, and animatable full-body human avatarswith free-viewpoint rendering. Existing methods for relightable full-bodyavatars rely on explicit 3D intrinsic decomposition with analytic re-flectance models, which require accurate geometry registration and care-ful optimization to capture realistic light transport effects. This tightcoupling of relighting with avatar modeling has hindered expressiveness:to our knowledge, no existing method demonstrates strong facial anima-tion alongside relighting, limiting applicability in telepresence, gaming,and virtual production. We propose to decouple relighting entirely fromavatar modeling by treating it as an image-space post-process: a learnedtranslation from flat-lit, albedo-like renderings to a target HDR illumina-tion. To this end, we leverage the strong generative prior of a pre-trainedvideo diffusion relighting model, fine-tuned via LoRA on paired flat-litand relit frames captured in a light stage. The flat-lit driving framesare produced by an independent expressive full-body avatar frameworktrained under white-light conditions, requiring no modification to sup-port relighting, making D-Rex directly applicable to any white-lightavatar system. We demonstrate that D-Rex enables view- and tempo-rally consistent relighting while faithfully preserving expressive motionand fine-grained facial detail, outperforming physically-based relightableavatar baselines. Project page: https://vcai.mpi-inf.mpg.de/projects/DRex/

Abstract:
Visual feedback is critical for motor skill acquisition in sports and rehabilitation, and psychological studies show that observing nearperfect versions of one’s own performance accelerates learning more effectively than watching expert demonstrations alone. We propose to enable such personalized feedback by automatically editing a person’s motion to reflect higher skill. Existing motion editing approaches are poorly suited for this setting because they assume paired input-output data—rare and expensive to curate for skill-driven tasks—and explicit edit guidance at inference. We introduce ExpertEdit, a framework for skill-driven motion editing trained exclusively on unpaired expert video demonstrations. ExpertEdit learns an expert motion prior with a masked language modeling objective that infills masked motion spans with expert-level refinements. At inference, novice motion is masked at skill-critical moments and projected into the learned expert manifold, producing localized skill improvements without paired supervision or manual edit guidance. Across eight diverse techniques and three sports from Ego-Exo4D [15] and Karate Kyokushin [52], ExpertEdit outperforms state-of-the-art supervised motion editing methods on multiple metrics of motion realism and expert quality. Project page and benchmark available at: https://vision.cs.utexas.edu/projects/expert_edit/

Abstract:
Generative visual models fundamentally struggle with precise spatial control. This arises from a core disconnect: models can process textual descriptions of space but cannot directly map numerical coordinates onto the 2D image canvas (as illustrated in Fig. 2). We introduce MetaPoint, a method that bridges this gap by representing a continuous 2D coordinate as a single, special token. Crucially, MetaPoint requires no new architectural components; it directly leverages the model’s inherent positional encoding schemes to interpret these coordinates, treating our token as a virtual point on the canvas. This lightweight approach enables pixel-level control of an object’s position with one token or its bounding box with two, all without requiring architectural changes or bespoke attention masking. The MetaPoint tokens are designed to be compositional, serving as spatial primitives. This allows a planner agent to decompose a high-level user request into a structured sequence of primitives for the generator. By providing a simple, precise, and scalable building block for spatial control, MetaPoint unlocks more powerful compositional generative agents and enables intuitive, interactive editing systems.

Abstract:
The ability to transform a flat sheet into a complex threedimensional structure is a fundamental test of physical intelligence. Unlike cloth manipulation, origami is governed by strict geometric axioms and hard kinematic constraints, where a single invalid crease or collision can invalidate the entire folding sequence. As a result, origami demands long-horizon constructive reasoning that jointly satisfies precise physical laws and high-level semantic intent. Existing approaches fall into two disjoint paradigms: optimization-based methods enforce physical validity but require dense, precisely specified inputs, making them unsuitable for sparse natural language descriptions, while generative foundation models excel at semantic and perceptual synthesis yet fail to produce longhorizon, physics-consistent folding processes. Consequently, generating valid origami folding sequences directly from text remains an open challenge. To address this gap, we introduce Learn2Fold, a neuro-symbolic framework that formulates origami folding as conditional program induction over a crease-pattern graph. Our key insight is to decouple semantic proposal from physical verification. A large language model generates candidate folding programs from abstract text prompts, while a learned graph-structured world model serves as a differentiable surrogate simulator that predicts physical feasibility and failure modes before execution. Integrated within a lookahead planning loop, Learn2Fold enables robust generation of physically valid folding sequences for complex and out-of-distribution patterns, demonstrating that effective spatial intelligence arises from the synergy between symbolic reasoning and grounded physical simulation.

Abstract:
Existing single-view 3D human generation methods remain limited in modeling clothed human–object interactions (CHOI), due to two key challenges: (1) the lack of reliable guidance for human–object spatial relationships resulting in implausible layouts, and (2) the absence of physical interaction modeling between entities leading to unrealistic contacts. To address these issues, we propose PartCHOI, a part-aware method that formulates the CHOI task as generating a human-centered scene with objects treated as parts of the human body. Specifically, to model spatial relationships between objects and the human, we design a Human-Guided Pose Estimator (H-GPE) that efficiently infers objectpart poses by separating and localizing them within the coupled interaction scene. Furthermore, we devise an Object-Guided Refinement Module (O-GRM) to perform fine-grained human surface deformation for preventing penetration into objects, thus yielding realistic contact. Benefiting from the above design, PartCHOI generates plausible and detailed interactive scenes while avoiding lengthy layout optimization. Extensive experiments demonstrate our approach surpasses state-of-the-art methods in generation and interaction quality.

Abstract:
This paper introduces ID-PreFeR, a robust identity-preservingface restoration method that tackles the ill-posed face restoration prob-lem by incorporating personalized identity information. Existing approachesoften suffer from high training and storage costs and are sensitive tothe quality of reference images. To address these issues, we propose alightweight personalization injector that enables efficient personalizationwithout the need for regularization data. We also introduce an iden-tity–quality disentanglement training strategy to ensure robust identitylearning, even when some reference images are of poor quality. Further-more, we propose an identity-preserving sampling strategy to enhanceidentity fidelity during inference. Extensive experiments on both syn-thetic data and a newly collected real-world mobile-phone dataset verifythe effectiveness and practicality of the proposed method.

Abstract:
Recent advances in video diffusion models have remarkably improved camera-controlled video generation, but most methods rely solely on supervised fine-tuning (SFT), leaving online reinforcement learning (RL) post-training largely underexplored. In this work, we introduce an online RL post-training framework that optimizes a pretrained video generator for precise camera control. To make RL effective in this setting, we design a verifiable geometry reward that delivers dense segment-level feedback to guide model optimization. Specifically, we estimate the 3D camera trajectories for both generated and reference videos, divide each trajectory into short segments, and compute segment-wise relative poses. The reward function then compares each generated-reference segment pair and assigns an alignment score as the reward signal, which helps alleviate reward sparsity and improve optimization efficiency. Moreover, we construct a comprehensive dataset featuring diverse large-amplitude camera motions and scenes with varied subject dynamics. Extensive experiments show that our online RL post-training clearly outperforms SFT baselines across multiple aspects, including camera-control accuracy, geometric consistency, and visual quality, demonstrating its superiority in advancing camera-controlled video generation.

Abstract:
4D scene synthesis from monocular videos has made significant progress in recent years. However, existing methods are typically constrained by view interpolation. As a result, they struggle to infer unseen regions beyond the observed views. In this paper, we reformulate the task as 4D scene synthesis with unseen regions, which extends beyond traditional interpolation settings. Camera-conditioned video generation enables unseen region synthesis by guiding generation along specified cameras. However, these methods lack explicit 3D priors and are optimized with random camera trajectories. This design leads to severe inconsistencies under large trajectory deviations. To address this limitation, we build a unified training and inference framework with panoramic trajectory guidance. While this design improves cross-view consistency, the panoramic representation alone fails to model dynamic content effectively. Object motion in panoramic space introduces scale and shape distortions. To address this, we propose PanoGaussian, a unified PanoramicGaussian representation that distills the panoramic representation into an explicit dynamic Gaussian representation to capture dynamic physical priors of the 4D scene. Experiments demonstrate that PanoGaussian achieves consistent 4D scene synthesis even under large viewpoint variations.

Abstract:
3D human avatars have shown impressive visual fidelitydriven by pose-conditioned models, yet they still lack the physical abil-ity required for interactions with each other and environments. Althoughrecent studies have made various attempts to incorporate physical char-acteristics into 3D avatars, they only exhibit limited physical deforma-tions, often leading to constrained interaction behaviors. To resolve thisissue, we present PIAvatar, a framework to simultaneously enable physi-cally aware interactions between avatar-avatar and avatar-environment,and a non-rigid deformable human body simulation. In this work, ourkey insight is to decouple kinematic velocity from deformation gradi-ent. When external forces act on avatars, the kinematic velocity inducesstress which hinders the avatar’s ability to achieve a desired pose. Inaddition, we integrate a skeletal framework within the avatar. It allowsestimating its poses and real-time tracking in a closed form, even duringnon-rigid physical interactions. Our approach is implemented within aconventional Material Point Method framework to ensure physically con-sistent dynamics. We lastly evaluate the method on both human-objectand human-human interaction scenarios to assess its behavior under di-verse interaction settings.

Abstract:
Cinematic camera control relies on a tight feedback loop between director and cinematographer, where camera motion and framing are continuously reviewed and refined. Recent generative camera systems can produce diverse, text-conditioned trajectories, but they lack this “director in the loop” and have no explicit supervision of whether a shot is visually desirable. This results in in-distribution camera motion but poor framing, off-screen characters, and undesirable visual aesthetics. In this paper, we introduce VERTIGO, the first framework for visual preference optimization of camera trajectory generators. Our framework leverages a real-time graphics engine (Unity) to render 2D visual previews from generated camera motion. A cinematically fine-tuned vision–language model then scores these previews using our proposed caption-based consistency similarity mechanism, which aligns renders with text prompts. This process provides the visual preference signals for Direct Preference Optimization (DPO) post-training. Both quantitative evaluations and user studies on Unity renders and diffusion-based Camera-to-Video pipelines show consistent gains in condition adherence, framing quality, and perceptual realism. Notably, VERTIGO reduces the character off-screen rate from 38% to nearly 0% while preserving the geometric fidelity of camera motion. User study participants further prefer VERTIGO over baselines across composition, consistency, prompt adherence, and aesthetic quality, confirming the perceptual benefits of our visual preference post-training.

Abstract:
Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal self-attention, causing compute and latency to grow quadratically with the number of frames. Existing efficiency methods improve scalability but often lose accuracy relative to full self-attention, for example through aggressive frame/token dropping or coarse attention approximations. We introduce StateKV, an inference-time method that adapts pretrained long-video VLMs to linear-time video prefill by carrying cross-frame context in a fixed-capacity, importance-based recurrent state, paired with a second full per-frame cache used for decoding. Across three long-video benchmarks and seven models spanning three families and multiple scales, StateKV remains close to full self-attention and consistently outperforms dominant sliding-window / recency-based streaming approximations, without fine-tuning or architectural changes. StateKV also reduces video-prefill cost measured FLOPs, enabling stronger accuracy at a fixed compute budget by running larger models. These results suggest a practical step toward scalable long-video understanding.

Abstract:
Deep video understanding requires long-horizon, perceptioncentric reasoning that repeatedly revisits a video to gather temporally distributed evidence. However, existing benchmarks are either relatively easy (perception-centric but often solvable after a single view) or logicheavy with simplified visuals, and thus do not faithfully measure multimodal test-time thinking that depends on repeated perception. We introduce PerceptionComp, a fully manually annotated benchmark designed so that no single moment is sufficient: answering requires evidence from multiple temporally separated segments under compositional constraints. PerceptionComp contains 1,114 five-choice questions over 279 high-scene-complexity videos spanning diverse domains. Videos are selected using automatic proxies for scene complexity (SAM2 instance counts and optical-flow magnitude), and each question requires 10–20 minutes of annotation. Human evaluation confirms the intended difficulty: PerceptionComp requires substantially longer response times than prior benchmarks, and under a single-view setting (no rewatching) human accuracy drops to near chance (18.97%), while experts can reach 100% accuracy with unrestricted rewatching and sufficient time. Stateof-the-art MLLMs perform notably worse: the best model in our evaluation (Gemini-3-Flash) reaches only 45.96% accuracy, and open-source MLLMs remain below 40%. Test-time reasoning helps but remains far from human-level (e.g., GPT-o3 exceeds GPT-4o by 11.04%; Gemini- 2.5-Pro exceeds Gemini-2.5-Flash by 6.19%), and increasing test-time compute via larger thinking-token budgets or more input frames further improves performance. Finally, among the strongest frontier models we tested (Gemini-3 variants and GPT-o3), accuracies cluster in the mid-40s, suggesting a bottleneck in perception-centric long-horizon video reasoning. PerceptionComp provides a focused testbed for diagnosing these limitations and advancing multimodal visual thinking.

Abstract:
Implicit neural representations (INRs) have recently emerged as a promising approach to video compression, delivering competitive ratedistortion performance alongside rapid decoding. However, existing neural video codecs struggle to balance complexity and scalability. Lightweight models often suffer from degraded compression performance when scaled to different bitrate/quality levels, whereas high-performance models exhibit limited scalability, as their model complexity typically increases with quality. This lack of a unified architecture capable of maintaining consistent complexity across a wide range of bitrates severely limits their diverse real-world deployment. To address these challenges, we introduce NVRC++1, a novel INR-based video codec that utilizes a lightweight INR with multiple high-resolution feature grids, providing high scalability at any given complexity level. This is paired with an optimization framework that enables efficient overfitting on high-resolution grids for long video sequences, thereby exploiting spatio-temporal redundancies without prohibitive computational or memory overhead. Additionally, an advanced entropy model is designed for efficiently compressing the high-dimensional grid parameters. As a result, NVRC++ provides four complexity levels (from 7kMACs/pixel to 360kMACs/pixel), each spanning wide bitrate and quality ranges while supporting real-time decoding. The experimental results show that NVRC++ offers a much faster decoding speed (up to 7.6x) compared to the SOTA INR-based video codec, NVRC, while delivering comparable performance.

Abstract:
Audio-video generation has recently gained unprecedentedresearch attention, aiming to synthesize high-quality sounding video con-tent with fine-grained synchronization and semantic alignment betweenthe auditory and visual components. The preceding methods predomi-nantly adopt a dual-branch design with separate tokenization and gen-eration modules per modality, neglecting the representation gap whilenecessitating intensive computational resources for proper training. In-spired by recent advancements in one-dimensional visual tokenization,we present AVTok, a novel unified tokenizer designated for holisticaudio-video generation. AVTok features a dual-stream transformer-basedarchitecture with shared encoder-decoder and modal-specific learnablequeries to efficiently and effectively encode an audio-video pair into acompact one-dimensional latent representation with a unified codebook.To cope with the heterogeneous information imbalance that hinders AV-Tok from exploiting aligned audio-visual information, we devise a hier-archical training strategy to progressively realize reconstruction capabil-ities for each modality. Extensive experiments demonstrate that AVTokexcels both in audio-video reconstruction and when integrated into down-stream pipelines for audio-to-video, video-to-audio, and class-conditionaljoint audio-video generation. AVTok paves the way for the challenge ofjoint audio-video tokenization and provides a potential direction to buildunified large multimodal models for audio-video generation.

Abstract:
Long-form audio description (AD) requires more than de-scribing visible actions: it must preserve characters, events, relationships,and story context across scenes so that blind and low-vision (BLV) audi-ences can follow a film. Modern video–language models (VLMs) are effec-tive on short clips, but they often treat each moment independently, pro-ducing descriptions that miss who characters are, why events matter, andhow the current scene connects to earlier narrative context. We proposeStoryTeller, a training-free framework for story-aware long-form AD. In-stead of relying only on local visual cues, StoryTeller maintains a verifiednarrative memory that carries forward story-relevant information acrossscenes, enabling later descriptions to remain coherent, grounded, andcontextually informative. Given only raw video and a movie title, Sto-ryTeller can optionally retrieve public movie metadata to resolve namesand story context, while accepting only facts that are supported by thevideo through semantic filtering and VLM verification. The method re-quires no subtitles, scripts, AD transcripts, aligned captions, characterbanks, precomputed face identities, or task-specific fine-tuning. To evalu-ate whether generated AD preserves narrative information, we introduceStoryAD-QA1 , a question-answering benchmark that tests whether alanguage model can answer story-context questions using only the gener-ated descriptions. Experiments on standard AD benchmarks and diverselong-form videos show that StoryTeller consistently improves narrativecoherence, factual grounding, and story comprehension over strong base-lines in automatic, QA-based, and human evaluations.

Abstract:
Text-conditioned human interaction generation must cap-ture both long-range temporal causality within each individual and tightlycoupled coordination between partners. Existing interaction diffusionmodels typically denoise full sequences using bidirectional attention, whichobscures causality and hinders streaming and long-horizon generation.Autoregressive alternatives enforce causality but often suffer from tem-poral drift, leading to coordination degradation and unstable interactiondynamics over time. We propose InterCMDM, a block-causal latent diffu-sion framework for autoregressive two-person interaction generation. In-terCMDM introduces a Dual-Stream Causal Diffusion Transformer thatmaintains separate causal streams for each person while modeling inter-person dependencies via unified dual-stream attention with multi-taskattention masks. These masks unify interaction modeling within a sin-gle attention mechanism and support diverse coordination behaviors, in-cluding simultaneous actions, reactive responses, leader–follower dynam-ics, and independent motion. By training a single model across thesemask configurations as a form of data augmentation, InterCMDM en-ables controllable interaction generation by simply selecting the desiredattention mask at inference time. Finally, a block-wise diffusion objec-tive enables stable latent rollout over long sequences without repeateddecode–encode cycles. InterCMDM achieves state-of-the-art performanceon InterHuman and Inter-X, improving text–motion alignment, realism,and long-horizon continuity.

Abstract:
We address the problem of training on long-tailed data forvideo action recognition. We propose to augment the training set usinga text-to-video generative model, conditioned on diverse text promptsgrounded in action profiles and training exemplars. Our approach, calledGen2Balance, converts an imbalanced training set into a balanced com-bination of real and generated video clips. To effectively learn from suchdata, we employ a two-stage training strategy that mitigates domainshift and yields significant improvements.We evaluate on long-tailed versions of standard benchmarks: UCF-101(UCF-LT) and a 100-class subset of Kinetics (K100-LT) selected to pri-oritise temporally challenging actions. Gen2Balance improves accuracyover the strongest baselines for long-tailed learning by 5.1% and 7.0% onthe respective datasets. On rare actions from the RareAct dataset (e.g.,cut keyboard ), Gen2Balance improves accuracy by 31.9%, demonstrat-ing effectiveness for scarce actions. By varying the amount of syntheticdata added, we show that partial balancing already achieves 79% of theperformance gains at 27% of the compute cost on K100-LT, highlightingthe practical scalability of Gen2Balance.

Abstract:
Controllable traffic simulation is critical for testing autonomousdriving systems, yet existing approaches often require retraining largegenerative models with extensive annotated data. We introduce a lightweightcontrol adaptation framework that enables multi-modal controllability(sketch, latent behavior codes, and text) for pretrained state-of-the-artdiffusion and autoregressive traffic models. By modulating intermediatefeatures through identity-initialized FiLM layers, our method efficientlyadds new control modalities while preserving the base model’s generativeprior. Evaluated on Waymo Open Sim Agents Challenge, our approachdemonstrates strong controllability with less than 1% of the paired con-trol data. Through context-aware condition transfer, our framework en-ables counterfactual scenario generation and long-tail synthesis whilemaintaining stable closed-loop driving realism and safety. Our framework Equal contribution.unlocks new possibilities for controllable traffic simulation, enabling tar-geted scenario generation through lightweight adaptation of pretrainedgenerative models. Project page: https://ecosim-web.github.io/

Abstract:
Current sign language translation (SLT) systems attempt tolearn all aspects of signing—manual gestures, high-speed fingerspelling,and asynchronous non-manual facial cues—within a single end-to-endnetwork. Learning multiple tasks without detailed supervision leads topoor recognition of fingerspelled proper nouns and technical terms, andleaves rich disambiguating information from lip movements largely unex-ploited. We introduce SignBind-LLM, a modular framework that ad-dresses these limitations through three dedicated expert streams: one forcontinuous signing, one for fingerspelling, and one for lipreading. Eachexpert is pre-trained independently using CTC on approximately twomillion automatically generated pseudo-gloss sequences, removing theneed for manual gloss annotation. A lightweight transformer with learnedtemporal alignment fuses the expert outputs, and a pre-trained languagemodel translates the resulting pseudo-gloss sequences into fluent spokenEnglish. At matched decoder scale (250M parameters), our architecturealready surpasses all prior methods, confirming that the gains are archi-tectural rather than a consequence of scaling the language model. Scal-ing to a larger decoder sets a new state-of-the-art across How2Sign: 23.1,BOBSL: 7.0, and ChicagoFSWild+: 73.6%, while requiring significantlylower training cost than prior approaches.

Abstract:
Video Temporal Grounding (VTG) aims to localize specificvideo segments corresponding to natural language queries. While recentLarge Vision-Language Models (LVLMs) employ Reinforcement Learn-ing to generate Chains-of-Thought (CoT), they typically rely solely onoutcome-based supervision. Consequently, this often leads to halluci-nations, where the reasoning process becomes disconnected from thevisual content and the final prediction. Existing attempts to mitigatethis by relying on external supervision from larger models or separatereward models are computationally expensive and prone to rigid pat-terns. To address these challenges, we propose TAR (Temporal Anchor-Constrained Reasoning), a framework that introduces the temporal an-chor (T-anchor) as a transparent and auditable checkpoint mechanism.T-anchor enforces progressive refinement within the CoT, compellingthe model to continuously ground its intermediate thoughts in visual evi-dence and iteratively calibrate temporal predictions, thereby significantlyenhancing the faithfulness and autonomy of the reasoning process and fi-nal accuracy. Furthermore, we introduce a bootstrapping paradigm thatautomatically harvests high-quality CoT data using only a standard 7Bmodel, eliminating the dependency on ultra-large models. Extensive ex-periments demonstrate that TAR achieves state-of-the-art performanceand generates faithful, autonomous, and progressively refined reasoningtraces.

Abstract:
Diffusion-based video relighting enables controllable relighting from a single input video, but modern video diffusion backbones are trained on short clips and applied to long-horizon videos through chunked sliding-window inference, often causing temporal discontinuities at chunk boundaries. We address this by reframing long-horizon relighting as temporally conditioned latent domain 38 translation. Our framework enforces cross-chunk continuity by propagating targetdomain latents across boundaries and makes this behavior learnable using masked target-domain self-conditioning, training the model to continue from temporally masked propagated context. We further introduce warm-start prompting with a relit prompt anchor from a controllable generative model, which establishes the initial target-domain state and creates a general interface for prompt-based relighting. Experiments on in-the-wild long-horizon videos show markedly improved temporal consistency, with chunk-boundary artifacts largely reduced and unwantedappearancechangesacrosschunksgreatlysuppressed.Visitourwebpage at https://research.nvidia.com/labs/sil/projects/horizonrelight/.

Abstract:
The deployment of long-form video-language models (Video-VLMs) is fundamentally constrained by the prohibitive scaling of atten-tion complexity and KV-cache overhead as temporal and spatial res-olutions increase. Existing pruning methods rely on local frame-levelheuristics, which fail to capture shifting information density and oftenresult in cumulative budget misallocation over long horizons. To addressthese challenges, we propose S3 -prune, a stability-aware token budgetingframework that jointly models spatio-temporal demand while ensuringconsistent allocation over time. S3 -prune characterizes token demand byintegrating spatial uncertainty (SU) from patch-level embedding shiftsand segment transition (ST) from inter-segment semantic variations. Tomitigate temporal instability, we introduce stability accumulation (SA)via a Kalman-filtered latent estimator, which smoothens stochastic spikesand provides uncertainty-aware margins for adaptive budgeting. Guidedby these stabilized budgets, S3 -prune executes a hierarchical two-stageselection process. Extensive evaluations across seven benchmarks andvarious backbones demonstrate that S3 -prune significantly advances theefficiency-accuracy frontier, reducing inference latency while remainingrobust even under aggressive token retention settings.

Abstract:
Driven by Artificial Intelligence-Generated Content (AIGC),the authenticity of audio-visual content is facing severe challenges. Tem-poral Forgery Localization (TFL) aims to precisely identify manipulatedsegments within untrimmed sequences. However, existing methods arelimited by CNNs’ local receptive fields or Transformers’ quadratic com-plexity, while emerging linear models often struggle to balance globalauthentic context compression with local abrupt forgery perception. Toaddress this, we propose MG-RWKV, a multi-granularity framework thatleverages the data-dependent state evolution of RWKV to achieve ef-ficient full-sequence processing with O(T ) complexity. Our frameworkfeatures three core innovations: (1) a Bidirectional RWKV architec-ture that captures bidirectional temporal contexts without quadraticoverhead; (2) a Multi-Granularity Mixture of Experts (MG-MoE) that performs dynamic routing over explicit temporal recep-tive fields, adaptively selecting granularities based on forgery dura-tion to significantly enhance decision interpretability; and (3) Cross-Granularity Consistency (CGC), which aligns adjacent feature pyra-mid levels through hierarchical scale-wise pairing and spatial boundary-aware weighting, effectively reducing false positives in authentic regions.Extensive experiments on Lav-DF, TVIL, and Psynd datasets demon-strate that MG-RWKV achieves state-of-the-art performance with lowcomputational cost.

Abstract:
Upgrading embedding models typically requires expensivedatabase re-indexing, as new query embeddings are incompatible withexisting database embeddings. While Backward Compatible Training(BCT) mitigates this by enforcing compatibility during training, ex-isting approaches often require updating the backbone model. This isimpractical because of significant training cost, the risk of performanceregression, and limited access to proprietary model weights. We intro-duce Multi-modal Knowledge Preserving Adapter (MKP-Adapter),the first adapter-only BCT approach for Multi-Modal Large LanguageModels (MLLMs) that requires no backbone updates. We identified thatthe primary challenge in adapter-only BCT is preserving the knowledgeof the new embeddings while enforcing backward compatibility. Hence,we propose a multi-level preservation loss that maintains the geometricstructure of the embedding spaces throughout BCT. Furthermore, a focalre-weighting strategy is integrated to prioritize learning from challeng-ing samples. Experiments demonstrate that our method achieves strongbackward compatibility across diverse multi-modal benchmarks (image,text, visual document, and video retrieval tasks) and model types. No-tably, MKP-Adapter is trained solely on pre-extracted embeddings andrequires only negligible additional latency relative to the original back-bone forward pass, highlighting its efficiency.

Abstract:
Current multimodal latent reasoning often relies on externalsupervision (e.g., auxiliary images), ignoring intrinsic visual attentiondynamics. In this work, we identify a critical Perception Gap in distil-lation: student models frequently mimic a teacher’s textual output whileattending to fundamentally divergent visual regions, effectively relyingon language priors rather than grounded perception. To bridge this, wepropose LaViT, a framework that aligns latent visual thoughts ratherthan static embeddings. LaViT compels the student to autoregressivelyreconstruct the teacher’s visual semantics and attention trajectories priorto text generation, employing a curriculum sensory gating mechanism toprevent shortcut learning. Extensive experiments show that LaViT sig-nificantly enhances visual grounding, achieving up to +16.9% gains oncomplex reasoning tasks and enabling a compact 3B model to rival largermodels on selected evaluated benchmarks.

Abstract:
Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) withverifiable rewards improves accuracy, it still struggles to achieve reliablespatio-temporal grounding during the reasoning process. Moreover, im-proving grounding typically relies on scaled training data or inference-timeperception tools, which increases annotation cost or computational cost.To address this challenge, we propose VisionCoach, an input-adaptiveRL framework that improves spatio-temporal grounding through visualprompting as training-time guidance. During RL training, visual promptsare selectively applied to challenging inputs to amplify question-relevantevidence and suppress distractors. The model then internalizes theseimprovements through self-distillation, enabling grounded reasoning di-rectly on raw videos without visual prompting at inference. VisionCoachconsists of two components: (1) Visual Prompt Selector, which predictsappropriate prompt types conditioned on the video and question, and(2) Spatio-Temporal Reasoner, optimized with RL under visual promptguidance and object-aware grounding rewards that enforce object identityconsistency and multi-region bounding-box IoU. Extensive experimentsdemonstrate that VisionCoach achieves state-of-the-art performanceacross diverse video reasoning, video understanding, and temporal ground-ing benchmarks (V-STAR, VideoMME, World-Sense, VideoMMMU, Per-ceptionTest, and Charades-STA), while maintaining a single efficientinference pathway without external tools. Our results highlight the effec-tiveness of training-time visual prompting as a lightweight mechanismfor improving grounded video reasoning.

Abstract:
When should an intelligent assistant speak up without beingasked? Continuous egocentric video offers rich, evolving context that en-ables a new form of assistance: one that is proactive rather than merelyreactive. Yet existing approaches either wait passively for user queries ortreat every detected event as requiring a response, without consideringthe user’s history, current activity, or whether assistance would actuallybe welcome. We reframe proactive assistance as a context-dependent de-cision problem: the agent must not only perceive what is happening,but reason over accumulated temporal context to determine when andwhether to intervene. To this end, we present Vinci2, a proactive ego-centric assistance system that advances the on-device assistant Vincifrom reactive response toward proactivity. On the evaluation side, wepresent EgoServe, the first large-scale benchmark for proactive assistancein continuous egocentric video. EgoServe comprises over 3,000 serviceinstances organized along 4 temporal memory horizons, ranging fromimmediate safety alerts to long-term habit coaching, across 10 servicecategories. On the modeling side, we propose EgoMemo, a training-free,memory-augmented agent that maintains three complementary mem-ory representations: multi-scale temporal summaries, a semantic knowl-edge graph, and visual embedding archives. At each timestep, EgoMemoperforms retrieval-augmented reasoning to determine whether assistanceis warranted and, if so, produces contextually grounded responses. Ex-periments demonstrate that EgoMemo establishes strong baselines onEgoServe while remaining competitive on existing egocentric benchmarks.Our benchmark and code are publicly available at Vinci2.

Abstract:
While video streaming understanding has made significantstrides, real-world applications, such as live sports broadcasting, au-tonomous driving, and multi-screen collaboration, inherently demandcontinuous, multi-stream interactions. However, existing benchmarks areconfined to single-stream paradigms, leaving a critical gap in evaluatingonline, cross-stream reasoning. To bridge this, we introduce X-Stream,the first benchmark dedicated to multi-stream streaming understand-ing. Comprising 4,220 rigorously curated QA pairs across 932 videos,X-Stream evaluates 11 subtasks across multi-window, multi-view, andmulti-device scenarios. Crucially, our dataset is constructed using a noveldual-verification pipeline that prevents over-reliance on a single stream.Furthermore, we pioneer the conceptualization of multi-modal large lan-guage models (MLLMs) as naive multiplexers, systematically evaluat-ing their performance through the lens of Signal Multiplexing Theory. Ourextensive online inference experiments reveal a stark reality: state-of-the-art MLLMs struggle significantly with concurrent streams, achieving only∼50% score and exhibiting a poor proactive ability. Ultimately, X-Streamexposes the trade-off of current multiplexing schemes, providing both apractical evaluation protocol and empirical guidance for next-generationmulti-stream agents. Code and data are released at homepage.

Abstract:
Video Large Language Models (Video LLMs) aim to under-stand and reason over dynamic visual content. However, videos inher-ently exhibit low task-relevant information density because only a smallfraction of frames and regions provide meaningful cues. As a result, theuseful signals are sparse across space and time. We present STVFocus, atraining-free framework that unifies spatio-temporal focusing to capturesalient contexts and build informative video representations. STVFocusconsists of three components. First, Hierarchical Temporal Focus appliesa coarse-to-fine frame selection strategy that captures global temporalstructure while selectively emphasizing informative moments without ex-cessive query bias. Second, Query-informed Spatial Focus uses spatialsaliency to highlight query-relevant intra-frame regions. Finally, Tem-poral Reallocation reinvests the saved spatial capacity into additionalhigh-value frames, enabling richer temporal coverage. By jointly leverag-ing temporal and spatial saliency, STVFocus produces compact yet se-mantically enriched video representations. Experiments on VideoMME,LongVideoBench, and MLVU show consistent gains with meaningfulmargins, confirming the effectiveness of unified spatio-temporal focus-ing.

Abstract:
Distilled autoregressive di!usion models facilitate real-timeshort video synthesis but su!er from severe error accumulation duringlong-sequence generation. While existing Test-Time Optimization (TTO)methods prove e!ective for images or short clips, we identify that they failto mitigate drift in extended sequences due to unstable reward landscapesand the hypersensitivity of distilled parameters. To overcome these limi-tations, we introduce Test-Time Correction (TTC), a training-free alter-native. Specifically, TTC utilizes the initial frame as a stable referenceanchor to calibrate intermediate stochastic states along the sampling tra-jectory. Extensive experiments demonstrate that our method seamlesslyintegrates with various distilled models, extending generation lengthswith a slight overhead while matching the quality of resource-intensivetraining-based methods on 30-second benchmarks.

Abstract:
Talking-head generation from text typically relies on cas-caded pipelines that synthesize audio before video, leading to error ac-cumulation, latency, and audiovisual misalignment. Existing end-to-endmodels address some of these issues but often lack autoregressive capabil-ities. In this paper, we present VoCa, a unified autoregressive frameworkthat jointly generates speech and talking-head video from text transcriptsand a reference image. VoCa employs a large language model (LLM) tomap text into shared representations, which are concurrently processedby dedicated speech and video decoders to ensure strict synchronization.To bridge the semantic gap between audio-optimized features and hu-man motion, we introduce a speech refiner with a tailored multi-stagetraining strategy. Furthermore, to enable step-by-step autoregressive co-generation, we propose a unified window partitioning mechanism andan associated improved rolling-window denoising strategy. This adaptsa bidirectional diffusion transformer into a causal architecture synchro-nized with the LLM’s autoregressive generation windows. Extensive ex-periments show that VoCa matches or surpasses state-of-the-art baselinesin visual quality, improves audio–lip synchronization, and identity preser-vation. Code and models will be released upon acceptance.

Abstract:
Recent research on Large Vision-Language Models (LVLMs)has explored visual chain-of-thought (CoT) mechanisms to enhance visual-centric reasoning, effectively mitigating the hallucination issues that plaguecurrent models. However, existing paradigms typically rely on externalvisual tools and multi-turn interactions. While recent latent visual rea-soning approaches achieve tool-free, single-turn inference by supervisingintermediate representations with auxiliary images, they incur signifi-cant annotation costs. To address this, we propose Contrastive-GuidedSelf-Supervised Latent Visual Reasoning (CoLVR), a novel framework forself-supervised training of visual latent tokens in an annotation-free man-ner. Specifically, we leverage visual contrastive principles to identify keyvisual regions and employ Reinforcement Learning (RL) to align the gen-erated latent tokens with these salient regions. Extensive experimentson hallucination benchmarks demonstrate that our method significantlyoutperforms mainstream de-hallucination approaches and even surpassesfully supervised visual reasoning baselines.

Abstract:
Vision-Language Models (VLMs) have demonstrated remark-able success across diverse visual tasks, yet their performance degradesin complex visual environments. Existing enhancement approaches re-quire additional training, rely on external segmentation tools, or operateat coarse-grained levels, overlooking VLMs’ innate attention capabilities.To bridge this gap, we investigate VLMs’ attention patterns and discoverthat: (1) visual complexity strongly correlates with attention entropy,negatively impacting reasoning performance; (2) attention progressivelyrefines from global scanning in shallow layers to focused convergencein deeper layers, with the degree of convergence determined by visualcomplexity; (3) theoretically, under a multiplicative decomposition as-sumption, we show that contrasting attention maps between general andtask-specific queries approximately separates visual signal into seman-tic and visual noise components. Building on these insights, we proposeContrastive Attention Refinement for Visual Enhancement (CARVE),a training-free method that extracts task-relevant visual signals throughattention contrasting at the pixel level. Experiments on seven bench-marks show that CARVE improves performance on visual perceptiontasks, with notable gains on both recent and earlier-generation models.Our analysis reveals how visual complexity affects attention mechanismsand demonstrates an effective strategy for improving visual reasoningthrough attention contrasting.

Abstract:
We propose a societal bias evaluation method for large visionlanguage models (LVLMs) in the era of strong safety guardrails. Existing benchmarks rely on prompts that ask models to infer attributes of people in images (e.g., “Is this person a CEO or a secretary?”). However, we find that LVLMs with strong guardrails, such as GPT and Claude, often refuse these prompts, making evaluations unreliable. To address this, we change the prior evaluation paradigm by decoupling the task from the depicted person: instead of inferring person’s attributes, we use prompts that do not ask about the person (e.g., “Write a fictional story about an imaginary person.”) and attach the image as provisional user information to implicitly provide demographic cues, then compare outputs across user demographics. Instantiated across three tasks — story generation, term explanation, and exam-style QA — our method avoids refusals even in guardrailed LVLMs, enabling reliable bias measurement. Applying it to 20 recent LVLMs, both open-source and proprietary, we find that all models undesirably use user demographic information in person-irrelevant tasks; for instance, characters in stories are often portrayed as mechanic for male users and nurse for female users. Although still biased, proprietary models like GPT-5 show lower bias than opensource ones. We analyze potential factors behind this gap, discussing continuous model monitoring and improvement as a possible contributor for reducing bias.

Abstract:
Large pretrained vision encoders are central to multimodalintelligence, powering applications from on-device vision processing tovision-language models. Since these applications often demand real-timeprocessing of massive visual data, reducing the inference cost of visionencoders is critical. Quantization offers a practical path, but it remainschallenging even at 8-bit precision due to so-called outliers. In this work,we propose RegCache, a training-free algorithm that mitigates outliers inlarge-scale pretrained vision encoders and serves as a plug-in module thatcan be applied on top of other quantization methods. RegCache intro-duces outlier-prone yet semantically meaningless prefix tokens into thevision encoder, which prevent other tokens from having outliers. Notably,we observe that outliers in vision encoders behave differently from thosein language models, motivating two technical innovations: middle-layerprefixing and token deletion. Experimental results show that our methodconsistently improves quantized model performance across various visionencoders, particularly in extremely low-bit regimes (e.g., 4-bit).

Abstract:
Contrastive vision–language models (VLMs) such as CLIP,EVACLIP, ImageBind, and SigLIP exhibit impressive zero-shot general-ization, yet their performance on specific data distributions can furtherbe enhanced by improving the synchronization of the vision and languagerepresentations. For this purpose, we present SyncVL, a bi-directionalVL synchronization framework that enables fully unsupervised adapta-tion of VLMs. SyncVL jointly refines visual and textual representationsthrough two lightweight transformer-based encoder-decoder synchroniz-ers (Vision-to-Text and Text-to-Vision) that progressively project VLrepresentation into a shared latent subspace without requiring labeleddata. To ensure robust synchronization, we form groups using multipleimage-views and text templates. Both synchronizers are trained using areward function motivated by group relative policy optimization whilekeeping the original VLMs frozen. Additionally, a mutual-distillationmechanism is proposed to iteratively reinforce each synchronizer. Weextensively evaluate SyncVL on 24 benchmark datasets spanning unsu-pervised VL adaptation, out-of-distribution matching, unsupervised clus-tering, cross-modal retrieval, object detection, and segmentation. Acrossall tasks, SyncVL consistently improves over state-of-the-art baselines,establishing a new standard for unsupervised adaptation of VLMs.

Abstract:
Vision-language models (VLMs) can describe urban scenesin rich detail, yet consistently fail to produce reliable human preferencelabels in domain-specific tasks such as safety assessment and aestheticevaluation. The standard fix, fine-tuning or RLHF, requires large-scaleannotations and model retraining. We ask a different question: can afrozen VLM be aligned with human preferences without modifying anyweights? Our key insight is that VLMs are strong concept extractorsbut poor decision calibrators. We propose a three-stage post-hoc pipelinethat exploits this asymmetry: (i) interpretable evaluation dimensionsare automatically mined from consensus exemplars; (ii) an Observer–Debater–Judge chain extracts robust concept scores from the frozenVLM; and (iii) locally-weighted ridge regression on a hybrid manifoldcalibrates these scores to human ratings. Applied as UrbanAlign onPlace Pulse 2.0, the framework reaches 70.8% accuracy (κ=0.41) acrosssix perception categories, outperforming all baselines by +9.6 pp andzero-shot VLM by +14.1 pp, with full interpretability and zero weightmodification.

Abstract:
Visual prompt tuning has emerged as a parameter-e!cientfine-tuning approach for adapting large-scale Vision Transformers (ViTs)to downstream tasks. As its learnable prompts are applied in input andfeature spaces, prior to jointly going through attention in transformerlayers, the most commonly used scheme for fusing image and prompttokens is concatenation or addition. In this paper, we aim to study afundamental yet essential problem in visual prompt tuning: whethera single fusion scheme tends to yield better results, and whether thatwould be beneficial to develop a hybrid fusion scheme. To this end,we formulate the task as a bi-level optimization problem, and solve itleveraging di"erentiable architecture search. In this context, the learnableprompts and their fusion schemes are jointly optimized. To enrich thesearch space in the architecture search, we propose two additional fusionschemes, namely, a!ne transformation and cross-attention, in additionto concatenation and addition. Extensive experiments on 34 datasetsspanning VTAB-1k, FGVC, and HTA show consistent gains over prompt-tuning baselines. With a frozen ViT backbone, our method delivers afavorable accuracy–latency–parameter trade-o" compared with VPT-Deep and recent variants. Our findings reveal that how prompts fusewith image tokens plays a significant role in visual prompt tuning, anda hybrid fusion fashion can more e"ectively leverage layer semantics ofViTs, contributing a novel perspective for visual prompt-tuning research.

Abstract:
Natural images exhibit strong geometric regularities: local structures, such as edges, corners, and textures, appear in many orientations and mirror configurations. Since Vision Transformers (ViTs) operate on square image patches, these transformations naturally correspond to the dihedral symmetry group D 8, also known as the octic group. Recent work has shown that ViTs can be made reflection equivariant and more e!cient than standard ViTs simultaneously by implementing the linear layers in the Fourier domain of the reflection group. In this work, we extend the equivariance to reflections and rotations and analyze the scalability of the resulting networks. Our Quick ViTs, based on octic equivariant linear layers, achieve 5.33x reductions in FLOPs and up to 8x reductions in memory compared to ordinary linear layers. By analyzing the arithmetic intensity of these layers, we identify theoretical limits on how much the FLOP savings translate into throughput improvements on modern GPUs. However, these limitations disappear as the embedding dimensions increase. Enabled by their computational e!ciency, we conduct a broader empirical evaluation of equivariant ViTs than in previous work. Upon training supervised (DeiT-III) and self-supervised (DINOv2) on ImageNet-1K, we find that our Quick ViTs match or exceed baseline accuracy while at the same time providing substantial e!ciency gains.

Abstract:
Vision-language models (VLMs) are commonly trained by directly inserting image tokens from a pretrained vision encoder into the text stream of a language model. This allows text and image information to fully attend to one another within the model, but becomes rapidly costly for long multi-image conversations or streaming video applications, both in terms of memory and compute. VLMs leveraging cross-attention are an efficient alternative to token insertion as image tokens are not added to the KV cache. Despite being introduced early on, multimodal cross-attention models are scarce in the current VLM literature and often underperform their token insertion counterparts. In this work, we reinvestigate the effectiveness of cross-attention for vision-language modeling: (i) We analyze the core differences between the cross-attention and selfattention mechanisms, (ii) we train cross-attention VLMs both from a text-only LLM and by adapting a pretrained insertion-based VLM, showing that simple cross-attention is more competitive with token insertion than previously reported, and (iii) we demonstrate the practical advantages of cross-attention on real-time video captioning, where it naturally maintains low latency and near-constant memory cost.

Abstract:
In real-world deployment, vision-language models often en-counter disturbances such as weather, occlusion, and camera motion.Under such conditions, their understanding and reasoning degrade sub-stantially, revealing a gap between clean, controlled (i.e., unperturbed)evaluation settings and real-world robustness. To address this limitation,we propose ROVA, a novel training framework that improves robustnessby modeling a robustness-aware consistency reward under spatio-temporalcorruptions. ROVA introduces a difficulty-aware online training strategythat prioritizes informative samples based on the model’s evolving capa-bility. Specifically, it continuously re-estimates sample difficulty via self-reflective evaluation, enabling adaptive training with a robustness-awareconsistency reward. We also introduce PVRBench, a new benchmark thatinjects real-world perturbations into embodied video datasets to assessboth accuracy and reasoning quality under realistic disturbances. Weevaluate ROVA and baselines on PVRBench, UrbanVideo, and VisBench,where open-source and proprietary models suffer up to 35% and 28%drops in accuracy and reasoning under realistic perturbations. ROVAeffectively mitigates performance degradation, boosting relative accuracyby at least 24% and reasoning by over 9% compared with baseline models(QWen2.5/3-VL, InternVL2.5, Embodied-R). These gains transfer to cleanstandard benchmarks, yielding consistent improvements.

Abstract:
Despite rapid progress, pretrained vision–language models still struggle when answers depend on tiny visual details or on combining clues spread across multiple regions, as in documents and compositional queries. We address this by framing grounding as test-time evidence retrieval: given a query, the model should actively identify where to look next to resolve ambiguity. To this end, we propose a training-free, model-intrinsic grounding method that uses uncertainty as supervision. Specifically, we compute the entropy of the model’s next-token distribution and backpropagate it to the visual token embeddings to obtain an entropy-gradient relevance map, without auxiliary detectors or attentionmap heuristics. We then extract and rank multiple coherent regions to support multi-evidence queries, and introduce an iterative zoom-andreground procedure with a spatial-entropy stopping rule to avoid over-refinement. Experiments on seven benchmarks across four VLM architectures demonstrate consistent improvements over existing methods, with the largest gains on detail-critical and high-resolution settings, while also producing more interpretable evidence localizations.

Abstract:
The practical deployment of autoregressive (AR) Vision-Language-Action (VLA) models is severely constrained by inference speed bottlenecks, particularly in high-frequency and dexterous manipulation tasks. While recent studies have explored Jacobi decoding as a more efficient alternative to traditional autoregressive decoding, its practical benefits are marginal due to the lengthy iterations. To address this problem, we introduce consistency distillation to teach the model to predict multiple correct action tokens in each iteration, thereby reducing the total iterations. While the distillation brings moderate speedup, we identify that certain redundancy iterations remain a critical limitation. To tackle this, we propose an adaptive early-exit decoding strategy that moderately relaxes convergence conditions, which further improves average inference efficiency. Experimental results show that the proposed method achieves more than 4× inference acceleration across different base models while maintaining high task success rates in both simulated and real-world robot tasks. These experiments validate that our approach provides an efficient and general paradigm for accelerating multimodal decision-making in robotics. Our code and videos could be found in the supplementary files.

Abstract:
Large Vision-Language Models (VLMs) have achieved re-markable multimodal performance yet remain prone to factual halluci-nations, particularly in long-tail or specialized domains. Moreover, cur-rent models exhibit a weak capacity to refuse queries that exceed theirparametric knowledge. In this paper, we propose a systematic frame-work to enhance the refusal capability of VLMs when facing such un-known questions. We first curate a model-specific "Visual-Idk" (Visual-Idon’t know) dataset, leveraging multi-sample consistency probing to dis-tinguish between known and unknown facts. We then align the modelusing supervised fine-tuning followed by preference-aware optimization(e.g., DPO, ORPO) to effectively delineate its knowledge boundaries. Re-sults on the Visual-Idk dataset show our method improves the TruthfulRate from 57.9% to 67.3%. Additionally, internal probing also demon-strates that the model genuinely recognizes its boundaries instead of justmemorizing refusal patterns. Our framework further generalizes to out-of-distribution medical and perceptual domains, providing a robust pathtoward more trustworthy and prudent visual assistants.

Abstract:
Multimodal Large Language Models (MLLMs) have recentlyachieved remarkable success in visual-language understanding, demon-strating superior high-level semantic alignment within their vision en-coders. An important question thus arises: Can these encoders serve asversatile vision backbones, capable of reliably performing classic vision-centric tasks as well? To address the question, we make the following con-tributions: (i) We identify that the vision encoders within MLLMs exhibitdeficiencies in their dense feature representations, as evidenced by theirsuboptimal performance on dense prediction tasks (e.g., semantic seg-mentation, depth estimation); (ii) We propose VersaViT, a well-roundedvision transformer that instantiates a novel multi-task framework for col-laborative post-training. This framework facilitates the optimization ofthe vision backbone via lightweight task heads with multi-granularity su-pervision; (iii) Extensive experiments across various downstream tasksdemonstrate the effectiveness of our method, yielding a versatile visionbackbone suited for both language-mediated reasoning and pixel-levelunderstanding. The project page is available here.

Abstract:
Affordance prediction, which identifies interaction regionson objects based on language instructions, is critical for embodied AI.Prevailing end-to-end models couple high-level reasoning and low-levelgrounding into a single monolithic pipeline and rely on training over an-notated datasets, which leads to poor generalization on novel objects andunseen environments. In this paper, we move beyond this paradigm byproposing A4-Agent, a training-free agentic framework that decouplesaffordance prediction into a three-stage pipeline. Our framework coordi-nates specialized foundation models at test time: (1) a Dreamer thatemploys generative models to visualize how an interaction would look;(2) a Thinker that utilizes large vision-language models to decide whatobject part to interact with; and (3) a Spotter that orchestrates vi-sion foundation models to precisely locate where the interaction area is.By leveraging the complementary strengths of pre-trained models with-out any task-specific fine-tuning, our zero-shot framework significantlyoutperforms state-of-the-art supervised methods across multiple bench-marks and demonstrates robust generalization to real-world settings.

Abstract:
E!ective collaboration begins with knowing when to ask forhelp. For example, when trying to identify an occluded object, a humanwould ask someone to remove the obstruction. Can MLLMs exhibit asimilar “proactive” behavior by requesting simple user interventions?To investigate this, we introduce ProactiveBench, a benchmark builtfrom seven repurposed datasets that tests proactiveness across di!erenttasks such as recognizing occluded objects, enhancing image quality, andinterpreting coarse sketches. We evaluate 22 MLLMs on ProactiveBench,showing that (i) they generally lack proactiveness; (ii) proactiveness doesnot correlate with model capacity; (iii) “hinting” at proactiveness yieldsonly marginal gains. Surprisingly, we found that conversation historiesand in-context learning introduce negative biases, hindering performance.Finally, we explore a simple fine-tuning strategy based on reinforcementlearning: its results suggest that proactiveness can be learned, evengeneralizing to unseen scenarios. We publicly release ProactiveBench asa first step toward building proactive multimodal models.

Abstract:
Token pruning has emerged as an essential technique for enabling efficient and resilient inference for foundation models, showing great potential in accelerating the Reinforcement Learning (RL) optimization of Vision–Language–Action (VLA) Models. However, our indepth analysis indicates that directly transferring prior pruning strategies to RL is non-trivial. Inference-time pruning introduces a train–test discrepancy, whereas dynamically adjusting the pruning configuration leads to inconsistent policy rollout and improvement. Therefore, we propose PARL-VLA, a pruning-aware RL framework that enforces explorationexploitation pruning consistency by recording the rollout pruning configuration and replaying it during policy updates, enabling end-to-end co-adaptation between token pruning and policy optimization. PARLVLA further trains across a spectrum of token budgets to learn a single policy that remains reliable under different information budgets at test time. On LIBERO and LIBERO-Plus, PARL-VLA discards 55% of visual tokens, speeds up rollout policy forward by about 1.3× and policy updates by about 1.7×, and improves LIBERO-Plus robustness by up to 6.6 points (3.2 on average) without sacrificing in-distribution success. On RoboTwin2.0, PARL-VLA achieves 46.9% real-robot success over 405 trials, improving over RLinf at 37.0%.

Abstract:
Self-collision remains a persistent challenge in SMPL-basedhuman pose estimation and motion generation. Under extreme articula-tions or stochastic motion synthesis, generated meshes frequently exhibitself-penetrations, leading to physically implausible results. We proposePoseShield, a neural collision constraint defined directly in SMPL posespace. We formulate collision correction as a constrained optimizationproblem and connect the learned constraint with the Eikonal equation.Enforcing Eikonal regularization ensures non-vanishing gradients nearthe collision boundary, improving numerical stability and robustness ofthe optimization process. Unlike prior methods that operate in the meshspace or rely on heuristic penalties, our approach operates directly in thelow-dimensional space of human poses and is theoretically grounded. Thesame learned constraint extends to human motion sequences, providinga generator-agnostic post-hoc collision corrector without retraining theunderlying motion model. Experiments on a newly constructed SMPLpose benchmark show that our method achieves a 95.8% success rateand outperforms state-of-the-art baselines.

Abstract:
Controlling continuous, physically valid 3D surface evolutionis a fundamental challenge in computer vision and graphics. Current neu-ral representations typically rely on unstructured latent spaces or discreteiterations, lacking explicit geometric interpretability and struggling toproduce smooth deformations. While classical scale-space theory offersclear physical interpretability, its numerical irregularity isolates it frommodern deep learning. In this paper, we bridge this gap by establishing alearnable scale-space foundation. Our framework comprises three syner-gistic components: (1) the Variational Neighborhood Curvature (VNC)operator, an efficient, parallelizable, and scale-stable geometric metric,(2) the spacetime-balanced VNC-Flow algorithm, which translates ir-regular physical smoothing into structured deformation trajectories, and(3) a Controllable Variational Autoencoder (C-VAE) that learns thesetrajectories conditioned on normalized evolution time. Departing fromtraditional discrete iterations or unstructured latent interpolations, ourcontinuous neural surrogate induces a structured radial latent organiza-tion. This enables precise control over continuous shape abstraction andestablishes a structural prior for downstream applications.

Abstract:
Automatic extraction of road networks from aerial imageryis a fundamental task, yet prevailing methods rely on discrete poly-lines that inherently lack the high-order geometric continuity required bydownstream applications. While parametric curve representations, suchas our Bézier Graph, offer analytic smoothness and topological compact-ness, their extraction is bottlenecked by the absence of curve-level vectorground-truth (GT) and the fragility of heuristic vectorization pipelines.To overcome these challenges, we introduce DOGE, a novel differen-tiable rendering-driven framework for robust, curve GT-free road net-work extraction. DOGE elegantly decouples the extraction process viaa discrete-continuous coupling mechanism: a DiffAlign module contin-uously optimizes the graph’s geometric alignment against target masksusing differentiable rendering, while a TopoAdapt module applies dis-crete operators to evolve the network’s topology. This coupling preventstopological bottlenecks from paralyzing the gradient-based geometric op-timization. Extensive experiments demonstrate that our method sets anew state-of-the-art on the large-scale SpaceNet and City-Scale bench-marks. Driven purely by mask guidance, DOGE successfully reconstructshighly accurate, geometrically smooth, and topologically compact vectormaps, advancing a new paradigm for structured road network reconstruc-tion.

Abstract:
Existing 3D generative models predominantly rely on implicit volumetric representations, which inherently enforce watertight topology and struggle to faithfully represent thin-shell and non-manifold geometries such as garments. While geometry image-based approaches o(cid:27)er a surface-centric alternative, existing methods typically rely on discrete binary occupancy maps whose resolution-dependent boundary encoding causes staircase artifacts and information loss upon downsampling, while surface reconstruction remains a non-di(cid:27)erentiable postprocessing step disconnected from the learning pipeline. 1 To address this, we propose Di(cid:27)erentiable Geometry Image (Di(cid:27)GI) , an end-to-end 3D-to-2D mapping framework that seamlessly integrates surface representation and geometric optimization. Di(cid:27)GI replaces conventional binary maps with a continuous 2D Truncated Signed Distance Function (TSDF), which encodes boundary position at subpixel precision within a (cid:28)xed grid resolution, e(cid:27)ectively eliminating resolutiondependent staircase artifacts even under aggressive downsampling. Building on this continuous (cid:28)eld, we introduce a di(cid:27)erentiable Marching Squares algorithm based on analytical linear interpolation, allowing gradients from 3D surface losses to propagate seamlessly back to the 2D latent space. Leveraging this di(cid:27)erentiable pipeline, we train a Di(cid:27)GI-VAE augmented with a geometry-aware normal rendering loss to compress complex 3D surfaces into an ultra-compact 32 × 32 latent space. Finally, we instantiate a transformer-based latent di(cid:27)usion model on top of this space for conditional 3D generation, showing that the proposed representation readily supports e(cid:30)cient generative modeling. Extensive experiments on garment and object datasets demonstrate that our method achieves superior reconstruction (cid:28)delity and boundary precision compared to prior geometry-image and voxel-based approaches, while requiring signi(cid:28)cantly fewer computational resources. • •

Abstract:
MeanFlow training commonly enforces the meanflow identitywith Jacobian-vector products (JVP). This is computationally expensiveand hard to scale, since the JVP path often weakens fused-kernel andcompiler-level optimization benefits. We revisit JVP-free MeanFlow froman error-driven perspective. Rather than a drop-in JVP replacement, finitedifferencing exposes controllable error structure that directly informs al-gorithm design. We identify two dominant error channels: step-truncationerror and trajectory-velocity error.Guided by this decomposition, we introduce a JVP-free differential Mean-Flow framework for from-scratch MeanFlow-style one-step image gen-eration. For step-truncation error, we use stochastic signed one-sideddifferencing, which needs only one additional forward pass and recoverssecond-order truncation behavior in expectation. For trajectory-velocityerror, we propose DuoFlow, which predicts mean and instantaneous veloc-ities on the same sampled state to improve trajectory consistency in dif-ferential updates. We further introduce progressive self-bootstrapping todrive trajectory velocity toward a fixed-point-like self-consistent tendencyduring training. As a fast derivative-evaluation scheme, our one-forwarddifferential estimator is between 7.17× and 34.83× faster than JVP ondirectional-derivative micro-benchmarks for batch sizes from 1 to 64, andreduces peak memory by a factor between 1.71× and 2.68×. On ImageNet256×256 from-scratch training, it improves FID by 30% over a matchedJVP-based MeanFlow baseline. Code will be available at here.

Abstract:
Transformer has recently garnered significant interest in un-supervised domain adaptation tasks due to its superior generalizationability. State-of-the-art methods leverage masked image modeling withconsistency regularization to improve target domain performance. How-ever, such strategy becomes less effective when the domain gap becomeslarge, as the domain gap between masked domain and original domainis not minimized. How to address the adaptation problem when domaingap becomes large is an important research problem in domain adap-tation. In this paper, we propose Dual Masked Generative AdversarialTransformer (Dual-MGAT), which simultaneously aligns the maskedtarget and masked source domain towards source and target domainto address the large domain gap problem. Specifically, we formulate ageneral framework for masked domain learning. To reduce the domaingap between the masked domain (e.g. masked target) and the originaldomain (e.g. source), we note that there exists two types of discrepan-cies, namely the information gap due to masked image modeling and thedomain distribution discrepancy. To this end, we propose the maskedgenerative adversarial adaptation technique, which introduces a trans-former decoder to bridge the masked feature space towards the originalfeature space via [CLS] token feature generation and utilizes a class con-ditional domain discriminator for adversarial distribution alignment toreduce the domain distribution discrepancy. We further investigate thedual branch of masked source to target domain adaptation, which boostsperformance. We provide a theoretical analysis of our framework from amasked domain adaptation perspective. Extensive experimental studiesdemonstrate the superiority of our framework.

Abstract:
Reconstructing 3D Human-Object Interaction (HOI) from asingle image is challenging due to the diversity of the reconstructed tar-gets. Ordinarily, the human body follows a fixed parametric template,whereas objects exhibit diverse and irregular shapes. Such differenceslead to inconsistent geometric priors and scale variations, making jointmodeling within a unified framework highly difficult. Existing vertex-level methods struggle to effectively handle this structural discrepancy.We propose to bridge this gap by introducing a unified triplane featuremap that models both human and object meshes within a shared 3Dlatent space. Our framework comprises three components: (1) a HOI Tri-plane Variational AutoEncoder (HOIT-VAE) that learns a compact andstable triplane latent space from large-scale human-object mesh data; (2)a HOI Triplane Transformer (HOIT-T) that predicts triplane featuresfrom a single image, regularized by the learned priors of HOIT-VAE;(3) a pose-fitting method for estimating the human pose from the re-constructed human mesh. Experiments on the BEHAVE and InterCapdatasets show that our approach achieves competitive reconstruction per-formance, validating the effectiveness of the unified triplane representa-tion in capturing the mesh structures and interactions between humansand objects.

Abstract:
We introduce GenSP, a data-driven framework that learns consistent spherical parameterizations across a collection of genus-0 shapes. Instead of optimizing the parameterization of each shape independently, our method learns a neural generative model that predicts a continuous mapping from the unit sphere to shapes in a dataset. Under this formulation, spherical parameterizations are obtained through the inverse mappings of the learned generator, which encourages similar shapes to share consistent parameterizations. To make this formulation practical, we address several key challenges in learning such a generative model. First, we introduce a continuous neural deformation model that predicts surface points from sphere coordinates and latent shape codes, avoiding discretization artifacts common in mesh-based formulations. Second, we augment the training space with intermediate shapes that bridge the sphere and input shapes, allowing the model to learn meaningful deformations across a heterogeneous shape collection. Third, we compute reliable initial correspondences by propagating mappings along a spanning tree of training shapes in the latent space. Experiments on the ShapeNet [9] dataset demonstrate that our approach significantly reduces geometric distortion and improves cross-shape consistency compared with state-of-the-art spherical parameterization methods.

Abstract:
The rapid rise of generative image synthesis has intensified interest in the graphics and vision community in producing realistic, controllable illumination. We present a generative method for screenspace global illumination (GI) that produces geometrically aligned, temporally stable indirect lighting using a single-step latent diffusion model. Unlike iterative and video diffusion methods that are costly and hard to control, our model conditions a one-step image generator on sceneintrinsic signals and sparse lighting hints for the current frame, encoded via adapter modules. To address flicker without the overhead of video diffusion, we introduce a temporal VAE decoder that plugs into a latent diffusion pipeline at inference time, delivering long-range temporal coherence while preserving spatial fidelity. On test data, our approach synthesizes high-quality indirect illumination well-aligned to inputs and improves temporal stability. Our quality evaluation demonstrates that our method generates visually and quantitatively plausible illumination in generative forward rendering, outperforming state-of-the-art methods.

Abstract:
Multimodal CAD generation faces a fundamental scalability challenge. Design-history methods are confined to small annotated datasets, while BRep topology is discrete and non-differentiable. Meanwhile, millions of unannotated 3D meshes remain untapped, since existing CAD methods cannot leverage them without explicit CAD annotations. We propose DreamCAD, a multimodal generative framework that bridges this gap by representing shapes as C0-continuous Bézier patches with differentiable tessellation, enabling direct point-level supervision on large-scale 3D meshes without CAD-specific annotations. We further introduce CADCap-1M, the largest CAD captioning dataset with 1M+ GPT-5-generated descriptions to advance text-to-CAD research. DreamCAD achieves state-of-the-art performance on ABC and Objaverse across text, image, and point modalities, surpassing 75% user preference. Finally, we show that DreamCAD’s accurate, compact geometry enables topology recovery into production-ready CAD models, exportable as editable STEP files. Project page is available at https://s adilkhan.github.io/dreamcad2026/.

Abstract:
Novel view synthesis under trajectory changes is essential for autonomous driving simulation, yet existing methods struggle to generate consistent videos when extrapolating to unseen viewpoints such as lane shifts. Reconstruction-based approaches maintain geometric consistency but degrade in visual quality under large viewpoint changes, while reconstruct-then-restore methods often produce visually plausible frames yet suffer from geometric and temporal inconsistencies. We present GeoV2V, a geometry-grounded video-to-video diffusion framework that synthesizes temporally coherent driving videos along novel trajectories by integrating LiDAR and depth-derived geometric priors with full-video conditioning in the Wan 2.1 backbone. A key challenge in learning cross-lane transformations is the lack of synchronized multitrajectory supervision in real-world datasets. To address this, we also introduce Para4D, a synthetic dataset capturing synchronized multi-lane driving videos within dynamic scenes, which provides explicit supervision for lane-shift generation and serves as a strong training prior for geometry-consistent view synthesis. Experiments on Waymo, nuScenes, and Para4D demonstrate state-of-the-art performance in visual quality, geometric consistency, and robustness to complex lighting dynamics under large trajectory shifts. Project page: https://xiyuche.github.io/ GeoV2V/

Abstract:
Recent indoor occupancy prediction methods adopt Gaussian primitives as a sparse 3D representation for computational efficiency. However, their training relies on voxel classification, which imposes only local constraints and lacks global supervision on the distribution of the primitives. Therefore, they inevitably predict spurious primitives in empty regions, undermining both representational and computational efficiency. To address this, we propose Feed-forward Likelihood Maximization (FLM), a novel framework that reformulates occupancy prediction as voxel distribution estimation. In FLM, a network is trained to predict a mixture model that maximizes the likelihood over groundtruth occupied voxels in a feed-forward manner. To enable end-to-end training of networks and voxelization of a standard mixture model, we define mixture weights as normalized primitive volumes to implicitly enforce simplex constraints and derive novel voxelization formulas. Based on FLM, our FLM-Occ, a novel method that is capable of relocating randomly initialized primitives over long distances to model a scene. On Occ-ScanNet, FLM-Occ achieves superior accuracy using only 32 superquadrics, 2.7% of the prior SoTA, while running 3.7× faster.

Abstract:
3D Gaussian Splatting (3DGS) has been widely used in 3D reconstruction and 3D generation. However, the rapid adoption of 3D Gaussian Splatting raises growing concerns about information leakage and unauthorized use, urging the exploration of effective watermarking techniques. However, existing methods are limited by low capacity, fragility under geometric perturbations, and the infeasible requirement for costly fine-tuning or pipeline modifications, motivating the need for a generalizable, feed-forward framework capable of robust multi-modal embedding with minimal intrusion. In this paper, we propose a new framework X-SG2S which can simultaneously inject 1D to 3D watermarks for copyright protection, while keeping the high fidelity of original 3DGS scenes. Specifically, we first split the watermarks into message patches. A self-adaptive gate is developed to select the injection positions of the watermark messages. Then, we use an XD (multi-dimensional) injection head to inject multi-modal messages into sorted 3DGS points. To restore watermarking messages, a learnable gate is developed to recognize the watermarked locations, from which our XD-extraction heads are used to restore hidden messages. X-SG2S is the first framework to unify 1Dto-3D watermarking and enable simultaneous multi-modal watermark embedding in 3DGS, achieving this with minimal rendering interference and zero modifications to parameters or pipelines. Extensive experiments demonstrate that X-SG2S effectively preserves consistency between the watermark and the original 3DGS, exhibits robustness against model degradation, and maintains accurate judgment capabilities.

Abstract:
Photorealistic and animatable hand avatars are essential forapplications such as AR/VR, gaming, and telepresence. Recent 3D Gaus-sian Splatting (3DGS) based avatar methods enable real-time renderingof articulated humans, but modeling hands remains challenging due totheir compact structure, frequent self-occlusions, and complex finger in-teractions. Existing approaches primarily rely on pose-driven transfor-mations while representing Gaussians in Euclidean space, lacking an ex-plicit structural association with the underlying skeleton, which makespreserving fine-grained hand structures under complex articulations diffi-cult. In this work, we introduce the Structural Coordinate Space (SCS),a skeleton-relative representation that assigns each Gaussian primitivean explicit structural coordinate with respect to the articulated handskeleton. SCS is constructed using a hybrid static–virtual bone basis to-gether with a distance–rotation structural descriptor that encodes thegeometric relationship between Gaussians and bones. Based on SCS, weenforce both intra-pose and cross-pose structural consistency by com-bining per-Gaussian residual embeddings for local appearance modelingwith structural correspondence across poses. Experiments demonstratethat our approach significantly improves structural consistency and pre-serves fine geometric details under challenging hand articulations com-pared with existing 3DGS-based avatar methods.

Abstract:
3D Gaussian Splatting (3DGS) enables efficient novel-viewsynthesis, yet its extension to simultaneous RGB and thermal infrared(TIR) reconstruction is hindered by a fundamental spectral mismatch:RGB relies on reflected illumination while TIR captures emitted radi-ance. This disparity in frequency characteristics and physical sensingmechanisms causes gradient interference when naively sharing a unifiedrepresentation. Common methods typically treat TIR as an auxiliary in-tensity channel, ignoring the physical radiative transfer governing ther-mal imaging. Critically, enforcing a single opacity field across modalitiesis physically invalid due to cross-spectral visibility disparities, constrain-ing representational capacity and yielding thermally implausible recon-structions. To address these limitations, we propose Physically GroundedDual-Opacity Gaussian Splatting, a framework that unifies RGB andTIR reconstruction under a shared geometric scaffold with modality-aware visibility modeling. We introduce Radiative Attribute Parameter-ization, explicitly modeling each Gaussian’s thermal response throughemissivity, temperature, and reflectance. Dual-Opacity Rendering en-ables spectrally aware visibility handling by assigning modality-specificopacities to shared location parameters, resolving cross-modal occlusionconflicts. Training jointly optimizes photometric reconstruction, physicalpriors, and RGB-guided geometric regularization to resolve parameterambiguities. Experiments demonstrate competitive performance in bothradiometric accuracy and photorealistic novel view synthesis, highlight-ing the potential of 3DGS for joint thermal field with high-fidelity RGBreconstruction.

Abstract:
Closed-loop simulation for autonomous driving requires synthesizing free-viewpoint driving scenes. However, existing datasets and generative pipelines rarely provide consistent off-trajectory observations, limiting large-scale evaluation and training. While recent generative models demonstrate strong visual realism, they struggle to simultaneously achieve interpolation consistency and extrapolation realism without perscene optimization. To address this, we propose FreeGen, a feed-forward reconstruction-generation co-training framework for free-viewpoint driving scene synthesis. The reconstruction model provides stable geometric representations to ensure interpolation consistency, while the generation model performs geometry-aware enhancement to improve realism at unseen viewpoints. Through co-training, generative priors are distilled into the reconstruction model to improve off-trajectory rendering, and the refined geometry in turn offers stronger structural guidance for generation. Experiments demonstrate that FreeGen achieves state-of-the-art performance for free-viewpoint driving scene synthesis.

Abstract:
Recent 3D Gaussian Splatting (3DGS) GANs for humanheads synthesize and render photorealistic 3D models in real-time andoffer a vast variety in identity and appearance. However, controlling spe-cific semantic attributes such as hair color or glasses remains challenging,as edits in the entangled latent space often induce unintended changesin identity or appearance. Although there are several methods that aimto disentangle the latent space post training by estimating directionsthat only modify certain features, these methods cannot guarantee com-plete disentanglement and often require pre-trained classifiers. In ourapproach, we propose a new generator architecture that synthesizes com-ponents, such as hair, skin, glasses, and torso, completely independently.This allows for changing the latent vector for one region while keepingthe remaining parts fixed. Further, we achieve this separation using onlysparse information such as the hair or skin color, eliminating the re-quirement of segmentation masks or geometric priors, often seen in priorwork. To ensure matching shape and lighting conditions during editing,we allow minimal shared information via context tokens between the in-dependent generators. These tokens even allow us to control the shapeand light, without any prior annotation. Compared to existing works onGAN-based generation and editing, our method shows better disentan-glement, more precise editing control, and competitive visual quality.

Abstract:
Reconstructing strand-level 3D hair from a single-view image is highly challenging, especially when preserving consistent and realistic attributes in unseen regions. Existing methods rely on limited frontalview cues and small-scale/style-restricted synthetic data, often failing to produce satisfactory results in invisible regions. In this work, we propose a novel framework that leverages the strong 3D priors of video generation models to transform single-view hair reconstruction into a calibrated multi-view reconstruction task. To balance reconstruction quality and efficiency for the reformulated multi-view task, we further introduce a neural orientation extractor trained on sparse real-image annotations for better full-view orientation estimation. In addition, we design a two-stage strand-growing algorithm based on a hybrid implicit field to synthesize the 3D strand curves with fine-grained details at a relatively fast speed. Extensive experiments demonstrate that our method achieves state-ofthe-art performance on single-view 3D hair strand reconstruction on a diverse range of hair portraits in both visible and invisible regions.

Abstract:
Generating complete 3D scenes from sparse, unconstrainedviews is a fundamental challenge in 3D vision which requires reason-ing beyond observed content while remaining computationally tractable.Existing feed-forward reconstruction methods are inherently limited tocontent visible in the input images, while 3D generative modeling is hin-dered by the high computational cost of dense volumetric representationsand the scarcity of large-scale 3D supervision. We introduce SPAR3S, asparse voxel-aligned 3D latent generative model for conditional scenecompletion without requiring ground-truth 3D data for supervision. Ourkey insight is to formulate 3D scene generation in a structured, compact,voxel-aligned 3D latent space where only occupied voxels are represented.We learn this sparse latent space directly from multi-view images usingphotometric supervision via differentiable 3D Gaussian Splatting. Givena partial set of observed voxels encoded from sparse input views, scenecompletion reduces to predicting the missing latent tokens and theirspatial support within the voxel grid. To this end, we train a maskedautoregressive transformer that jointly models voxel occupancy and la-tent token values, enabling efficient and spatially consistent generation ofunseen regions. We demonstrate the effectiveness of our method on syn-thetic indoor scenes, achieving higher novel-view quality than prior work.We further validate its generalization on RealEstate10k, highlighting itsapplicability to real-world data.

Abstract:
While 2D di x001B usion models have achieved remarkable successin identity-preserving personalization, extending this capability to 3Dassets remains a signi x001C cant challenge due to the complexities of multi-view consistency and spatial control. Inspired by these 2D advance-ments, we present a novel personalization method for text-guided 3Dediting that enables compositional, object-level control through naturallanguage. Given a 3D input, we render orthogonal views and extractobject-level segmentation masks to isolate semantic components. Wethen learn distinct token embeddings for each component through a tai-lored two-phase optimization strategy: multi-view textual inversion withattention alignment, followed by full x001C ne-tuning of multi-view di x001B usionmodel. During inference, these disentangled tokens seamlessly composewith editing prompts to generate multi-view consistent images, whichare subsequently lifted into high- x001C delity textured 3D meshes. Extensiveevaluations across diverse editing scenarios demonstrate that our methodsuccessfully transfers the x001D exibility of 2D personalization to 3D, achiev-ing state-of-the-art edit faithfulness and identity preservation comparedto existing baselines.

Abstract:
Portrait animation has witnessed tremendous quality im-provements thanks to recent advances in video diffusion models. How-ever, these 2D methods often compromise 3D consistency and speed, lim-iting their applicability in real-world scenarios, such as digital twins ortelepresence. In contrast, 3D-aware feedforward facial animation methods– built upon 3D representations, such as neural radiance fields or Gaus-sian splatting – ensure 3D consistency and achieve faster inference speed,but come with inferior expression details. In this paper, we address thisportrait animation trilemma (speed, 3D consistency, and expressiveness)and propose a pipeline that instantly converts an in-the-wild single im-age into a 3D-consistent, fast yet expressive animatable representationvia a feed-forward encoder. Unlike previous computationally intensiveglobal fusion mechanisms (e.g., multiple attention layers) for fusing 3Dstructural and animation information, our design employs an efficientlightweight local fusion strategy to achieve high animation expressivity.Furthermore, our animation representation is decoupled from the face’s3D representation and learns motion implicitly from data, eliminatingthe dependency on pre-defined parametric models that often constrainanimation capabilities. Our method runs at 107.31 FPS for animationand pose control, representing a 3-4 order of magnitude speedup versusthe state of the art while achieving comparable animation quality, thussurpassing alternative designs that trade speed for quality or vice versa.

Abstract:
The reconstruction of dynamic 3D scenes using 3D GaussianSplatting has shown significant promise. A key challenge, however, re-mains in modeling realistic motion, as most methods fail to align the mo-tion of Gaussians with real-world physical dynamics. This misalignmentis particularly problematic for monocular video datasets, where failingto maintain coherent motion undermines local geometric structure, ulti-mately leading to degraded reconstruction quality. Consequently, manystate-of-the-art approaches rely heavily on external priors, such as opti-cal flow or 2D tracks, to enforce temporal coherence. In this work, wepropose a novel method to explicitly preserve the local geometric struc-ture of Gaussians across time in 4D scenes. Our core idea is to introducea view-space ray grouping strategy that clusters Gaussians intersectedby the same ray, considering only those whose α-blending weights ex-ceed a threshold. We then apply constraints to these groups to maintaina consistent spatial distribution, effectively preserving their local geom-etry. This approach enforces a more locally coherent motion model byensuring that local geometry remains stable over time, eliminating thereliance on external guidance. We demonstrate the efficacy of our methodby integrating it into two distinct baseline models. Extensive experimentson challenging monocular datasets show that our approach significantlyoutperforms existing methods, achieving superior temporal consistencyand reconstruction quality.

Abstract:
We present a real-time LiDAR-based framework for Gaus-sian Splatting SLAM that tightly couples fast G-ICP registration withspherical rasterization-based dense mapping for large-scale sequences.Leveraging LiDAR geometry rather than appearance, we reuse tracking-estimated local covariances to initialize Gaussians with range-aware scalesand to derive surface normals for geometry-aware map optimization. Wefurther introduce a covariance-derived geometry score that measures lo-cal complexity and drives pruning in planar regions and selective densifi-cation in structurally rich areas, while optimized Gaussians and LiDAR-specific confidence cues are fed back to improve tracking robustness. Onthe Newer College dataset, our method achieves an F-score of 86.78%using purely online trajectories at real-time speed (>20 FPS), and addi-tional experiments on other datasets confirm its stability and scalability.Code is available at github.com/Lab-of-AI-and-Robotics/LiDAR-GS-SLAM.Project Page: lab-of-ai-and-robotics.github.io/GS-SLAM-Family

Abstract:
Dynamic 3D Gaussian splatting faces a fundamental tension between motion consistency and visual fidelity. Deformation-based approaches preserve temporal correspondence but suffer from motion over-factorization, oversmoothing high-frequency dynamics. In contrast, 4D-primitive methods capture fine visual details yet incur temporal overparameterization, breaking object identity and leading to severe storage overhead. To resolve this, we introduce Multi4D, a framework for highfidelity dynamic Gaussian Splatting based on multi-level competitive allocation. Instead of a monolithic representation, we distribute modeling capacity across three structured levels: static structure, persistent dynamic geometry, and transient appearance primitives. Through shared rasterization and residual-driven optimization, these levels dynamically compete to explain photometric error, enabling adaptive specialization without pre-assigned decomposition. This allocation preserves long-term motion consistency while capturing fine dynamic detail, achieving stateof-the-art rendering quality and real-time performance with significantly fewer dynamic primitives. Furthermore, because our representation explicitly tracks compact persistent Gaussians over time, semantic features can be embedded afterward, enabling Multi4D to achieve state-of-the-art 4D segmentation accuracy with an order-of-magnitude speedup.

Abstract:
3D Gaussian Splatting achieves impressive novel view syn-thesis for ground-level scenes. However, its performance degrades sig-nificantly in aerial domains due to sparse viewpoint sampling imposedby platform constraints. Existing sparse-view methods typically rely onstructure-from-motion reconstruction, which often yields incomplete ge-ometry due to limited feature correspondences under repetitive texturesand sparse viewpoint overlap. We present Geometry-Propagated Gaus-sian Splatting (GeoProp-GS), which overcomes the limitations of in-sufficient initialization. Our approach introduces two core components:Depth-guided Geometric Initialization (DGI) generates dense point cloudsand extends coverage to unobserved regions; Anchor-constrained Gaus-sian Optimization (AGO) stabilizes under-supervised regions by decom-posing proposal Gaussians into reliable anchors and learnable residuals.Extensive experiments demonstrate that GeoProp-GS achieves state-of-the-art performance and serves as an effective plug-and-play module thatconsistently improves existing methods. Code available at GeoProp-GS .

Abstract:
Dynamic view synthesis has seen significant advances, yet re-constructing scenes from uncalibrated, casual videos remains challengingdue to ambiguity between camera and object motion, inadequate viewcoverage and slow optimization. In this work, we present Minute4D,a novel and efficient approach for high-fidelity 4D scene reconstructionfrom monocular videos using Gaussian Splatting. Our Gaussian initial-ization begins with an efficient geometric recovery leveraging pre-trainedvisual foundation models. To improve reconstruction fidelity, we intro-duce a Segmentation-Tracking Enhancement module that leverages 2Dsemantic priors and 3D point tracking to jointly enhance the geometricaccuracy and completeness of the initial Gaussians. To reduce redun-dancy and accelerate Gaussian optimization, we propose a Loss-GuidedDensity Control strategy that adaptively densifies and prunes Gaussiansbased on multi-view photometric loss. Our method reduces the optimiza-tion time to approximately 40 seconds for a typical 400-frame video,yielding a speed-up of at least 5×. Extensive experiments show thatour method not only enables substantially faster optimization but alsoachieves superior performance across several benchmarks.: Equal contribution.†: Co-corresponding authors.

Abstract:
Bumblebees play a key role in agriculture, ecology, and bio-inspired robotics, making accurate 3D surface reconstruction essential forquantitative behavioral analysis. However, their dark dense hairs, com-plex structure and flexible limbs prone to visual occlusion and annotationdifficulties, resulting in difficulties in reconstruction. Current template-free or generic template-based models offer limited structural detail andbiological realism, while high-quality bumblebee templates and reliablemorphological priors are lacking. To address these challenges, we pro-pose BioMTBee, a template-based 3D mesh reconstruction frameworkfor bumblebees. We built a high-fidelity articulated mesh template frommicro-CT scans to provide an accurate morphological prior. A multi-view3D pose estimator with spatio-temporal filtering (BPST) is then intro-duced to extract robust 3D keypoints from 2D detections, guiding stablemesh template fitting. Building on this template, we combine 3D poseand shape supervision with biological constraints—bilateral symmetry,kinematic coupling, and temporal smoothness—to achieve anatomicallyconsistent and temporally stable surface reconstructions. Experiments on3D reconstruction from multi-view images show improvements in mor-phological accuracy and biological plausibility over representative base-lines, capturing fast limb movements and fine structural and texturaldetails, and also generalize well to cross-species reconstruction.

Abstract:
Recent breakthroughs in 3D generation have advanced no-tably with the development of text-to-image diffusion model. However,existing methods remain two practical challenges: (1) They primarilygenerate single 3D object, but struggle to generate multi-object compo-sitional 3D assets due to the lack of the modeling for Gaussian prim-itives in reasonable interactions. (2) They often suffer from cross-viewinconsistency during 3D optimization, as Score Distillation Samplinginherently performs on each single view, inevitably resulting in cross-view hallucinations. To solve above issues, we propose I2 C-3D, a noveloptimization-based method to generate multi-view consistent composi-tional 3D assets with reasonable interactions. Specifically, we proposean Inclusive Interactive Collisions strategy to guide Gaussian primitivesappearing in reasonable interaction regions naturally, thereby ensuringobjects in the compositional scene interact in a physically plausible andvisually coherent way. Additionally, to enhance multi-view consistency,Multi-View Adaptive Score Distillation Sampling is devised to distillmulti-view consistency prior and layout prior from pre-trained diffusionmodel by modulating attention map of instance token and spatial to-ken across viewpoints. Benefiting from above elaborate designs, I2 C-3Dnot only generates high-fidelity multi-view consistent compositional 3Dassets but also supports 3D editing flexibly, facilitating complex scenegeneration. Extensive experiments demonstrate our I2 C-3D outperformsexisting methods in generation quality and multi-view consistency.

Abstract:
Recent advances integrate physically grounded Newtonian dynamics with neural rendering frameworks, narrowing the gap between photorealistic scene reconstruction and physics-based animation. However, existing approaches focus on mechanically driven dynamics while neglecting temperature, a fundamental yet invisible physical factor underlying phenomena such as melting, solidification, and other thermomechanical processes. In this paper, we propose MeGAS, a novel framework that incorporates thermomechanical phase-change dynamics into 3D Gaussian Splatting (3DGS). Specifically, we propose a new thermomechanical dynamic Gaussian Splatting representation that augments 3DGS with temperature attributes and employs a heat advection-diffusion solver with MPM dynamics incorporating phase transitions, enabling physically plausible and visually realistic synthesis of thermophysical phenomena. Furthermore, a new topology-adaptive Gaussian rendering strategy is proposed to mitigate cracking and floaters under extreme deformation. Extensive experiments demonstrate that MeGAS produces physically consistent thermomechanical behavior while maintaining highfidelity photorealistic rendering, advancing toward physics-integrated world models.

Abstract:
Merging multiple 3D Gaussian Splatting (3DGS) scenes intoa single unified Gaussian representation is essential for large-scale 3Dmapping and long-term map management. Despite its importance, thisarea remains underexplored, and existing solutions exhibit several limi-tations. Learning-based methods attempt direct correspondence betweenGaussian primitives and require training on large 3DGS datasets. Image-based optimization methods depend heavily on coarse initialization fromgeneric foundation models and often incur expensive refinement. Wepresent Graph-GSReg. Our method constructs a 3D scene graph froma 3DGS and its rendered images, reformulating 3DGS registration asa graph registration problem. The proposed 3D scene graph representseach 3DGS at a higher-level representation, enabling a globally consis-tent understanding of semantic information and structural context foraccurate registration. To further construct a seamless unified scene, weintroduce a Self-Supervised Test-Time Optimization. Naively mergingtwo 3D Gaussian scenes often suffers from occlusion artifacts such ashollows and floaters. To alleviate this issue, we refine the merged Gaus-sians to preserve visual consistency between the original scenes and themerged scene. We evaluate our method on real and synthetic bench-marks, demonstrating competitive registration accuracy and merged scenerendering quality.

Abstract:
Adversarial attacks on point clouds require effective constraints to ensure imperceptibility. However, existing methods often overlook the intrinsic hierarchical organization of 3D shapes, thereby limiting their ability to preserve structural coherence. In this paper, we propose HHA, a novel framework that generates hierarchy-aware adversarial perturbations by leveraging hyperbolic geometry. HHA first decomposes the input point cloud into semantic and geometric substructures to capture its multi-scale organization. Then, each substructure is embedded into hyperbolic space, where localized constraints limit distortion and maintain hierarchical consistency. This hyperbolic regularization keeps perturbations aligned with the underlying structure and thereby enhances imperceptibility. Extensive experiments validate that HHA produces adversarial point clouds with improved structural coherence and imperceptibility, outperforming state-of-the-art methods.

Abstract:
Masked autoregressive (MAR) models unify the strengths ofmasked and autoregressive generation by predicting tokens in a fixed or-der using bidirectional attention for image generation. Although effective,MAR models incur substantial computational overhead because they re-compute attention and feed-forward representations for every token ateach decoding step, even though the majority of tokens remain seman-tically unchanged across steps. We propose a training-free generationframework MARché to address this inefficiency through two key com-ponents: cache-aware attention and selective KV refresh. Cache-awareattention partitions tokens into active and cached sets, enabling sepa-rate computation paths that allow efficient reuse of previously computedkey/value projections without compromising full-context modeling. How-ever, a cached token cannot be used indefinitely without recomputationdue to the changing contextual information over multiple steps. MARchérecognizes this challenge and applies a technique called selective KV re-fresh. Selective KV refresh identifies contextually relevant tokens basedon attention scores from newly generated tokens and updates only thosetokens that require recomputation, while preserving image generationquality. MARché significantly reduces redundant computation in MARwithout modifying the underlying architecture. Empirically, MARchéachieves up to 1.7× speedup with negligible impact on image quality,offering a scalable and broadly applicable solution for efficient maskedtransformer generation.

Abstract:
Scalable generation of outdoor driving scenes requires 3Drepresentations that remain consistent across multiple viewpoints andscale to large areas. Existing solutions either rely on image or video gen-erative models distilled to 3D space, harming the geometric coherenceand restricting the rendering to training views, or are limited to small-scale 3D scene or object-centric generation. In this work, we propose a3D generative framework based on Σ-Voxfield grid, a discrete representa-tion where each occupied voxel stores a fixed number of colorized surfacesamples. To generate this representation, we train a semantic-conditioneddiffusion model that operates on local voxel neighborhoods and uses 3Dpositional encodings to capture spatial structure. We scale to large scenesvia progressive spatial outpainting over overlapping regions. Finally, werender the generated Σ-Voxfield grid with a deferred rendering moduleto obtain photorealistic images, enabling large-scale multiview-consistent3D scene generation without per-scene optimization. Extensive experi-ments show that our approach can generate diverse large-scale urbanoutdoor scenes, renderable into photorealistic images with various sen-sor configurations and camera trajectories while maintaining moderatecomputation cost compared to existing approaches.

Abstract:
Vision-based 3D occupancy prediction fundamentally relieson the 2D-to-3D view transformation. Current paradigms predominantlyutilize explicit physical projection, which artificially restricts the routingmatrix to strict, sparse camera rays. While computationally efficient, thisimposes a severe Locality Bottleneck, preventing the network from con-structing holistic contextual understanding and degrading sharply whencamera extrinsics are unreliable or absent. To break this bottleneck, weabstract view transformation as unconstrained bipartite routing and pro-pose Factorized Dense Routing (FDR). By approximating dense2D-to-3D mixing through hierarchical tensor contractions, FDR guar-antees a fully-global receptive field with tractable, sub-quadratic com-plexity. Crucially, the mandatory spatial contraction in dense routingexposes a fundamental Resolution-Context Trade-off. To address this,we introduce a Resolution-Context Decoupled Architecture. Wefactorize the 3D space into a global macroscopic topological anchor (viaFDR) and precise local geometric planes (via explicit projection). Thisdecoupling enables global semantic inference and exact surface localiza-tion to complement each other without mutual compromise. Extensiveexperiments demonstrate that our framework achieves state-of-the-artperformance on the Occ3D-nuScenes and Occ3D-Waymo benchmarks.More notably, in an uncalibrated setting where physical extrinsics arewithheld, our global routing internalizes the implicit multi-camera rigtopology and exhibits substantially stronger structural robustness thanphysical-projection baselines under the same protocol.

Abstract:
Unbounded 3D world generation is emerging as a founda-tional task for scene modeling in computer vision, graphics, and robotics.In this work, we present WorldFlow3D, a novel method capable of gen-erating unbounded 3D worlds. Building upon a foundational property offlow matching – namely, defining a path of transport between two datadistributions – we model 3D generation more generally as a problemof flowing through 3D data distributions, not limited to conditional de-noising. We find that our latent-free flow approach generates causal andaccurate 3D structure, and can use this as an intermediate distribution toguide the generation of more complex structure and high-quality texture– all while converging more rapidly than existing methods. We enablecontrollability over generated scenes with vectorized scene layout condi-tions for geometric structure control and visual texture control throughscene attributes. We confirm the effectiveness of WorldFlow3D on bothreal outdoor driving scenes and synthetic indoor scenes, validating cross-domain generalizability and high-quality generation on real data distri-butions. We confirm favorable scene generation fidelity over approachesin all tested settings for unbounded scene generation.

Abstract:
Estimating the 6D pose of unseen objects is a fundamentalyet challenging problem for open-world robotics and embodied percep-tion. Model-based methods are accurate but depend on CAD assets orheavy onboarding, while most model-free approaches are still limitedto pairwise single-anchor matching and thus fail under occlusion andlarge viewpoint changes with low query–reference overlap. Therefore, wepresent PANY, a unified model-free framework that seamlessly sup-ports both RGB and RGB-D inputs, operates on one or sparse pose-freereference views, and generalizes effectively to novel objects. Built on amulti-view transformer geometry backbone, PANY moves beyond pair-wise matching by learning view-consistent geometry and cross-view align-ment cues that remain stable under wide baselines and limited overlap.When additional unposed assist views are available, PANY aggregatesthem via pose-graph canonical registration to increase geometric cover-age and reinforce the final pose. Extensive experiments show that PANYachieves state-of-the-art performance across multiple benchmarks, sub-stantially outperforming existing model-free methods, improving poseaccuracy by +12% on YCB-V and over +20% on LM-O. Further-more, PANY consistently performs well under both single-reference andsparse-reference settings, demonstrating strong robustness in real-worldenvironments.

Abstract:
Point cloud registration critically depends on local featuresthat are both distinctive and robust to arbitrary 3D rotations. Existinglearning-based methods typically approximate rotation invariance viafragile local reference frames or extensive data augmentation, providingonly empirical invariance and often degrading under unseen rotationaltransformations. In this paper, we propose SHReg, a strictly rotation-equivariant point cloud registration framework grounded in the repre-sentation theory of SO(3). By representing local geometric features asirreducible representations of SO(3), SHReg guarantees exact equivari-ance under arbitrary rotations without relying on local reference frames.Built upon a spherical-harmonics-based equivariant backbone, SHRegjointly learns rotation-invariant descriptors for robust correspondencematching and rotation-equivariant features that preserve fine-grained ori-entation information. The preserved equivariant structure enables eachcorrespondence to directly hypothesize a rigid transformation, reducingreliance on large-scale hypothesis sampling in conventional RANSAC-based pipelines and leading to improved robustness under challengingrotational variations. Extensive experiments on 3DMatch, 3DLoMatch,and KITTI demonstrate that SHReg consistently outperforms state-of-the-art methods in registration accuracy, particularly under large rota-tional perturbations.

Abstract:
Critical mineral discovery is a positive-only problem: deposits are observed as sparse locations, while unlabeled regions are not reliable negatives, and similar geophysical signatures can arise from different subsurface states. We therefore model mineral targeting as learning a conditional spatial distribution over occurrence locations, π(p | d), given geo-images d, rather than predicting a deterministic per-pixel score map. We introduce GeoCFM, a conditional flow-matching model that generates mineral occurrence point sets conditioned on multi-channel geo-images; GeoCFM learns a point-wise transport field in R2, using UNet features with point-conditioned velocity prediction to bridge dense rasters and sparse supervision without pseudo-negatives. On a synthetic magnetics–geochemistry benchmark with latent activation and on USGS Earth MRI data with a spatially disjoint tile split, GeoCFM improves geometric agreement with observed occurrences over score-map and nonconditional baselines, while representing epistemic uncertainty through conditional sampling.

Abstract:
This study introduces a masked coordinate pooling-basedattention network (MCPNet), which is a novel framework designed toimprove medical landmark detection. Coordinate features are essentialin accurately localizing medical landmarks. Recently, many studies haveused coordinate information in final regression heads; however, its directincorporation into the attention mechanism remains largely unexplored.To address this issue, we propose MCPNet, which directly extracts posi-tional features from CNN feature maps and analyzes them in the coordi-nate domain. Specifically, MCPNet employs learnable masks and coordi-nate maps to effectively encode diverse and richer anatomical positionalcues of the landmarks. Moreover, the proposed mask maps are utilizedwhen transforming the analyzed coordinate features back into the origi-nal CNN feature space, ensuring smooth reconstruction within the net-work. Extensive experiments on four public benchmark datasets demon-strate that the proposed MCPNet outperforms state-of-the-art methods,offering improved accuracy and efficiency.

Abstract:
State-of-the-art super-resolution (SR) transformers stack 16–30+ blocks of small shifted-window attention. Information between dis-tant positions must therefore traverse multiple attention layers, eachbounded by the local window—an indirect path that grows with net-work depth. We show that direct wide attention over large position-fixedwindows (64×64 at training, up to 128×128 at inference) can matchor surpass these deep narrow-window designs with only 12 blocks. Akey enabler is 2D Rotary Position Embeddings (RoPE), which encoderelative positions through query-key rotations rather than additive biasmatrices, enabling memory-efficient attention over thousands of tokens.To fully exploit this large spatial context, we replace the standard linearQKV projection with a rich nonlinear projection module that producesmore expressive features for each attention operation; sharing a singlemodule across all blocks keeps the model at 20M parameters while pro-viding the representational capacity that would otherwise require 115M.The shared module jointly produces spatial and channel QKV througha unified output. RoPE further enables resolution-adaptive inference viaYaRN-style scaling [22] with an inverted temperature correction tailoredfor SR. With only 20M parameters, our model, WARP, achieves PSNRcompetitive with or superior to state-of-the-art methods on standardbenchmarks.

Abstract:
As an important 3D sensing method, photometric stereo estimates surface normals from multiple photos taken under varying lighting. The surface normal quality highly relies on large number of input images, which requires long capturing time, high data burden and limits high-speed applications for photometric stereo. Recently, event-based photometric stereo has emerged as an efficient and rapid approach, but the low resolution of event sensors and the noise of event signals limit its high-quality application. In this paper, we propose DualResPS, a novel photometric stereo pipeline utilizing a frame-event hybrid camera. We design a lighting and capturing strategy tailored to a hybrid-camera setup to utilize observations of different spatial and temporal resolutions. The algorithm explicitly models non-ideal factors including shadow effects and specular reflection to achieve high-resolution, high-quality normal reconstruction. Our proposed DualResPS is validated on both semi-real datasets from the DiLiGenT, DiLiGenT-Pi, and real captured data. The results demonstrate that DualResPS surpasses its frame-based counterpart with 18.1% data bandwidth.

Abstract:
Fine-grained 3D point cloud segmentation is essential forrobot manipulation and CAD editing. A key unresolved challenge is thatdifferent parts of the same object often share similar local geometry, mak-ing part boundaries difficult to distinguish. Existing methods constructlocal patches via K-nearest search and rely on deeper and larger networksto learn discriminative features. However, these approaches mainly focuson local geometry and lack global structural awareness, which leads toambiguous predictions under sampling noise and partial observations.In this work, we propose Topological Graph Attention Network (To-poGAT), a lightweight, plug-and-play backbone refinement module thatintegrates topological data analysis with Graph Attention Networks tointroduce global structural information into point-wise feature learning.When combined with existing backbones, TopoGAT improves segmenta-tion accuracy with a slight parameter increase. Extensive experiments onfine-grained 3D part segmentation validate the effectiveness of the pro-posed TopoGAT and show up to 1.0% improvement on ShapeNetPartdataset and 3.3% improvement on S3DIS dataset.

Abstract:
Multimodal Large Language Models (MLLMs) have demon-strated remarkable potential in medical image analysis. However, theirapplication in gastrointestinal endoscopy is currently hindered by twocritical limitations: the misalignment between general model reasoningand standardized clinical cognitive pathways, and the lack of causal as-sociation between visual features and diagnostic outcomes. In this paper,we propose a novel Clinical-Cognitive-Aligned (CogAlign) framework toaddress these challenges. First, we endow the model with rigorous clinicalanalytical capabilities by constructing the hierarchical clinical cognitiondataset and employing Supervised Fine-Tuning (SFT). Unlike conven-tional approaches, this strategy internalizes the hierarchical diagnosticlogic of experts, ranging from anatomical localization and morphologicalevaluation to microvascular analysis, directly into the model. Second, toeliminate visual bias, we provide a theoretical analysis demonstratingthat standard supervised tuning inevitably converges to spurious back-ground correlations. Guided by this insight, we propose a counterfactual-driven reinforcement learning strategy to enforce causal rectification. Bygenerating counterfactual normal samples via lesion masking and opti-mizing through clinical-cognition-centric rewards, we constrain the modelto strictly ground its diagnosis in causal lesion features. Extensive exper-iments demonstrate that our approach achieves State-of-the-Art (SoTA)performance across multiple benchmarks, significantly enhancing diag-nostic accuracy in complex clinical scenarios.

Abstract:
Lensless imaging enables compact and versatile computa-tional cameras by replacing bulky optics with thin coded elements. How-ever, reconstruction from the resulting measurements is challenging: large-footprint point-spread functions (PSFs) produce highly multiplexed ob-servations, making inversion severely ill-conditioned and sensitive to cal-ibration errors and model mismatch. While deep learning approaches,including hybrid models that incorporate physics priors, have shownpromise, explicitly maintaining data fidelity throughout the network hier-archy remains difficult. Here, we propose the Integrated Forward–InverseNetwork (IFIN), a physics-guided architecture that interleaves differen-tiable forward projections with learnable inverse updates at every scale,enabling complementary cues to be exploited jointly in the measurementand image domains. This bidirectional coupling supports progressive,physics-consistent refinement and permits system-constrained PSF ker-nel adaptation under model uncertainty. On challenging lensless bench-marks, including a newly introduced dataset, IFIN achieves state-of-the-art reconstruction quality. We further observe competitive performanceon Gaussian deblurring and simulated inline holography reconstruction,suggesting that the same interleaving principle can extend beyond lens-less cameras.

Abstract:
Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likelihoods. This manifests itself as either overconfident incorrect predictions or under-confident correct predictions. Label smoothing (LS) enhances model calibration by introducing entropy regularization during training through redistributing probability mass from the ground-truth label to the remaining classes. LS, including Margin-based LS (MbLS), have restrictive assumptions: they rely on predefined, uniform smoothing rules and only tackle overconfidence. In reality, samples exhibit diverse characteristics, such as difficulty/ambiguity, that interact with the evolving nature of the model being trained. In training, samples may have various degrees of underor overconfidence. To overcome this, a mechanism that identifies the specific confidence state of each sample and determines the appropriate degree of smoothing in each training step is needed, tailoring the adjustment to the individual sample. We propose FeDLaS: Feature-Modulated Bidirectional Label Smoothing, a plug-and-play algorithm for label smoothing-based losses. In FeDLaS, we introduce a Feature Norm-based Confidence Indicator (NCI) to control smoothing and a Bidirectional Calibration Gating (BCG) module to detect both over and under-confidence. Our algorithm can be integrated with LS and MbLS based losses when applied to standard DNNs, enhancing performance. Extensive experiments on standard and fine-grained high-resolution vision benchmarks show that FeDLaS consistently improves calibration compared to modern baselines, reducing Expected Calibration Error (ECE) and Adaptive ECE while maintaining Top-1 accuracy. Code: github.com/nadarasarbahavan/FEDLAS

Abstract:
Spike camera, a type of neuromorphic sensor with high-temporal resolution, shows great promise for high-speed visual tasks.Unlike traditional cameras, spike camera continuously accumulates pho-tons and fires asynchronous spike streams. Due to unique data modality,spike streams require reconstruction methods to become perceptible to thehuman eye. However, under low-light high-speed conditions, spike streamsbecome highly sparse and noisy, making faithful pixel-wise recovery in-trinsically difficult. In this work, we propose Diff-SPK, a diffusion-basedframework for perceptual reconstruction from low-light spike streams.Diff-SPK leverages generative priors to produce visually plausible re-constructions while remaining constrained by spike-derived structuralconditions. Specifically, it first employs an Enhanced Texture f rom Inter-spike Interval (ETFI) to aggregate sparse structural information fromlow-light spike streams. Then, the encoded ETFI by a suitable encoderserves as the input of ControlNet for high-speed scenes generation. Toimprove the quality of results, we introduce an ETFI-based feature fusionmodule during the generation process.

Abstract:
We present an RGB-only method for 6D object pose esti-mation that leverages monocular depth cues from a single image. Al-though monocular depth estimation has advanced substantially, its pre-dictions remain scale-ambiguous and locally unreliable, limiting theiruse in metric pose refinement. We address this gap by closing the loopbetween depth correction and pose refinement: monocular depth is notonly a regularizer but is iteratively calibrated and filtered using pose-induced geometric consistency, enabling stable metric pose updates fromRGB alone. We propose a dynamic depth outlier removal module basedon metric consistency and infer object pose from dense 2D correspon-dences. Both components are embedded into a recurrent optimizationloop, enabling iterative depth correction and pose refinement. Experi-ments on seven BOP datasets demonstrate state-of-the-art performanceamong RGB-only methods, without requiring real depth input.

Abstract:
Universal photometric stereo (UniPS) aims to recover sur-face normals from images captured under unconstrained illumination,which fundamentally differs from calibrated photometric stereo (CPS),where lighting conditions are known. This gap forces most existing UniPSmethods to adopt fully end-to-end learning that struggles to disentangleillumination and surface geometry due to the lack of explicit physical con-straints. In this paper, we propose Bridge-UniPS, a novel frameworkthat bridges CPS and UniPS through a lighting adapter trained withoutdirect intermediate supervision. The adapter transforms images capturedunder unconstrained illumination into a sequence of image–illuminationpairs directly compatible with CPS models, allowing CPS to act as aphysics-aligned intermediate representation and thereby improving theaccuracy of surface normal recovery. The lighting adapter receives nodirect supervision on Bridge Images or target lighting directions, butis optimized through the final normal-estimation objective with gra-dients propagated through a frozen CPS network. It can still produceCPS-compatible Bridge Images while preserving illumination-geometryconsistency. This behavior suggests that modern learning-based CPSmodels provide useful physical priors to guide the reconstruction. Ex-tensive experiments on multiple benchmarks demonstrate that Bridge-UniPS achieves state-of-the-art performance under unconstrained illu-mination, exhibiting strong generalization and significantly improvingfine-scale surface normal accuracy.

Abstract:
Error-diffusion-based binary-defocusing fringe projection pro-filometry has been widely adopted for high-speed 3D imaging of dynamicobjects due to its high measurement efficiency. However, existing errordiffusion methods often neglect the intrinsic relationship between fringepattern structures and the diffusion direction, resulting in systematicphase errors in the generated binary patterns. To overcome this limi-tation, we develop a theoretical model of phase errors in the ditheringalgorithm that elucidates the mechanisms linking phase errors to thediffusion direction. Based on this analysis, a binary fringe generationstrategy is designed. In this approach, fringe patterns in the four-stepphase-shifting method are grouped, and binarization is performed us-ing both the Floyd Steinberg algorithm and its inverse. This processexploits inter-fringe error-compensation effects to enhance measurementaccuracy. Experimental results demonstrate that the proposed methodreduces binarization-induced errors by 23.77% without additional com-putational overhead, confirming its superior performance in terms of bothaccuracy and efficiency.

Abstract:
Generating clean line art from conti, rough storyboard sketchesused in webtoon production, remains a labor-intensive process requiringsubstantial artistic expertise. Conti sketches provide only coarse geom-etry and sparse semantic cues, making it difficult to recover detailedline art while preserving consistent character identity across sequentialpanels. Existing sketch-to-image or diffusion-based translation modelsprocess images independently and therefore struggle to maintain cross-panel identity coherence. We present PanelDiff, a diffusion transformerframework for conti-to-line-art generation that explicitly models cross-panel character consistency. PanelDiff integrates three components. Amulti-reference character conditioning module encodes character labelsand multiple line-art exemplars into compact identity tokens for robustidentity guidance under ambiguous conti inputs. A memory-augmentedrepresentation bank accumulates character-aware features from previ-ously generated panels, while a similarity interpreter dynamically re-trieves relevant entries for the current panel. A panel-aware diffusiontransformer then jointly attends to conti structure, textual descriptions,identity tokens, and retrieved memory features to produce coherent line-art sequences. To support this task, we construct a conti–line-art datasetwith character identity annotations and sequential panel structures. Ex-periments show that PanelDiff improves line-art fidelity, identity preser-vation, and cross-panel consistency over strong baselines, and ablationstudies verify the contribution of each component. The code and datasetare available online (huggingface) ⋆ .

Abstract:
Monocular depth estimation remains challenging for trans-parent objects, where refraction and transmission are difficult to modeland break the appearance assumptions used by depth networks. As a re-sult, state-of-the-art estimators often produce unstable or incorrect depthpredictions for transparent materials. We propose SeeClear, a novelframework that converts transparent objects into diffusion-generated opaqueimages, enabling stable monocular depth estimation for transparent ob-jects. Given an input image, we first localize transparent regions andtransform their refractive appearance into geometrically consistent opaqueappearances using a diffusion-based generative opacification module. Theprocessed image is then fed into an off-the-shelf monocular depth esti-mator without retraining or architectural changes. To train the opaci-fication model, we construct SeeClear-396k, a synthetic dataset con-taining 396k rendered images across 66k paired transparent-opaque con-figurations. Experiments on both synthetic and real-world datasets showthat SeeClear significantly improves depth estimation for transparentobjects.

Abstract:
Hyperspectral imaging (HSI) promises material-level perception beyond RGB vision, yet current models remain tightly coupled to sensing hardware. Minor changes in spectral discretization or spatial resolution can invalidate trained networks, despite all sensors observing the same material reflectance physics. We introduce OneHSI, a unified hyperspectral foundation model that aligns representations across heterogeneous sensing configurations through physically motivated inductive biases. Rather than treating bands and resolutions as arbitrary tensor dimensions, OneHSI reparameterizes spectral channels in a shared continuous wavelength space, separates scale-invariant relational attention from scale-conditioned feature transformation, and balances cross-domain contributions to learn sensor-invariant representations. Cross-domain experiments demonstrate state-of-the-art performance and strong robustness under sensor and scale shifts, supporting physics-aligned representation learning as a principled path toward cross-sensor generalization.

Abstract:
Bundle adjustment (BA) remains a critical refinement mod-ule for image-based 3D reconstruction and continues to improve geomet-ric accuracy even in learning-based pipelines. However, in low-parallaxand near-rotational regimes, classical Schur-based Levenberg–Marquardt(LM) often becomes ill-conditioned and yields unreliable pose and cali-bration estimates. We propose Gate-Guided CSS-BA, a solver-side mod-ification of Schur-LM that preserves the classical BA objective and trust-region framework while constraining each update to a geometrically in-formed low-dimensional subspace. By integrating Column Space Search(CSS) with geometry-aware gating, the method stabilizes the Schur-LMupdate without altering the estimation problem. In contrast to keyframeor state-selection approaches, all camera and point parameters remainin the optimization problem; only the update direction is restricted. Themethod serves as a drop-in replacement for existing BA pipelines. Exper-iments on both generic and challenging weak-geometry scenarios showmore stable optimization, improved relative pose accuracy, and compet-itive calibration behavior while maintaining reprojection quality.

Abstract:
Structured Light (SL) is a popular technique that delivers precise 3D shape acquisition across diverse materials and object sizes. SL requires, however, careful modeling of the set-up, including geometric and radiometric calibration of the camera-projector pair; without it, reconstruction quality degrades. We propose NeLU3D, a neural inverse SL method without explicitly modeling the projector. We calibrate the camera–projector system using a neural network that maps 3D positions to a set of projected patterns. Then, with as few as four monochromatic images (or two RGB images), our approach uses differentiable volume rendering to fit a surface to match SL captures. We scan over twenty-five objects of different shapes and reflectances to demonstrate the feasibility and quality of our method in a handful of projector-camera set-ups, including an extremely low-cost projector and an analog projector with fixed RGB pattern. We also showcase sub-millimeter accuracy with suboptimal patterns, where previous methods recover noisy 3D surfaces. We release an open-source implementation at https://github.com/ geometryprocessing/neural-lookup.

Abstract:
Deploying safety-critical agents requires anticipating the consequences of actions before they are executed. While world models offer a paradigm for this proactive foresight, current approaches relying on visual simulation incur prohibitive latencies, often exceeding several seconds per step. In this work, we challenge the assumption that visual processing is necessary for failure prevention. We show that a trained policy’s latent state, combined with its planned actions, already encodes sufficient information to anticipate action outcomes, making visual simulation redundant for failure prevention. To this end, we introduce DILLO (DIstiLLed Language-ActiOn World Model), a fast steering layer that shifts the paradigm from “simulate-then-act” to “describe-thenact.” DILLO is trained via cross-modal distillation, where a privileged Vision Language Model teacher annotates offline trajectories and a latentconditioned Large Language Model student learns to predict semantic outcomes. This creates a text-only inference path, bypassing heavy visual generation entirely, achieving a 14× speedup over baselines. Experiments on MetaWorld and LIBERO demonstrate that DILLO produces high-fidelity descriptions of the next state and is able to steer the policy, improving episode success rate by up to 15pp and 9.3pp on average across tasks. Code is available at github.com/MaxPappa/DILLO.

Abstract:
The strong generalization capability to diversified atmospheric conditions is crucial for autonomous driving models. However, models trained on existing datasets struggle with real-world complexity due to the homogeneous weather, limited maneuvers, and insufficient scale in training data. To overcome this issue, we introduce DiverseAD, a largescale driving dataset comprising 150K scenes that feature a great diversity in atmospheric condition, road types, and driving actions. Based on this dataset, we further propose a novel end-to-end autonomous driving model robust across diverse atmospheric conditions. More specifically, an atmospheric-invariant feature learning mechanism is proposed, which spots and disentangles atmospheric-agnostic features from visual inputs by using driving intent and scene structure cues as stable anchors. Our method thus allows the extracted features to be more invariant to changes in atmospheric conditions. Experiments show that DiverseAD is superior to existing public datasets in diversity, hence is valuable for training and benchmarking. Extensive comparisons also illustrate the superior performance of our proposed method.

Abstract:
In autonomous driving perception, the fusion of LiDAR andcamera modalities has become the dominant paradigm for 3D object de-tection. However, current multi-modal frameworks heavily rely on mas-sive visual backbones pretrained on 2D semantic tasks. This relianceintroduces substantial parameter redundancy and a structural misalign-ment, as 2D priors are ill-equipped to handle the extreme sparsity of Li-DAR projections required for Bird’s-Eye-View geometry. To address this,we present DeGuNet, an ultra-compact and plug-and-play image back-bone explicitly designed for depth-guided representation learning. By in-corporating sparsity-aware feature extraction mechanisms, DeGuNet ef-fectively aligns multi-view images with unstructured LiDAR depth whilestrictly preventing invalid-region contamination. Extensive experimentson the nuScenes dataset demonstrate DeGuNet’s broad plug-and-playapplicability and superior efficiency. When integrated into establishedbaselines, it fundamentally eliminates architectural redundancy, reduc-ing GPU memory consumption by up to 66.5% and achieving a 1.16×inference speedup. Concurrently, DeGuNet delivers up to a 6.20 absolutemAP gain, establishing a new paradigm for parameter-efficient multi-modal 3D perception.

Abstract:
Humans can navigate an unfamiliar city and gradually forma coherent spatial mental map spanning tens of square kilometers. CanAI build spatial representations at a comparable scale? Although recentfoundation models have advanced scene reconstruction and embodied in-telligence, scaling to entire cities remains an open challenge, primarilydue to the lack of city-scale data. To bridge the gap, we introduce Wild-City, a real-world multimodal dataset collected by autonomous fleetstraversing complex urban environments. Our dataset includes 18 tra-jectories, each averaging 83.7 kilometers in length, and preserves thecore challenges of in-the-wild perception, e.g., dynamic objects, lightingvariations, and imperfect camera poses. We further establish an urban-tailored reconstruction baseline and convert the reconstructed environ-ments into a closed-loop simulator. Beyond the dataset and baseline, wesystematically analyze the key challenges on the path to simulation-readyurban digital twins: scalability, extrapolation, and uncertainty. Ul-timately, WildCity aims to catalyze progress not only in city-scale render-ing, but more broadly in the pursuit of AI that can perceive, remember,and reason across space at a scale comparable to human cognition.

Abstract:
Embodied visual navigation requires agents with real-time perception, logical reasoning and efficient decision-making capabilities. Existing collaborative modular frameworks struggle to balance accurate decision-making and efficient online execution, leading to error propagation and inference blocking. To tackle this limitation, we propose AMCoNav, an Asynchronous Multi-module Collaborative Framework that unites real-time lightweight backbone networks with on-demand zeroshot large model modules (ZLMM) for robust and real-time embodied navigation. The framework has four tightly coupled components: (1) a Real-Time Multimodal Decision Module (RMDM) for continuous decision making; (2) Zero-shot Large Model Modules (ZLMM) for on-demand asynchronous reasoning; (3) a latency-tolerant Multimodal Shared Context (MSC) for cross-module coordination; and (4) a Bayesian Probabilistic Decision Fusion Module (BPDFM) for robust score fusion and mode switching. Experiments on three representative embodied navigation benchmarks (HM3D-OVON, SG3D, and GOAT-Bench) demonstrate that AMCoNav consistently improves success rates while maintaining real-time execution. The most significant gain appears on HM3DOVON, where AMCoNav achieves an average 8.7% gain over prior stateof-the-art results, with 60.0%/60.0%/53.3% success rate (SR) on Val Seen/Val Seen Synonyms/Val Unseen. Ablation results show that asynchronous guidance from MSC and ZLMM and the BPDFM both contribute to performance improvement, and their combination yields the best overall results. These results show that asynchronous multi-module collaboration can improve navigation capability without sacrificing realtime responsiveness.

Abstract:
Constructing world models for AI agents in visual environ-ments has long been a significant challenge, chiefly due to the di!cultyof achieving precise alignment of the dynamics between the world modeland the environment, given noisy pixel-level observations. Existing worldmodels solely rely on video or image generation, which are exceedinglycomplex and expensive to train, yet still su"er from hallucinations andviolations of basic rules due to misalignment. In this paper, we show thata few rules su!ce to convert a pretrained large language model (LLM)to an accurate world model for a specific visual environment. We pro-pose a training-free approach to e!ciently learn these complementaryrules from multimodal agent trajectories collected in the visual environ-ment. In particular, we convert the multimodal input into structuredtext and extract symbolic rules using an LLM by comparing agents’ truetrajectories with world model predictions if they have conflicts. We usethis neurosymbolic approach to induce, update, and prune rules dur-ing exploration, resulting in a precise world model. It allows us to builda novel model-predictive control (MPC) agent, “WALL-EVE”, with-out training any policy. Before taking each action, WALL-EVE utilizesan LLM as a look-ahead optimizer to determine the next k-step actionsthrough interactions with our world model. On challenging tasks in ALF-World and Minecraft, WALL-EVE achieves higher success rates thanexisting methods, while reducing inference times and the number of to-kens required for reasoning. In ALFWorld, WALL-EVE surpasses thestate-of-the-art method RAFA with only 17% of RAFA’s token usage.In Minecraft, WALL-EVE outperforms baselines by 8-30% in successrate but costs 8–20 fewer replanning rounds and only 60–85% tokens.Code is available here.

Abstract:
Human demonstrations provide strong priors for robot ma-nipulation, yet it is non-trivial to transfer them to execute on real robotsdue to the kinematic gap. In dexterous manipulation, it remains chal-lenging to track long-horizon, contact-rich sequences even in simulators:a reference-tracking policy must keep objects on their target trajectorieswhile preserving demonstrated joint motion and contact timing. Exist-ing approaches often rely on hand-crafted reward tuning that requireper-sequence tuning and break under limited interaction budgets. We in-troduce ConTrack, a reinforcement learning (RL) framework that scaleswith tracking data. ConTrack treats object tracking as a constraint andallocates remaining control authority to motion fidelity, which allows itto adapt task–style trade-offs online using a dual-variable update. In ad-dition, ConTrack also stabilizes long-horizon learning with an adaptivemid-trajectory reset library that reuses policy-reachable simulator states.Our qualitative and quantitative results in simulation tracking and realrobot demonstrate that ConTrack improves success and object pose ac-curacy significantly over prior arts while preserving joint and contactfidelity. Website: https://www.lyt0112.com/projects/ConTrack.

Abstract:
Humans achieve stable and adaptive grasps by seamlessly integrating visual perception and tactile feedback, a capability that remains challenging to replicate in robotic systems. Existing robotic grasping approaches predominantly rely on visual inputs and lack mechanisms for tactile-guided adaptation after contact, limiting robustness and generalization. To address this challenge, we propose a uni(cid:28)ed visuo-tactilefusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive re(cid:28)nement. At its core, our method introduces an e(cid:30)cient visuo-tactile representation that tightly fuses object geometry with tactile feedback by associating tactile signals with (cid:28)nger identities. This uni(cid:28)ed representation supports contact-aware grasp pose generation during planning and tactile-guided re(cid:28)nement after contact, enabling the system to reason about (cid:28)ne-grained (cid:28)nger-object interactions and adjust grasps dynamically. Comprehensive experiments in both simulation and real-world environments demonstrate that our approach signi(cid:28)cantly enhances grasp success rates and generalization across diverse objects. • •

Abstract:
Pruning visual foundation models has attracted considerableattention. However, existing methods focus on rigid point-to-point tokenalignment on a single dataset for pruning, suffering from two limitations:i) robustness degradation, and ii) task-specificity deficiency. To addressthese limitations, we propose a task-specific pruning pipeline, namedCut-ViT. Specifically, we first construct gram anchoring matrices fromboth spatial and semantic perspectives, and perform the subspace de-composition to extract the corresponding subspace bases. Basis-agnosticand residual constraints are then adopted to align the gram subspaces be-tween the native and pruned DINOv3 models along spatial and channeldimensions, enabling subnetworks to inherit robust feature representa-tions of native DINOv3. Furthermore, we design spectral entropy adapta-tion, which quantifies the information density of feature manifolds alongspatial and channel dimensions, thereby adapting the pruning objectiveto specific downstream tasks. Experiments show that Cut-ViT requiresapproximately one minute on a single A100 GPU to obtain subnetworksat various sparsity levels, using only 20.9% of the time and 45.5% of theGPU memory compared with previous methods, while achieving SOTAperformance on six tasks across nine datasets.

Abstract:
In this work, we introduce FSU-QA, a VQA dataset for au-tonomous driving scenarios designed to advance research on ForesightIntelligence—the ability to anticipate and reason about complex, long-horizon futures. Unlike existing benchmarks that mainly focus on imme-diate perception or reactive planning, FSU-QA evaluates future-orienteddriving understanding through multi-agent-aware and rule-grounded coun-terfactual QA. Rather than holding surrounding agents fixed or target-ing geometric path generation, our benchmark requires models to infersemantic future outcomes from front-view historical observations andpast ego trajectories. A comprehensive evaluation on the accompanyingFSU-Bench reveals that state-of-the-art VLMs still face significant chal-lenges in anticipating future events. Furthermore, beyond model perfor-mance, we examine whether WM-generated predictions remain seman-tically consistent by using VLM-based proxy judges, and validate thisevaluation protocol through shuffled control experiments. Fine-tuningmodels on FSU-QA leads to substantial improvements in foresight under-standing, demonstrating the dataset’s effectiveness and offering a prin-cipled foundation for future research.

Abstract:
Rapid progress in embodied AI is bringing robots closer to performing complex tasks in real-world environments. In these settings, robots must operate over long time horizons, making decisions based on experiences collected over hours or even days. Vision-language models (VLMs) have recently shown strong capabilities in planning and control, making them promising high-level controllers for embodied agents. However, current VLMs can process only a limited number of images at once, highlighting the need for more efficient mechanisms to manage long-term memory in embodied contexts. To meaningfully evaluate these models for long-horizon control, a benchmark must target scenarios where memory is essential. Existing long-video QA benchmarks neglect embodied challenges like object manipulation and navigation, which require lowlevel skills and fine-grained reasoning over past interactions. Moreover, effective memory integration in embodied agents involves both recalling relevant historical information and executing actions based on that information, making it essential to study these aspects together. In this work, we introduce FindingDory, a new benchmark for long-range embodied tasks in the Habitat simulator. FindingDory evaluates memorycentric capabilities across 60 tasks requiring sustained engagement and contextual awareness in an environment. The tasks can also be procedurally extended to longer and more challenging versions, enabling scalable evaluation of memory and reasoning. We further present baselines that integrate state-of-the-art closed-source and fine-tuned open-source VLMs with low-level navigation policies, assessing their performance on these memory-intensive tasks and highlighting key areas for improvement.

Abstract:
Bridging abstract semantics and precise physical control re-mains a fundamental challenge in open-world robotic manipulation. Whilerecent data-driven policies show promise, their reliance on isolated con-tact points or latent affordance embeddings lacks the rigorous kinematicconstraints necessary for complex articulated objects.To overcome thelimitation, we introduce RelAfford6D, a novel training-free frameworkcentered on a Relational 6D Affordance Graph. Given a free-form in-struction, our system deduces a semantic topology linking a primary in-teracting part to its physical anchor. By elevating these topological nodesinto precise metric SE(3) poses via vision foundation models, we analyti-cally formulate downstream execution as a kinematic constraint satisfac-tion problem. The robot synthesizes continuous trajectories by trackingstrictly defined physical manifolds (e.g., revolute or prismatic orbits).Coupled with a closed-loop tracking mechanism for dynamic replanningagainst disturbances, our physically grounded approach achieves supe-rior zero-shot success rates, cross-category generalization and executionrobustness in both simulation and the real world environments, outper-forming existing data-driven baselines.

Abstract:
Conventional visual navigation policies often struggle withmyopic decision-making and mode collapse in complex environments.While world models offer a promising alternative, existing paradigms typ-ically isolate perception, generation, and control, failing to capture theirshared spatio-temporal dynamics. In this paper, we propose NavWM, aunified navigation world model that seamlessly integrates latent worldreasoning, multimodal action prediction, and controllable visual gener-ation. At its core, NavWM leverages latent world tokens to distill geo-metric and semantic priors, endowing the agent with robust structuralunderstanding. To overcome the limitations of deterministic policies, weintroduce an anchor-based multimodal trajectory forecasting frameworkthat generates a diverse action space. This inherent diversity explicitlyempowers the generative world model to act as a robust closed-loop plan-ner, utilizing visual foresight to evaluate and select the optimal path.Extensive experiments across diverse robotics datasets demonstrate thatNavWM significantly advances the state-of-the-art, delivering remark-able improvements in both high-fidelity future state generation and zero-shot navigation success.

Abstract:
Imitation Learning (IL) offers an effective approach for robotmanipulation by learning a mapping from visual inputs to actions. How-ever, this paradigm suffers from poor robustness under visual domainshifts (e.g., lighting conditions, camera viewpoints, etc.), often failingto perform manipulation. Our key insight in tackling this problem is toconstruct a Structure Map encoding the fine-grained object structuresthat remain invariant across visual domains and vital for manipulation.Based on this insight, we propose StructPolicy, a method elaboratelydesigned to incorporate the domain-invariant Structure Map into the ILpolicy through two modules: StructCon and StructEncoder. StructCon isan automated Structure Map construction module that leverages struc-ture primitives to enable flexible transformation and composition to forma wide range of objects. StructEncoder is a hierarchical network thatefficiently captures structural relationships and affordance from Struc-ture Map. This provides the IL policy with both domain-invariant andstructurally-aware features, guiding it toward robust manipulation un-der visual domain shifts. We extensively evaluate StructPolicy across 49manipulation tasks in multiple benchmarks and diverse real-world tasksunder various visual changes. The results demonstrate consistent andsignificant performance improvements across all tasks, validating thatStructPolicy enhances the effectiveness and robustness against visual do-main shifts of the IL policy, improving manipulation accuracy.

Abstract:
World models have demonstrated significant promise for data synthesis in autonomous driving. However, existing methods predominantly concentrate on single-modality generation, typically focusing on either multi-camera video or LiDAR sequence synthesis. In this paper, we propose UniDriveDreamer, a single-stage unified multimodal world model for autonomous driving, which directly generates multimodal future observations without relying on intermediate representations or cascaded modules. Our framework introduces a LiDAR-specific variational autoencoder (VAE) designed to encode input LiDAR sequences, alongside a video VAE for multi-camera images. To ensure cross-modal compatibility and training stability, we propose Unified Latent Anchoring (ULA), which explicitly aligns the latent distributions of the two modalities. The aligned features are fused and processed by a diffusion transformer that jointly models their geometric correspondence and temporal evolution. Additionally, structured scene layout information is projected per modality as a conditioning signal to guide the synthesis. Extensive experiments demonstrate that UniDriveDreamer outperforms previous state-of-the-art methods in both video and LiDAR generation, while also yielding measurable improvements in downstream driving tasks.

Abstract:
Dense 3D semantic occupancy perception is critical for mo-bile robots operating in pedestrian-rich environments, yet it remains un-derexplored compared to its application in autonomous driving. To ad-dress this gap, we present MobileOcc, a semantic occupancy dataset formobile robots operating in crowded human environments. Our dataset isbuilt using an annotation pipeline that incorporates static object occu-pancy annotations and a novel mesh optimization framework explicitlydesigned for human occupancy modeling. It reconstructs deformable hu-man geometry from 2D images, then refines and optimizes it using as-sociated LiDAR point data. Using MobileOcc, we establish benchmarksfor two tasks: i) Occupancy prediction and ii) Pedestrian velocity predic-tion, using different methods, including monocular, stereo, and panopticoccupancy, with metrics and baseline implementations for reproduciblecomparison. Beyond occupancy prediction, we further assess our annota-tion method on 3D human pose estimation datasets. Results demonstratethat our method exhibits robust performance across different datasets.Our code and dataset are released at https://autonomousrobots.nl/paper_websites/mobileocc

Abstract:
In this paper, we propose GTA-VLA (Guide, Think,Act), an interactive Vision-Language-Action (VLA) framework thatenables spatially steerable embodied reasoning by allowing users to guiderobot policies with explicit visual cues. Existing VLA models learn adirect "Sense-to-Act" mapping from multimodal observations to robotactions. While effective within the training distribution, such tightly cou-pled policies are brittle under out-of-domain (OOD) shifts and difficultto correct when failures occur. Although recent embodied Chain-of-Thought (CoT) approaches expose intermediate reasoning, they stilllack a mechanism for incorporating human spatial guidance, limitingtheir ability to resolve visual ambiguities or recover from mistakes. Toaddress this gap, our framework allows users to optionally guide thepolicy with spatial priors, such as affordance points, boxes, and traces,which the subsequent reasoning process can directly condition on. Basedon these inputs, the model generates a unified spatial-visual Chain-of-Thought that integrates external guidance with internal task planning,aligning human visual intent with autonomous decision-making. Forpractical deployment, we further couple the reasoning module with alightweight reactive action head for efficient action execution. Extensiveexperiments demonstrate the effectiveness of our approach. On thein-domain SimplerEnv WidowX benchmark, our framework achievesa state-of-the-art 81.2% success rate. Under OOD visual shifts andspatial ambiguities, a single visual interaction substantially improvestask success over existing methods, highlighting the value of interactivereasoning for failure recovery in embodied control.

Abstract:
Intelligent cabin is gradually evolving from passive voicebased interfaces into multimodal collaborative systems supported by multi-camera and multi-sensor setups, where spatial reasoning becomes a core capability for enabling proactive perceptual interaction. Unlike traditional single-scene spatial tasks, cabin environments involve both interior and exterior vehicle regions, multiple occupants and traffic participants, and inherently require cross-view observations. To fill this research gap, we introduce CabinSI, a benchmark for evaluating spatial intelligence in cross-cabin environments, consisting of two components: RelCabin for relational reasoning tasks and RefCabin for spatial referring localization tasks. The benchmark is built upon real-world captured multi-view cabin data and systematically covers in-cabin, out-of-cabin, and cross-cabin scenarios under single-view, multi-view, and cross-cabinview settings. Furthermore, we propose a cognitive-map-based framework that projects multi-view observations onto a normalized top-down plane and constructs an explicit spatial graph as model input, allowing multimodal large language models to focus on structured reasoning. Experimental results demonstrate that such explicit spatial representation significantly improves the stability of cross-view reasoning in MLLMs. Data and code is available at CabinSI.

Abstract:
Building robust world models for visual reinforcement learn-ing remains challenging under complex visual conditions. When facedwith task-irrelevant visual distractions, the representations learned byworld models tend to collapse. Pre-trained vision models (PVMs) providestrong visual priors and structured representations, yet those learned forvisual perception are incompatible with dynamic control in RL. In thiswork, we present Vision-Priors-Aligned World Model (VPA-WM1 ): aunified framework that organically integrates PVMs and world models toachieve robust visual control. We first introduce Control-Oriented Align-ment, which effectively guides PVMs to focus on control-relevant signalsthrough two auxiliary objectives: instant action prediction and short-horizon return estimation. We then design Spatio-Temporal Collabora-tive Integration mechanism, which guides the world model to adaptivelyattend to the visual cues of PVMs through two complementary designs(spatially grounded guidance and experience query integration), therebylearning representations that are visually robust and dynamically con-sistent. Superior performance on visually-distracted control benchmarksdemonstrates that VPA-WM effectively bridges the semantic gap be-tween visual perception and dynamic control, and successfully leveragesvisual priors to enhance the robust control of world models.

Abstract:
Visual Active Tracking (VAT) requires an embodied agent tocontrol cameras to follow a designated target, which is essential for ap-plications like robot navigation and security monitoring. However, trans-ferring VAT trackers to the real world faces two primary bottlenecks: (1)severe visual domain shifts between simulated environments and the realworld, leading to performance collapse during deployment; and (2) tar-get distribution shift caused by similar-looking distractors in real-worldscenes. To address these, we propose VATA, a novel test time adaptationmethod with two complementary strategies. First, we introduce a CriticValue Maximization strategy that leverages value signals from a criticmodel to filter reliable test samples, enabling unsupervised model up-dates to mitigate visual shift. Second, we propose an Action Smooth-ness Regularization term that exploits the continuity of real-worldtarget motion to bound action variations, correcting irrational actionsunder target shift. Experiments demonstrate VATA achieves a 24.4%SR improvement on the EVT-Benchmark and reaches 80% TSR in 10real-world scenarios (vs. 30% by the SOTA TTA method), providing arobust and plug-and-play solution for VAT deployment.

Abstract:
We study action-conditioned world modeling as a scalableway to learn transferable dynamics priors for robot learning. By pre-training a model to predict how actions drive visual scene evolution,the resulting world model captures reusable interaction dynamics be-yond appearance-level video generation. Concretely, we pretrain a multi-view interactive base diffusion world model, A2World, on large-scalerobot manipulation data with real action annotations. We validate thelearned dynamics priors from two complementary perspectives. First,we adapt A2World into a task- or scene-specialized real-world simula-tor, A2World-sim, whose long-horizon rollouts support simulator-basedpolicy evaluation and scalable what-if analysis by replacing real-robotrollouts with world model rollouts. Second, starting from the same pre-trained weights, we adapt A2World into a video-action joint predictionmodel, A2World-policy, that predicts actions under visual and instruc-tion conditioning. Experiments across simulation benchmarks and real-robot settings demonstrate that action-conditioned world model pre-training yields transferable dynamics priors that benefit both simulator-centric and policy-centric robot learning.

Abstract:
Visual grounding (VG) localizes target objects in an imagefrom natural-language expressions. In real-world perception, RGB cuesoften degrade under low illumination and adverse weather, making visualgrounding substantially more challenging. However, existing VG bench-marks are largely RGB-only and provide limited, structured coverage ofsuch conditions, hindering systematic robustness evaluation and cross-spectral comparison. We present RGBT-GroundBench, the first large-scale benchmark for RGB-Thermal (TIR) visual grounding in complexenvironments. It contains over 40K images (21,535 RGB-TIR pairs) and38,760 object instances with referring expressions, bounding boxes, andfine-grained annotations at three levels: scene types, environmental con-ditions (illumination and weather), and object properties (size and oc-clusion). As a benchmark suite, RGBT-GroundBench provides not onlycurated RGB-TIR grounding annotations but also a unified evaluationprotocol supporting RGB-only, TIR-only, and RGB+TIR inputs. Underthis protocol, we benchmark 11 representative VG models across diversescenes and environmental conditions. Our results show that groundingaccuracy is strongly correlated with scene complexity, LoRA-based mod-els are more robust in complex scenes, and low-illumination conditionscause significant performance degradation that has been rarely explored.Guided by these observations, we introduce RGBT-VGNet, a simpleand reproducible reference baseline under the unified protocol, featuringAsymmetric Modality Adaptation, Language-Aware Visual Synergy, andTri-Prior Fusion for reliability-aware RGB-TIR integration. Resources,annotations, code, checkpoints, and evaluation scripts have been publiclyreleased.

Abstract:
Human demonstrations are widely considered the corner-stone of end-to-end (E2E) autonomous driving despite human demon-stration’s scarcity for long-tail and safety-critical scenarios. Nonetheless,current E2E autonomous driving (AD) training paradigms continue torely on human demonstrations. Imitation learning (IL) requires humandemonstrations for training, whereas reinforcement learning (RL) hasemerged as a promising alternative to reduce this dependency. How-ever, most existing RL methods for E2E AD still rely implicitly on hu-man demonstrations. A pure rewards-based RL method can overcomethe need for human demonstrations, but general RL policy gradientmethods suffer from the cold-start problem. In this paper, we proposeZTRS (Zero-human demonstration end-to-end autonomous driving withTRajectory Scorer) — a complete RL-based E2E planning paradigmtrained solely on real-world images and rule-based rewards, entirely with-out human demonstration. Through our proposed Exhaustive PolicyOptimization (EPO), a policy gradient variant tailored for enumer-able trajectory actions and dense supervision, ZTRS enables the modelto generalize better to long-tail driving scenarios. We demonstrate thisgeneralization through our SOTA performance against IL approaches onboth long-tail Navhard and closed-loop HUGSIM datasets. Project page:https://zhenxinli.net/ZTRS/.

Abstract:
Promptable foundation segmentation models exhibit impres-sive capabilities across diverse tasks, yet their performance deterioratessubstantially on medical images due to domain and sub-domain shifts.Online test-time adaptation (OTTA) helps alleviate these shifts, but ex-isting methods remain limited in effectiveness and efficiency. To thisend, we introduce Semantic-Preserving Dual-Perturbation Adaptation(SPDA), a resource-efficient OTTA framework tailored for promptablemedical segmentation. SPDA constructs diverse and robust learning sig-nals by enforcing feature and prediction consistency across two semantic-preserving perturbations: (i) a domain-adaptive low-frequency radial-spectrum perturbation that simulates task-irrelevant intra-sub-domainvariations without out-of-domain distortions, and (ii) a prompt under-sampling perturbation that reduces prompt density while preservingtarget semantics. These consistency constraints drive the optimizationof a lightweight post-encoder adapter, facilitating efficient adaptationwith minimal computational overhead. Extensive experiments on varied2D/3D medical data demonstrate that SPDA consistently outperformsstate-of-the-art OTTA baselines under box and point prompts, whileusing substantially fewer resources. Ablation studies validate each com-ponent and indicate the suitability for real-time clinical deployment.

Abstract:
Class-incremental learning (CIL) aims to continually acquirenew classes while preserving previously learned knowledge, posing thewell-known stability–plasticity dilemma. With the emergence of largepre-trained models, parameter-efficient fine-tuning methods such as LoRAhave become a practical paradigm for rehearsal-free CIL by restrictingadaptation to low-rank weight updates. Recent LoRA-based approachesoften mitigate cross-task interference by enforcing orthogonality betweentask updates. However, under strict rank budgets and long task se-quences, uniformly protecting previously used directions can becomeoverly conservative, progressively shrinking the effective update spaceand limiting plasticity. To address this limitation, we propose Varia-tional Direction-aware LoRA (VD-LoRA), a Bayesian framework thatenables adaptive consolidation within the low-rank update space. VD-LoRA maintains a recursive variational posterior over LoRA directionsto estimate direction-wise uncertainty, using posterior precision to adap-tively control protection strength. Based on this uncertainty signal, wefurther design a direction selection mechanism that favors updates thatare both task-relevant and safe to adapt. This enables selective reuseof low-risk directions while preserving critical ones, maintaining a task-aligned update subspace under fixed rank constraints. Extensive experi-ments on multiple rehearsal-free CIL benchmarks demonstrate that VD-LoRA consistently outperforms existing LoRA-based methods while im-proving long-horizon stability and plasticity. Our code is available at

Abstract:
The impact of real-world noise on Open Vocabulary ObjectDetectors (OV-ODs) remains poorly understood due to their architec-tural complexity. We present our comprehensive analysis, Robust Onion,an empirical study that uses controlled synthetic visual degradations topeel OV-ODs layer-by-layer, revealing how, why, and where robustnessdegrades, systematically analyzing feature collapse. Our findings revealthat models with similar vision backbones exhibit comparable robust-ness, driven by similar feature collapse at similar layers, while factorssuch as pretraining strategy, architectural nuances, and caption supervi-sion contribute little. Robustness is primarily governed by the image do-main rather than annotations, explaining the similar robustness impacton COCO and LVIS, and why datasets like ODinW-13 can give an im-pression of inflated robustness due to large, isolated objects. Finally, wevalidate our insights by improving robustness on real-world BDD-100K,WiderFace, and VisDRONE via our lightweight plug-and-play NN &TK0 approach, using 96× fewer trainable parameters than end-to-endtraining. We also explain the prior works’ robustness observations.

Abstract:
Sparse Mixture-of-Experts (MoE) offers an attractive pathto scaling discrete diffusion vision–language models (dVLMs), but wefind that naïvely combining MoE with multimodal diffusion training canfail catastrophically. Specifically, MoE-dVLMs suffer from expert selec-tion collapse on visual tokens, where most image tokens are routed toonly a few experts and overall multimodal performance degrades sharply.We trace this failure to an upstream geometric mismatch at the modalityboundary rather than router optimization. Visual embeddings producedby deep Pre-Norm vision encoders have much larger norms than text em-beddings; when injected into a Pre-Norm language tower, the residualdynamics cause attention drowning and representational inertia, yield-ing highly concentrated, low-rank visual representations that are difficultfor the router to discriminate. To resolve this issue, we propose Tower-Align, a lightweight post-projector modality-wise norm alignment thatglobally rescales visual tokens to match text norm statistics while pre-serving their relative structure. TowerAlign stabilizes MoE routing andimproves multimodal accuracy with negligible overhead. On a practicalMoE-dVLM built from LLaDA-MoE-7B-A1B with a SigLIP2-SO400M-Patch14-384 vision encoder, TowerAlign delivers consistent gains acrossdiverse benchmarks and largely closes the gap to dense diffusion baselinesunder matched data budgets.

Abstract:
Accurate motion prediction of surrounding agents and safemotion planning are two closely coupled key tasks for social robot nav-igation in crowded environments. Deploying these systems on resource-constrained edge devices necessitates compact, unified models that canperform both tasks simultaneously. However, within these compact sharedencoders, recent unified models often overlook severe representationalconflicts that arise from the distinct objectives of predicting neighbor be-haviors versus ego-centric safety planning. To address this issue, we firstidentify the Skill Conflict—a phenomenon where overlapping parameterassignments cause distinct tasks to compete for the same weights, pre-venting the model from fully specializing in individual skills. To resolvethis, we propose a novel model-merging-based framework, Disjoint Pa-rameter Training (DPT). DPT mitigates performance degradationcaused by Skill Conflict through distributed parameter learning, whichseparates the key parameter regions of each task while preserving theircore capabilities prior to merging. In addition, we observe that sparsemerging, which selectively integrates only the most influential param-eters for each task rather than combining all task-specific parameters,yields optimal performance by preventing interference among adjacentfeatures and concentrating representational capacity. DPT can be ap-plied in parallel with a variety of merging methods. Evaluated on stan-dard crowd navigation benchmarks (JRDB and JTA), our frameworkdemonstrates superior performance, validating its versatility and effec-tiveness for safe, resource-efficient robot navigation. The project page isavailable at: https://dpt2026.github.io/

Abstract:
Psychiatric diagnosis is moving from subjective clinical ob-servation toward objective multimodal assessment using audio, visual,and textual signals. Federated learning offers a natural framework formulti-center collaboration without centralizing sensitive patient data,but psychiatric data introduce a form of non-IID heterogeneity thatis not captured by standard label or domain skew: patients with thesame diagnosis can exhibit highly polymorphic manifestations, form-ing multi-peak within-class distributions. Existing prototype-based FLmethods usually aggregate client or class features into coarse centroids,which can collapse distinct symptom modes into semantically ambigu-ous prototypes. We propose FedMental, a topology-aware federatedprototype framework for multi-center multimodal psychiatry. FedMen-tal first preserves subsampled sample-level embeddings before prema-ture client-mean aggregation, then applies Topology-Aware Proto-type Refinement (TAPR) on the server to discover class-view la-tent modes through parameter-free clustering. On the client side, Intra-View Mode Alignment (IVMA) softly anchors local representationsto matched mode prototypes, while Cross-View Semantic Contrast(CVSC) promotes view-invariant semantics across modalities and cen-ters. Experiments on the real-world Psycare psychiatric benchmark andtwo standard heterogeneous FL benchmarks show that FedMental consis-tently improves over strong FL baselines, achieving +8.83% over FedAvgon Psycare, +11.42% on Office-Caltech, and +4.41% on Digits.

Abstract:
With an increasing number of Object Detection (OD) modelsbeing deployed on edge devices, Zero-Shot Quantization for OD (ZSQ-OD) aims to quantize these models when access to the original trainingdata is prohibited. Existing research on Zero-Shot Quantization-AwareTraining (QAT) for OD synthesizes training sets through noise optimiza-tion. However, this approach struggles to maintain performance in low-bit regions. In this paper, we introduce GoodQ (Generative off-the-shelfmodels for object detector Quantization), a QAT pipeline that utilizesoff-the-shelf generative models to construct a training set. We first iden-tify three challenges that arise when introducing a generative model tothe ZSQ-OD task: 1) each image contains dense information with mul-tiple instances, 2) the class-wise distribution in the original dataset isimbalanced, and 3) the pseudo-labels assigned to the generated imagescan potentially act as noisy signals during QAT. GoodQ addresses thesechallenges by 1) introducing an Information-Dense Prompting strategyto generate multi-instance images, 2) applying Intrinsic Distribution-Aware Selection to match the pretrained class distribution, and 3) em-ploying Teacher-guided Adaptive Noise Reduction to mitigate noise aris-ing from the QAT process. Our framework achieves state-of-the-art per-formance in low-bit ZSQ (W4A4) and extends quantization to extremebit-widths (W3A3). Furthermore, we conduct an extensive analysis touncover the underlying factors contributing to the efficacy of GoodQ.The constructed dataset is available at GoodQ.

Abstract:
Vision-Language Models (VLMs) enable autonomous GUInavigation, but agents still struggle to process and learn from dense,continuous visual histories. This bottleneck hinders both immediate errorcorrection within a single episode (intra-trial) and experience distilla-tion across multiple attempts (cross-trial). We trace these challenges toan empirical informational asymmetry in GUI navigation: while expectedtransitions can often be compressed into lightweight textual summaries,unexpected outcomes benefit from preserved screenshots as causal evi-dence for accurate diagnosis. Building on this insight, we propose An-chorGUI, a unified framework driven by the Cognitive State Anchor(CSA). The CSA acts as a per-step primitive that actively comparesexpected and observed transitions, converting passive multimodal tra-jectories into explicit prediction-error signals. These signals orchestratea dual-scale learning mechanism via an asymmetric memory. Forintra-trial correction, a sliding window selectively retains visual evidencefor detected mismatches, providing immediate, visually-grounded feed-back. For cross-trial distillation, this asymmetric memory focuses thecomputationally expensive credit assignment search space on likely fail-ure steps. Experiments across four benchmarks validate the effectivenessof our approach. On AndroidWorld, AnchorGUI achieves a 57.3% successrate with a 2.4× token reduction per step. Furthermore, cross-trial distil-lation reaches 69.2% success (+11.9% gain), significantly outperformingstandard reflection methods while maintaining sub-linear context scaling.

Abstract:
Heterogeneity in data distributions and model architectures poses significant challenges in federated learning (FL). While various heterogeneous FL (HtFL) approaches have been proposed to address these challenges, prototype-based FL (PBFL) has emerged as a promising framework that exchanges prototypes—per-class mean activations from the penultimate layer—rather than model parameters. However, existing PBFL methods suffer from suboptimal prototype separation in the feature space, which limits their discriminative capacity. We propose Prototype Normalization (ProtoNorm), a novel PBFL framework that systematically addresses this limitation. Inspired by the Thomson problem in classical physics, ProtoNorm optimizes global prototype configurations on a unit hypersphere, maximizing separation between class prototypes. Extensive experimental evaluation demonstrates that ProtoNorm consistently outperforms existing HtFL methods across diverse heterogeneous settings. Importantly, ProtoNorm maintains communication efficiency while operating entirely on the server side with less computation than existing PBFL methods, making it well-suited for resource-constrained environments.

Abstract:
Split inference (SI) offloads computation from edge devicesand is often considered privacy-friendly since raw inputs remain local.However, recent data reconstruction attacks (DRA) show that interme-diate features can be inverted to recover sensitive content. Existing de-fenses suppress feature information through pruning, noise injection, ordecorrelation, yet strong reconstructions persist. In this paper, we ar-gue that reconstruction vulnerability is not solely determined by mutualinformation between inputs and representations, but also by the localorganization of representations that facilitates reliable inversion in prac-tice. Therefore, we propose ReTarget, a lightweight transformation thatpreserves task-discriminative representations while perturbing inversion-consistent features. ReTarget combines adversarial reconstruction, taskpreservation, and semantic regularization training to substantially de-grade inversion performance in practice without sacrificing utility. Ex-periments on CLIP-ViTB/16, CLIP-RN50 and DINOv2 across multiplesplit points and datasets show substantial degradation in reconstructionquality. Under both state-of-the-art and adaptive attacks, particularlyat shallow split points where prior defenses fail, our method remainseffective, where achieve up to 35% improvement while maintaining com-petitive task accuracy.

Abstract:
Vision-language alignment—the stage that bridges pretrainedvision encoders and large language models—is widely treated as a formof pretraining requiring full-parameter updates. We challenge this viewand investigate what happens when low-rank adaptation is applied to theLLM during this stage instead. We find that low-rank alignment not onlyreduces computational costs but also outperforms full-parameter align-ment on most benchmarks. To understand this phenomenon, we system-atically characterize the implicit biases introduced by low-rank adap-tation during alignment. Empirically, we find that low-rank alignmentshifts model behavior from hallucinatory to conservative and preservesper-token linear separability of visual features that full-parameter align-ment disrupts—a phenomenon we term LS-curse. Geometrically, low-rank aligned models exhibit more homogeneous and structurally stablevisual representations, maintaining modality-specific knowledge ratherthan prematurely fusing entity-level semantics. Theoretically, we estab-lish two theorems showing that low-rank alignment induces preferencesfor parameter subspaces with flat gradients and feature subspaces robustto perturbations, providing a principled explanation for the observedstructure-preserving behavior. Extensive experiments cover ablation over100 alignment configurations, three families of low-rank operators, andvarious rank, encoder, and other settings.

Abstract:
On-policy exploration is a crucial component for trainingrobust Vision-Language Navigation (VLN) agents, as it exposes the pol-icy to a broader state distribution. However, such exploration inevitablyleads to trajectories that deviate from expert demonstrations, resulting ina semantic mismatch between the executed visual stream and the originallanguage instruction. In this work, we address this challenge by introduc-ing Φ-Nav, a unified on-policy framework that leverages hindsight rea-soning to align instructions with the agent’s actual exploratory journey.Specifically, Φ-Nav operates through a three-stage dual-supervision cy-cle: 1) the agent performs oracle-guided on-policy exploration, samplinga trajectory while learning from expert action feedback, 2) a hindsightspeaker synthesizes a path-level hindsight instruction grounded in thecollected visual observations, and 3) the agent conducts a second imita-tion pass, treating the synthesized trajectory–instruction pair as an ad-ditional expert demonstration. Through this process, Φ-Nav bridges thecritical semantic supervision gap inherent in on-policy methods, trans-forming semantically unlabeled movement into dense training signals.Evaluations on the R2R-CE and RxR-CE benchmarks show that Φ-Navyields competitive performance while requiring only a fraction of the ex-pert demonstrations used by current baselines. These results underscorethe necessity of semantic exploration in VLN, positioning Φ-Nav as aeffective solution for training embodied agents with limited data.

Abstract:
Recent studies have revealed critical vulnerabilities in Large Vision-Language Models (LVLMs) to sophisticated adversarial attacks that induce the model into generating adversary-specific content. However, existing LVLM-oriented detectors largely rely on external judging pipelines that incur substantial computational overhead, precluding their use in real-time scenarios. Towards this end, we propose PTI (PrefillTime Intervention), a method that probes an LVLM’s internal states to detect adversarial images before any response token is generated. PTI combines two complementary modules trained only on benign data: (i) a masked reconstruction module in the vision tower that identifies visual anomalies through token-wise reconstruction errors, and (ii) a dynamic modeling module in the language model backbone that tracks layer-wise trajectories of salient tokens to detect semantic inconsistencies. We establish a comprehensive evaluation framework covering both black-box and white-box attacks, including L -bounded, unrestricted, p and patch-based perturbations. Extensive experiments demonstrate that PTI achieves state-of-the-art detection performance and robust generalization across diverse threat models. Crucially, its minimal inference overhead paves the way for secure, real-time LVLM applications.

Abstract:
Multi-oriented text is ubiquitous in real-world scenes and remains a major challenge for scene text recognition (STR). Existing rotation-aware methods explicitly estimate text orientation. However, due to the lack of theoretical guarantees, they are prone to error accumulation, increased computational cost, and strong reliance on data. In this work, we incorporate rotation invariance into the STR framework to address these limitations. Specifically, we adopt an encoder– decoder architecture, embedding rotation equivariance in the encoder and rotation invariance in the decoder to construct a fully rotationinvariant network. On the decoder side, we first identify and prove the rotation-invariant property of the cross-attention mechanism and use it to formulate a rotation-invariant text decoder that maps visual features to output text in a rotation-invariant manner. On the encoder side, we propose a rotation-equivariant local–global extraction network that integrates deep equivariant convolutions with self-attention, enabling rotation-equivariant feature extraction while modeling inter-character dependencies and preserving fine-grained visual details. By integrating the encoder and decoder, we obtain an end-to-end Rotation-Invariant Scene TExt Recognition network (RISTER). RISTER provides rotation invariance with theoretical guarantees, enhancing robustness on multioriented samples without introducing additional inference computation or relying on data-driven orientation correction. Experiments show that RISTER achieves state-of-the-art performance on both standard and multi-oriented benchmarks, surpassing the second-best model by 4.0% in accuracy on the general multi-oriented dataset.

Abstract:
Generating temporally coherent 4D content from monocularvideo is a challenging yet practically important problem. Recent advancesin layout-aware 3D generation have enabled producing objects with theirspatial arrangements from a single image, naturally providing scene-levelunderstanding that is valuable for 4D generation. However, applyingsuch single-image models independently to each video frame yields severetemporal inconsistencies—flickering textures, structural discontinuities,and jittering poses. We present Tempo-SAM3D, a training-free frame-work that extends layout-aware 3D generation to coherent 4D generationfrom monocular video. Our approach introduces Dual-Path TemporalAttention, which maintains importance-driven KV caches in both cross-attention and self-attention to propagate visual memory and structuralmemory across frames, respectively. We further incorporate gradient-guided velocity correction that constrains the flow matching samplingtrajectory, and HexPlane-based temporal smoothing that regularizes la-tent representations via low-rank spatiotemporal decomposition. Tempo-SAM3D requires no model retraining and inherits layout awareness forspatially-grounded 4D generation. Experiments demonstrate clear im-provements over per-frame baselines and existing video-to-4D methods.

Abstract:
Deep learning models exhibit fairness concerns when pre-dictions are inadvertently influenced by sensitive attributes. However,existing attempts to make Path-Specific Counterfactual Fairness opti-mizable rely on estimating marginal potential outcome probabilities—anapproach that fundamentally requires high-dimensional conditional den-sity estimation and breaks down in modalities such as medical images,where the curse of dimensionality renders reliable estimation infeasible.To address this limitation, we reduce the problem of enforcing Path-Specific Counterfactual Fairness to a causal conditional independenceconstraint and prove that satisfying this constraint is sufficient to elim-inate the unfair causal effect. This reduction replaces intractable coun-terfactual estimation with a discriminative optimization objective thatremains scalable in high-dimensional settings.

Abstract:
Optimizing vertex deformations from video for mesh anima-tion is constrained by rendering-based reconstruction losses. While ex-isting approaches improve mesh representations, supervision signals, oranimation paradigms, their supervision remains confined to the 2D do-main. Such 2D supervision is limited: it provides no signal for occludedregions and only indirect cues for visible areas. Consequently, these meth-ods often suffer from severe shape and motion artifacts. To address thislimitation, we propose Shape Flow Guidance (SFG), a sequence of 3Dshapes derived from videos, which serves as explicit 3D supervision formesh animation. Specifically, SFG is elicited by intervening in the sam-pling process of a pretrained mesh generator in a training-free manner.We further tailor a skeletal animation model that separates local defor-mation from global transformation. This model enables SFG to guidecomplex local motion while reserving rendering-based losses for simpleglobal motion. Extensive experiments confirm that our method signifi-cantly outperforms prior work qualitatively, quantitatively, and in termsof processing speed. Qualitative results are available on our project page:https://sfgmesh.pages.dev/.

Abstract:
We study open-vocabulary 3D indoor layout generation, whichsynthesizes diverse and physically plausible indoor scenes from unla-beled 3D assets given free-form language instructions. Recent text-guidedlayout generation methods leverage large language models (LLMs) andvision-language models (VLMs) to synthesize structured scenes directlyfrom text descriptions. However, most existing approaches model inter-asset relations implicitly or rely on local pairwise constraints during lo-cal optimization. Such formulations are misaligned with the global andhighly non-convex feasible layout space, often producing locally plausiblebut globally inconsistent or physically infeasible scenes. To address theselimitations, we introduce a graph-based intermediate representation thatdecouples semantic coherence and physical feasibility, and propose ahybrid search-and-refinement strategy to generate indoor layouts withglobal semantic consistency and physical feasibility. First, we proposeGlobal Semantic Verification (GSV), which represents scenes as struc-tured scene graphs and enforces semantic constraints through rule-basedgraph verification. This explicit structural validation prunes contradic-tory configurations and produces a globally consistent semantic scaffoldfor scene generation. Second, we introduce Global Physical FeasibilitySearch (GPFS), a hybrid optimization framework that combines evo-lutionary search for global exploration with gradient-based refinementfor local exploitation. GPFS reduces dependence on VLM-proposed ini-tialization and improves robustness in highly non-convex and discon-tinuous feasible spaces. Together, GSV and GPFS shift layout genera-tion from local relational modeling and initialization-sensitive optimiza-tion toward globally consistent reasoning and exploration. Experimentsdemonstrate that our method achieves state-of-the-art performance onopen-vocabulary 3D indoor layout generation, improving both semanticconsistency and physical plausibility.

Abstract:
Recent 3D human motion generation models demonstrateremarkable reconstruction accuracy yet struggle to generalize beyondtraining distributions. This limitation arises partly from the use of pre-cise 3D supervision, which encourages models to fit fixed coordinate pat-terns instead of learning the essential 3D structure and motion–semanticcues required for robust generalization. To overcome this limitation, wepropose LaxMotion, a framework that synthesizes realistic 3D motionswithout direct 3D pose supervision. Instead of regressing toward exactcoordinates, LaxMotion learns 3D motion as a consistent explanationof global trajectories and monocular 2D kinematic cues. We introducea structured motion factorization together with a reformulated trainingparadigm under relaxed observability. This design is further supported byrelaxed regularization objectives that enforce view-consistent alignment,orientation coherence, and structural stability. Under this relaxed super-vision paradigm, LaxMotion generates diverse, temporally coherent, andsemantically aligned 3D motions, achieving performance comparable toor surpassing fully 3D-supervised methods. These results indicate thatshifting supervision from exact coordinate matching to structural consis-tency promotes stronger reasoning and improved generalization, offeringa scalable and data-efficient paradigm for 3D motion generation.

Abstract:
Population-level morphometric measurements are central toecological and evolutionary studies, but traditionally require controlledimaging or physical specimen handling, limiting their scalability. Wepresent WildProp, a framework for estimating wildlife body-proportiondistributions directly from large-scale, unconstrained image repositories.We cast morphometric estimation as a retrieval-driven correspondenceproblem: given a single user-annotated canonical image, WildProp per-forms pose-aware retrieval using foundation model features, transferspart endpoints via dense patch-level matching, filters predictions us-ing geometric consistency, and aggregates measurements across retrievedimages to estimate length-ratio distributions. Unlike supervised keypointpipelines, our approach adapts to arbitrary species and user-defined bodyparts without per-species training. Evaluations on three large morpho-metric datasets spanning birds and amphibians show median relative er-rors of 10–20%. We further demonstrate broad applicability through casestudies measuring proportions across diverse taxa, including birds, frogs,insects, and flowers. Ablations show that pose-aware retrieval is criticalfor stable estimation, while robust aggregation mitigates keypoint andpose noise. Our results suggest that carefully filtered 2D correspondencesover web-scale imagery can provide scalable morphometric proxies forcomparative analyses across taxa, geography, and seasonality.

Abstract:
WiFi Channel State Information (CSI) enablesprivacy-preserving human pose sensing in camera-denied environments,but existing WiFi-based pose estimators often fail under environmentshifts and rely on costly camera-based annotation pipelines that limitscale. We propose WiFi-JEPA, a self-supervised framework that learnsCSI-native representations by predicting masked latent embeddings in-stead of reconstructing raw CSI signals that may contain hardware-specific artifacts. WiFi-JEPA makes three contributions: (i) CSI-specifictokenization and link masking tailored to the CSI tensor over channel,time, and link (C, T, L); masking entire Tx–Rx antenna links forces themodel to predict one spatial link view from others, capturing cross-linkcorrelations informative of 3D spatial structure. (ii) A ray-tracing CSIsimulation pipeline that generates diverse unlabeled CSI from random-ized geometric primitives, providing scalable pre-training data withoutpose annotations. (iii) State-of-the-art results on Person-in-WiFi-3D:WiFi-JEPA outperforms prior WiFi-CSI baselines on both single- andmulti-person 3D pose estimation under the same evaluation protocol.We also show that simulated CSI provides complementary pre-trainingsignal to real CSI, and that four vision-native SSL objectives degrade per-formance below training from scratch, whereas WiFi-JEPA consistentlyimproves downstream pose estimation.

Abstract:
Video frame interpolation (VFI) remains a challenging task,particularly when dealing with large, non-linear motions and complexocclusions. While flow-based methods are prevalent, they often strugglewith ambiguous correspondences. Recent VFI methods based on selectiveState Space Models (SSMs) are still limited by static grid-based scanningthat misaligns with physical motion. In this paper, we propose Motion-Aligned Selective Scan (MASS), a novel framework that reformulates fea-ture scanning from static spatial grids to dynamic motion trajectories.MASS builds a feature sequence along each pixel’s flow-guided trajectoryand aggregates it with an SSM. Specifically, we introduce a learnable non-linear path integration to approximate complex curved trajectories viaresidual velocity updates, and a velocity-aware SSM that dynamicallyadjusts the sampling budget and step size based on motion magnitude.This adaptive strategy allocates denser sampling to fast-motion regionswhile keeping static regions efficient. Furthermore, the aggregated statesguide a refinement module to rectify intermediate flows and masks in anend-to-end manner. Extensive experiments indicate that MASS achieveshighly competitive overall performance on standard benchmarks, estab-lishing state-of-the-art results particularly in challenging scenarios withlarge displacements and complex dynamics.

Abstract:
The global rose cultivation industry has experienced continued expansion in recent years. There has been a set of value evaluation criteria covering multiple dimensions for roses. However, the evaluations are mainly conducted by human experts after harvesting, which is time-consuming and labor-intensive. 3D computer vision technologies are promising to automate the process before harvesting, but the lack of large-scale datasets with localization annotations that capture plant architecture hinders the progress. To bridge this gap, we propose Rosetum3D, which is a large-scale 3D vision dataset for preharvest roses. The dataset is constructed via occlusion-robust multi-view RGB-D capture protocols in commercial greenhouses. We provide fine-grained 2D localization labels using bounding boxes and botanically defined keypoints, and then obtain the 3D structures recovered through depth backprojection. Rosetum3D contains 21,114 images and 46,848 annotated rose objects. Models trained on Rosetum3D have achieved 2D/3D rose localization, which is a crucial step for automated preharvest quality grading and growth monitoring. Beyond localization, Rosetum3D serves as a benchmark for agricultural vision tasks, including 2D rose object detection, local feature matching, depth estimation, and instance reidentification. By enabling data-driven precision agriculture, Rosetum3D paves the way for robotic harvesting systems and AI-driven yield prediction in protected cultivation. The dataset are available at https: //huggingface.co/datasets/WaterMelon2333/Rosetum3D/tree/main.

Abstract:
Cross-View Geo-Localization (CVGL) is crucial for naviga-tion in GPS-restricted environments. However, bridging the severe per-spective gap between ground and aerial views remains challenging. Exist-ing approaches either rely on coarse image-level retrieval, suffer from un-stable optimization in pose regression, or require expensive fine-grainedannotations. These limitations suggest that current formulations of cross-view geo-localization are insufficient for reliable fine-grained localization.To address this limitation, we introduce a new task, Sector-Level Cross-View Geo-Localization (SLCVGL), which aims to perform fine-grainedsector-level geo-localization of a limited Field-of-View (FoV) ground im-age within an omnidirectional aerial image, while simultaneously infer-ring its ground-view orientation without manually annotated pose labels.To operationalize this task, we propose an Azimuthal Scanning frame-work. It decomposes aerial feature maps into multiple azimuthal sectors,instead of relying on holistic matching. By aligning ground features withthese sectors, the model explicitly retrieves a sector-level sub-region andimplicitly estimates orientation, eliminating the need for continuous poseregression supervision. This design also reduces interference from unob-served overhead regions. To benchmark the proposed task, we repur-pose standard panoramic datasets into a limited-FoV sector-level proto-col with random FoV cropping and soft labeling. Extensive experimentson CVUSA and CVACT demonstrate improvements over state-of-the-art methods, with a 30.17% R@1 improvement on CVACT under the 70°FoV setting, while maintaining strong robustness across varying FoVs.

Abstract:
State Space Models (SSMs) have surfaced as a promisingarchitecture in Video Frame Interpolation (VFI), as they can capturelong-range dependencies with linear computational complexity. However,their predefined scanning order limits their effectiveness in modelingthe dynamic motion trajectories inherent in VFI problems. To tacklethis challenge, we propose Motion-Guided Mamba for Video FrameInterpolation (MGMVFI), an adaptation of the selective state spacemodel tailored explicitly for VFI. MGMVFI introduces Motion-GuidedSerialization (MGS), which leverages optical flow to define a motion-adaptive 1D input order for the SSM. This aligns the causal state up-dates with semantically related tokens, enabling motion-consistent fea-ture propagation, particularly for large and dynamic motions. Addition-ally, to mitigate the unreliable feature representations caused by inaccu-rate optical flow estimates, we introduce contextual synthesis that uti-lizes the surrounding spatial context for robust inter-frame feature syn-thesis. These components are seamlessly integrated within our tailoredMamba architecture, which also employs a lightweight refinement blockto enhance local detail reconstruction at a reduced computational cost.Extensive experiments on standard VFI benchmarks demonstrate thatMGMVFI achieves state-of-the-art performance, particularly on complexand dynamic motions, thereby establishing a new direction for sequencemodeling in video interpolation.

Abstract:
Different stages of manipulation tasks exhibit varying lev-els of difficulty, suggesting stage-dependent motion speeds and temporalprediction horizons. However, existing IL-based visuomotor policies typ-ically imitate the execution speed of expert demonstrations and operatewith a fixed temporal prediction horizon, limiting flexibility and over-all task throughput. In this paper, we introduce AutoSpeed, a model-agnostic learning framework that enables existing visuomotor policies topredict trajectories with stage-adaptive motion speeds, without requir-ing speed or stage annotations. We treat future trajectories at differentspeeds as candidate optimization targets, evaluate each candidate us-ing a composite cost that trades off prediction error against predictionhorizon, and optimize the policy toward the minimum-cost candidate.With a fixed-length action sequence, speed modulation adjusts the ef-fective temporal prediction horizon: simple stages are executed fasterwith a longer prediction horizon, whereas complex stages are executedmore slowly with a shorter prediction horizon. Specifically, we implementspeed modulation in the frequency domain via the discrete cosine trans-form (DCT), which enables smooth, non-integer speed scaling and thuspreserves motion continuity. Extensive evaluations show that AutoSpeedsubstantially reduces task execution time while also improving successrates. Under the AutoSpeed framework, the inferred motion speeds ex-hibit a strong correspondence with task stages.

Abstract:
Robust and efficient local feature matching remains a bot-tleneck for real-time visual localization, especially in environments withlow texture, repetitive structures, or illumination changes. Most priorapproaches model either appearance or geometry in isolation, failing toexploit the semantic–geometric interactions crucial for disambiguatingchallenging matches. To address this limitation, we propose SemLight,built on the insight that compact semantic cues can serve as a lightweightprior for resolving matching ambiguity under challenging conditions.Specifically, high-level semantic priors are first distilled into compact,matching-oriented descriptors via a task-aware student network. Thesedescriptors then conditionally re-weight appearance and geometric chan-nels locally, enabling efficient disambiguation with minimal computa-tional overhead. Extensive experiments on relative pose estimation, ho-mography estimation, and visual localization demonstrate state-of-the-art performance. Under comparable computational cost to XFeat, ourmethod achieves a 17.8% relative improvement in AUC@5 over XFeat(50.2 vs. 42.6).

Abstract:
Streaming autoregressive (AR) video generators combined with few-step distillation achieve low-latency, high-quality synthesis, yet remain difficult to align via reinforcement learning from human feedback (RLHF). Existing SDE-based GRPO methods face challenges in this setting: few-step ODEs and consistency model samplers deviate from standard flow-matching ODEs, and their short, low-stochasticity trajectories are highly sensitive to initialization noise, rendering intermediate SDE exploration ineffective. We propose AR-CoPO (AutoRegressive Contrastive Policy Optimization), a framework that adapts the Neighbor GRPO contrastive perspective to streaming AR generation. ARCoPO introduces chunk-level alignment via a forking mechanism that constructs neighborhood candidates at a randomly selected chunk, assigns sequence-level rewards, and performs localized GRPO updates. We further propose a semi-on-policy training strategy that complements onpolicy exploration with exploitation over a replay buffer of reference rollouts, improving generation quality across domains. Experiments on SelfForcing demonstrate that AR-CoPO improves both out-of-domain generalization and in-domain human preference alignment over the baseline, providing evidence of genuine alignment rather than reward hacking.

Abstract:
Autoregressive (AR) models deliver high-quality image gen-eration but suffer from severe inference latency due to sequential decod-ing. While speculative decoding (SD) successfully accelerates large lan-guage models, we demonstrate that state-of-the-art methods like EAGLE-3 provide limited gains for visual AR models. We attribute this to theintrinsic nature of visual tokens: unlike text, their probability distribu-tions are flat and non-discriminative, which hinders drafter–target align-ment and causes verification to collapse under sampling. To bridge thisgap, we propose LISA, a Locality-Informed Speculative framework forAutoregressive image generation. LISA overcomes alignment barriers viatwo key components: (i) Locality-Informed Distillation, which alignsthe drafter by prioritizing supervision on structured uncertainty; and(ii) Geometry-Aware Soft Verification, which leverages embeddingproximity and target confidence to enable more permissive yet safe to-ken acceptance. Experiments across T2I and C2I tasks demonstrate thatLISA improves the speed–quality trade-off, achieving up to 4.12× accel-eration and outperforming recent competitive methods.

Abstract:
The quality and diversity of instruction-based image editing datasets are continuously increasing, yet large-scale, high-quality datasets for instruction-based video editing remain scarce. To address this gap, we introduce OpenVE-3M, an open-source, large-scale, and high-quality dataset for instruction-based video editing. It comprises two primary categories: spatially-aligned edits (Global Style, Background Change, Local Change, Local Remove, Local Add, and Subtitles Edit) and non-spatially-aligned edits (Camera Multi-Shot Edit and Creative Edit). All edit types are generated via a meticulously designed data pipeline with rigorous quality filtering, a process consuming in excess of 10,000 GPU-days. OpenVE-3M surpasses existing open-source datasets in terms of scale, diversity of edit types, instruction length, and overall quality. Furthermore, to address the lack of a unified benchmark in the field, we construct OpenVE-Bench, containing 431 video-edit pairs that cover a diverse range of editing tasks with three key metrics highly aligned with human judgment. We present OpenVE-Edit, a 5B model trained on our dataset that demonstrates remarkable efficiency and effectiveness by setting a new state-of-the-art on OpenVE-Bench, outperforming all prior open-source models including a 14B baseline.

Abstract:
Diffusion Transformers (DiTs) have significantly advancedaudio-driven portrait animation, but their high computational cost leadsto substantial inference latency. Although training-free diffusion cachingaccelerates inference significant, existing methods are primarily devel-oped for text-conditioned generation and overlook the spatial and modal-ity imbalances inherent in audio-driven portrait animation. In this pa-per, we propose SyncCache, a training-free caching acceleration methodtailored for DiT-based portrait animation that explicitly exploits asym-metric dynamics. Specifically, high-frequency dynamics driven by audioconditions and concentrated in human regions are more challenging andcritical to cache and reuse than the low-frequency visual background inportrait animation. First, we introduce Spatially-Asymmetric Probingto prioritize error sensitivity in dynamic human region. Second, throughModality-Decoupled Caching, we bypass heavy DiT block by reusingstable inter-block residuals, while continuously recomputing lightweightaudio blocks to preserve precise lip synchronization. Furthermore, weintroduce a cache ratio to control cache capacity and formulate memory-adaptive cache selection as an offline dynamic programming problemwithout online overhead. Extensive experiments demonstrate that Sync-Cache achieves superior speed–quality trade-offs, delivering up to 4.12×acceleration on HunyuanVideo-Avatar and 3.75× on Wan-S2V with near-lossless visual fidelity and precise audio alignment.

Abstract:
The substantial training cost of di!usion models hinderstheir deployment. Immiscible Di!usion [20] showed that reducing themixing of images’ di!usion destination in the noise space via linear as-signment can accelerate di!usion training. However, concerns regardinglimited image diversity and exploding execution times under large batchsizes limit its feasibility for large-scale training. In this work, we startfrom thoroughly tackling these limitations: For the image diversity, wedemonstrate the bijective nature of the denoising process of vanilla dif-fusion, underlying that being immiscible cannot hurt the diversity. Mov-ing beyond the noise layer, we refine immiscible di!usion’s concept to abroader miscibility reduction at any layer, which enables us to proposea new family of its implementations much more e"cient to execute un-der high batch sizes, including K-nearest neighbor (KNN) noise selectionand image scaling. Overall, the immiscible di!usion family achieves upto 4→ faster training across diverse models and tasks, including uncon-ditional/conditional generation, image editing, and robotics planning.Extensive analysis shows step-by-step on how immiscibility eases denois-ing and improves e"ciency. Besides, our analysis of immiscibility o!ersa novel perspective on how optimal transport (OT) enhances di!usiontraining. By identifying trajectory miscibility as a fundamental bottle-neck, we believe this work establishes a potentially new direction forfuture research in high-e"ciency di!usion training.

Abstract:
Quality assessment of AI-generated content is vital for modeloptimization, yet most existing evaluation datasets and models rely oncoarse-grained single scores, failing to provide targeted guidance. Tobridge this gap, we introduce Q-Real, a fine-grained quality assessmentdataset specifically designed for AI-generated images. This dataset com-prises 10K images generated by multiple models, annotated along twocritical dimensions, naturalness and distortion which are widely re-garded as the most significant aspects of AI-generated image quality.For each image, we localize major entities and provide a set of judgmentquestions and attribution descriptions along these dimensions to facili-tate comprehensive evaluation. Based on this dataset, we establish theQ-Real Bench to rigorously evaluate models on the challenging tasks ofjudgment and grounding with reasoning. And to tackle these challenges,we further propose a fine-grained training pipeline for Multimodal LargeLanguage Models (MLLMs), empowering them to judge, localize prob-lematic entities with detailed analysis, and predict quality score. Ex-perimental results demonstrate the high quality and significance of ourdataset, as well as the effectiveness of our proposed training pipeline.The dataset is available at https://huggingface.co/datasets/AGI-Eval/Q-Real.

Abstract:
While diffusion models excel at generating images with con-ventional dimensions, pushing them to synthesize ultra-high-resolutionimagery at extreme aspect ratios (EAR) often triggers catastrophic struc-tural failures, such as object repetition and spatial fragmentation. Thislimitation fundamentally stems from a lack of robust spatial priors, asstatic text-to-image models are primarily trained on image distributionswith conventional dimensions. To overcome this bottleneck, we presentScrollScape, a novel framework that reformulates EAR image synthe-sis into a continuous video generation process through two core inno-vations. By mapping the spatial expansion of a massive canvas to thetemporal evolution of video frames, ScrollScape leverages the inherenttemporal consistency of video models as a powerful global constraint toensure long-range structural integrity. Specifically, Scanning PositionalEncoding (ScanPE) distributes global coordinates across frames to actas a flexible moving camera, while Scrolling Super-Resolution (ScrollSR)leverages video super-resolution priors to circumvent memory bottle-necks, efficiently scaling outputs to an unprecedented 32K resolution.Fine-tuned on a curated 3K multi-ratio image dataset, ScrollScape ef-fectively aligns pre-trained video priors with the EAR generation task.Extensive evaluations demonstrate that it significantly outperforms ex-isting image-diffusion baselines by eliminating severe localized artifacts.Consequently, our method overcomes inherent structural bottlenecks toensure exceptional global coherence and visual fidelity across diverse do-mains at extreme scales.

Abstract:
Image editing has become an important capability of modernvisual generative models. Most existing approaches generate the targetimage directly given an input image and an editing instruction. Whileeffective for simple appearance modifications, this paradigm often failswhen edits require multi-step inference over spatial, temporal, causal, orcommonsense relationships, which entails reasoning about how the sceneshould evolve through a series of plausible intermediate states from thesource image to the desired result. Notably, modern video generationmodels naturally capture such transformation processes: by modelinghow scenes evolve over time, they encode strong temporal coherence andimplicit physical priors. However, directly applying video models to imageediting remains challenging due to the mismatch between editing instruc-tions and video generation objectives. To bridge this gap, we proposeVGEdit, a reinforcement learning framework that empowers video gener-ation models for image editing. Our method consists of two components:(1) Instruction transfer : an MLLM converts the editing instruction into avideo description, reframing the edit as a plausible transformation process.(2) Reinforcement learning: we adapt the video model with reward-basedoptimization, treating intermediate frames as implicit reasoning chainsand the last frame as the target edited image. This formulation eliminatesthe need for ground-truth editing videos and enables direct optimizationfrom rewards that capture instruction faithfulness, contextual consistency,and user preference alignment. Experiments show that VGEdit signifi-cantly outperforms prior methods on reasoning-informed image editingbenchmarks, demonstrating that connecting image editing and videogeneration in a process-centric manner provides a scalable path towardmore physically grounded and reasoning-capable visual editing.

Abstract:
Recent studies indicate that test-time scaling (TTS) or re-flection mechanisms can substantially improve performance in multi-modal generative tasks. At the core of TTS lies verification, which as-sesses the semantic alignment between textual instructions and generatedimages. Such reflection inherently couples generation and understanding,its integration into a single unified model for visual comprehension andsynthesis remains largely unexplored. Existing methods typically dependon external Visual Large Language Models (VLLMs) for verification, re-sulting in additional computational overhead and fragmented pipelines.We introduce UniReflect, the first unified framework for visual un-derstanding and generation that performs in-model verificationvia self-reflection tuning. The proposed model is explicitly trained to as-sess the correspondence between the prompt and the generated image,produce a structured analysis, and output a special token that guides thedecoding of a refined image, which enables seamless TTS within a singleunified model, eliminating the need for external verifiers or extra edit-ing prompts. Extensive experiments demonstrate that UniReflect notonly advances image generation fidelity but also provides reliable visualverification, setting a new benchmark for unified multi-modal models.

Abstract:
Unified multimodal models have demonstrated remarkableversatility in multimodal understanding and generation. However, theystruggle with image generation under complex compositional instructions,a setting frequently encountered in real-world applications. To addressthis, we present MANGO, a novel model that unleashes image generationcapability at both its performance floor and ceiling. (1) To raise theperformance floor, we introduce Functionality-Oriented Transformers,an architectural design that assigns each Transformer branch to a dedi-cated function, such as image generation or image understanding. Thisfunction-disentangled design alleviates cross-functional interference inher-ent in mainstream modality-oriented architectures, thereby unleashingthe model’s image generation capacity. (2) To elevate the performanceceiling, we propose Paint-CoT, a framework that enables image generationthrough a human-like artistic chain of thought, consisting of planning,acting, reflection, and correction. Leveraging the model’s multimodalunderstanding and reasoning capabilities, this reasoning-guided processfurther unleashes image generation capacity. Further, to overcome the dif-ficulty and high cost of collecting multi-step aligned chain-of-thought datafor end-to-end training, we develop a multi-task joint training paradigmthat decomposes the full reasoning-generation pipeline into manageablesubtasks. This strategy maximizes the utilization of existing or readilyconstructed supervision, enabling e!ective Paint-CoT-based image gener-ation without requiring fully aligned multi-step annotations. Extensiveexperiments demonstrate that MANGO consistently achieves superiorperformance across diverse image generation benchmarks, delivering sub-stantial improvements in complex image generation scenarios.

Abstract:
Instruction-based image editing enables natural-language con-trol over visual modifications, yet existing models falter under Instruc-tion–Visual Complexity (IV-Complexity), where intricate instructionsmeet cluttered or ambiguous scenes. We introduce RePlan (Region-alignedPlanning), a plan-then-execute framework that couples a vision–languageplanner with a diffusion editor. The planner decomposes instructions viastep-by-step reasoning and explicitly grounds them to target regions;the editor then applies changes using a training-free attention-region in-jection mechanism, enabling precise, parallel multi-region edits withoutiterative inpainting. To strengthen planning, we apply GRPO-based rein-forcement learning using 1K instruction-only examples, yielding substan-tial gains in reasoning fidelity and format reliability. We further presentIV-Edit, a benchmark focused on fine-grained grounding and knowledge-intensive edits. Across IV-Complex settings, RePlan consistently outper-forms strong baselines trained on far larger datasets, improving regionalprecision and overall consistency. Project page: https://replan- iv-edit.github.io/

Abstract:
Recent diffusion- and flow-based image editing methods en-able high-quality text-guided image editing. However, improving editstrength often degrades identity and background preservation, a phe-nomenon known as the edit—preservation trade-off. This trade-off iswidely observed and commonly regarded as an inherent limitation ofgenerative editing. In this work, we revisit this assumption and iden-tify trajectory instability as a previously overlooked factor behind theedit–preservation trade-off. Through empirical analysis across multipleediting methods and backbones, we find that trajectory instability isclosely associated with degraded semantic preservation while showinglittle association with edit alignment. We characterize instability by us-ing directional consistency and update variability of latent update ve-locities during integration, revealing that unstable integration dynamicscontribute substantially to the edit–preservation trade-off. Motivated bythis insight, we propose V-HOLD, a simple training-free method thatstabilizes editing trajectories by maintaining a target-oriented updatedirection across multiple integration steps instead of recomputing veloc-ities at every step. Experiments on PIE-Bench and the FlowEdit-Datashow that our method consistently improves semantic preservation whilemaintaining competitive edit alignment across multiple backbones. Inaddition, V-HOLD reduces the number of velocity-field evaluations dur-ing editing, improving computational efficiency. These results highlighttrajectory stability as a key factor for improving semantic preservationwithout sacrificing edit alignment.

Abstract:
The advancement of generative AI models capable of pro-ducing text and image marks a critical step forward in the realm of Equal Contribution, B Corresponding Author, † Project Leadermultimodal intelligence, particularly for tasks involving the interleavingof both modalities. To advance this intelligence to the next stage, it iscrucial for models to autonomously generate free-form interleaved text-image sequences. In this paper, we introduce ILLUME-X, an advancedunified multimodal paradigm that enables high-quality, free-form inter-leaved text-image generation by improving multimodal data efficiencyand stabilizing the multimodal training process. ILLUME-X comprisesthree key components: (i) an expanded training data pipeline optimizedfor interleaved text-image generation, (ii) a progressive training strategywith self-adaptive objectives for free-length multimodal token sequences,and (iii) an objective and comprehensive evaluation method ILScore forinterleaved text-image sequences. Notably, our ILLUME-X outperformsprevious unified models across multiple interleaved text-image generationtasks like style transfer, image decomposition and storytelling.

Abstract:
Inference-time scaling for text-to-image generation has pro-gressed from simple Best-of-N (BoN) sampling to guided search meth-ods that verify and steer candidate trajectories at intermediate denoisingsteps. These approaches focus on when and how often to verify duringdenoising but largely treat the cost of generation itself as fixed. More-over, the standard practice of comparing methods by number of functionevaluations (NFEs) counts only denoising forward passes and ignores ver-ifier overhead, which can distort e!ciency rankings. We show that underwall-clock evaluation, simple BoN already matches or outperforms sev-eral guided search techniques, suggesting that compute is better spent onbroader exploration than on repeated intermediate verification. This mo-tivates Flash-BoN, which generates a large pool of inexpensive draft can-didates by combining three complementary acceleration knobs: timesteptruncation, layer skipping, and activation proxies into a single config-uration optimized once per model. An e!cient multi-stage verificationprocedure then identifies the most promising draft, which is refined at fullquality. Across three benchmarks and three model scales, Flash-BoN con-sistently outperforms all baselines under fixed wall-clock budgets, withgains that grow at larger model scales (+8% AUC). We further showthat our strategy combines well and improves existing orthogonal tech-niques such as reflection-based prompt optimization (+16% AUC). Thegains correlate with increased candidate diversity, which also enablesdraft-guided selection to accelerate RL post-training convergence.

Abstract:
Image-based Virtual Try-On (IVTON) has greatly advancedthrough diffusion models, yet existing methods require many samplingsteps and depend on masks with costly auxiliary networks. In addition,the absence of large-scale mask-free paired datasets further limits thedevelopment of mask-free IVTON. We propose FDM-MFVT, a few-stepdiffusion model for mask-free IVTON, integrating an Outfit-aware NoiseOptimization Module (OANO) and an Instruction-driven Try-on Module(IDT) to enhance efficiency and flexibility.The OANO module initializesthe alignment space with noise using the input image and only needs 6steps to generate a higher-fidelity try-on image compared to 30 steps.TheIDT module uses virtual try-on prompts and efficient adaptation to gen-erate high-quality results from garment and person images alone. Wefurther introduce MFVT, a 30,000-pair mask-free IVTON dataset. Ex-periments show that FDM-MFVT achieves superior quantitative andqualitative results with fewer inference steps than mask-based and mask-free baseline methods.

Abstract:
Deciphering human visual intent and manipulating visual con-tent via brain signals represents a significant milestone in neuro-generativeAI. Recent studies have demonstrated impressive image reconstructionfrom brain activity, yet they often rely on slow iterative diffusion inversionprocesses, makes it difficult to achieve real-time editing and fails to unifygeneration with precise context modification. Rectified Flow has recentlyemerged as a powerful solution for fast, deterministic generation. Inspiredby this, we propose MinD-Flow, a pioneering inversion-free framework forunified brain-driven synthesis and editing. It incorporates a Neuro-awareEncoder to achieve unified encoding of heterogeneous brain signals. Com-bined with the Neuro-Perceiver Bridge (NPB), it aligns brain semanticswith visual anchors, mapping the ‘Mind Space’ to the generative latentspace. Furthermore, the Decoupled Flow Guidance (DFG) and AdaptiveSoft Masking overcome the conflict between structural preservation andsemantic changes, enabling precise local editing without noise inversion.We introduce Brain-Gen Benchmark to standardize the evaluation ofNeuro-context/creation/editing tasks. Comprehensive experiments showSOTA performance, providing a versatile tool for BCI applications.

Abstract:
Given an input image, style transfer aims to recast it into the style of a reference image while preserving its content. While this problem is well addressed for natural images, we find that structured images such as charts, diagrams and tables demand capabilities fundamentally distinct from natural image stylization, i.e. visual elements encode data through geometry (bar heights, arc angles, point positions), and dense text must be preserved exactly. We term this structured style transfer as changing appearance while keeping strict element-level fidelity. Even frontier models like GPT-Image-1.5 and Nano-Banana-Pro often struggle here, producing inconsistent transfer, structural distortion, and content leakage. The obvious fix, training on data that restyles a content chart toward a reference, inherits these failures: it distorts the content and leaks the reference into the target. We introduce ChartForge, a data pipeline that instead builds triplets in reverse: starting from a style image, it produces a target of similar style but different content, then a matching content chart. Because the content is derived from the target, the two stay structurally are disentangled from the start, avoiding forwardpipeline leakage. With style-space resampling and multi-dimensional filtering, ChartForge yields ChartStyle-100K, over 100K triplets across 36 chart types and 3 visualization families. For evaluation, we further build ChartStyle-Bench, a benchmark of 300 content-style pairs with 6 metrics. Using ChartStyle-100K, Training ReChart by progressively fine-tuning Qwen-Image-Edit on ChartStyle-100K achieves the best overall performance across all baselines, including GPT-Image-1.5 and Nano-Banana-Pro.

Abstract:
Achieving simultaneous preference alignment and distillation acceleration in video diffusion models remains an open challenge. Existing methods optimize the two objectives over mismatched representation spaces, where improving one objective often compromises the other. To overcome this, we propose Reward Lightning, a unified framework that aligns and accelerates a video diffusion model within a single shared representation. Its central principle is homology: both objectives are evaluated on identical latent features, which mitigates the gradient conflicts that arise when they are optimized over disjoint representations. As a foundational component, we first introduce a latent reward model (LRM) that scores videos directly in the latent space, without decoding back to the pixel space. Building on the LRM, homologous preference distillation (HPD) reuses this shared backbone to perform adversarial distillation and preference alignment jointly, yielding few-step generators that remain faithful and well aligned. Extensive experiments demonstrate that the LRM surpasses pixel-level and latent-level reward baselines by 11.0% and 14.7% in preference accuracy, and that Reward Lightning generates high-fidelity videos in merely 1 to 4 steps, improving the average VBench score by 2.1% while leading in text alignment, motion quality, and visual quality. Project page: https://reward-lightning.github.io.

Abstract:
Most image colorization systems operate in Lab space by predicting chroma (ab) while preserving an input-derived luminance channel (L). While effective on standard benchmarks, this fixed-luminance design restricts brightness changes and becomes unreliable when grayscale formation deviates from natural-image luminance, as in historical orthochromatic photography. We propose a luminance-agnostic colorization framework that formulates colorization as full-RGB image editing using a foundation image-editing model. To bridge modern panchromatic and historical orthochromatic conditions, we introduce a mixed grayscale objective that trains the model under both standard luminance grayscale and a red-insensitive grayscale formation. Experiments on COCO, ImageNet, and a multi-instance benchmark show that our method is competitive on standard grayscale inputs and substantially more robust under orthochromatic inputs, with qualitative comparisons and a human study indicating fewer visible color artifacts.

Abstract:
Existing methods for instruction-guided image editing are usually confined to text-based CoT reasoning for semantic understanding and reasoning, and lack of visual awareness necessary for fine-grained synthesis. In this work, we propose CORE-V, a novel Visual CoT framework to explicitly perform visually-interactive reasoning process for controllable image editing. Specifically, CORE-V refines global semantics into detailed entities and generates visual imagination images based on attribute description in the instructions. These visual imagination images, together with spatial conditions of input images (i.e., segmentation masks, depth maps and edge maps) are applied within the reasoning process. To achieve visual reasoning training, we build the CORE-Edit-800K dataset that contains both reference images and spatial controls aligned with reasoning pipelines. Furthermore, we introduce CORE-Bench to assess directional editing similarity. Comprehensive experiments on EmuEdit, Reason-Edit and CORE-Bench demonstrate superior performance of CORE-V for visual-semantic alignment and structural integrity.

Abstract:
Visual autoregressive (VAR) models have recently emerged as a promising alternative to diffusion models, achieving competitive performance in text-to-image generation while offering substantially faster inference. Although conditional image generation has been widely studied, training-free prompt-guided image editing remains largely unexplored despite its importance for practical applications. In this paper, we present Visual AutoRegressive Inverse Noise (VARIN), the first noise inversion framework for training-free text-based image editing in visual autoregressive models. VARIN introduces Location-aware Argmax Inversion (LAI), a pseudo-inverse function for the argmax operator that reconstructs the inverse Gumbel noise used during discrete autoregressive sampling. The recovered inverse noise enables accurate reconstruction of the source image while providing controllable guidance for promptdriven image editing. Extensive experiments demonstrate that VARIN produces edits that closely follow the target prompts while effectively preserving the background and structural details of the source image. These results establish VARIN as a simple, efficient, and practical image editing framework for visual autoregressive models.

Abstract:
We present Spectral Consistent Flow (SC-Flow), a 3D med-ical image translation framework with a single function evaluation (1-NFE) in the latent space. This approach reformulates medical imagetranslation as a stochastic Brownian bridge process that directly con-structs a mapping between source and target modalities by predictingthe support regularized mean velocity field. To mitigate modality entan-glement, over-smoothing, and artifacts induced by the implicit low-passmodulation of the latent average velocity, we introduce a Spectral Consis-tency Corrector that dynamically regularizes the evolution of the powerspectral density via learnable frequency-domain gain modulation. Thismechanism establishes an explicit bridge between spatial textures andspectral energy flow, enabling the model to recover fine-grained anatom-ical fidelity while maintaining global structural coherence. Extensive ex-periments on four datasets demonstrate that SC-Flow delivers signifi-cantly more accurate, consistent, and robust performance across varioustranslation scenarios.

Abstract:
Realistic motion generation from a single example remainschallenging due to the scarcity of large-scale, high-quality motion datasets,particularly for animals and custom characters. Existing approaches mainlyoperate in temporal or spatial domains, where controllability is imposedthrough joint-level or time-based constraints. However, motion signalsexhibit a natural hierarchical structure across frequency scales: low-frequency components encode the fundamental rhythm and movementpattern, while high-frequency components capture individual details andvariations. This observation suggests an alternative control dimensionbased on frequency-selective motion synthesis. We present CMDer (pro-nounced Commander), a Controllable Mode Decomposition-based SingleMotion Synthesis framework that leverages diffusion models to generatediverse motions from a single motion example. CMDer employs Multi-variate Variational Mode Decomposition (MVMD) to represent motionsequences as intrinsic frequency modes, providing an interpretable andstructured representation for motion synthesis. A lightweight U-Net dif-fusion model then learns to generate these modes, while a Mode En-hancement Block (MEB) improves local motion modeling. By perform-ing diffusion directly in the decomposed motion modes, CMDer enablesfrequency-selective control that allows users to preserve global rhythmicpatterns while varying fine-grained motion details. Experiments demon-strate that CMDer achieves generation quality comparable to state-of-the-art temporal-domain approaches while introducing a new control-lable dimension for motion synthesis. We further showcase diverse appli-cations including mode-based synthesis, cross-modality motion genera-tion, and controllable crowd animation.

Abstract:
Ultra-high-resolution image generation is increasingly vital for applications requiring fine-grained textures and global structural fidelity, yet state-of-the-art text-to-image diffusion models such as FLUX and SD3 remain confined to sub 2MP (< 1K × 2K) resolutions due to the quadratic complexity of attention mechanisms and the scarcity of high-quality high-resolution training data. We present UltraImageGen, a novel framework that introduces hierarchical local attention with low-resolution global guidance, enabling efficient, scalable, and semantically coherent image synthesis at ultra-high resolutions. Specifically, high-resolution latents are divided into hardware aligned fixed-size local windows to reduce attention complexity from quadratic to near-linear, while a low-resolution latent with scaled positional embeddings injects global semantics as an anchor. A lightweight LoRA adaptation bridges global and local pathways during denoising, ensuring consistency across structure and detail. To maximize efficiency and achieve scalable ultrahigh-resolution generation, we repermute token sequence in window-first order, so that the GPU-friendly dense local blocks in attention calculation equals to the fixed-size local window in 2D regardless of resolution. Together UltraImageGen reliably scales pretrained models to resolutions higher than 8K with more than 10× speed up and significantly lower memory usage. Extensive experiments demonstrate that UltraImageGen achieves superior quality while maintaining computational efficiency, establishing a practical paradigm for advancing ultra-high-resolution image generation. Our project page and code are available here.

Abstract:
From a Bayesian perspective, score-based di x001B usion solves in-verse problems through joint inference, embedding the likelihood withthe prior to guide the sampling process. However, this formulation failsto explain its practical behavior: the prior o x001B ers limited guidance, whilereconstruction is largely driven by the measurement-consistency term,leading to an inference process that is e x001B ectively decoupled from thedi x001B usion dynamics. We show that the di x001B usion prior in these solversfunctions primarily as a warm initializer that places estimates near thedata manifold, while reconstruction is driven almost entirely by measure-ment consistency. Based on this observation, we introduce DAPS++,which fully decouples di x001B usion-based initialization from likelihood-drivenre x001C nement, allowing the likelihood term to guide inference more directlywhile maintaining numerical stability and providing insight into whyuni x001C ed di x001B usion trajectories remain e x001B ective in practice. By requiringfewer function evaluations (NFEs) and measurement-optimization steps,DAPS++ achieves high computational e x001E ciency and robust reconstruc-tion performance across diverse image restoration tasks.

Abstract:
Modern one-step diﬀusion models achieve impressive qual-ity through distribution-based timestep distillation. Yet, they rely on acritical assumption: Teacher and Student must inhabit the same latentspace. This Shared-Space constraint prevents knowledge transfer frommodern high-capacity Teachers (e.g., SD 3.5 and Flux) into compact,deployment-friendly Students such as SD 1.5, whose latent resolutionand VAE parameterization diﬀer from the Teacher. We formalize thisoverlooked regime as Cross-Space Distillation, where Teacher andStudent diﬀer in both latent resolution and VAE space. To enable distil-lation under this mismatch, we introduce the Bridge (Bϕ ), a lightweightlatent interface that maps Student latents into the Teacher space with-out modifying the Student backbone. Bridge combines a frozen StudentVAE decoder as a spatial prior with a compact learnable projector, andis trained with latent reconstruction and attention ﬁdelity objectives forstable Teacher-space alignment. Across diverse modern Teachers, Bridgeenables substantial gains for compact one-step Students; for example, itimproves SD 1.5 from 5.4 to 9.4 HPSv3 while preserving one-step in-ference, low latency, and broad ecosystem compatibility. These resultsshow that heterogeneous large Teachers can be distilled into eﬃcient,deployable backbones through a lightweight latent-space interface.

Abstract:
Diffusion models have achieved remarkable success across di-verse domains, with performance closely related to the denoising back-bones that parameterize the score function. In this paper, we presenta systematic, phase-aware analysis of diffusion components and showthat abrupt, early-stage fluctuations in deep latents are strongly asso-ciated with artifacts. Guided by these findings, we introduce DUNE(Diffusion Unified Network refiNEr), a training-free refinement frame-work that detects abrupt deviations in deep low-noise internal latentsusing a shared EMA-based criterion, and applies backbone-specific sup-pression to the detector-selected entries. Although derived from U-Net,the same detect–suppress principle extends naturally to Transformer-based diffusion models by acting on the latents of deep self-attentionblocks. Extensive experiments across multiple backbones indicate thatDUNE improves fidelity while reducing hallucinations, offering new in-sight into where and when diffusion backbones should be controlled.

Abstract:
Dataset distillation provides an effective approach to reducememory and computational costs by optimizing a compact dataset thatachieves performance comparable to the full original. However, for large-scale datasets and complex deep networks (e.g., ImageNet-1K with ResNet-101), the vast optimization space hinders distillation effectiveness, limit-ing practical applications. Recent methods leverage pre-trained diffusionmodels to directly generate informative images, thereby bypassing pixel-level optimization and achieving promising results. Nonetheless, theseapproaches often suffer from distribution shifts between the pre-traineddiffusion prior and target datasets, as well as the need for multiple dis-tillation steps under varying settings. To overcome these challenges, wepropose a novel framework that is orthogonal to existing diffusion-baseddistillation techniques by utilizing the diffusion prior for patch selectionrather than generation. Our method predicts noise from the diffusionmodel conditioned on input images and optional text prompts (with orwithout label information), and computes the associated loss for eachimage-patch pair. Based on the loss differences, we identify distinctiveregions within the original images. Furthermore, we apply intra-classclustering and ranking on the selected patches to enforce diversity con-straints. This streamlined pipeline enables a one-step distillation process.Extensive experiments demonstrate that our approach consistently out-performs state-of-the-art methods across various metrics and settings.

Abstract:
Acquiring a complete set of magnetic resonance imaging (MRI)contrasts is time-intensive and uncomfortable for patients, despite thediagnostic value of multi-contrast imaging. This motivates synthesizingmissing contrasts from those already acquired, which is an inherently3D problem requiring anatomical coherence across axial, sagittal, andcoronal planes. However, fully 3D generative models are often impracti-cal under computational resources that scale cubically with volume size.We propose a unified Multi-Plane Autoregressive Diffusion (MPAD), alatent diffusion framework that achieves full-volume 3D synthesis usingefficient plane-wise 2D operations while preserving volumetric coherence.A 3D autoencoder first compresses MRI scans into an isotropic 3D la-tent representation. A 2D diffusion model is then trained to reconstructmasked latent slices of the target contrast, conditioned on both source-contrast slices and unmasked target-contrast slices. During inference,we introduce plane-wise autoregressive synthesis with inter-plane priors.Slices are generated autoregressively in random order within one planeorientation to maintain intra-plane continuity, then propagated as con-ditioning priors to orthogonal plane orientations to enforce inter-planeconsistency. Compared to 3D latent diffusion baselines, MPAD reducestraining and inference FLOPs by 7× and 3×, respectively, while alsolowering inference time and peak memory consumption. Experimentson multiple datasets demonstrate that MPAD achieves superior perfor-mance, generating high-fidelity 3D volumes and supporting one-to-manytranslation within a single unified model.

Abstract:
Video diffusion transformers (DiTs) generate high-fidelityand temporally coherent videos, yet motion control remains implicit,primarily relying on text prompts. As a result, achieving desired motionoften requires extensive prompt engineering and repeated resampling.While fine-tuning models with additional spatial prompts (e.g., bound-ing boxes or point trajectories) enables explicit control, it demands sub-stantial data curation and computation, and may compromise the gener-ative capabilities of pretrained models. Consequently, training-free mo-tion control using such spatial prompts has been explored in U-Net–basedvideo diffusion models, but remains largely unexplored for DiTs. We in-troduce Qwerty, a training-free framework that enables flexible motioncontrol in pretrained image-to-video DiTs via user-defined object warpingand optical flow. We carefully manipulate the 3D full attention of DiTsby warping the frame-invariant semantic subspace of queries. We findthat the noise predicted by the query-warped DiT naturally guides thediffusion trajectory toward the desired motion, and further show thatleveraging this noise as self-guidance for latent optimization improvescontrol stability and visual quality. Experiments show that Qwertyachieves the most effective motion control among existing training-freeapproaches on a recent image-to-video DiT, with performance compara-ble to fine-tuning–based methods.

Abstract:
When labeled data are scarce, off-the-shelf diffusion modelscan augment training sets for few-shot medical image classification, butnot all generated samples are equally useful for the downstream task. Ex-isting approaches largely improve synthetic data by increasing realism,diversity, or domain adaptation, while overlooking a more fundamen-tal question: how should sample usefulness for classification be measuredand optimized? We address this with Class-Contrastive Influence (C2I),a criterion that quantifies a sample’s usefulness through its gradient-based influence on the classifier. We find that effective samples exhibita strong C2I gap: their loss gradients align with validation gradientsfrom the same class and oppose those from other classes. Our anal-ysis further suggests that such high-C2I samples are hard, boundary-proximal examples that help refine the decision boundary and improverobustness. Building on this insight, we fine-tune diffusion models withreinforcement learning using a C2I-based reward to steer generation to-ward class-informative samples. Across several few-shot medical imagingbenchmarks, C2I-guided generation improves downstream accuracy androbustness over diffusion-based augmentation baselines, showing thatsynthetic augmentation is most effective when guided by task usefulnessrather than image quality alone.

Abstract:
Recent advances in 3D content generation from text or im-ages have achieved impressive results, yet view inconsistency from 2Dgenerators and the scarcity of high-quality 3D data remain significantbottlenecks. Existing solutions [21, 28] typically adapt large-scale pre-trained text-to-image latent diffusion models to generate 3D GaussianSplats (3DGS). However, these approaches often rely on training complexcascade pipelines that are computationally expensive and scalability-limited. Most critically, the quality of generated 3D assets is inherentlyconstrained by each component capacity and compressed latent space,leading to decoding artifacts and accumulated errors. To address theselimitations, we propose PixGS, a single-stage pipeline for direct high-quality 3DGS generation, which leverages recent advances in pixel-spacediffusion to bypass lossy latent compression while still benefiting from thevast 2D generative priors. By directly denoising 3D Gaussian attributesat each timestep, our method enables precise, splat-level regularizationof both appearance and geometry. Furthermore, we introduce a compre-hensive supervision strategy that incorporates surface normals, depth,and high-frequency structural information, which is often overlooked inprior works. Experiments demonstrate that PixGS outperforms currentstate-of-the-art methods while maintaining a fast inference speed (≈ 1son a single A100 GPU), offering a robust and efficient alternative tomulti-stage generation pipelines.

Abstract:
High-resolution Synthetic Aperture Radar (SAR) imageryis critical for precision analysis such as automatic target recognition,yet its acquisition is costly. Although generative image super-resolution(ISR) models offer a promising alternative, current smooth-approximation-based diffusion frameworks often struggle to preserve the coherent scat-tering statistics, causing stochastic structural distortions that are lessconsistent with real SAR physics. To address this, we propose Semantic-Prototype-Guided Super-Resolution (ProSR), reformulating SAR ISR asa semantically-guided discrete token prediction task within a quantizedlatent space. By mapping signal features to discrete scattering primitives,ProSR preserves the impulsive nature of SAR without over-smoothing.Furthermore, we integrate a Self-Supervised Learning backbone intoSAR ISR to extract label-free semantic priors, overcoming label scarcity.Guided by these priors, we introduce Semantic-Aligned Detail Encodingto decouple high-frequency signals into discrete scattering primitives. Inparallel, the Semantic Prototype Map Generator explicitly constructssemantic prototype maps, allowing Prototype-Map-Guided Attentionto route the information flows within identical categories and mitigateinter-class interference. To validate our approach, we present a large-scale0.25 m resolution benchmark from the Umbra Open Dataset. Experimen-tal results show ProSR achieves superior visual quality while preservingessential scattering characteristics required for practical SAR applications.

Abstract:
Aligning generative real-world image super-resolution mod-els with human visual preference is challenging due to the perception–fidelity trade-off and diverse, unknown degradations. Prior approachesrely on offline preference optimization and static metric aggregation,which are often non-interpretable and prone to pseudo-diversity understrong conditioning. We propose OARS, a process-aware online align-ment framework built on COMPASS, a MLLM-based reward that eval-uates the LR→SR transition by jointly modeling fidelity preservation andperceptual gain with an input-quality-adaptive trade-off. To train COM-PASS, we curate COMPASS-20K spanning synthetic and real degrada-tions, and introduce a three-stage perceptual annotation pipeline thatyields calibrated, fine-grained training labels. Guided by COMPASS,OARS performs progressive online alignment from cold-start flow match-ing to full-reference and finally reference-free RL via shallow LoRA opti-mization for on-policy exploration. Extensive experiments and user stud-ies demonstrate consistent perceptual improvements while maintainingfidelity, achieving state-of-the-art performance on Real-ISR benchmarks.

Abstract:
Recent di x001B usion-based super-resolution (SR) methods oftenemploy large text-to-image (T2I) backbones as generic priors. While thisyields strong perceptual quality, restoration performance can becomeoverdependent on text-driven semantics. T2I pipelines typically rely onclassi x001C er-free guidance (CFG), doubling the number of function evalu-ations at inference time. To address these limitations, we propose theReference-to-Image Aware Latent Di x001B usion Model for Image Super-Resolution (ReAL), a purely Reference-to-Image (R2I) model condi-tioned only on the LR input and a retrieved reference. The ReferenceFusion Module encodes the reference once, caches its key x0015 value tensors,and injects them into the denoising model's self-attention layers at everytimestep, providing strong feature-level guidance for texture and struc-ture recovery. The R2I design substantially reduces dependence on T2Ipriors and improves semantic consistency, achieving state-of-the-art per-formance compared to T2I-based SR baselines.

Abstract:
Image morphing aims to produce a smooth and semantically consistent transition between two input images. Existing diffusionbased morphing methods either require expensive per-pair optimization or rely on implicit spatial alignment, which easily fails under large layout discrepancies. To address these limitations, we propose AlignMorph, a novel tuning-free diffusion framework guided by the principle of transport-then-denoise. We explicitly decouple geometric alignment from generative denoising to avoid structural entanglement. Our framework consists of two core components. (1) Global Semantic Transport, which achieves diffusion-compatible semantic alignment via entropic optimal transport and reliability-aware latent warping; and (2) Coordinate-Aligned Generation, which uses a symmetric bi-phase attention handoff to maintain consistent spatial coordinates throughout denoising. Without any tuning, AlignMorph effectively eliminates ghosting and achieves superior structural coherence and temporal smoothness on morphing benchmarks. Code is available at https://github.com/ 51xOne/Alignmorph.

Abstract:
Reasoning agents increasingly rely on external tools such asweb search to answer complex queries. Reinforcement learning (RL) fine-tuning algorithms such as GRPO have improved long-form reasoningin text-only language models, particularly for coding and mathematics.Reliable tool use in multimodal agents, however, remains challengingbecause models must interpret text and images while integrating noisyretrieved evidence, often under sparse outcome-level supervision withoutexplicit verification signals. We present Self-Verification via Reinforce-ment Learning (SVRL), an RL-only finetuning framework that trainsmultimodal agents to verify and filter retrieved evidence within theirown reasoning traces, reducing reliance on external verifiers at infer-ence time. SVRL also introduces a search-aware penalty that discour-ages unnecessary tool calls and a query-diversity reward that encouragesdiverse, well-formed search queries, providing fine-grained feedback onwhen and what to search. Finetuning Qwen-2.5-VL-7B with SVRL ononly 5,000 visual question answering examples yields consistent gainsin multi-hop VQA generalization and tool efficiency across benchmarks.Overall, SVRL narrows the gap between compact agents and much largerproprietary models while requiring substantially lower training and in-ference cost.

Abstract:
Video spatial reasoning is essential for navigation-oriented perception and long-video question answering, where models must infer spatial relations across long horizons under changing viewpoints. However, existing multimodal large language models (MLLMs) remain largely semantic-centric, and often fail to reliably aggregate consistent spatial evidence from redundant video observations, leading to inefficient or unstable reasoning. To address these issues, we propose ConsiSpace, a geometry-consistency-aware framework for geometry-sensitive video spatial reasoning that turns spatial consistency into both an evidence organization principle and an explicit post-SFT learning signal. We build a geometry-consistent memory (GCM) including implicit evidence tokens and explicit geometric cues, and leverage efficient organization strategies to compactly preserve task-related spatial evidence. Furthermore, we utilize unified consistency self-supervised reinforcement learning (UCSSRL) after supervised fine-tuning to improve cross-view stability, with answer-, metric-, and topology-consistency rewards. Extensive experiments on three spatial-reasoning benchmarks, VSI-Bench, OSI-Bench, and MMSI-Video-Bench, show consistent gains, improving the average score by 12.6 points over the strongest baselines.

Abstract:
With the increasing development of Vision-Language Mod-els, it becomes imperative that their predictions are readily explainableto relevant stakeholders. However, the field of explainability has notkept pace with the multimodal surge. While recent Multimodal Explain-able AI (MxAI) methods generate explanations to attribute the interac-tion between different modalities, current evaluation protocols lack theground truth required to distinguish between true cross-modal reason-ing (e.g. spatial composition) and shallow cross-modal shortcuts (e.g.Bag-of-Words attribute matching). It remains unknown whether MxAImethods faithfully capture synergistic interactions or merely hallucinatereasoning on models acting as simple feature detectors. In this paper,we introduce GridVQA-X, the first diagnostic framework specificallydesigned to evaluate cross-modal explainability. Unlike natural datasets,GridVQA-X leverages a closed-world synthesis logic to generate unique,mathematically guaranteed explanations. We utilize this controlled envi-ronment to train paired ground-truth models on identical architectures:Mpure , which learns robust spatial-relational reasoning and Mspur , whichis structurally forced to rely on cross-modal shortcuts. This behavioraldivergence creates a rigorous testbed: a faithful explainer must reportdistinct reasoning pathways for each model.Our findings reveal that widely used methods fail to distinguish betweenmodels relying on genuine spatial-relational reasoning and those exploit-ing cross-modal shortcuts, highlighting a critical gap in capturing truecross-modal synergy and misrepresenting how multimodal models actu-ally make decisions. Our code is available at GitHub, and the dataset andmodels are available on Hugging Face at gridvqa-dataset and gridvqa-models, respectively.

Abstract:
005 We present Lunima-OmniLV (abbreviated as OmniLV), 005006 a universal multimodal multi-task framework for low-level vision that ad- 006007 dresses over 100 sub-tasks across four major categories, including image 007008 restoration, image enhancement, weak-semantic dense prediction, and 008009 stylization. OmniLV leverages both textual and visual prompts to offer 009010 flexible, user-friendly interactions. Built on Diffusion Transformer (DiT)- 010011 based generative priors, our framework supports arbitrary resolutions 011012 — achieving optimal performance at 1K resolution — while preserving 012013 fine-grained details and high fidelity. Through extensive experiments, 013014 we demonstrate that separately encoding text and visual instructions, 014015 combined with co-training using shallow feature control, is essential to 015016 mitigate task ambiguity and enhance multi-task generalization. Our find- 016017 ings also reveal that integrating high-level generative tasks into low-level 017018 vision models can compromise detail-sensitive restoration. These insights 018019 pave the way for more robust and generalizable low-level vision systems. 019

Abstract:
Stereotype biases in Large Multimodal Models (LMMs) per-petuate harmful societal prejudices, undermining the fairness and equityof AI applications. As LMMs grow increasingly influential, addressingand mitigating inherent biases related to stereotypes, harmful generations,and ambiguous assumptions in real-world scenarios has become essential.However, existing datasets evaluating stereotype biases in LMMs oftenlack diversity, rely on synthetic images, and often have single-actor im-ages, leaving a gap in bias evaluation for real-world visual contexts. Toaddress the gap in bias evaluation using real images, we introduce theBBQ-Vision (BBQ-V), the most comprehensive framework for assessingstereotype biases across nine diverse categories and 50 sub-categories withreal and multi-actor images. BBQ-V benchmark contains 14,144 image-question pairs and rigorously evaluates LMMs through carefully curated,visually grounded scenarios, challenging them to reason accurately aboutvisual stereotypes. It offers a robust evaluation framework featuring real-world visual samples, image variations, and open-ended question formats.BBQ-V enables a precise and nuanced assessment of a model’s reasoningcapabilities across varying levels of difficulty. Through rigorous testingof 19 state-of-the-art open-source (general-purpose and reasoning) andclosed-source LMMs, we highlight that these top-performing models areoften biased on several social stereotypes, and demonstrate that the think-ing models induce more bias in the reasoning chains. This benchmarkrepresents a significant step toward fostering fairness in AI systems andreducing harmful biases, laying the groundwork for equitable and sociallyresponsible LMMs. Dataset and evaluation code are available here.

Abstract:
Knowledge-based Visual Question Answering (KB-VQA) re-quires models to retrieve visual entities matching the query image fromlarge-scale encyclopedic knowledge bases and answer related questions.Existing multimodal Retrieval Augmented Generation (mRAG) meth-ods rely on global visual features to match candidate entities, yet whenthe knowledge base contains numerous visually similar entities, the re-triever struggles to distinguish them, populating the candidate set withvisually similar but factually mismatched distractors. Since subsequentprocessing steps such as noise filtering are also confined to this fixedcandidate set, errors from failed retrieval inevitably propagate to the fi-nal answer. To address these challenges, we propose MMAgent-R2 , anagentic mRAG framework that integrates visual reranking and active re-jection as its internal verification mechanism. Visual reranking directlycompares query and candidate images, capturing discriminative detailsbeyond textual descriptions to precisely identify the target entity amongsimilar candidates; active rejection discards unreliable results and re-trieves additional candidates when no confident match is found, movingbeyond the fixed candidate pool. We design a composite reward functionwith step-level verification rewards and achieve joint optimization of ex-ternal retrieval, internal verification, and answer generation via GRPOtraining. Experiments on InfoSeek, E-VQA, and MMhops demonstratethat MMAgent-R2 achieves state-of-the-art performance, with particu-larly notable advantages in challenging retrieval scenarios and complexmulti-image multi-hop reasoning tasks.

Abstract:
Visual Speech Recognition (VSR) tasks in complex multi-speaker scenarios are severely hindered by rapid head motions, occlu-sions, and subtle lip articulations. Traditional RGB-based methods strug-gle here due to low rates and motion blur of frames. To overcome these,we propose LipsFlow, a neuromorphic-inspired VSR framework thatconverts RGB videos into high-temporal-resolution event streams. Formulti-speaker, we employ ByteTrack tracking and TalkNet active speakerdetection to temporally segment scenes into single-speaker clips, enablingfocused per-speaker analysis. By explicitly capturing microsecond-levelarticulatory dynamics via learnable event-based representations, Lips-Flow achieves inherent robustness against visual degradation. To effi-ciently model these dense event-based features and adapt to speaker-specific articulatory patterns, we introduce Optimal Transport Condi-tional Flow Matching (OT-CFM). It enforces deterministic, straight-line trajectory generation in a semantic latent space, slashing inferencelatency to just two Ordinary Differential Equation (ODE) steps. Further-more, we design a Dual-Level Semantic Supervision mechanism combin-ing token-level BERT weight tying and sentence-level priors to resolvehomophene ambiguities. Validated on competitive benchmarks, LipsFlowachieves a state-of-the-art WER of 22.3% at 240 ms latency, estab-lishing a highly robust and efficient paradigm for event-based VSR.

Abstract:
Unified Multimodal Models (UMMs) have emerged as apromising paradigm that integrates multimodal understanding and gen-eration within a unified modeling framework. However, current gener-ative training paradigms suffer from inherent limitations. We presentSemantically-Grounded Supervision (SeGroS), a fine-tuning frameworkdesigned to resolve the granularity mismatch and supervisory redundancyin UMMs. At its core, we propose a novel visual grounding map toconstruct two complementary supervision signals. First, we formulatesemantic Visual Hints to compensate for the sparsity of text prompts.Second, we generate a semantically-grounded Corrupted Input to explic-itly enhance the supervision of masking-based UMMs by restricting theText-to-Image loss to core text-aligned regions. Extensive evaluations onGenEval, DPGBench, and CompBench demonstrate that SeGroS signif-icantly improves generation fidelity and cross-modal alignment acrossvarious UMM architectures.Project page: https://segros-project.github.io/

Abstract:
While many tasks require reasoning across multiple images, open-source Large Multimodal Models (LMMs) remain unreliable in these settings. We analyze multi-image LMMs and identify a phenomenon we term attention fragmentation: in each image, tokens at similar background locations act as attention sinks, absorbing disproportionate attention. Causal masking further skews this effect, as earlier images accumulate more sink attention than later images. Using an entropy score over per-image attention, we find that visual focus remains highly dispersed across images rather than isolating key evidence. By applying Pinsker’s inequality, we establish a theoretical bound showing that this high-entropy dispersion, combined with stronger early-image sinks, strictly reduces the usable non-sink attention available to earlier images, providing a mechanistic link to image order sensitivity. Motivated by this diagnosis, we propose Attention Remasking (AR), a post-training edit that blocks sink keys and opens a sparse set of cross-image links, routing the recovered attention to task-relevant tokens. AR improves accuracy and reduces order sensitivity across multi-image benchmarks, narrowing the gap between open-source LMMs and leading commercial models.

Abstract:
As Text-to-Video (T2V) models progress towards higher vi-sual realism, the artifacts and errors they produce are highly nuanced,fine-grained, and spatio-temporally localized. Vision Language Models(VLMs) are actively being adopted as automatic evaluators for videogeneration, driven by the promise of their perception and reasoning abili-ties. Yet, it remains unclear whether they can detect, localize, and explainfine-grained errors in modern high-fidelity video generations. We intro-duce Spotlight, a novel benchmark to rigorously assess whether cur-rent VLMs can precisely localize and explain nuanced video-generationerrors. Spotlight comprises 600 videos generated by state-of-the-artT2V models (Veo3, Seedance, LTX-2), annotated with over 1,600 fine-grained error localizations and explanations spanning physics, semanticsand anatomy. Our experiments reveal that current VLMs lag behindhumans significantly, with humans outperforming our best baselines bynearly 2× on the task. Our analysis highlights key areas of improvementfor utilizing VLMs as automated video evaluators, including the need formore robust perception and hallucination mitigation. Data and code isavailable at https://spotlight-video.github.io.

Abstract:
Large Vision-Language Models (VLMs) typically process hun-dreds or even thousands of visual tokens per image or video frame, in-curring quadratic attention costs and significant information redundancy.Existing token-reduction methods either ignore the textual query or relyon deep attention maps, which become unstable under aggressive prun-ing and often lead to degraded semantic alignment. To address this, weintroduce FlashVLM, a text-guided visual token selection frameworkthat dynamically adapts visual inputs to the given query. Rather thanrelying on noisy attention weights, FlashVLM computes an explicit cross-modal similarity between projected image tokens and normalized textembeddings within the LLM space. It then fuses this extrinsic text-imagerelevance with intrinsic visual saliency using log-domain weighting andtemperature-controlled sharpening. Furthermore, a diversity-preservingpartitioning mechanism retains a minimal yet representative set of back-ground tokens to preserve global context. Evaluated under identical to-ken budgets and protocols, FlashVLM achieves beyond-lossless compres-sion—slightly surpassing the unpruned baseline while discarding up to77.8% of visual tokens on LLaVA-1.5. Remarkably, it maintains 92.8%performance even under an aggressive 94.4% compression rate. Extensiveexperiments across 14 image and video benchmarks demonstrate thatFlashVLM establishes a new state-of-the-art in efficiency-performancetrade-offs, exhibiting strong robustness and broad generalization acrossmainstream VLMs.

Abstract:
Vision-language-action (VLA) models that generate contin-uous action chunks via flow matching lack an internal signal for judgingwhether a given prediction is reliable. Distribution shift and long-horizonrollouts can push backbone representations away from the region the ac-tion head decodes reliably, yet the policy has no mechanism to detector react to this drift. We observe that the cost of transporting observa-tion features to the action representation in a shared feature space risesprecisely when such drift occurs, providing a per-step reliability esti-mate without extra supervision. Building on this observation, we proposeDiG (Discrepancy Gate), a lightweight plug-in module for flow-matchingVLA policies. DiG computes a sliced Wasserstein transport cost betweenbackbone features and the action expert’s own input projection, maps itthrough an exponential gate, and uses the gate to modulate both a resid-ual feature refinement and the training loss. At inference time, the gateenables DiG-Refine, an iterative refinement process that corrects actionchunks before execution. Experiments on both simulation and real-worldscenarios show that DiG consistently improves success rates, with thelargest gains under distribution shift and on long-horizon tasks.

Abstract:
Achieving human-like reasoning in Vision-Language Models(VLMs) remains a long-standing challenge. Recent approaches leverageChain-of-Thought (CoT) rationales generated by human annotators orproprietary models to improve reasoning, which is costly and difficultto scale. Self-training offers a promising alternative by using model’sown outputs as supervision. However, existing methods often suffer fromvisual hallucinations - where rationales describe non-existent visual con-tent, and language shortcuts - where predictions rely on textual priorsrather than true visual grounding, as rationales are typically filtered onlyby answer correctness without verifying visual perception. To addressthis limitation, we propose a perception-verified self-training frameworkthat enforces visually grounded reasoning. First, our method employs aCoT template (caption-reasoning-conclusion) that disentangles percep-tion from reasoning, enabling independent verification of visual under-standing. To compensate for the absence of ground-truth captions, wepropose PerceptEval, an unsupervised method that evaluates captionquality based on its alignment with visual and textual elements presentin the image. Using caption verification together with answer correct-ness, we partition the data into three subsets: easy (correct caption andconclusion), medium (correct caption but incorrect conclusion), and hard(incorrect caption). Building on this partitioning, we design a two-stagecurriculum learning strategy. In Stage 1, the model is trained on easyexamples and subsequently in Stage 2, medium samples are incorporatedthrough a caption-guided reasoning enhancement procedure thatregenerates reasoning conditioned on verified captions. Only regeneratedsamples with the correct conclusions are retained. This ensures trainingin both stages is done exclusively on perceptually grounded reasoning,reducing hallucinations and language shortcuts. Extensive experimentsacross diverse domains and models demonstrate improvements of up to16% over standard self-training baselines. This demonstrates that theproposed framework is a scalable and cost-effective solution to advanc-ing multimodal reasoning without manually annotated CoT rationales.

Abstract:
Despite remarkable progress toward general-purpose videomodels, a critical question remains unanswered: how far are these modelsfrom achieving true multimodal reasoning? Existing benchmarks fail toaddress this question rigorously, as they remain constrained by straight-forward task designs and fragmented evaluation metrics that neglectcomplex multimodal reasoning. To bridge this gap, we introduce CLVG-Bench, an evaluation framework designed to probe video models’ zero-shot reasoning capabilities via Context Learning in Video Generation.CLVG-Bench comprises more than 1,000 high-quality, manually anno-tated metadata across 6 categories and 47 subcategories, covering com-plex scenarios including physical simulation, logical reasoning, and inter-active contexts. To enable rigorous and scalable assessment, we furtherpropose an Adaptive Video Evaluator (AVE) that aligns with human ex-pert perception using minimal annotations, delivering interpretable tex-tual feedback across diverse video context tasks. Extensive experimentsreveal a striking answer to our central question: while state-of-the-art(SOTA) video models, such as Seedance 2.0, demonstrate competenceon certain understanding and reasoning subtasks, they fall substantiallyshort with logically grounded and interactive generation tasks (achiev-ing success rates < 25% and ∼ 0%, respectively), exposing multimodalreasoning and physical grounding as critical bottlenecks. By systemati-cally quantifying these limitations, the proposed method provides action-able feedbacks and a clear roadmap toward truly robust, general-purposevideo models. CLVG-Bench and code are released at here.

Abstract:
Recent advances in multimodal large language models (MLLMs)have shown impressive reasoning capabilities. However, further enhanc-ing existing MLLMs necessitates high-quality vision-language datasetswith carefully curated task complexities, which are both costly and chal-lenging to scale. Although recent self-improving models that iterativelyrefine themselves offer a feasible solution, they still suffer from two corechallenges: (i) most existing methods augment visual or textual data sep-arately, resulting in discrepancies in data complexity (e.g., over-simplifieddiagrams paired with redundant textual descriptions); and (ii) the evo-lution of data and models is also separated, leading to scenarios wheremodels are exposed to tasks with mismatched difficulty levels. To ad-dress these issues, we propose SyncLoop, an automatic, closed-loopself-improving framework that jointly evolves both training data andmodel capabilities. Specifically, given a base dataset and a base model,SyncLoop enhances them by a cross-modal data evolution loop and adata-model evolution loop. The former loop expands the base datasetby generating complex multimodal problems that combine structuredtextual sub-problems with iteratively specified geometric diagrams ormathematical functions, while the latter loop adaptively selects the gen-erated problems based on the performance of the base model, to conductsupervised fine-tuning and reinforcement learning alternately. Conse-quently, our method continuously refines its model and training data,and consistently obtains considerable performance gains across multiplemathematical reasoning benchmarks.

Abstract:
Multimodal Large Language Models (MLLMs) have demon-strated promising spatial reasoning capabilities, while these abilities re-main underexplored in the emerging visual modality of panoramic im-agery. The full 360◦ ×180◦ field of view of panoramas essentially supportscomplex global multi-step reasoning, which is also the fundamental ad-vantage of panoramas in applications such as embodied intelligence. How-ever, existing panoramic benchmarks largely focus on simplistic queriesthat rely on local cues or single-/few-step reasoning, thereby ignoring thefundamental advantage of panoramas and failing to fully exploit theirpotential. To address this gap, we introduce OmniCoT , a panoramicspatial reasoning suite designed to enable MLLMs to use global evi-dence and perform multi-step inference across viewpoints. It includesOmniCoT-B (6.7K data) for evaluation, which measures both answeraccuracy and reasoning quality, OmniCoT-Real (1K data) as a man-ually annotated real-world subset to quantify the Sim-to-Real gap. Fortraining, OmniCoT-T (14.3K data) is purpose-built with structuredstepwise Chain-of-Thought annotations that explicitly link intermediatereasoning steps to panoramic evidence. Based on OmniCoT-T, we intro-duce OmniCoT-R1 and adopt a two-stage training strategy tailoredto the geometrically complex panoramic space, where Supervised Fine-Tuning (SFT) anchors reasoning to panoramic evidence (e.g., bearings,proximity) and GRPO penalizes geometrically incoherent paths to con-solidate global 360◦ spatial consistency. Through OmniCoT, we aim torecalibrate the difficulty of panoramic spatial reasoning to bet-ter align with the intrinsic capabilities of panoramic imagery ,thereby fostering meaningful progress in this research area.

Abstract:
Vision-language models (VLMs) commonly formulate visualgrounding and detection as a coordinate-token generation problem, serial-izing each 2D box into multiple 1D tokens that are learned and decodedlargely independently. This token-by-token decoding mismatches the cou-pled structure of box geometry and creates a practical inference bottleneckdue to strictly sequential generation. We introduce LocateAnything,a unified generative grounding and detection framework based on Par-allel Box Decoding (PBD). By decoding geometric elements such asbounding boxes and points as atomic units in a single step, LocateAny-thing preserves intra-box geometric coherence and unlocks substantialparallelism. We show that PBD improves both decoding throughput andlocalization accuracy. We further develop a scalable data engine andcurate LocateAnything-Data, a large-scale dataset with more than138 million training samples, substantially increasing data diversity forhigh-precision localization. Extensive evaluations show that LocateAny-thing advances the speed–accuracy frontier, achieving significantly higherdecoding throughput while improving high-IoU localization quality acrossdiverse benchmarks. The results highlight the complementary benefits ofParallel Box Decoding and large-scale training data in enabling efficientand precise unified visual grounding and detection.

Abstract:
Chain-of-Thought fine-tuning enhances the reasoning capabilities of Large Vision-Language Models, but can paradoxically exacerbate hallucinations, revealing a reasoning-faithfulness trade-off. We identify a key cause of this phenomenon as the “Confidence Trap”: under visual ambiguity or degradation, models may produce low-entropy, high-confidence errors instead of expressing appropriate uncertainty. This failure mode challenges conventional mitigation strategies that rely on uncertainty cues or contrastive decoding signals, since confident hallucinations can remain highly stable under visual perturbations. To address this problem, we propose Hallucination-Entropy Regulated Optimization (HERO), an on-policy reinforcement learning framework for mitigating confident hallucinations. HERO introduces a dynamic entropy-aware objective that assigns larger optimization weights to low-entropy erroneous outputs, and further improves training efficiency through variance-gated sample selection that focuses updates on informative hard negatives. Experiments on POPE, THRONE, and AMBER show that HERO achieves strong overall hallucination mitigation, substantially improves truthfulness in critical low-entropy regions, and maintains competitive general reasoning capabilities. These results suggest that explicitly targeting confidence-misaligned errors is an effective direction for improving multimodal faithfulness in CoT-style LVLMs.

Abstract:
Multimodal Large Language Models (MLLMs) show promising results as decision-making engines for embodied agents operating in complex, physical environments. However, existing benchmarks often prioritize high-level planning or spatial reasoning, leaving the fine-grained action intelligence required for embodied physical interaction underexplored. To address this gap, we introduce CFG-Bench, a new benchmark designed to systematically evaluate this crucial capability. CFG-Bench consists of 1,368 curated videos paired with 19,562 question-answer pairs spanning three evaluation paradigms targeting four cognitive abilities: 1) Physical Interaction, 2) Temporal-Causal Relation, 3) Intentional Understanding, and 4) Evaluative Judgment. Together, these dimensions provide a systematic framework for assessing a model’s ability to translate visual observations into actionable knowledge, moving beyond mere surface-level recognition. Our comprehensive evaluation on CFG-Bench reveals that leading MLLMs struggle to produce detailed instructions for physical interactions and exhibit profound limitations in the higher-order reasoning of intention and evaluation. Moreover, supervised fine-tuning (SFT) on our data demonstrates that teaching an MLLMs to articulate fine-grained actions directly translates to significant performance gains on established embodied benchmarks. Our analysis highlights these limitations and offers insights for developing more capable and grounded embodied agents. Project page: https://cfg-bench.github.io/

Abstract:
Video temporal grounding (VTG) aims to localize relevant moments and predict saliency scores in untrimmed videos based on natural language queries. Recently, DETR-inspired methods have achieved instance-level moment predictions through query-based semantic evidence aggregation. However, unlike spatial objects with clear physical edges, video moments often exhibit ambiguous temporal boundaries due to the semantic gradient. Consequently, relying solely on global semantics can lead to severe boundary misalignment, yielding sub-optimal grounding results. To address this issue, we propose a novel Dual Evidence Detection Transformer (DE2TR) framework for better boundary localization. In particular, we introduce a dual-branch decoder that simultaneously captures global semantic evidence and fine-grained boundary cues. To prevent feature conflict between these distinct objectives, we channel-wise decouple the moment queries, facilitating independent evidence learning under the supervision of evidence alignment losses. Building upon this dual-branch design, we introduce a prior-guided refinement mechanism that employs explicit prior maps to steer queries toward potential regions of interest. Furthermore, a boundary augmentation strategy synthesizes ideal boundary patterns via simple temporal splicing, effectively enhancing the model’s boundary awareness. Extensive experiments on multiple popular benchmarks demonstrate that DE2TR significantly outperforms state-of-the-art approaches.

Abstract:
3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable pretraining and transfers well to per-point tasks such as semantic and panoptic segmentation, but transfer to 3D detection remains weaker. We analyze recent SSL methods and find that most objectives are defined only on measured LiDAR returns from visible surfaces, leaving occluded and unobserved regions unconstrained. This visible-surface bias can be sufficient for point-wise prediction, but 3D detection requires robustness to missing structure. To address this gap, we propose GhostPoint, an SSL framework that hallucinates latent features in local neighborhoods around discovered instances, generated via a novel instance voxel dilation. In GhostPoint, an encoder processes observed returns, and an additional predictor infers neighborhood representations from observed context. In addition to standard encoder-level supervision, we introduce a predictor-level supervision scheme on sampled voxels from generated neighborhoods. Specifically, observed (visible/masked) voxels match teacher-encoder targets, while unobserved voxels match teacher-predictor hallucinations. This design encourages the learned representation to explicitly model structure beyond observed returns. Extensive evaluations on nuScenes and Waymo demonstrate that our method achieves state-of-the-art performance, consistently improving downstream 3D detection, especially under sparse scans and limited labels.

Abstract:
Facial Expression Recognition (FER) in the wild is inher-ently affected by annotation ambiguity and noisy label. Recent label dis-tribution learning (LDL) methods alleviate this issue by exploiting neigh-borhood information. However, existing approaches typically rely onstatic or heuristic mechanisms to estimate neighbor contribution weights,limiting their capacity to model complex dependencies among neighbor-ing samples. In this paper, we propose Mamba-driven Neighbor WeightRefinement (Mamba-NWR), a robust FER framework that integratescross-attention routing (CAR) for initial neighbor weight estimation andMamba-based iterative refinement (MIR) for distribution aggregation,effectively handling noisy labels. Specifically, our CAR mechanism intro-duces a multi-route local feature projection for both the input and neigh-bor samples. The subsequent cross-attention-based interaction amongthese features serves as the foundation for computing the initial neigh-bor weights. Then, we employ a Mamba-based state space model to iter-atively refine the contribution weights of neighboring label distributions,enabling dynamic balance between preserving historical states and ab-sorbing new neighborhood information. The optimized neighbor distribu-tions are then aggregated with the original logical label through a learn-able fusion factor to construct the final supervision information. Exten-sive experiments on multiple benchmark datasets demonstrate that ourapproach consistently improves robustness under ambiguous and noisyannotations, achieving state-of-the-art performance.

Abstract:
Cross-domain Few-shot Segmentation (CD-FSS) aims to learn generalizable segmentation capability from abundant annotated samples in the source domain, enabling accurate segmentation of novel classes in the target domain with only a few annotated samples. Existing CDFSS methods mainly focus on mitigating feature distribution shifts caused by style gaps while ignoring significant differences in class semantic granularity and discriminative attributes across domains, leading to two key degradations in support-query matching: semantic over-alignment and attribute over-alignment. To this end, we propose the Dual Hierarchical Aggregation Network (DHANet), which comprises three key modules. First, the Hierarchical Spatial Aggregation (HSA) module performs multi-scale region aggregation of pixel features along the spatial dimension, generating hierarchical semantic-enhanced features to alleviate semantic over-alignment. Additionally, the HCA module conducts multi-scale attribute aggregation along the channel dimension, generating hierarchical attribute-enhanced features to mitigate attribute overalignment. Finally, we propose the Online Probabilistic Semantic Bank (OPSB), which progressively constructs and updates class probability distributions from query predictions during inference, and samples multiple pseudo-prototypes as additional support information to mitigate insufficient support. Extensive experiments on four target-domain datasets demonstrate that our method achieves state-of-the-art performance.

Abstract:
We present UniMotion, to our knowledge the first unifiedframework for simultaneous understanding and generation of humanmotion, natural language, and RGB images within a single architecture.Existing unified models handle only restricted modality subsets (e.g.,Motion–Text or static Pose–Image) and predominantly rely on discretetokenization, which introduces quantization errors and disrupts temporalcontinuity. UniMotion overcomes both limitations through a core princi-ple: treating motion as a first-class continuous modality on equal footingwith RGB. A novel Cross-Modal Aligned Motion VAE (CMA-VAE) andsymmetric dual-path embedders construct parallel continuous pathwaysfor Motion and RGB within a shared LLM backbone. To inject visual-semantic priors into motion representations without requiring images atinference, we propose Dual-Posterior KL Alignment (DPA), which distillsa vision-fused encoder’s richer posterior into the motion-only encoder. Toaddress the cold-start problem—where text supervision alone is too sparseto calibrate the newly introduced motion pathway—we further proposeLatent Reconstruction Alignment (LRA), a self-supervised pre-trainingstrategy that uses dense motion latents as unambiguous conditions toco-calibrate the embedder, backbone, and flow head, establishing a stablemotion-aware foundation for all downstream tasks. UniMotion achievesstate-of-the-art performance across seven tasks spanning any-to-any un-derstanding, generation, and editing among the three modalities, withespecially strong advantages on cross-modal compositional tasks.

Abstract:
Zero-shot object counting aims to count objects in images from givenclass names. Existing methods typically select representative image patches orextract text embeddings as exemplars to match image regions for counting. How-ever, a limited number of selected patches is insufficient to cover intra-class di-versity, and the accumulated errors in exemplar detection and selection substan-tially degrade counting accuracy. While the text embeddings remain at a genericsemantic level, blind to the specific instance diversity of target objects in animage. To handle these issues, this paper proposes the Hyper-Exemplar Repre-sentation (HER) counting framework (HER-Count) to represent the objects ofa specific category in a given image. Rather than image patches or text embed-dings, HERs are synthesized from holistic images and textual class names viaan multi-modal large-language model. By jointly integrating all target objects ineach image, HERs avoid exemplar generation errors and are more customizedrepresentative, thus yielding more accurate counting results. To effectively ex-ploit HERs in counting, HER-Count introduces a hierarchical fusion strategy thatinjects HERs into multiple intermediate layers of the vision encoder when pre-dicting density maps. This enables the model to progressively refine countingcues across layers, significantly enhancing the performance. A global discrimi-nation enhancement constraint at both image sample and semantic class levels isdesigned to further improve the capacities for both representation and discrimi-nation of HERs. Extensive experiments on widely used benchmarks demonstrateboth the state-of-the-art performance and generalization ability of the proposedHER-Count. Benefiting from the compact end-to-end design, HER-Count alsoachieves promising inference speed.

Abstract:
Domain Adaptive Object Detection (DAOD) adapts detec-tors from labeled source domains to unlabeled target domains. Currentteacher-student methods rely solely on proposal-level pseudo-boxes assupervision, which suffer from substantial noise under domain shift. Wemake an intriguing empirical observation: image-level predictions, ob-tained by aggregating proposal scores, achieve significantly higher ac-curacy than proposal-level pseudo-boxes. This superiority stems fromaggregation’s inherent noise reduction and robustness to localization er-rors. Despite this finding, image-level predictions have been completelyoverlooked as a supervision source in DAOD, though extensively usedin weakly supervised detection. We propose Multi-Granularity Pseudo-Labeling (MGPL), which for the first time incorporates image-level su-pervision alongside proposal-level pseudo-boxes in teacher-student DAOD.Our framework supervises the student detector at both granularities:proposal-level boxes provide spatial precision while image-level predic-tions offer reliable category information. We further introduce coarse-to-fine collaborative filtering, where image-level predictions guide class-adaptive threshold selection for proposals, reducing false negatives whilemaintaining precision. Extensive experiments demonstrate state-of-the-art performance across multiple benchmarks. For example, on PascalVOC → Clipart1k, we achieve 51.4% mAP, surpassing previous best andfully-supervised Oracle by 2.3% and 6.4% respectively. Our work revealsthe untapped potential of multi-granularity learning in domain adapta-tion. Code will be made publicly available.

Abstract:
Radar sensors provide reliable perception under adverse weatherand lighting conditions, but their sparse, noisy, and weakly semanticmeasurements make dense semantic segmentation challenging. Most ex-isting radar segmentation methods rely on grid-based encodings andpairwise interactions, which struggle to capture the higher-order rela-tional structure formed by multiple radar returns from the same phys-ical object. We introduce a unified higher-order structural alignmentframework for multi-view radar segmentation. The proposed method re-fines radar feature representations using learnable hypergraphs to cap-ture higher-order dependencies among spatially related responses. To en-sure consistency across heterogeneous radar projections, we further alignview-specific features using Unbalanced Optimal Transport (UOT), en-abling correspondence-free alignment under varying measurement den-sities and partial observations. An adaptive attention mechanism thenfuses complementary radar views while emphasising structurally informa-tive responses under sparsity and noise. The resulting architecture learnsstructurally consistent representations across Range Angle (RA), RangeDoppler (RD), and Angle Doppler (AD) views and is trained using super-vised segmentation together with cross-view consistency regularisation.Experiments on the CARRADA and RADIal benchmarks demonstrateconsistent improvements over strong radar-specific baselines, achieving63.8% mIoU on CARRADA and 83.4% mIoU on RADIal, im-proving the previous best methods by +1.7 and +2.3 mIoU, respec-tively. These results highlight the importance of higher-order relationalmodelling for robust radar perception.

Abstract:
Open-world semi-supervised learning (OWSSL) aims to rec-ognize known and unknown classes from unlabeled samples, where la-beled samples only include known classes. Recent studies introduce class-specific textual descriptions as semantic information and employ learn-able textual prompts to optimize these descriptions, thereby enhancingthe model’s ability to recognize classes. However, these methods oftenoverlook the model bias toward known classes during textual prompttuning, and establish only limited cross-modal connections, leading tosevere sparsity in the graph structure and hindering the propagation ofvisual and semantic information across the graph. These issues collec-tively limit the model’s ability to discover unknown classes, particularlyin scenarios with a large number of classes. To address these issues, wepropose a novel OWSSL method, which consists of two core components:(1) textual prompt optimization aimed at mitigating the model bias to-ward known classes, and (2) bimodal graph enhancement designed toalleviate the sparsity issue in the graph structure. Experimental resultsacross multiple datasets indicate that our method achieves an averageaccuracy 20.7% higher on unknown classes than state-of-the-art OWSSLmethods (relative improvement).

Abstract:
Zero-shot Human-Object Interaction (HOI) detection has achieved significant progress by leveraging the open-vocabulary capabilities of Vision-Language Models. However, the generalization of existing methods is severely restricted by a two-fold representational bottleneck. First, at the feature level, essential fine-grained interaction cues are frequently overshadowed by high-level global semantics. Second, at the decision level, models exhibit a strong background shortcut bias, often relying on scene co-occurrences rather than the actual humanobject interactions. To address these challenges, we propose a novel zeroshot HOI detection framework, DRIC, which is explicitly formulated for Disentangling and Reusing Interaction Cues. Specifically, we introduce the Context-Orthogonal Residual Disentangling (CORD) module, which projects features onto a global prototype subspace to decouple fine-grained residual details from global semantic biases, subsequently reintegrating them for enhanced local discriminability. Furthermore, the proposed Stratified Local Fusion (SLF) mechanism mitigates background shortcuts by aggregating multi-depth features from the image encoder of CLIP. This recovers essential contact-level details, guiding the model toward actual interactions. Extensive experiments demonstrate that DRIC consistently outperforms state-of-the-art methods across zero-shot evaluation settings, validating the robustness of the proposed framework in handling interaction ambiguity and cross-scene transfer.

Abstract:
Generalization remains a critical bottleneck in AI-generatedimage detection. Because many modern generators are proprietary oradversarially modified, existing detectors overfit to the low-level tex-tural patterns of accessible training data, resulting in severe failureson unseen domains. Conventional regularization techniques (e.g., L1 /L2norms, Dropout) apply indiscriminate parametric constraints and fail toprovide the domain-invariant structure necessary for cross-generator ro-bustness. To address this, we propose Feature-Augmented Implicit Reg-ularization (FAIR). FAIR introduces an orthogonal, macro-structuralprior, specifically, Scene Composition Structure (SCS), during train-ing to geometrically constrain the model’s optimization trajectory. Byaugmenting the primary feature space with domain-invariant SCS fea-tures, FAIR explicitly penalizes texture-biased shortcut learning. Cru-cially, this structural prior is entirely discarded at inference, yielding asmoothed, generalized decision boundary with zero architectural or com-putational overhead. Extensive evaluations across five massive bench-marks demonstrate that integrating FAIR into state-of-the-art detectorssignificantly improves cross-generator generalization, boosting accuracyby up to 8.04% and establishing new state-of-the-art robustness in zero-shot transfer scenarios.

Abstract:
High Dynamic Range (HDR) imaging is indispensable forautonomous driving to ensure robust perception under changing light-ing conditions, such as tunnel exits or night driving. However, exist-ing learning-based HDR methods suffer from excessive computationalcosts and latency, making them impractical for real-time edge deploy-ment. In this paper, we propose SplitHDR, an extremely lightweightHDR framework tailored for machine vision. Leveraging advanced sen-sor technologies designed to minimize temporal displacement, our ap-proach avoids relying on heavy alignment modules and introduces aluminance-aware dynamic inference mechanism. Specifically, the modelconstructs a saturation mask based on input intensity, efficiently routingsaturated regions to a multi-frame fusion mode and unsaturated regionsto a lightweight denoising mode. This task decomposition ensures thatcomputational resources are allocated only where necessary. Experimen-tal results demonstrate that our method achieves state-of-the-art effi-ciency with only 0.002M parameters and 3.63 GMACs, reducing the pa-rameter count by over 99% compared to existing multi-exposure models.Despite the extreme compression, the HDR images reconstructed by ourapproach effectively preserve critical structural details, yielding highlycompetitive object detection performance crucial for safety-critical au-tonomous systems.

Abstract:
Textual image classification remains challenging due to thediversity of document layouts and textual structures. We propose TiCRL(Textual Image Classification with Reinforcement Learning-based Cur-riculum Learning), which combines a text-independent difficulty mea-surer with an RL-based training scheduler. The measurer estimates sam-ple difficulty using visual-textual features and loss signals without re-quiring OCR, while the RL agent dynamically adapts the curriculumbased on the learner’s state. TiCRL substantially improves both accu-racy and data efficiency. On a lightweight CNN trained from scratch,it achieves 83.67% accuracy using only 82% of the training data, out-performing curriculum learning baselines by up to 13.8%p. Applied toa strong transformer baseline (DiT), TiCRL reaches 93.02% accuracyusing only 58% of the data, surpassing transformer baselines trained on80%. The learned curriculum policy generalizes to new datasets and ar-chitectures without retraining, demonstrating that RL-based curriculumlearning is an effective and efficient training strategy for textual imageclassification.

Abstract:
Zero-shot anomaly detection aims to identify defects in ar-bitrary novel domains; however, existing models assume that the auxil-iary data contains a rich diversity of anomalies, neglecting the far morecomplex and unpredictable variations in real-world target domains. Thisstudy introduces DIVE, the first approach to investigate the scenario oflimited auxiliary anomaly priors and resolve the resulting substantial per-formance degradation. Through a shallow-and-deep text embedding in-jection strategy during visual encoding, DIVE learns to abstract genericanomaly concepts shared across the auxiliary training domain and di-verse target domains. Moreover, we propose a disentanglement mech-anism to tackle the suboptimal alignment between visual embeddingsentangled with object semantics and object-agnostic textual prompts.Experiments demonstrate that, under the setting of limited anomalypatterns in auxiliary data, DIVE outperforms SOTA baselines by up to16.2% and 28.5% on two classification metrics, and 23.4%, 24.1%, and47.0% on three segmentation metrics, in terms of average performanceacross twelve datasets. Furthermore, it maintains highly competitive per-formance when auxiliary data exhibits sufficient anomaly diversity.

Abstract:
The recently established ‘Out of Sight, Not out of Mind’ (OSNOM) task for egocentric videos focuses on tracking objects that are moved by the camera wearer, online, maintaining knowledge of instance locations throughout the video even when they leave the field of view or become heavily occluded. In this paper, we propose the first learningbased solution to the OSNOM task: Whareformer, a transformer-based model with two components: an updatable memory of established tracks and a track assignment module that associates observations with existing tracks in a feed-forward manner. Whareformer jointly reasons over evolving object appearance (what) and updated 3D location (where), and employs a dedicated New Track token to reason about novel objects. Thanks to its design choices of using relative distances and evolving track representations, Whareformer is trained on a small set of 56 videos but achieves SOTA performance on 260 long test videos from three datasets: EPIC-KITCHENS-100 (unseen videos), IT3DEgo, and HD-EPIC, with significant absolute improvements over prior work.

Abstract:
Multi-animal tracking (MAT) is critical for wildlife monitor-ing and behavioral analysis, yet remains challenging due to uniform ap-pearance, high density, and irregular motion. Existing methods typicallyfollow heuristic- or query-based paradigms: the former relies on hand-crafted geometric associations without end-to-end optimization, whereasthe latter enables joint optimization but relies heavily on appearanceembeddings. In such conditions, continuous geometric embeddings canbe unstable, as small coordinate perturbations may disproportionatelyalter cross-frame attention weights, degrading identity association perfor-mance. To address this limitation, we propose HieDG, a HierarchicalDiscrete Geometry-guided tracking framework that reformulates geo-metric dynamics as structured discrete representations within a query-based tracker. Instead of directly using raw geometric signals, HieDGemploys a two-stage residual codebook to discretize position, scale, andvelocity cues, transforming unstable continuous geometry into struc-tured, stable discrete tokens. These tokens are aligned with visual em-beddings and integrated into the tracking queries to enhance identityconsistency. Extensive experiments on animal-specific benchmarks (Ani-malTrack, BFT, and BuckTales) demonstrate state-of-the-art associationperformance with significant improvements in HOTA, AssA, and IDF1.Additional evaluations on generic multi-object tracking benchmarks, in-cluding DanceTrack and SportsMOT, show competitive performance,indicating the broader applicability of discretized geometric modelingbeyond animal-specific scenarios.

Abstract:
In the task of human mesh recovery (HMR), multi-personscenes are particularly difficult to handle due to the many entities thatappear and occlusions between them over time. In particular for videoinputs, there is a need to track each entity reliably and consistently.Existing methods rely on pretrained human detection modules, increas-ing their runtime and limiting the number of tracked entities. We presentDETRAM, a unified framework for multi-person HMR and tracking thatsimultaneously detects, reconstructs, and tracks humans across time,both automatically and via user prompts. DETRAM uses a single trans-former decoder with an identity-consistent set of learnable query embed-dings that persist across frames: detection queries discover new people,tracking queries maintain pose and shape for existing individuals, andprompt queries follow user-specified identities. Our approach achievesstate-of-the-art tracking results on PoseTrack21, 3DPW, BEDLAM, andMuPoTS-3D, and competitive reconstruction accuracy on BEDLAM and3DPW, while uniquely supporting prompt-based tracking of individualsin multi-person scenes. To our knowledge, this is the first method tounify promptability and multi-person HMR with tracking in an end-to-end trainable framework, enabling user-directed human analysis in

Abstract:
Aerial-ground person re-identification (AG-ReID) is signifi-cantly challenged by heterogeneous geometric regimes and severe foreground-background imbalance. Traditional shared-backbone architectures suf-fer from transformation conflicts when modeling disparate aerial andground views, while residual connections in transformers often lead toidentity embedding contamination from persistent background activa-tions. To address these issues, we propose View-conditioned and Identity-Guided Adaptation (VIGA), a unified framework that mitigates AG-ReID-specific aerial-ground discrepancies at both the parameter and ac-tivation levels. VIGA incorporates View-Conditioned Low-Rank Adap-tation to dynamically modulate feed-forward weights using view-awarelow-rank residuals, thereby alleviating view-dependent geometric map-ping conflicts without substantial parameter overhead. Simultaneously,we introduce Identity-Guided Sparsification, which employs a soft mask-ing mechanism guided by identity semantics to suppress irrelevant back-ground noise while preserving the 2D spatial topology. Extensive evalua-tions on the LAGPeR and AG-ReID.v2 datasets demonstrate that VIGAachieves state-of-the-art performance, particularly in challenging cross-platform retrieval scenarios, by effectively learning robust, view-invariantrepresentations.

Abstract:
Compositions of frozen foundation models offer a trainingfree route to instance segmentation, yet a clear gap remains between current systems and their empirical upper bounds. We introduce progressive oracle replacement, a diagnostic procedure that decomposes this gap into stage-level components, and find that the scoring stage accounts for most of it. Across diverse encoders and mask generators, we trace this gap to Quality Misalignment: a mismatch between semantic matching confidence and mask geometric fidelity that yields ranking errors when the two signals are used independently. This pattern persists across all tested configurations, suggesting that it is intrinsic to decoupled scoring. We therefore propose Semantic-Geometric Quality Alignment (SGQA), which replaces decoupled scoring with a single quality score formed by the geometric mean of the two signals and adds no hyperparameters to the scoring formula. Under a fixed backbone of DINOv2 ViT-L paired with SAM2 Hiera-L, SGQA outperforms existing training-free methods and matches or exceeds fine-tuning-based methods with comparable or smaller backbones.

Abstract:
Hierarchical structure is common in image data, where finegrained clusters often merge into larger, coarser semantic clusters. In biological cell images, current self-supervised learning models tend to suppress the inherent hierarchical structure, as coarse factors (such as the imaging modality) systematically obscure finer attributes (e.g., morphological details) in the latent space. We propose a novel hierarchy-aware self-supervised training framework to address this problem. Our method integrates two components: we propose (i) a distillation framework by introducing a segmentation teacher that improves the latent space’s morphological awareness, and (ii) a hierarchy-aware contrastive loss based on HDBSCAN to improve decision boundaries between closely related yet distinct subtypes at each hierarchical level. Both components counteract the self-supervised learning’s tendency to overemphasize coarse factors by aligning embeddings with semantic and morphological cues, yielding biologically meaningful sub-clusters driven by fine morphological detail. We train and evaluate our method on a curated corpus of 2.3M single cells aggregated from 20 microscopy datasets (labeled and unlabeled), covering 208 cell classes. Our method improves upon the counterparts and baseline, with the average top-K accuracy up by +2.8%, top-9 retrieval on the dataset with a deeper hierarchy up by +6.3%, and an F1-score on biologically relevant classification of administered drug from perturbed cell morphology downstream up by +7.8%.

Abstract:
Human body parsing datasets and benchmarks define partlabels for intact anatomies and focus on body parts and clothing. Widelyused datasets implicitly assume intact anatomies and therefore do not in-clude residual-limb or prosthetic classes. This leaves people with limb de-ficiencies excluded from body parsing model training and standard evalu-ation, creating a fairness gap. To the best of our knowledge, InclusiveHuman-10K (IH-10K) is the first human parsing benchmark centered on in-dividuals with limb deficiencies, with explicit labels for residual limbs,prostheses, and mobility assistive devices. Specifically, IH-10K containsmore than 10k images and around 15k person instances from everyday,sports, and clinical scenes, each with pixel-wise body parsing masks. Thisbenchmark can support e-commerce, human-machine interaction, reha-bilitation and assistive technology, human-centric image understanding,and editing. Moreover, we extend conventional body parsing categorieswith explicit classes for residual limbs, prostheses, and mobility assis-tive devices. On our IH-10K, body parsing models can be trained andevaluated on disability-related regions for this population with limb de-ficiencies, instead of treating them as background or noise. To assesswhether disability-related regions are systematically underserved, we re-port group-wise evaluation metrics across disability-related and commonclasses. Our experiments show that supervised models struggle on theseregions due to large appearance variation and broken limb structure.Meanwhile, zero-shot methods perform poorly, suggesting that these con-cepts are absent from previous benchmarks and under-covered in open-vocabulary pretraining. This further highlights the representational andmeasurement gaps and signifies the necessity of our IH-10K benchmark.We hope this work helps build vision systems that serve people with limbdeficiencies more reliably and fairly, increasing the likelihood of mean-ingful social benefit.

Abstract:
Vision-language models offer a promising approach for zero-shot anomaly detection (ZSAD). However, due to object-centric bias,normal and anomalous text prototypes exhibit a high semantic over-lap. While enforcing strict orthogonality between them improves dis-criminability, mapping highly contiguous visual inputs onto drasticallyorthogonal prototypes introduces a geometric dilemma, disrupting thepre-trained structural continuity. To address this problem, we proposeProximity-CLIP, a framework that visually calibrates the semantic mar-gin to guide visual adaptation. First, we introduce a visually-calibratedsemantic proximity learning mechanism that uses a bounded dynamicregularization to learn an appropriate semantic margin, ensuring dis-criminative separation while preserving structural alignment. Second, wedesign an Anomaly Query Module (AQM) driven by these text priors.Using the calibrated anomalous prototype as a semantic query, the AQMactively retrieves localized defect cues from contextual visual patches,mitigating the dilution of subtle anomalies during global pooling. Exten-sive experiments demonstrate that Proximity-CLIP outperforms currentstate-of-the-art methods across multiple ZSAD benchmarks with mini-mal architectural modifications.

Abstract:
We address the problem of discovering repeated elementsfrom a single image. In contrast to existing approaches that depend onlarge annotated datasets, curated multi-image collections, or object seg-mentation masks, we show that a single image can suffice to learn ameaningful object model in a completely bottom-up fashion, without anyprior knowledge beyond a coarse scale prior. Our method learns a tunableimage-space prototype of the repeated elements through a reconstructionobjective, enabling the model to identify and synthesize consistent objectinstances within the same image. Experiments on 116 real images fromthe FSC-147 dataset demonstrate that our method successfully learnscoherent element models and captures intra-category variation on chal-lenging images. Qualitative results reveal superior reconstructions andinterpretable decompositions compared to classical decomposition, jointalignment, and 3D object modeling methods, while maintaining a simple2D formulation. These results suggest that meaningful object discoverycan emerge from single-image learning alone.

Abstract:
Composed Image Retrieval (CIR), a task towards personalizing visual intelligence, aims to retrieve a target image from a database based on users’ multimodal query, which contains a reference image and a modification text. The text specifies how to alter the reference image to form a “mental image”, based on which CIR should find the target image in the database. The fundamental challenge of CIR is that this “mental image” is not physically available and is only implicitly defined by the query. The contemporary literature pursues zero-shot methods and uses a Large Multimodal Model (LMM) to generate a textual description for a given multimodal query, and then employs a Vision-Language Model (VLM) for textual-visual matching to search for the target image. In contrast, we address CIR from first principles by directly generating the “mental image” for more accurate matching. Particularly, we prompt an LMM to generate a “mental image” for a given multimodal query and propose to use this “mental image” to search for the target image. As the “mental image” has a synthetic-to-real domain gap with real images, we also generate a synthetic counterpart for each real image in the database to facilitate matching. In this sense, our method uses LMM to construct a “paracosm”, where it matches the multimodal query and database images. Hence, we call this method Paracosm. Notably, Paracosm is a training-free zero-shot CIR method. It significantly outperforms existing zero-shot methods on challenging benchmarks, achieving state-of-the-art performance for zero-shot CIR.

Abstract:
Existing referring image segmentation (RIS) models passively process static images captured from fixed perspectives, limiting their applicability in Embodied AI, where agents must perform active perception in the continuous 360◦ environments. To bridge this gap, we introduce a novel task: Active Panoramic Referring Segmentation (APRS). In this setting, an agent is required to adjust its viewing direction (∆θ,∆ϕ) to explore the 360◦ environment, seeking the object specified by a user instruction for segmentation. To tackle this challenging task, we propose PanoSeeker, a memory-augmented agent for efficient APRS. Rather than relying on heuristic scanning, PanoSeeker integrates a Vision-Language Model (VLM) with EgoSphere—an explicit spatial visual memory. By progressively integrating sequential local observations into a unified 360◦ representation, EgoSphere enables the agent to plan efficient and non-redundant search trajectories. Once the target is found, the agent performs active viewpoint alignment and outputs the segmentation mask. Furthermore, we curate an expert-annotated search trajectory dataset with memory timelines for Supervised FineTuning, followed by Reinforcement Learning post-training to explicitly optimize PanoSeeker’s exploration efficiency. Extensive experiments on our newly established APRS benchmark demonstrate that PanoSeeker achieves superior search efficiency and segmentation accuracy, significantly outperforming adapted state-of-the-art baselines.

Abstract:
Face recognition systems (FRSs) are increasingly deployed in critical real-world services for authentication, such as banking applications and airport identity checks, necessitating stringent security configurations. Consequently, the security vulnerabilities of FRSs have garnered significant attention. While existing studies have extensively explored FRS security, prior analyses have primarily focused on mediumsecurity threshold settings, which are not directly applicable to FRSs operating under high-security constraints. In this paper, we propose the first successful impersonation attack against FRSs under high-security threshold settings. Among various threat models, we focus on a practical and challenging scenario: score-based impersonation attacks under strict rate limits. To precisely evaluate the feasibility of such attacks, we provide a principled mathematical analysis characterizing the gaps in each stage of the attack pipeline. Our method significantly enhances impersonation capabilities in score-based attacks, even under elevated decision thresholds. On the LFW benchmark, with a budget of only 100 confidence score queries per identity, our attack achieves an impersonation success rate exceeding 92% against Amazon Rekognition at a confidence score threshold of 99—recommended setting for law enforcement scenarios. We further observe consistently robust performance across multiple open-source FRSs evaluated at similarly stringent decision thresholds.

Abstract:
The rapid advancement of diffusion models has exacerbatedprivacy risks by enabling realistic manipulations of personal facial im-ages. Existing proactive defenses primarily target model-specific internalmodules under restrictive white-box assumptions, rendering their pro-tective efficacy highly vulnerable to unseen architectures and manipula-tion paradigms. To bridge this gap, we propose FaceArmor, a universalfacial image protection framework against both unknown generative ar-chitectures and real-world environmental transformations. Unlike priormodel-dependent approaches, FaceArmor focuses on the intrinsic fea-ture composition of facial images. It leverages an ensemble of surrogateextractors to decompose representations into a structural tier targetinglatent reconstruction and fine textures, and a semantic tier obscuringprompt alignment and biometric identities. These complementary di-mensions are universally exploited by mainstream diffusion-based ma-nipulation methods. FaceArmor then employs an attention-based softweighting scheme to generate effective adversarial perturbations acrossthese heterogeneous features. By extracting intrinsic attention heatmapsdirectly from the surrogate models, this strategy smoothly decouplescompeting gradients and resolves spatial optimization conflicts. Exten-sive experiments demonstrate that FaceArmor not only preserves visualimperceptibility but also achieves exceptional zero-knowledge transfer-ability, robustly thwarting diverse malicious manipulations across state-of-the-art architectures.

Abstract:
Knowledge Distillation (KD) transfers knowledge from a largeteacher model to a smaller student by aligning their predictive distri-butions. However, conventional KD formulations - typically based onKullback–Leibler divergence - assume that the teacher provides reliabletargets. In practice, teacher predictions are often noisy or overconfident,and existing correction-based approaches rely on ad-hoc heuristics andextensive hyper-parameter tuning, which hinders generalization. We in-troduce REDistill (Robust Estimator Distillation), a simple yet prin-cipled framework grounded in robust statistics. REDistill replaces thestandard KD objective with a power divergence loss, a generalizationof KL divergence that adaptively downweights unreliable teacher out-put. This formulation provides a unified and interpretable treatmentof teacher noise, requires only logits, integrates seamlessly into existingKD pipelines, and incurs negligible computational overhead. Extensiveexperiments on CIFAR-100 and ImageNet-1k demonstrate that REDis-till consistently improves student accuracy in diverse teacher-student ar-chitectures. Remarkably, it achieves these gains without model-specifichyper-parameter tuning, underscoring its robustness and strong general-ization to unseen teacher–student pairs.

Abstract:
Despite significant advances in image segmentation, evenstate-of-the-art models produce masks with imperfect boundaries, se-mantic inconsistencies, and structural errors. Mask refinement addressesthese limitations, yet current approaches rely on simplistic syntheticnoise that fails to capture the complex error patterns of real segmen-tation models. We introduce Phoenix, a novel framework that lever-ages adversarial learning to generate semantically meaningful noise pat-terns and contrastive learning to model refinement relationships. Ourapproach consists of two key innovations: (1) Adversarial Mask Per-turbation, which employs embedding attacks to create semantic-awarenoise that mimics real segmentation errors, and (2) Contrastive MaskRefinement Learning, which establishes a tri-directional framework thatensures feature consistency within semantic regions while maintainingseparation between classes. Experiments demonstrate that Phoenix sig-nificantly outperforms existing methods across diverse tasks, while con-sistently enhancing state-of-the-art segmentation models with substan-tial improvements. Our code and project page are publicly available atTargetMaskMorphologicalMask (b) Morphological NoiseLess-Semantic, Unrealistic, and Non-Contextual NoiseAdversarialMask(ours)Semantic, Realistic, and Contextual False-Negative & False-Positive Noise(a) Qualitative Samples of Morphological and Adversarial Noise Masks (c) Adversarial NoiseFig. 2: Qualitative Comparison of noise patterns (a) between morphological andour adversarial noise masks. (b, c) Distribution of the Pearson correlation betweennoise location and image edge/texture maps on LVIS val. (b) Morphological noise isnarrowly concentrated near 0 (semantically uncorrelated), while (c) our adversarialnoise spans [−0.6, 0.8], where positive values indicate alignment with semantic struc-tures and negative values indicate alignment with homogeneous regions, mirroring thediverse error patterns of real models.

Abstract:
Concept erasure and machine unlearning have emerged asa critical paradigm for enabling models to remove unwanted knowledgefrom learned representations at the level of classes, attributes, or higher-level semantics, motivated by the safety concerns surrounding moderngenerative AI models. However, existing approaches face three persis-tent challenges: (1) they often degrade utility on the remain concepts,(2) they often leave residual feature-level signals that enable recoveryof the forget concepts via x001C ne-tuning or representation probing, and (3)they impose substantial computational or memory overhead, limitingscalability. Prior work, such as Erasing Space Concept (ESC), moves to-ward structured removal in representation space, but achieving robusterasure with low overhead remains a challenge. To address this issue, wepropose Robustly Erasing Space Concepts (R-ESC), a framework for ro-bustly erasing space concepts via stochastic feature remapping. R-ESCintroduces three components: (i) prototype-orthogonal projection thatreduces interference by decorrelating forget and remain prototypes priorto erasure, (ii) stochastic multi-target remapping that blends forget pro-totypes into multiple remain prototypes to prevent re-separation andhinder recovery, and (iii) activation-mean prototypes that compress theerasure procedure to a single forward pass, yielding linear-time computa-tion with constant additional memory. Across extensive experiments, R-ESC maintains remain data utility, strengthens resistance to feature-levelrecovery, and scales to large models and datasets, providing a practicalpathway toward robust and e x001E cient concept erasure.

Abstract:
Despite the remarkable capabilities of Large Vision-LanguageModels (LVLMs), they remain highly susceptible to hallucinations. Exist-ing inference-time mitigation strategies largely rely on visual attention asa diagnostic proxy. However, we demonstrate that this approach is insuf-ficient in certain cases due to the “anchoring trap,” where parameterizedlinguistic priors override correct visual grounding. In this paper, we pio-neer a representation geometry perspective, identifying hidden states asa complementary diagnostic signal beyond visual attention. We modelthe evolution of hidden states across layers and generation steps as con-strained sequences governed by a static visual subspace and a dynamiclinguistic subspace within a high-dimensional Euclidean space. We revealthat hallucinations inherently stem from abrupt structural deviations,specifically, the spontaneous departure of latent representations from thevisual subspace and their subsequent drift into the linguistic domain.To quantify this phenomenon, we propose the Geo-Semantic TurbulenceIndex (GSTI) to adaptively monitor visual misalignment and semanticvolatility. Building upon GSTI, we introduce Orthogonal Residual-BasedIntervention (ORBIT), an architecture-agnostic, plug-and-play mecha-nism that recalibrates divergent representation sequences by employinga closed-loop intervention encompassing residual extraction, visual re-anchoring, and norm-preserving injection of suppressed visual evidence.Extensive experiments across diverse LVLM architectures demonstratethat ORBIT significantly mitigates hallucinations and enhances generalvision-centric performance with exceptional computational efficiency.

Abstract:
Differentially private training adds isotropic Gaussian noiseto clipped gradients, corrupting every singular direction equally. In vi-sion models, where spatial correlation concentrates gradient energy intoa low-rank subspace, most of this noise falls in directions that carrylittle signal. Spectral gradient orthogonalization via polar decomposi-tion is introduced as a post-processing step that recovers directionalsignal from the noisy gradient’s low-rank structure at zero additionalprivacy cost. A phase transition governs the utility of this approach:orthogonalization improves accuracy only when the per-direction spec-tral signal-to-noise ratio (SNR) suffices for singular vector recovery; inlow-SNR regimes, the directional bias of the gradient is replaced by anearly random orthogonal update, and the transformation is harmful.The recovery threshold is determined by the spectral gap of the gradientand is surpassed at large batch sizes. Empirically, the benefit scales withmodel capacity: spectral orthogonalization achieves a +20.9% improve-ment over DP-SGD on WRN-28-10 (B = 4096) and +14.9% on ResNet-18, while reducing inter-run variance by a factor of two to three. In thefine-tuning regime, spectral orthogonalization matches the stability ofDP-Adam while maintaining a first-order memory footprint. Combiningspectral with temporal denoising yields 50.3% on CIFAR-10 (ε = 4), thehighest accuracy in any tested configuration. These gains are specific tomoderate-to-high-SNR regimes such as large-batch training of higher-capacity models. Small-batch or low-SNR settings are better served byDP-SGD or temporal denoising.

Abstract:
Self-supervised learning (SSL) has revolutionized representation learning, with Joint-Embedding Architectures (JEAs) emerging as an effective approach for capturing semantic features. Existing JEAs rely on implicit or explicit batch interaction – via negative sampling or statistical regularization – to prevent representation collapse. This reliance becomes problematic in regimes where batch sizes must be small, such as high-dimensional scientific data, where memory constraints and class imbalance make large, well-balanced batches infeasible. We introduce IConE (Instance-Contrasted Embeddings), a framework that decouples collapse prevention from the training batch size. Rather than enforcing diversity through batch statistics, IConE maintains a global set of learnable auxiliary instance embeddings regularized by an explicit diversity objective. This transfers the anti-collapse mechanism from the transient batch to a dataset-level embedding space, allowing stable training even when batch statistics are unreliable, down to batch size 1. Across diverse 2D and 3D biomedical modalities, IConE outperforms strong contrastive and non-contrastive baselines throughout the small-batch regime (from B = 1 to B = 64) and demonstrates marked robustness to severe class imbalance. Geometric analysis shows that IConE preserves high intrinsic dimensionality in the learned representations, preventing the collapse observed in existing JEAs as batch sizes shrink.

Abstract:
Sign language video generation demands precise hand andfacial articulation, yet modern video diffusion models, trained predom-inantly on spoken-language video, produce artifacts that render signingunintelligible. We propose SignRefine, a sign language video genera-tion model that produces comprehensible signing from 2D keypoint con-ditioning alone, generalizing across appearances and visual conditions.Our approach builds on a pretrained video diffusion transformer and in-troduces local adapters with spatial grounding to selectively refine handand face regions, steering the strong base model’s prior toward accu-rate articulation. To enable this work and support broader sign languageresearch, we present NVSign, a large-scale dataset of video content na-tively produced in sign language, offering diverse signer appearances,environments, and natural conversational settings. Trained on this data,our model shows up to 30% improvement in hand pose precision metricsover the strongest baseline and is preferred by sign language users forvisual quality and comprehensibility in more than 80% of comparisons.

Abstract:
Generating human motion from language in 3D scenes re-quires both semantic alignment with textual intent and physically plau-sible human-scene interactions. Existing methods struggle because im-plicit language-scene fusion entangles motion synthesis with scene rea-soning, while explicit grounding via fixed-vocabulary detectors or affor-dance maps lacks generalization and fails to capture full-body, temporallycoherent interactions. We address this representation gap by identifyingmutual distance as an effective interaction representation for modelingfull-body spatiotemporal human-scene relations, and propose MDNet,a two-stage diffusion framework that explicitly generates and groundssuch interactions. Since mutual distance encodes only relative geometryand is ambiguous for stochastic generation, we introduce VLM-based se-mantic anchors to provide absolute spatial grounding. We further modelmutual distance in the frequency domain to enhance temporal stabilitybefore synthesizing final motions. Experiments on HUMANISE demon-strate state-of-the-art semantic alignment, physical plausibility, and tem-poral coherence, with cross-dataset generalization to unseen scenes.

Abstract:
Photographs of people taken by professional photographerstypically present the person in beautiful lighting, with an interestingpose, and flattering quality. This is unlike common photos people takeof themselves in uncontrolled conditions. In this paper, we explore howto canonicalize a person’s "in-the-wild" photograph into a controllable,high-fidelity avatar—reposed in a simple environment with standard-ized minimal clothing. A key challenge is preserving the person’s uniquewhole-body identity, facial features, and body shape while stripping awaythe complex occlusions of their original garments. While a large paireddataset of the same person in varied clothing and poses would simplifythis, such data does not exist. To that end, we propose two key insights:1) Our method transforms the input photo into a canonical full-body UVspace, which we couple with a novel reposing methodology to model oc-clusions and synthesize novel views. Operating in UV space allows us todecouple pose from appearance and leverage massive unpaired datasets.2) We personalize the output photo via multi-image finetuning to ensurerobust identity preservation under extreme pose changes. Our approachyields high-quality, reposed portraits that achieve strong quantitativeperformance on real-world imagery, providing an ideal, clean biometriccanvas that significantly improves the fidelity of downstream applicationslike Virtual Try-On (VTO).

Abstract:
To serve as a scalable data source for embodied AI, worldmodels should act as true simulators that infer interaction dynamicsstrictly from user actions, rather than mere conditional video genera-tors relying on privileged future object states. In this context, egocentricHand Object Interaction (HOI) world models are critical for predictingphysically grounded first-person rollouts. However, building such modelsis profoundly challenging due to rapid head motions, severe occlusions,and high-DoF hand articulations that abruptly alter contact topolo-gies. Consequently, existing approaches often circumvent these physicschallenges by resorting to conditional video generation with access toknown future object trajectories. We introduce EgoHOI, an egocentricHOI world model that breaks away from this shortcut to simulate pho-torealistic, contact-consistent interactions from action signals alone. Toensure physical accuracy without future-state inputs, EgoHOI distillsgeometric and kinematic priors from 3D estimates into physics-informedembeddings. These embeddings regularize the egocentric rollouts towardphysically valid dynamics. Experiments on the HOT3D dataset demon-strate consistent gains over strong baselines, and ablations validate theeffectiveness of our physics-informed design.

Abstract:
Synthesizing large-scale, explorable, and geometrically accurate 3D urban scenes is a challenging yet valuable task for immersive and embodied applications. The challenge lies in the lack of large-scale and high-quality real-world 3D scans for training generalizable generative models. In this paper, we take an alternative route to create largescale 3D scenes by leveraging readily available satellite imagery for realistic coarse geometry and open-domain diffusion models for high-quality close-up appearance synthesis. We propose Skyfall-GS, a novel hybrid framework that synthesizes immersive city-block scale 3D urban scenes by combining satellite reconstruction with diffusion refinement, eliminating the need for costly 3D annotations, and also featuring real-time, immersive 3D exploration. We tailor a curriculum-driven iterative refinement strategy to progressively enhance geometric completeness and photorealistic texture. Extensive experiments demonstrate that SkyfallGS provides improved cross-view consistent geometry and more realistic textures compared to state-of-the-art approaches. Project page: https://skyfall-gs.jayinnn.dev/

Abstract:
Autoregressive diffusion enables real-time frame streaming,yet existing sliding-window caches discard past context, causing fidelitydegradation, identity drift, and motion stagnation over long horizons.Current approaches preserve a fixed set of early tokens as attentionsinks, but this static anchor cannot reflect the evolving content of agrowing video. We introduce MemRoPE, a training-free framework withtwo co-designed components. Memory Tokens continuously compress allpast keys into dual long-term and short-term streams via exponentialmoving averages, maintaining both global identity and recent dynamicswithin a fixed-size cache. Online RoPE Indexing caches unrotated keysand applies positional embeddings dynamically at attention time, ensur-ing the aggregation is free of conflicting positional phases. These twomechanisms are mutually enabling: positional decoupling makes tempo-ral aggregation well-defined, while aggregation makes fixed-size cachingviable for unbounded generation. Extensive experiments validate thatMemRoPE outperforms existing methods in temporal coherence, visualfidelity, and subject consistency across minute- to hour-scale generation.

Abstract:
We present EmbodiedHead, a speech-driven talking headframework that equips LLMs with real-time visual avatars for conver-sation. A practical embodied avatar must achieve real-time generation,unified listening-speaking behavior, and high rendered visual quality si-multaneously. Our framework couples the first Rectified-Flow DiffusionTransformer (DiT) for this task with a differentiable renderer, enablingdiverse, high-fidelity generation in as few as four sampling steps. Priorlistening-speaking methods rely on dual-stream audio, introducing an in-terlocutor look-ahead dependency incompatible with causal user–LLMinteraction. We instead adopt a single-stream interface with explicitper-frame listening-speaking state conditioning and a Streaming AudioScheduler, suppressing spurious mouth motion during listening while en-abling seamless turn-taking. A two-stage training scheme of coefficient-space pretraining and joint image-domain refinement further closes thegap between motion-level supervision and rendered quality. Extensive ex-periments demonstrate state-of-the-art visual quality and motion fidelityin both speaking and listening scenarios.

Abstract:
While image stylization has been studied extensively, video stylization remains a critical and largely unsolved challenge in the field of intelligent content creation. Existing methods, usually utilizing a reference image as the style prior, suffer from content leakage, data scarcity and limited adaptability to long videos, leading to suboptimal results with severe style drift and motion distortion. For these issues, we present EchoStyle, a scalable text-driven framework to achieve high-quality stylization of videos with arbitrary lengths. To start with, we construct a video-to-video architecture to appropriately re-fuse the video content and the text style. To address data scarcity, we pioneer an automatic reverse-synthesis pipeline to establish V-Style20k, a large-scale stylization dataset of 20k high-quality video pairs. To facilitate long video stylization, we devise an init-follow-mode mechanism along with a slidingwindow inference strategy. Extensive experiments demonstrate EchoStyle’s excellent performance across a wide range of artistic styles, even comparable to leading closed-source solutions.

Abstract:
Most advances in human mesh recovery (HMR) have focusedon pelvis-centered recovery, overlooking metric 3D localization and detec-tion accuracy in the camera coordinate system – two key factors for real-world applications such as human–robot interaction and social scene un-derstanding. Current evaluation protocols often ignore these aspects, em-phasizing per-person, root-centered recovery rather than camera-spaceperception. As a result, existing approaches rely on fixed camera as-sumptions or handcrafted post-processing, limiting their robustness andpractical deployment. We introduce Multi-HMR 2, a simple yet ro-bust DETR-based framework for Multi-person camera-centric Humandetection, Mesh Recovery, and tracking. Multi-HMR 2 predicts a scene-consistent camera together with human meshes, enabling metric 3D lo-calization without ground-truth intrinsics. Moreover, by distilling image-based memory features from SAM2, Multi-HMR 2 extends to tracking,achieving consistent identity association without video supervision. De-spite its conceptual simplicity – no handcrafted components, no videoinput, and no ground-truth cameras – Multi-HMR 2 achieves state-of-the-art pelvis-centered performance while substantially improving detec-tion accuracy and metric 3D localization.

Abstract:
Recent 3D generative models produce high-quality geometryfrom a single image using large-scale priors and diffusion architectures.However, they assume complete visibility and single-view inputs, limitingapplicability in multi-view, occluded, or editing scenarios. Although priorworks address these challenges individually, they lack a unified frameworkfor controllable 3D completion under diverse conditioning signals.We present Axolotl3D, a multi-modal and occlusion-aware 3D generationmodel that jointly conditions on images, visibility masks, camera param-eters, and a partial point cloud. The point cloud serves as a geometricanchor promoting faithful shape completion, while camera parametersensure consistent multi-view alignment in a shared 3D coordinate sys-tem. A unified training strategy synthesizes diverse conditioning regimesfrom large-scale 3D data, enabling robust cross-modal reasoning.Experiments on Toys4K and OmniObject3D demonstrate state-of-the-art performance under both clean and occluded settings, as well as strongresults in real-world reconstruction and geometry-consistent editing.

Abstract:
Generating temporally continuous and socially coherent hu-man motion from text remains a fundamental challenge, particularly inrealistic streams where people act alone, enter interactions, and later dis-engage. Most existing methods generate fixed-length motion clips understatic agent configurations, which makes them brittle to solo–social tran-sitions and unsuitable for incremental generation over long horizons. Wepropose ARMS, an Anchor–Relational Motion Streaming framework thatunifies solo motion and human–human interaction within a single causalgenerative process. ARMS introduces a dynamics-asymmetric represen-tation that decouples per-person temporal evolution from inter-personalignment via a partner-referenced relative-translation term, enablingseamless switching of social coupling without sacrificing long-horizonstability or spatial consistency between agents. On top of a causal la-tent space, a causal relational diffusion model progressively refines mo-tion segment by segment using only past context, capturing both intra-person temporal dependencies and inter-person relations. Mode-awarerelational gating activates or masks cross-agent connections, allowingthe same model to support both solo and interaction generation. Ex-periments show that ARMS improves transition smoothness and socialcoherence compared to interaction-centric baselines, while also achievingcompetitive results on human–human interaction benchmarks.

Abstract:
Generating motion-controlled videos—where user-specified actions drive physically plausible scene dynamics under freely chosen viewpoints—demands two capabilities: (1) disentangled motion control, allowing users to separately control the object motion and adjust camera viewpoint; and (2) motion causality, i.e., generative action–consequence dependency, ensuring that user-driven actions trigger coherent reactions from other objects rather than merely displacing pixels. Existing methods fall short on both fronts: they entangle camera and object motion into a single tracking signal and treat motion as kinematic displacement without modeling causal relationships between object motion. We introduce MoRight, a unified framework that addresses both limitations through disentangled motion modeling. Object motion is specified in a canonical static-view and transferred to an arbitrary target camera viewpoint via temporal cross-view attention, enabling disentangled camera and object control. We further decompose motion into active (user-driven) and passive (consequence) components, training the model to learn motion causality from data. At inference, users can either supply active motion and MoRight predicts consequences (forward reasoning), or specify desired passive outcomes and MoRight recovers plausible driving actions (inverse reasoning), all while freely adjusting the camera viewpoint. Experiments on three benchmarks demonstrate state-of-the-art performance in generation quality, motion controllability, and interaction awareness.

Abstract:
Reconstructing articulated 3D objects from a single imagerequires jointly inferring object geometry, part structure, and motionparameters from limited visual evidence. A key difficulty lies in theentanglement between motion cues and object structure, which makesdirect articulation regression unstable. Existing methods address thischallenge through multi-view supervision, retrieval-based assembly, orauxiliary video generation, often sacrificing scalability or efficiency. Wepresent MonoArt, a unified framework grounded in progressive struc-tural reasoning. Rather than predicting articulation directly from im-age features, MonoArt progressively transforms visual observations intocanonical geometry, structured part representations, and motion-awareembeddings within a single architecture. This structured reasoning pro-cess enables stable and interpretable articulation inference without ex-ternal motion templates or multi-stage pipelines. Extensive experimentson PartNet-Mobility demonstrate that MonoArt achieves state-of-the-artperformance in both reconstruction accuracy and inference speed. Theframework further generalizes to robotic manipulation and articulatedscene reconstruction.

Abstract:
Motion generation for rigged shapes is vital for scalable 4D asset production. However, template-based methods are limited by specific topologies and fail to generalize across diverse morphologies. Conversely, per-case optimization is computationally expensive, susceptible to local optima, and highly sensitive to viewpoint-induced ambiguities. In this paper, we present SkelMo, a diffusion-based framework designed for category-agnostic skeletal animation generation from 2D video guidance. To overcome the scarcity of high-quality training data, we have curated a large-scale dynamic dataset comprising approximately 25,200 diverse 3D animations, each featuring complete textures, skeletal rigging, and a wide array of comprehensive animation sequences. To bridge the kinematic gap between 2D visual motion cues and heterogeneous 3D skeletal structures, we propose a structural-semantic injection mechanism. Our model integrates texture and semantic attributes directly into skeletal joint representations. This allows it to map perceived visual dynamics to specific joint hierarchies and their functional roles. This enables SkelMo to synthesize high-fidelity animations that maintain anatomical consistency across a vast range of unseen categories, from existing biological species to fantastical beings. Extensive experiments demonstrate that our approach significantly outperforms existing methods, setting a new state-of-the-art benchmark for robust and efficient 4D asset generation. Project Page: https://research.davytao.me/skelmo/.

Abstract:
Reconstructing deformable objects from video requires a 4Drepresentation that should preserve geometry, explain motion throughphysics, and generalize to future or new physical conditions. Existingdynamic NeRF and Gaussian methods achieve strong view synthesis,yet their voxel or ellipsoidal primitives are primarily designed for ap-pearance rendering and typically driven by centers or predefined par-ticle bindings, limiting physically meaningful non-uniform deformationand sharp boundary evolution. We present PhysConvex, a physics-informed dynamic convex field for video-based reconstruction, physicalsystem identification, and simulation. PhysConvex represents a dynamicobject as material-space deformable convex primitives whose boundaryis advected by physical dynamics. The boundary-driven convex expressesnon-uniform deformation and evolving active supports while serving si-multaneously as a rendering element, deformation carrier, and physicalsupport where mass, elastic response, forces, and contacts are evalu-ated. We further introduce a mesh-free reduced-order convex simulatorin which neural skinning modes define physics-based deformation basesdirectly over deformable convex supports. Experiments show improveddynamic and physical reconstruction, efficiency, future prediction, andgeneralization to changed materials, forces, and boundary conditions.

Abstract:
Expressive human avatar creation from a single image ishighly challenging due to the inherently ill-posed nature of the prob-lem, as well as the complexities of appearance preservation and dynamichuman modeling. To address these challenges, this work presents a com-prehensive pipeline for training a large feed-forward model that efficientlygenerates fully controllable 3D avatars from single-image. Due to thescarcity of consistent training data, we construct a large-scale 3D Gaus-sian avatar dataset to support model training. To better recover appear-ance details, we propose an input-aware decoding scheme that fully lever-ages information from the input image. Furthermore, to achieve compre-hensive full-body control, we introduce a Gaussian blending–based facialenhancement module and apply Gaussian geometric constraints to sta-bilize expressive avatar generation. Extensive experiments demonstratethat our method enables one-shot reconstruction of photorealistic avatarswith whole-body control, outperforming existing works in terms of hu-man dynamic and appearance details.

Abstract:
Neural video codecs have surpassed classical codecs in codingefficiency but remain impractical for deployment due to cross-platformincompatibility and high computational cost. Existing quantization-basedsolutions fail to produce deterministic results across diverse hardwareplatforms, leading to catastrophic decoding failures. We introduce MLVC,a hardware-robust neural video codec designed for practical cross-platforminference. The key idea is to explicitly transmit scale parameters throughthe hyperprior, which guarantees entropy coding consistency across de-vices without requiring bit-exact arithmetic. While this increases bitrateoverhead, we recover most of the coding efficiency through architecturalimprovements (gated memory, ReGLU activation), a long-term refer-ence recovery mechanism, and domain-specific perceptual training. Onthe VCD video conferencing benchmark, MLVC achieves >70% BD-rate (MOS) improvement over hardware HEVC, the strongest deployablebaseline, while reaching subjective quality competitive with DCVC-RT,which cannot operate across diverse platforms. Both the encoder anddecoder run at 100 FPS on average on commodity NPUs from Apple,Intel, and Qualcomm. MLVC is the first neural video codec to com-bine competitive compression performance, real-time speed, and cross-platform robustness across diverse consumer devices, making it suitablefor widespread deployment. Code will be released.

Abstract:
Recent advances in neural decoding have enabled the direct reconstruction of visual stimuli from non-invasive brain signals. However, reconstructing continuous video streams remains highly challenging due to the continuous spatial and semantic transformations inherent in dynamic scenes. Most methods rely on a "reconstruct-thendescribe" paradigm, where captions are generated from reconstructed video keyframes, which are highly prone to semantic drift. To overcome these challenges, we propose a novel decoding framework, CortexVideo, to reconstruct video from functional magnetic resonance imaging (fMRI). Inspired by the visual dual-stream hypothesis, CortexVideo introduces dual-guidance decoding strategy. Specifically, we leverage subject-adaptive semantic information to guide video reconstruction, while concurrently integrating keyframe perceptual weights. Experiments demonstrate that CortexVideo provides a cognitive enhancement to existing hierarchical models along two critical dimensions: video semantic understanding and spatial localization. The reconstructed videos show greater fidelity to the visual stimuli in both semantic and spatial aspects, achieving state-ofthe-art results.

Abstract:
Driving scene manipulation using real-world sensor data hasemerged as a promising alternative to traditional driving simulators. De-spite advances in language control and neural scene representations, ex-isting methods treat grounding, editing, and simulation as loosely con-nected stages, relying on heuristic object localization, manual guidance,and single-agent validation—thereby constraining semantic expressive-ness and hindering scalable, reactive scenario generation. We introduceSIMSplat, a driving scene editor built on scene-graph-based 4D Gaus-sian Splatting augmented with language-aligned features. By embed-ding appearance, motion, and location semantics directly into Gaus-sian scene-graph nodes, SIMSplat makes reconstructed scenes queryablethrough free-form natural language, bridging language understanding toobject-level editing and multi-agent simulation within a single frame-work. Building on this language-grounded scene graph, SIMSplat sup-ports diverse edits including fine-grained pedestrian manipulation, whilea multi-agent path refinement module propagates changes across allagents to ensure reactive, physically plausible simulations. The pipelinefurther integrates with Vision-Language Models for automated scenariomining. Experiments show that SIMSplat more than doubles baselinegrounding accuracy, achieves the highest task completion rate, and pro-duces the lowest failure rates across diverse driving scenarios.

Abstract:
Precise control of 3D facial expressions from text is crucialfor virtual avatars, animation, and human–computer interaction, yet ex-isting text-to-3D methods jointly generate identity, expression, and tex-ture, making fine-grained expression control difficult. We instead for-mulate text-driven expression synthesis as a regression problem in thedisentangled parameter space of a 3D Morphable Model (3DMM). Thissetting, however, requires paired data linking detailed language to pre-cise expression parameters, which is missing from existing resources. Tofill this gap, we introduce Txt2Emote, a benchmark of diverse 3D facialexpressions with fine-grained textual annotations obtained from GPT-4o and a high-fidelity face tracker, providing both explicit descriptionsdetailing facial features and implicit descriptions referencing the situa-tional context behind the expression. Leveraging this dataset, we presentEmoteGPT, a text-to-3D expression framework based on a Multi-ModalLarge Language Model (MLLM) with a dedicated token to se-mantically ground expression representations, which are then decodedinto 3DMM parameters. We further improves EmoteGPT by augmentingtraining with large-scale image-to-3DMM data, surpasses state-of-the-arttext-to-3D face synthesis methods on emotion recognition metrics andin perceived expressiveness. Integrated into avatar pipelines, our methodenables photorealistic and stylized 3D avatars, as well as expressive 3D-consistent 2D face synthesis from textual input.

Abstract:
Despite recent progress, direct text-driven 4D object gen-eration remains challenging yet highly desirable. In this paper, we in-troduce DiTex4D, a native text-to-4D generation framework that en-ables both text-driven 4D generation from scratch and 3D animationfrom static mesh. Built upon large-scale pre-trained 3D generation mod-els, our framework DiTex4D avoids intermediate text-to-video pipelinesand costly per-object optimization. Specifically, (i) we achieve 4D spa-tiotemporal consistency via inflating 3D attention with mixed-4D RoPEand tailored correlated noise injection strategy. (ii) To enable 3D anima-tion, we introduce a mask-based diffusion model conditioned on multi-view global context to maintain strict consistency with the initial frame.We further fine-tune the framework for 4D interpolation to synthesizehigh-frame-rate sequences with smoother motion. Extensive experimentsdemonstrate that DiTex4D can achieve higher-quality, semantically align-ed, and spatiotemporally coherent 4D object generation, surpassing mostexisting state-of-the-art text-to-4D generation methods.

Abstract:
Our work addresses 3D hand trajectory prediction in egocentric interaction, where future hand motion is inferred from visual observations, past motion, spatial context, and intent. Real-world actions follow stage-aware interaction structures (e.g., approach, manipulate) describing how the hand interacts with objects over time. However, prior works typically treat trajectory prediction as continuous signal regression, decoupling motion from semantic supervision and ignoring interaction structure. Without stage-aware cues to infer intent, models struggle to separate purposeful motion from egocentric noise and generalize across diverse interactions. We therefore present EgoMAN, a unified framework for interaction-structured 3D hand trajectory prediction that models hand motion as stage-aware interactions between the hand and surrounding objects. EgoMAN introduces a novel Trajectory-Token Interface where a small set of tokens encodes interaction stages, temporal progression, and 6DoF pose, enabling interaction stage-aware reasoning to guide efficient long-horizon 3D trajectory generation while preserving physical interpretability. To support this formulation, we construct the EgoMAN dataset with 219K 6DoF trajectories, stage-aware annotations, and 3M semantic, spatial, and motion QA pairs. Experiments show that EgoMAN improves trajectory accuracy, smoothness, and generalization, enabling interaction-structured reasoning for egocentric hand motion prediction for applications in robotics and assistive systems.

Abstract:
Perceptual video compression leverages generative priors to reconstruct realistic textures and motions at low bitrates. However, existing perceptual codecs often lack native support for variable bitrate and progressive delivery, and their generative modules are weakly coupled with entropy coding, limiting bitrate reduction. Inspired by the next-scale prediction in the Visual Auto-Regressive (VAR) models, we propose ProGVC, a Progressive-based Generative Video Compression framework that unifies progressive transmission, efficient entropy coding, and detail synthesis within a single codec. ProGVC encodes videos into hierarchical multi-scale residual token maps, enabling flexible rate adaptation by transmitting a coarse-to-fine subset of scales in a progressive manner. A Transformer-based multi-scale autoregressive context model estimates token probabilities, utilized both for efficient entropy coding of the transmitted tokens and for predicting truncated fine-scale tokens at the decoder to restore perceptual details. Extensive experiments demonstrate that as a new coding paradigm, ProGVC delivers promising perceptual compression performance at low bitrates while offering practical scalability at the same time.

Abstract:
Autoregressive (AR) streaming models have emerged as a powerful paradigm for long video generation. However, the linearly growing Key-Value (KV) cache poses a significant bottleneck, leading to memory overload and degraded inference throughput. A common compression method is to drop redundant KV tokens, which often breaks long-range dependencies, resulting in temporal flickering and identity loss. In this paper, we propose Instance-Specific Parametric Absorption (ISPA), a novel framework that shifts the KV cache compression from discarding to distilling. The core idea is to transit a subset of layers from FullAttention (F-Layers) to memory-efficient Local-Attention (L-Layers) by "absorbing" historical context into the model’s weights. Specifically, during a brief warmup phase, ISPA monitors the output discrepancy between global and local attention. At the transition point, we solve a closed-form least-squares problem to compute an instance-specific weight modulation that compensates for the missing history. Experiments across architectures (1.3B to 14B) demonstrate that ISPA can remove up to 50% of the KV cache with near-lossless visual quality. We hope this perspective encourages future work to explore parametric memory consolidation beyond external token-level cache management for streaming generative models.

Abstract:
Table recognition (TR) aims to transcribe table images into semi-structured representations (e.g., HTML or Markdown). However, current methods still struggle with complex structures, particularly involving merged cells, which are essential for accurate table parsing and downstream understanding tasks. While some works improve TR through large-scale training and global supervision, these local fine-grained yet crucial merged-cell attributes are often overlooked, becoming a key bottleneck for further progress. To tackle this, we introduce Table-MCR2TR, a reinforced multimodal large language model framework that leverages the enhanced merged-cell recognition (MCR) ability as contextual guidance to improve table recognition quality. To enable this capability, we first develop a high-fidelity data pipeline to construct a large-scale table dataset covering many complex merged cells, alleviating their scarcity in existing datasets. Additionally, the merged-cell-aware table recognition (MCATR) optimization strategy is proposed to improve table parsing via collaborative reinforcement learning. The core of MCATR lies in integrating three complementary objectives: TR and MCR respectively learn the global table structure and fine-grained merged-cell information; based on these learned signals, the MCR2TR alignment task transfers merged-cell knowledge to guide TR generation. Experimental results show that Table-MCR2TR achieves SOTA across multiple benchmarks, with average TR/MCR accuracy of 92.3%/83.9%, outperforming top-tier models and further boosting downstream table QA performance.

Abstract:
Recent advances in video world models enable interactive environments with free navigation, making translation between first-person (egocentric) and third-person (exocentric) perspectives increasingly important. However, existing studies focus on unidirectional exocentric-toegocentric translation, overlooking reference-guided exocentric perspective synthesis. This capability is crucial for gaming and embodied AI applications. Motivated by this, we present WorldWander, an in-context learning framework tailored for translating between egocentric and exocentric worlds in video generation. Building upon advanced video diffusion transformers, WorldWander integrates (i) In-Context Perspective Alignment and (ii) Collaborative Position Encoding to model cross-view synchronization and character consistency. To support our task, we curate EgoExo-8K, a dynamic and scene-rich dataset containing synchronized egocentric–exocentric triplets from both synthetic and real-world scenarios. Experiments demonstrate that WorldWander achieves superior perspective synchronization, character consistency, and generalization, setting a new benchmark for egocentric-exocentric video translation.

Abstract:
Recent advances in Streaming Video Understanding has enabled a new interaction paradigm where models respond proactively to user queries. Current proactive VideoLLMs rely on per-frame triggering decision making, which suffers from an efficiency-accuracy dilemma. We propose Em-Garde, a novel framework that decouples semantic understanding from streaming perception. At query time, the InstructionGuided Proposal Parser transforms user queries into structured, perceptually grounded visual proposals; during streaming, a Lightweight Proposal Matching Module performs efficient embedding-based matching to trigger responses. Experiments on StreamingBench and OVO-Bench demonstrate consistent improvements over prior models in proactive response accuracy and efficiency, validating an effective solution for proactive video understanding under strict computational constraints. Code and model are available at https://air-embodied-brain.github.io/ Em-Garde/.

Abstract:
We present V-JEPA 2.1, a family of self-supervised models that learns dense, high-quality, and temporally consistent representations for visual scenes in both images and videos. V-JEPA 2.1 combines four key ingredients: (i) a Dense Predictive Loss, a masking-based objective in which all tokens—visible context and masked tokens alike— contribute to the training loss, encouraging explicit spatial and temporal grounding; (ii) Deep Self-Supervision, which applies the selfsupervised objective hierarchically at multiple intermediate encoder layers to improve representation quality; (iii) Multi-Modal Tokenizers that support unified training over images and videos; and (iv) effective model and data scaling. Empirically, V-JEPA 2.1 achieves state-ofthe-art results on a range of benchmarks: 7.71 mAP on Ego4D for shortterm object-interaction anticipation, 40.8 Recall@5 on EPIC-Kitchens for high-level action anticipation, and a 20% improvement in real-robot grasping success rate over VJEPA-2 AC. The model also achieves stateof-art performance in robotic navigation (5.687 ATE on Tartan Drive), depth estimation (0.307 RMSE on NYUv2 with a linear probe), and global recognition (77.7% on Something-Something-V2).

Abstract:
Time awareness is a fundamental ability of omni-modal largelanguage models, especially for understanding long videos and answeringcomplex questions. Previous approaches mainly target vision-languagescenarios and focus on the explicit temporal grounding questions, suchas identifying when a visual event occurs or determining what event hap-pens at a specific time. However, they often make insufficient use of theaudio modality, and overlook implicit temporal grounding across modal-ities (for example, identifying what is visually present when a characterspeaks, or determining what is said when a visual event occurs), de-spite such cross-modal temporal relations being prevalent in real-worldscenarios. In this paper, we first formally define the audiovisual tem-poral grounding task to systematically encompass both explicit andimplicit cross-modal temporal relations. To tackle this task, we proposeChronusOmni, an omni-modal large language model designed to en-hance temporal awareness for both explicit and implicit audiovisual tem-poral grounding. Specifically, we interleave text-based timestamp tokenswith visual and audio representations at each time unit, enabling uni-fied temporal modeling across modalities. Furthermore, to enforce correcttemporal ordering and strengthen temporal reasoning, we incorporate re-inforcement learning with specially designed reward functions. Moreover,we construct ChronusAV, a temporally-accurate, modality-complete, andcross-modal-aligned dataset to support the training and evaluation of theaudiovisual temporal grounding task. Experimental results demonstratethat ChronusOmni achieves state-of-the-art performance on audiovisualtemporal grounding datasets such as ChronusAV and LongVALE, out-performing the second-best results by 17.3% and 28.8%, respectively, anddelivers top-tier performance on visual-only temporal grounding datasetsincluding Charades-STA and ActivityNet. These achievements highlightthe strong temporal awareness of our model across modalities, while pre-serving its robust capabilities in general video and audio understanding.

Abstract:
History context is central to autoregressive video generation,driving consistency and storytelling for both commercial models andpersonal use cases. For example, personal users, offline workflows, andindividual-scale finetuning need to encode longer video histories undertight compute and memory budgets. We observe that content and identityconsistency is an essential requirement, and that complete, uninterruptedhistory coverage together with content query and interpretation capabil-ities is broadly desired. We present TinyHistory, a lightweight historyembedding learned through two-stage context learning. In the first stage,we pretrain the encoder on large-scale video data with a randomized framequery objective; in the second stage, we repurpose the pretrained encoderwithin an autoregressive video diffusion model to learn content-level con-sistency. As a result, we show that the learned lightweight embeddingsachieve consistency comparable (by VLM, VBench, ELO, etc.) to heavieralternatives, while reducing training overhead and extending the encod-able history length within a given memory budget. We conduct ablationstudies to analyze the influence and trade-offs of each component.

Abstract:
Video-to-audio (V2A) generation aims to synthesize realis-tic audio that is both semantically consistent with and temporally syn-chronized to a silent video. Despite recent progress, many methods stillrely on multi-stage training, resulting in high computational costs andlong runtimes, or transform visual input into text to leverage pretrainedtext-to-audio models, sacrificing fine-grained temporal cues. To over-come these limitations, we propose Flowley, an end-to-end, single-stagetraining architecture that produces soundtracks by combining visual fea-tures with textual prompts. Crucially, we introduce Progressive Soft-masked Cross-Attention, which embeds audio-visual synchronization di-rectly within its attention mechanism, adding zero additional computa-tional cost compared to standard attention layers. We further observethat existing V2A benchmarks lack sound-oriented descriptive captions,which can potentially degrade the quality of the synthesized audio. Toremedy this, we propose SoundCap, a plug-and-play pipeline for creatingdetailed, sound-aware captions that guide the model. Remarkably, with-out integrating any pretrained audio-visual alignment modules, Flow-ley achieves state-of-the-art performance on VGGSound across multiplemetrics. Moreover, by incorporating SoundCap, we further exceed theperformance of the strongest existing close-sourced methods in terms ofaudio quality in the zero-shot setting.

Abstract:
Visual generation is increasingly ubiquitous in diverse do-mains, from text-to-image/video synthesis to multimodal interactive cre-ation. Yet prevailing monolithic models remain fundamentally constrainedby their inability to learn cumulatively and evolve autonomously, whichis a limitation we term the “perpetual novice” problem. They lack mech-anisms for structuring experience into reusable knowledge and there-fore rely on brittle, “from-scratch” reasoning for each task, resultingin poor compositional generalization and inefficient knowledge reten-tion. Motivated by these limitations, we propose SymbOmni, an agenticomni-model designed for cumulative evolution through Symbolic Con-cept Learning. At its core is the Symbolic Concept Box, an optimizablememory module that abstracts low-level operations into reusable Sym-bolic Workflow Instructions. SymbOmni operates through an induction-transduction cycle: experiences are abstracted into symbolic concepts(induction), which are then adaptively composed to solve novel tasks(transduction). The training is done by verbalized backpropagation withlanguage-based feedback to enable continuous self-improvement withoutgradient-based model fine-tuning. Comprehensive experiments validatethat (I) SymbOmni significantly outperforms existing agent-based sys-tems for iterative creation and also surpasses closed-source models (e.g.,Nano Banana, GPT-Image-1) in both image quality and task successrates; (II) SymbOmni effectively reduces token consumption by over 40%while maintaining competitive generation quality; and (III) SymbOmnienables effective continual learning by achieving cumulative gains acrossmultiple online-learning benchmarks and setting a new state of the art.

Abstract:
Sign language translation (SLT) remains challenging due to its high spatio-temporal complexity, long sequences, and the need to model multiple articulators without relying on gloss annotations. Existing approaches are typically tailored to individual datasets or languages and struggle to scale, while overlooking the relationships between sign languages that could inform more effective cross-lingual transfer. We present SIGNET, a framework that enables motion-level knowledge transfer for cross-language sign language translation. Our key insight is that, although sign languages differ in grammar and lexicon, pretrained models capture motion-level visual patterns that can be reused across datasets and languages. SIGNET integrates multiple pretrained sign language backbones through an attention-based, hand-prior aggregation mechanism that guides a gated fusion network in dynamically selecting the most relevant experts. Comprehensive experiments on four benchmarks (How2Sign, Phoenix14T, CSL-Daily, and MeineDGS) demonstrate state-of-the-art translation performance, and SIGNET also surpasses prior methods on WLASL for sign language recognition.

Abstract:
Video large language models (Video-LLMs) have achieved re-markable progress in video understanding tasks, but their heavy compu-tational overhead during inference severely limits deployment in resource-constrained scenarios. This highlights the urgent need for efficient infer-ence acceleration frameworks tailored to Video-LLMs. Existing accelera-tion methods typically apply a homogeneous processing approach to allvideo frames, disregarding the fact that each frame within a video clipconveys distinct visual information. Additionally, many of them under-utilize text queries, or add text guidance only through costly pre-trainingor intrusive LLM modifications, underexploiting the text-video align-ment that is critical in long-form video understanding where redundantvisual content is common. To address this limitation, we propose Guide-Then-Refine (GTR), a plug-and-play inference acceleration frameworkfor Video-LLMs that realizes token compression through a two-stagemechanism. First, for all tokens in each video frame, we compute bothglobal scores and text-aware scores. Based on the sum of these scores forall tokens in a frame, we dynamically adjust the token retention ratio foreach frame. Subsequently, according to the retention ratio, we calculatethe local score of each token to evaluate its importance within the currentframe, thereby selecting critical visual tokens. Extensive evaluations onmainstream video benchmarks and various Video-LLMs demonstrate theeffectiveness of our approach. With only 15% of visual tokens retained,our method maintains an average of 95.8% of the original performanceacross four benchmarks.

Abstract:
Multimodal Large Language Models (MLLMs) have demon-strated remarkable success in various Remote Sensing (RS) tasks. How-ever, their ability to comprehend negation remains underexplored, limit-ing deployment in real-world applications where models must explicitlyidentify what is false or absent, e.g., emergency responders need to locatenon-flooded routes for evacuation. To comprehensively study this limi-tation, we introduce RS-Neg, the first benchmark to evaluate negationunderstanding across region-level to scene-level tasks. Specifically, we de-sign an automated data generation pipeline for RS imagery, using LLMsto synthesize diverse negation queries, and introduce a dynamic visualfocus module for verification. Our evaluation reveals that advanced RSMLLMs struggle with negation, exhibiting hallucinations and substantialperformance degradation. To close this gap, we propose NeFo, a noveltest-time adaptation method that explicitly incorporates the logical roleof negation into the model optimization. Remarkably, using about 5%unlabeled test samples, NeFo significantly improves the negation under-standing of models and shows strong generalization to unseen tasks.

Abstract:
Enabling humanoid robots to respond to human speech withsynchronized and semantically meaningful gestures is fundamental tonatural human-robot interaction. However, this task faces three criticalbarriers: the scarcity of semantically rich datasets, the "modality eclipse"where models ignore audio cues in favor of kinematic inertia, and the sim-to-real gap regarding physical safety. We propose RoboGesture, a robot-centric framework that co-designs data, modeling, and control to powera complete interactive human–humanoid system in which the robot lis-tens, responds, and gestures in real time. We first establish the Robo-Gesture dataset featuring over 300 gesture categories and develop anautomated pipeline to synthesize large-scale collision-free, robot-specificaudio-motion pairs. Our architecture features a Hierarchical Semantic-Acoustic Aligner that extracts multi-granular prosodic and semantic cuesdirectly from raw audio tokens. These cues drive a Streaming ConditionalMotion Generator based on a diffusion transformer with conditional flowmatching. To ensure high responsiveness, we introduce Anti-Inertia CFGMasking, which prevents the model from collapsing into repetitive his-torical patterns by compelling it to proactively mine control signals fromthe audio modality. Finally, an MPC-based safety filter ensures real-time, collision-free execution on physical hardware. Experiments on aUnitree G1 humanoid demonstrate that RoboGesture generates safer,more rhythmic, and more semantically appropriate responses comparedto state-of-the-art baselines.

Abstract:
Video understanding has become more and more importantwith the growth of Artificial Intelligence (AI) for video generation. Re-cently, Multimodal Large Language Model (M-LLM) has shown its ca-pability in video understanding. Video summarization, a specific domainof video understanding, has proven its importance for efficient naviga-tion and retrieval. Both video understanding and video summarizationrequire a good selection of key frames in a video. Current video summa-rization methods heavily focus on the selected key frames and correlatedsegment captions. However, existing approaches overlook the perspectiveof treating the importance of the frames globally. We argue that usingdiscrete selected frames for summarization will not only reduce the un-derstanding coherence, but also lost important information in the video,as well as wasting the original capacity of the MLLMs. In this paper, wepropose HAS, a Highlight-guided Attention Steering method for videosummarization. We consider a challenging but practical setting wherethe video given to MLLMs for summarize should be continuous but withhighlight guidance. HAS mainly consists of two parts: The first part isto find a continuous frame-level highlight distribution for the video glob-ally. The second part is to apply the highlight distribution as an attentionsteering vector for the MLLM, targeting a better understanding of thevideo, and thus during the model inference time, putting more attentionon the highlighted frames, while avoiding lost entire information on lesshighlighted frames through putting less attention instead of forgettingthem. We evaluated HAS on a variety of benchmarks, and it has shownconvincing performance in video summarization.

Abstract:
Recent advances in video diffusion models have enabled either long single-view generation through temporal autoregression, or short multi-view synthesis through bidirectional attention. However, generating long, multi-view consistent videos of dynamic scenes remains unsolved. In this work, we present MV-Forcing, a framework that composes temporal and view-wise autoregression within a single diffusion model by introducing a 4D geometric bridge between sequentially generated views. Our key insight is that an autoregressive 3D reconstruction model naturally interfaces between autoregressively generated views. Given a completed source view, we reconstruct its 3D structure and render a geometric prior of the next target viewpoint, which the diffusion model refines into a high-quality video. To extend generation beyond the teacher’s fixed temporal window, we introduce a joint denoising regime where both view slots are initialized from noise during training, enabling temporally unbounded generation. We distill the model via Distribution Matching Distillation with Spatio-Temporal Self-Forcing, closing the train-inference exposure bias gap for both temporal and view-sequential autoregression. Extensive experiments on both synthetic and real-world data demonstrate that MV-Forcing produces geometrically consistent multi-view videos of dynamic scenes at arbitrary lengths and viewpoint counts using a single few-step student model.

Abstract:
Despite advances in 3D scene understanding, existing 3DLarge Multimodal Models operate in offline settings, requiring completescene observations or predefined video clips. In this paper, we presentan online 3D vision-language model that enables real-time spatial un-derstanding from streaming video. Our approach adopts an autoregres-sive streaming control modeling based on the LLM’s next-token predic-tion objective to learn when to respond, and employs a lightweight Vi-sual–Spatial Feature Integration (VSFI) module to incrementally injecttemporally aligned geometry priors into the visual stream. To alleviatelong-context decoding overhead, we propose a plug-and-play Geometry-Adaptive Voxel Compression (GAVC) module for efficient visual tokencompression. To address the scarcity of streaming 3D–language data,we further develop a scalable data generation pipeline that curates over1M online spatio-temporal 3D QA pairs and establishes a comprehensivebenchmark spanning 29 tasks. Extensive experiments show that our ap-proach significantly outperforms both proprietary and open-source mod-els across online and offline 3D spatial understanding, reasoning, andgrounding tasks.

Abstract:
Human motion modeling must support generation from text,music, and speech, comprehension through captioning, temporal com-pletion, and multi-person coordination—yet existing methods typicallytackle each scenario with a separate, task-specific system. We arguethat discrete tokenization plus autoregressive (AR) modeling possessesunique compositional advantages for building a unified motion model:all modalities share a single vocabulary and training objective, tasks aredefined by token arrangement rather than architectural changes, andthe same model naturally supports both generation and understand-ing by simply reordering the sequence. However, realizing this potentialhas been blocked by two obstacles. First, current motion tokenizers useroot-relative, non-causal, frame-level encodings that discard world-spaceinformation, break consistency with forward-only AR decoding, and scalepoorly—confining existing discrete AR models to text–motion pairs insingle-human settings. Second, multi-modal motion data with paired textand audio annotations remains scarce. We address both obstacles. Weintroduce VerMoVQ, a causal spatio-temporal VQ-VAE that factorizeseach frame into low-dimensional body tokens while preserving world-space root trajectories, naturally enabling multi-person tokenization viaper-agent concatenation and arbitrary-length encoding through stricttemporal causality. We curate MotionHub, a ～ 380K-clip corpus in unifiedSMPL representation with large-scale multi-modal annotations spanningnine motion tasks. Built on these foundations, we train VersatileMo-tion, a single decoder-only AR Transformer that covers text\leftrightarrow motion,music\leftrightarrow dance, speech

Abstract:
Dataset condensation for action segmentation synthesizescompact, informative representations of long, untrimmed video datasets.The existing approach relies on Variational Autoencoders and an iter-ative latent optimization; it is computationally expensive and suffersfrom over-smoothed reconstructions and rigid temporal constraints. Thispaper proposes to shift the condensation paradigm from optimization-based inversion to deterministic latent mapping. By leveraging Denois-ing Diffusion Implicit Models, we represent action segments as continu-ous trajectories anchored by sparse latent points in the noise manifold.To maximize representational efficiency, we introduce an adaptive allo-cation mechanism that dynamically redistributes the anchoring budgetbased on segment-wise reconstruction difficulty. Extensive experimentsdemonstrate that our framework significantly outperforms state-of-the-art methods in segmentation performance across common datasets. No-tably, our approach achieves performance parity with real data trainingwhile maintaining a condensation ratio of 2.4% on Breakfast dataset.

Abstract:
Long-video language models (Video-LMMs) face a funda-mental bottleneck: temporal reasoning is often performed implicitly overdiscretized frames/clips and limited-context tokens. As a result, queriesthat require computable temporal structure—such as counting repeatedevents, comparing rates across time ranges, estimating intervals, or rea-soning about order—are handled by heuristic sampling, retrieval, or free-form summarization. We propose CTEPM, a continuous-time eventprocess memory that represents a video as a marked temporal pointprocess with learnable intensities and semantic marks. CTEPM first con-verts dense video features into a sparse set of latent events with contin-uous timestamps and mark embeddings, then fits an interaction-awaremarked point process that models global time trends and cross-eventtriggering/inhibition. Given a question, CTEPM executes a small libraryof temporal operators on the learned process to produce compact struc-tured evidence, which conditions the final answer generation. This designmakes temporal reasoning explicit, lightweight, and inspectable withoutperforming video generation. Experiments on long-video understandingbenchmarks demonstrate consistent improvements on temporally compo-sitional queries under tight context budgets, while retaining competitiveaccuracy on standard video QA.

Abstract:
Visual Autoregressive modeling (VAR) has emerged as ahighly efficient alternative to diffusion-based frameworks, achieving com-parable synthesis quality. However, as this paradigm extends to Space-time Autoregressive modeling (STAR) for video generation, scaling res-olution and frame counts leads to a "token explosion" that creates amassive computational bottleneck in the final refinement stages. To ad-dress this, we propose FastSTAR, a training-free acceleration frame-work designed for high-quality video generation. Our core method, Spa-tiotemporal Token Pruning, identifies essential tokens by integratingtwo specialized terms: (1) Spatial similarity, which evaluates structuralconvergence across hierarchical scales to skip computations in regionswhere further refinement becomes redundant, and (2) Temporal similar-ity, which identifies active motion trajectories by assessing feature-levelvariations relative to the preceding clip. Combined with a Partial Up-date mechanism, FastSTAR ensures that only non-converged regions arerefined, maintaining fluid motion while bypassing redundant computa-tions. Experimental results on InfinityStar demonstrate that FastSTARachieves up to a 2.01× speedup with a PSNR of 28.29 and less than 1%performance degradation, proving a superior efficiency-quality trade-offfor STAR-based video synthesis.

Abstract:
Current multimodal re x001D ection mechanisms for long video un-derstanding predominantly rely on closed-loop self-re x001D ection within in-ternal parameters. Lacking objective external evidence, models are fre-quently trapped in blind con x001C dence and often fail to correct errors.Furthermore, applying reinforcement learning to multi-stage re x001D ectionpipelines introduces severe policy coupling, which is exacerbated by acritical scarcity of dedicated training data. To address these limitations,this work proposes Re x001D ect-R1, the x001C rst Evidence-Driven self-correctionframework for long video understanding. The framework constructs athree-stage pipeline consisting of intuition, veri x001C cation, and arbitration.By dynamically retrieving objective visual evidence to verify initial in-tuitions and autonomously executing multiple temporal searches to re-solve con x001D icts, it completely breaks the hallucination loop. To over-come policy coupling, we design a stage-decoupled reinforcement learn-ing algorithm named SD-GRPO that independently computes advantagefunctions across di x001B erent reasoning stages. Concurrently, we constructa dataset of 120K samples to bridge the training data gap. Extensiveexperiments on benchmarks such as VideoMME and LongVideoBenchdemonstrate that Re x001D ect-R1 achieves state-of-the-art performance. Ourmethod signi x001C cantly improves the genuine recti x001C cation rate and enablesauthentic self-correction strictly grounded in objective evidence.

Abstract:
While Multimodal Large Language Models (MLLMs) have advanced video understanding, achieving precise temporal and crossmodal alignment in audiovisual video captioning remains a formidable challenge. Most existing approaches suffer from modality detachment and temporal incoherence, failing to accurately bind auditory events to visual entities or capture complex causal dynamics. To address these deficiencies, we propose TCA-Captioner, a framework specifically engineered to enhance Temporal and Cross-Modal Alignment for audiovisual video captioning. We first introduce the Observer-Checker-Corrector (OCC) framework, an iterative refinement strategy that generates high-fidelity, meticulously grounded training data. Leveraging a curated high-density human interaction dataset, TCA-Captioner is optimized to model sophisticated audiovisual interactions. Furthermore, we present TCA-Bench, a diagnostic benchmark utilizing a Decoupled Evaluation Protocol to isolate and quantify model proficiency in audiovisual binding and temporal relational reasoning. Extensive experiments demonstrate that TCACaptioner sets a new standard for temporally-coherent and synchronized audiovisual narratives.

Abstract:
Automatically detecting abnormal events in videos is cru-cial for modern autonomous systems, yet existing Video Anomaly De-tection (VAD) benchmarks lack the scene diversity, balanced anomalycoverage, and temporal complexity needed to reliably assess real-worldperformance. Meanwhile, the community is increasingly moving towardVideo Anomaly Understanding (VAU), which requires deeper seman-tic and causal reasoning but remains difficult to benchmark due to theheavy manual annotation effort it demands. In this paper, we introducePistachio, a new VAD/VAU benchmark constructed entirely through acontrolled, generation-based pipeline. By leveraging recent advances invideo generation models, Pistachio provides precise control over scenes,anomaly types, and temporal narratives, effectively eliminating the bi-ases and limitations of Internet-collected datasets. Our pipeline inte-grates scene-conditioned anomaly assignment, multi-step storyline gen-eration, and a temporally consistent long-form synthesis strategy thatproduces coherent 41-second videos with minimal human intervention.Extensive experiments demonstrate the scale, diversity, and complexityof Pistachio, revealing new challenges for existing methods and motivat-ing future research on dynamic and multi-event anomaly understanding.

Abstract:
Memorization in machine learning models enables high per-formance on rare in-distribution samples by capturing their atypicalpatterns. However, it also causes harmful retention of noise and out-liers, degrading generalization. While memorization has been extensivelystudied in both supervised and self-supervised learning in the visiondomain, it remains unexplored in multi-modal contrastive learning. Weaddress this gap by introducing MultiMem, the first metric designed toquantify memorization in multi-modal contrastive learning. Through oursystematic analysis, we demonstrate that cross-modal semantic misalign-ment has the strongest influence on memorization, with text being thedominant modality driving memorization, followed by video, image, andaudio. We show that targeted augmentations applied across all modalitieseffectively reduce memorization as measured by our MultiMem metricand improve model performance. Overall, this work establishes the firstframework for measuring and mitigating memorization in multi-modalcontrastive learning, preventing harmful data retention and contributingto higher-performing models. Full version with appendix available athttp://arxiv.org/abs/2606.22220

Abstract:
Foundation models have transformed vision and languageprocessing by providing rich, reusable representations that transfer acrossdiverse tasks. Sheet music, as a visual encoding of musical language,lacks such a strong domain-specific backbone. We introduce MuSViT(Music Score Vision Transformer): the first foundation vision modelfor sheet music representation—a ViT encoder pre-trained via MaskedAutoencoders on 9.7 million pages from the International Music ScoreLibrary Project (IMSLP). To handle the complexity of real-world scores,we adopt a two-stage curriculum: a synthetic warm-up on typeset scoresfollowed by large-scale training on the full IMSLP corpus. We evaluateMuSViT on four downstream tasks—full-page and staff-level music scorerecognition, music symbol detection, and score difficulty classification—under two scenarios: linear probing (frozen encoder) and fine-tuning.Under linear probing, MuSViT consistently outperforms modern visionencoders, revealing that general-purpose representations, regardless ofscale, fall systematically short on the structured symbolic propertiesMuSViT PaliGemma 2 Qwen3-VL MuSViT SoTAMuSViT-Light Kosmos-2.5 DINOv3-7B MuSViT-LightFull-Page Music Full-Page MusicScore Recognition Score RecognitionScore Difficulty Staff-Level Music Score Difficulty Staff-Level MusicClassification Score Recognition Classification Score RecognitionMusic Symbol Music SymbolDetection DetectionFig. 2: MuSViT performance across four downstream tasks. Left: Linear probing(frozen encoder)—MuSViT (solid) consistently outperforms general-purpose vision en-coders (dashed), demonstrating superior representation quality. Right: Fine-tuning—MuSViT generally outperforms state-of-the-art methods (SoTA). Axes represent nor-malized performance on each task (higher is better); see Section 3 for detailed results.of musical notation. Under fine-tuning, MuSViT generally improvesupon task-specific state-of-the-art methods. An additional embedding-transcription consistency analysis reveals that MuSViT encodes sym-bolic musical structure directly in its representation space—unlike otherencoders, whose embeddings do not correlate with music notation con-tent. These results establish MuSViT as a foundation backbone for sheetmusic understanding.

Abstract:
Parameter-efficient fine-tuning (PEFT) has become a prac-tical solution for adapting large pretrained vision transformers (ViTs)to downstream tasks while updating only a small subset of parame-ters. However, existing adapter-based methods perform adaptation in-dependently for each token, implicitly assuming that token refinementsshould be learned in isolation. This token-wise formulation overlooksthe structured relationships among tokens that naturally arise in visualscenes, potentially leading to redundant updates and spatially inconsis-tent feature refinement. In this work, we revisit the design of parameter-efficient adapters and propose to perform adaptation in hyperedge spacerather than token space. We introduce HyperAdapter, a hypergraph-based adapter architecture that enables structured, group-aware adap-tation through soft token routing. HyperAdapter constructs a soft hy-pergraph over ViT tokens using prototype-based assignments, aggregatestoken features into latent hyperedge representations, applies lightweightbottleneck adaptation at the hyperedge level, and diffuses the result-ing updates back to tokens via the hypergraph incidence structure. Thisdesign injects an explicit structural inductive bias into PEFT while pre-serving the modularity and efficiency of standard adapters. Extensiveexperiments across diverse visual benchmarks demonstrate that struc-tured hyperedge adaptation consistently outperforms strong PEFT base-lines under comparable parameter budgets, with particularly pronouncedgains on tasks requiring structured reasoning. Our results suggest thatthe choice of adaptation space is a critical yet underexplored dimensionin parameter-efficient transfer for ViTs.

Abstract:
Adapting pre-trained text-to-image diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended effects alone. We argue this framing is incomplete. Through sparse autoencoder analysis and zero-shot classification, we demonstrate that adaptation systematically damages semantically unrelated concepts in ways that aggregate metrics structurally cannot surface: when damage is severe enough for FID and KID to respond, the model is already nearly unusable; when the model remains functional, FID and KID stay flat while specific classes silently suffer worst-case zero-shot accuracy drops of up to 18.9 points and concept-level distributions shift dramatically. This pattern appears at both ends of the adaptation spectrum (concept customization and concept unlearning), suggesting it is a systematic consequence of weight-level modification rather than an artifact of any particular method. To surface this hidden drift before deployment, we introduce DriftScope, a prompt-level diagnostic tool that takes any two model checkpoints and returns a ranked list of tokens whose visual concepts have shifted most between them. DriftScope optimizes a soft prompt to attribute drift at the token level without requiring access to real data or model internals. The result is an interpretable, concept-level audit that aggregate evaluation cannot provide.

Abstract:
Self-supervised learning for imaging inverse problems is in-creasingly important in photon-limited settings, where acquiring cleanground truth is impractical and reconstruction must remain stable underdataset and acquisition shifts. This challenge is amplified under Poissonnoise, whose signal-dependent statistics interact with sampling operators(e.g., CFA mosaicing). Meanwhile, foundation vision encoders trained atweb scale offer distortion-invariant, content-related representations thatgeneralize well across domains, suggesting a promising route to buildpriors that transfer beyond the training distribution without expensivefine-tuning. This paper proposes an ADMM-inspired unrolled plug-and-play solver for Poisson inverse problems that decouples a closed-formdata-consistency update from a parameter-efficient prior. The prior isimplemented as a lightweight decoder operating on frozen CLIP RN50dense multi-scale features, adapting foundation representations with lesstrainable parameters. For self-supervision, the method integrates GR2Rmeasurement-domain re-corruption with an Equivariant Imaging regu-larizer via virtual acquisitions. Experiments on Poisson CFA demosaic-ing and deblurring show competitive quality, improved robustness undershifts, and self-supervised performance approaching supervised training.

Abstract:
Attention-based models often develop attention sinks, wherea small number of tokens repeatedly attract attention and accumulateunusually large activations. In vision transformers, these outliers areclosely related to registers, which have been diagnostically linked toglobal, low-frequency image structure. Existing work has largely studiedregisters through interpretability analyses and linear probes, leaving openwhether they can be operationalized as plug-and-play signals for genera-tion without retraining. We revisit this question in tokenized image gen-eration. Using OpenCLIP and DINOv2 on ImageNet, we find that test-time register features exhibit stronger low-frequency concentration thanboth [CLS] readouts and patch-mean features, and show a consistent (al-beit moderate) correlation with pixel-space DCT low-frequency energy.Motivated by these diagnostics, we introduce RegToken, a training-free procedure that converts register structure into a small set of globalprior tokens by (i) NFN-based layer localization, (ii) TokenRank-guidedsubspace extraction, and (iii) a projection-and-conservation update onthe register subspace. Inserted into a frozen compact 1D token gener-ation pipeline, RegToken improves ImageNet generation and alignmentmetrics (e.g., FID-5k 20.5 → 20.1, SigLIP 3.6 → 3.9) without modify-ing pretrained weights, and accelerates test-time optimization (Steps@τ74 → 52). Overall, our results suggest that structures often viewed asattention artifacts can be repurposed as lightweight global priors for to-kenized generation.

Abstract:
Understanding whether modern vision-language-action poli-cies internally represent task progress is central to interpretable robotcontrol. This paper presents a progress-based SafeSAE-VLA analysis ofOpenVLA on 750 LIBERO episodes, replacing a degenerate binary targetwith a suite-normalized high-vs.-low split over relative geometric progressand testing layer-20 sparse-autoencoder (SAE) features (dsae = 16,384)with non-parametric differential analysis and false-discovery-rate (FDR)control. The recovered signal is strong and sparse. Of 1,881 active fea-tures, 1,117 are significant (FDR < 0.05), a linear probe reaches 0.918AUROC (area under the ROC curve), and top-20 features alone retain0.894 AUROC. Performance is stable across suites (up to 0.985 on goaland 0.947 on object), while motion-only controls are markedly weaker(0.572–0.711), indicating separability is not explained by gross move-ment magnitude. Dense raw-activation probes are equal or stronger thanSAE readouts (e.g., raw LR 0.975 vs. full-SAE 0.947 under a pooledanalysis with bootstrap confidence intervals). We therefore position thecontribution not as predictive dominance but as showing that nearly allof the progress signal survives in a compact set of named, inspectable,intervention-compatible directions. Layer sweeps, split-robustness checks,and a success-labeled audit (in which the progress-tuned top-20 correctlyscores an at-chance 0.471 while the broader SAE basis reaches 0.968)support this scoping, and direct on-OpenVLA feature-setting interven-tions produce specific, task-local behavioral shifts rather than generalclosed-loop repair.

Abstract:
Pre-trained Vision Foundation Models (VFMs) provide strongvisual representations for diverse downstream tasks. The key challenge ofVFM adaptation stems from the prohibitive costs of full fine-tuning andcatastrophic forgetting. To address this, Low-Rank Adaptation (LoRA)has emerged as the prevailing paradigm for Parameter-Efficient Fine-Tuning (PEFT). However, LoRA is typically designed for transformerself-attention layers parameterized by 2D matrices. Since convolutionalkernels inherently couple spatial and channel information within a 4Dtensor, forcing them into a monolithic 2D matrix disrupts the inher-ent spatial topology. In this paper, we propose Low-Rank ConvolutionalAdaptation (LoCA), a convolution-aware PEFT framework that addressesspatial-channel entanglement by decoupling channel and spatial adapta-tion. LoCA introduces a low-rank channel adaptation for dense cross-channel mixing and refines spatial bases extracted from pre-trained ker-nels via Singular Value Decomposition (SVD). Experimental results showthat LoCA preserves pre-trained spatial priors and achieves competitiveor state-of-the-art performance across fine-grained classification, domain-generalized semantic segmentation, and generative benchmarks.

Abstract:
Generative vision-language models (VLMs) such as Qwen-VLand LLaVA achieve strong zero-shot performance on tasks overlappingwith their pretraining distribution, yet fail on specialized domains wherethe required discriminative features were never learned, a regime weterm distant out-of-distribution (OOD). Standard adaptation methodscannot overcome this representational absence because they operatewithin the encoder’s existing feature space. However, VLMs retain arobust descriptive capacity even when discrimination collapses: a modelthat cannot classify a medical scan can still articulate its visual patterns.Exploiting this asymmetry, we introduce Inductive Visual Logic (IVL), atraining-free framework that constructs classification knowledge from themodel’s surviving descriptive ability. IVL extracts visual traits from few-shot support images through dual-mode prompting, combining semanticdescriptions with primitive visual observations, and organizes them intoper-class trait dictionaries. At inference, hierarchical filtering identifiesspatially grounded trait evidence for classification. Across multiple distant-OOD benchmarks, IVL achieves the highest aggregate accuracy under twoVLM backbones while producing interpretable, trait-traceable predictions.

Abstract:
Transparency of neural networks’ internal reasoning is atthe heart of interpretability research, adding to trust, safety, and under-standing of these models. The field of mechanistic interpretability hasrecently focused on studying task-specific computational graphs, definedby connections (edges) between model components. Such edge-basedcircuits have been defined in the context of large language models, yetvision-based approaches so far only consider neuron-based circuits. Thesetell which information is encoded, but not how it is routed through thecomplex wiring of a neural network. In this work, we investigate whetheruseful mechanistic circuits can be identified through computational graphsin vision transformers. We propose an effective method for AutomaticVisual Circuit Discovery (Vi-CD) that recovers class-specific circuitsfor classification, identifies circuits underlying typographic attacks inCLIP, and discovers circuits that lend themselves for steering to correctharmful model behaviour. Overall, we find that insightful and actionableedge-based circuits can be recovered from vision transformers, addingtransparency to the internal computations of these models.

Abstract:
The deployment of deep learning in digital pathology holds transformative potential for cancer diagnosis, yet its clinical adoption is hindered by the opaque and vulnerable nature of black-box models. In this work, we introduce a mechanistic examination framework that combines sparse autoencoders and activation patching to dissect adversarial vulnerabilities in pathology models as several learned features may correspond to non-morphological artifacts rather than clinically meaningful patterns. By isolating the neural circuits responsible for these vulnerabilities, we demonstrate that models often rely on spurious, nongeneralizable cues, particularly in middle layers of Vision Transformers and later convolutional blocks of ResNets. To address this, we propose Mechanism-Informed Adversarial Training, a novel approach that targets vulnerable circuits with precision regularization, achieving state-of-theart robustness while preserving clean performance. Crucially, our framework aligns with rapidly emerging clinical and regulatory demands for alignment, accountability, and safety in AI-driven diagnostics. By providing a mathematically rigorous, interpretable, and actionable method to audit and fortify deep learning models, we seek to bridge the gap between AI research and clinical deployment, offering a pathway to trustworthy, regulation-ready AI in healthcare. Our findings not only inform the field of adversarial machine learning but also set a precedent for mechanistically grounded artificial intelligence in real-world medical applications.

Abstract:
Learning from inaccurate annotations has gained significantattention due to the high cost of precise labeling. However, despite thepresence of erroneous labels, models trained on noisy data often retainthe ability to make accurate predictions. This intriguing phenomenonraises a fundamental yet largely unexplored question: why models canstill extract correct label information from inaccurate annotations re-mains unexplored. In this paper, we conduct a comprehensive investiga-tion into this issue. By analyzing weight matrices from both empiricaland theoretical perspectives, we find that label inaccuracy primarily ac-cumulates noise in lower singular components and subtly perturbs theprincipal subspace. Within a certain range, the principal subspaces ofweights trained on inaccurate labels remain largely aligned with thoselearned from clean labels, preserving essential task-relevant information.We formally prove that the angles of principal subspaces exhibit mini-mal deviation under moderate label inaccuracy, explaining why modelscan still generalize effectively. Building on these insights, we proposeLIP, a lightweight plug-in designed to help classifiers retain principalsubspace information while mitigating noise induced by label inaccu-racy. Extensive experiments on tasks with various inaccuracy conditionsdemonstrate that LIP consistently enhances the performance of existingalgorithms. We hope our findings can offer valuable insights to under-stand of model robustness under inaccurate supervision.

Abstract:
Photographic style transfer aims to render a content imagewith the retouching style of a reference photo while preserving scenestructure and texture. Unlike artistic style, photographic style is dom-inated by subtle, continuous tone/color changes and smooth spatial ef-fects, which are difficult to represent with semantics-oriented or text-supervised image embeddings. We propose PETAL, a PhotographicEmbedding for Transfer with an Adaptive LUT. PETAL learns a dedi-cated photographic embedding through self-supervised photographic styleaugmentation and uses it to condition a lightweight adaptive 5D neu-ral LUT. The learned embedding outperforms prior representations inphotographic style retrieval, and the full framework enables reference-based style transfer without test-time optimization, achieving strongerquantitative and perceptual performance than state-of-the-art baselines.Project page: https://petal-pku.pages.dev/.

Abstract:
Visual token reduction has emerged as an effective strategy for accelerating Multimodal Large Language Models (MLLMs). Many existing methods prune tokens by ranking text–visual attention scores. However, we show that attention is often dominated by a model-induced prior: even without textual instruction, MLLMs tend to focus on certain task-agnostic regions. Consequently, the attention scores of instructionconditioned tokens are suppressed, increasing the risk that these tokens are discarded during pruning. To address this issue, we propose PriorCorrected Token Reduction (PriorTR), a training-free token reduction method that explicitly separates task-conditioned attention from the model-induced prior. PriorTR estimates the attention map of the prior, and contrasts it with the task-conditioned attention distribution to measure the additional usable information contributed by each visual token. Importantly, PriorTR computes both the model-induced prior and the task-conditioned posterior within a single forward pass by introducing a null token that serves as an instruction-agnostic probe in the attention block. This design avoids duplicated propagation. Extensive experiments across multiple multimodal benchmarks and MLLMs demonstrate that PriorTR consistently improves the trade-off between accuracy and efficiency over strong training-free baselines, particularly under aggressive token budgets.

Abstract:
Many real-world applications of spatial intelligence, such asrobotic control, autonomous driving, and automated assembly, requirespatial reasoning across multiple sequential steps. However, the extent towhich current Multimodal Large Language Models (MLLMs) possess thiscapability remains largely unexplored. Inspired by LEGO construction, arecreational activity that critically relies on multi-step spatial reasoning,we introduce LEGO-Puzzles: a benchmark designed to systematicallyevaluate the spatial reasoning capabilities of MLLMs from basic spatialunderstanding to multi-step planning. LEGO-Puzzles contains two tasksets. The Elementary set covers 11 visual question-answering (VQA)tasks with 1, 100 carefully curated samples to test elementary spatialreasoning skills that are cruical for LEGO assembly. The Planning setdirectly requires the model to generate a step-by-step plan for assemblinga target LEGO structure, where the tasks are organized into subsets withdifferent planning horizons ranging up to 8. Our evaluation of 29 state-of-the-art MLLMs shows that even the strongest models struggle withelementary reasoning tasks in LEGO construction, falling at least 20%behind human performance. The planning accuracy also quickly dropsto 0% as the number of planning steps increases, whereas our humanparticipants solve all the tasks perfectly. Switching the output formatfrom multiple choice to image generation degrades model performanceeven further, leading to zero accuracy even for planning 3 steps. Over-all, LEGO-Puzzles reveals critical limitations in current MLLMs’ spatialreasoning capabilities and highlights the need for substantial advances.

Abstract:
The rapid scaling of large language models (LLMs) and vision-language models (VLMs) has sharply increased compute and storagecosts, motivating extensive research on model pruning. Yet most prun-ing pipelines underuse data scaling during pruning, i.e., increasing thepruning-phase token budget. We revisit L0-regularized structured chan-nel pruning, valued for differentiability, incremental scheduling, and globaldecision-making. We uncover a long-horizon instability: the Lagrangemultiplier grows during training, amplifies pruning gradients, and causeslate-stage oscillations in both gradients and loss. We derive sufficient L2-norm bounds on pruning-specific gradients and reformulate the loss toenforce these bounds per update, replacing dual ascent with a coefficientdetermined by the gradient constraint. Building on this foundation, weintroduce DIGS (Differentiable, Incremental, Global, Scalable pruning),which enables stable and efficient structured channel pruning withoutauxiliary stabilizers while supporting much larger pruning token bud-gets. Applied to the Qwen2.5VL LLM backbone, DIGS scales pruningbeyond 100B tokens and achieves competitive results on public multi-modal benchmarks. Within this setting, larger pruning token budgetsyield monotonic performance gains at fixed sparsity.

Abstract:
Large Language Models (LLMs) have shown strong potentialfor medical reasoning, yet the scarcity and cost of expert-annotated dataconstrain their progress. While reinforcement learning offers a scalable al-ternative, standard outcome-based methods in medicine often suffer fromautoregressive credit assignment failure and gradient variance explo-sion. This leads to the “Right Answer, Wrong Reason” trap, where mod-els inadvertently reinforce spurious correlations and dataset shortcutsrather than valid clinical deduction. In this work, we propose Causally-Aligned Reasoning Exploration (CARE), a theoretically groundedframework for intrinsic experience curation. CARE is built upon tworigorous conditions for high-quality training trajectories: Causal Suf-ficiency, which utilizes an agreement-based self-verification mechanismto mimic do-calculus interventions and effectively debias gradients; andProximal Learnability, which employs dynamic entropy bounds to se-lect experiences within the model’s zone of proximal development forvariance-bounded optimization. These rigorously filtered experiences areoptimized via a dual-stream objective that combines on-policy group-relative exploration with difficulty-weighted experience replay. Extensiveexperiments on diverse medical multimodal and text-only benchmarksdemonstrate that CARE consistently outperforms other strong competi-tors, substantially reducing correct-but-inconsistent reasoning and im-proving training stability.

Abstract:
As Vision-Language Models (VLMs) become increasingly so-phisticated and widely used, it becomes more and more crucial to un-derstand their decision-making process. Traditional explainability meth-ods, designed for classification tasks, struggle with modern autoregressiveVLMs due to their complex token-by-token generation process and in-tricate interactions between visual and textual modalities. We presentDEX-AR (Dynamic Explainability for AutoRegressive models), a novelexplainability method designed to address these challenges by generat-ing both per-token and sequence-level 2D heatmaps highlighting imageregions crucial for the model’s textual responses. The proposed methodoffers to interpret autoregressive VLMs, accounting for the varying im-portance of layers and generated tokens—by computing layer-wise gradi-ents with respect to attention maps during the token-by-token generationprocess. DEX-AR introduces two key innovations: a dynamic head filter-ing mechanism that identifies attention heads focused on visual informa-tion, and a sequence-level filtering approach that aggregates per-tokenexplanations while distinguishing between visually-grounded and purelylinguistic tokens. Our evaluation on ImageNet, VQAv2, and PascalVOC,shows a consistent improvement in both perturbation-based metrics, us-ing a novel normalized perplexity measure, as well as segmentation-basedmetrics. Code is available at https://walidbousselham.com/DEX-AR

Abstract:
Large vision-language models (LVLMs) have demonstratedstrong general multimodal capability and are increasingly deployed indownstream systems. This trend has driven growing interest in LVLMpersonalization, which aims to enable models to quickly and effectivelylearn out-of-distribution multimodal concepts to meet user-specific needs.However, many existing methods rely on inference-time training, whichreduces efficiency. They also struggle to maintain accuracy in complexmulti-image, multi-concept settings. These limitations restrict the broaderdeployment of LVLM-based systems. Therefore, this paper proposes in-context prompt tuning (ICPT). Specifically, ICPT employs a lightweightprojection module capable of operating in complex scenarios to extractfine-grained visual semantics from multiple reference images, seamlesslytransforming these features alongside identity-label mappings into con-tinuous prompts. To maximize computational efficiency, this moduleadaptively determines the prompt length based on the intrinsic visualcomplexity of each concept. Crucially, to overcome the environmental bi-ases and cross-concept interference prevalent in real-world applications,we introduce two novel geometric regularizations. These constraints re-fine prompt representations by decoupling key identities from transientenvironmental states and separating concepts to avoid semantic confu-sion. Extensive experiments show that ICPT achieves state-of-the-artpersonalization accuracy across diverse tasks and LVLM backbones.

Abstract:
Hallucinations in vision–language models (VLMs) are com-monly treated as semantic errors, yet they often arise from partial or am-biguous visual evidence. Prior work mainly focuses on detecting or sup-pressing hallucinations at generation time, leaving the subsequent reason-ing stage largely unexplored. In this work, we study Post-HallucinationReasoning (PHR), the stage in which hallucinated semantics enter themodel’s inference context and influence downstream predictions. To sys-tematically investigate PHR, we introduce the HIVE (HallucinationInference and Verification Engine), an evaluation infrastructure that en-ables controlled comparisons between faithful and hallucinated captions.Across nine tasks and nine models, we observe structured modality-dependent patterns: hallucinated captions often improve accuracy onvision–language tasks, while text-only tasks exhibit limited or unstableeffects. Further analyses show that hallucinated cues broaden semanticcoverage and reshape reasoning dynamics while preserving stable infer-ence. These findings highlight that hallucinated semantics may influencedownstream reasoning once they enter the model’s inference context. Un-derstanding this post-hallucination stage is important for improving thereliability and interpretability of multimodal reasoning systems.

Abstract:
Layout understanding, or the interpretation of element orga-nization, is essential for document analysis, user interface (UI) creation,and graphic design. While recent vision-language models (VLMs) ex-cel at interpreting atomic layouts composed of independent elements,they struggle with compositional layouts that require reasoning over vi-sually entangled elements within hierarchical multi-layer structures. Inthis paper, we introduce a new task, compositional layout understanding,and present CoDeLayout, a VQA dataset of ∼20K real-world multi-layerlayouts annotated with compositional element pairs and design intent.Through empirical analysis on CoDeLayout, we identify two key chal-lenges for existing VLMs: semantic drift between textual metadata andvisual content, and structural ambiguity in hierarchical inter-elementrelationships. To address these challenges, we propose MASON, a post-training paradigm that integrates multimodal alignment (MA) and struc-tural perception (SP). MA enhances element interpretation by ground-ing metadata-defined elements to their visual counterparts, mitigatingsemantic drift, while SP models layer-aware inter-element spatial rela-tionships to improve hierarchical understanding and reduce structuralambiguity. Experiments reveal substantial gaps in existing VLMs: eventhe strongest baseline, GPT-o3, achieves only 79.68% accuracy, whereasQwen2.5-VL 7B with MASON reaches 91.66%. Notably, MASON sur-passes full-data Direct Finetune using only 30% of the training data andscales better with additional data.

Abstract:
Large vision–language models (LVLMs) incur substantial com-putational and memory overhead due to quadratic attention complexityand enlarged key–value (KV) caches. Training-free vision token prun-ing is promising, yet most methods rely on text-to-vision attention-scoremagnitudes, which often entangle instruction-relevant signals with po-sitional bias and visual attention sinks. To address this issue, we pro-pose DIVA, an instruction-aware, layer-wise vision token pruning methodbased on dual-probe attention discrepancy, with two key modules: DIVA-Pruner and VF-Calibrator. DIVA-Pruner selects important tokens us-ing the discrepancy between attention distributions of contextual andnon-contextual probe sets. A lightweight VF-Calibrator calibrates unim-portant tokens that bypass self-attention, mitigating representation mis-match and enabling effective reuse in subsequent LVLM blocks. On di-verse benchmarks and LVLM baselines, DIVA consistently outperformsprior methods across token budgets, even in highly constrained regimes.Notably, under a 97.2% token reduction, DIVA retains 92.6% of baselineperformance on LLaVA-NeXT-7B with a 10.8× reduction in KV cache.

Abstract:
The evaluation of long-term video quality understanding re-mains an open challenge for large vision-language models (LVLMs). Ex-isting video quality benchmarks predominantly focus on short clips andisolated distortions, overlooking the temporal continuity, cumulative degra-dation, and reasoning complexity inherent in long-duration content. Toaddress these limitations, we present LongVQUBench, a comprehen-sive benchmark for long-term video quality understanding. LongVQUBenchcontains over 1,200 diverse videos spanning movies, documentaries, surveil-lance footage, egocentric recordings, and animated content, accompa-nied by 1,500 multiple-choice and open-ended questions for validationand testing. To assess perceptual reasoning across different temporalscopes, we introduce three progressively complex evaluation levels: (i)local event quality understanding (LQU) for analyzing localized distor-tions; (ii) cross-event quality reasoning (CQR) for integrating multipledegraded events; and (iii) global quality understanding (GQU) for holis-tic perceptual evaluation over extended durations. Furthermore, a needledistortion question-answering (NDQA) paradigm is embedded across allthree levels, where spatial or temporal artifacts are sparsely inserted toprobe fine-grained detection and reasoning capabilities. Extensive ex-periments on 14 state-of-the-art LVLMs reveal significant performancedegradation with increasing video length and reasoning depth, highlight-ing their limited capacity for long-range temporal integration and percep-tual attribution. We envision LongVQUBench as a foundational step to-ward the systematic, hierarchical, and explainable evaluation of LVLMs’long-term video quality understanding.

Abstract:
Recent advances in Large Vision Language Models (LVLMs)have led to interest in alignment methods such as Direct PreferenceOptimization (DPO), which improves multi-modal instruction followingwithout costly supervision or explicit reward modeling. However, pre-vious attempts to improve DPO through data augmentation and pref-erence ranking fall short of bridging vision-language perceptual gaps,limiting consistent multi-modal alignment. In this paper, we proposea novel Vision-Language Consistency based Direct PreferenceOptimization (VLC-DPO), which automatically constructs prefer-ence data and leverages consistent information from visual inputs andcorresponding textual descriptions as alignment signals. The VLC-DPOconsists of three key components: (1) an initial response generation thatproduces image- and description-based responses to bridge the modal-ity gap in LVLMs by capturing complementary aspects of visual un-derstanding, (2) a preference data construction via vision-language con-sistency score that automatically selects high-quality preference pairsbased on response consistency, and (3) a VLC-DPO training that usesconsistency-based implicit rewards to optimize preferences, assigning dy-namic weights to less image-related or distant samples to improve DPOlearning. Extensive experiments on hallucination mitigation and zero-shot VQA demonstrate the e↵ectiveness of our method, which outper-forms existing approaches.

Abstract:
Understanding a painting is never a single act. Art histori-ans may analyze the same work through concepts of style, iconography,or historical context, dimensions that are not interchangeable, and eachcarries distinct semantic relationships between the visual and the tex-tual. Vision-Language Models (VLMs) like CLIP, which learn a singleshared embedding space, collapse this richness into a single homogeneousalignment, thereby losing the multi-relational structure that defines art-historical reasoning. We introduce CANVAS (Contrastive Art-awareNetwork for Vision-Language Alignment with Sheaves), a frameworkfor learning relation-aware multimodal representations inspired by sheaftheory. Each artwork is projected into multiple embeddings conditionedon the type of relation (i.e., the context), and a novel contrastive lossencodes contextual information during training, with no dependency onexternal data at inference. We evaluate on three newly introduced bench-marks of artworks for multi-relational art understanding: WikiArt+,derived from WikiArt and Wikipedia, HertzianaDP, from the Biblio-theca Hertziana collection, and SemArt+, refined from the SemArtdataset. In multimodal retrieval and art understanding, CANVAS out-performs the baselines, supporting the view that multi-relational align-ment is not just theoretically motivated but also practically essential.

Abstract:
Vision–language models (VLMs) achieve strong performanceon general-domain tasks but can degrade when visual distributions, ter-minology, and reasoning requirements diverge from general-domain pre-training assumptions, including in scientific imaging. We study this chal-lenge in materials-science transmission electron microscopy (TEM), adata-scarce setting that requires nanoscale visual grounding, modality-specific interpretation, and specialized materials-science knowledge. Weintroduce ATOMIC (Assistant for TEM Oriented Multimodal Instruc-tion and Conversation), a TEM-oriented vision–language model adaptedusing peer-reviewed, literature-derived data without manual annotation.Our pipeline extracts figure–caption pairs, isolates TEM subfigures, andgenerates multimodal instruction data via GPT-based instruction gen-eration. The training strategy separates two complementary supervisionsignals: VisionGround, which enforces image-only grounding, and Do-mainContext, which injects caption-conditioned scientific context. Theircombination, Blend, yields consistent improvements across perception-and knowledge-intensive queries. ATOMIC-7B-Blend achieves 75.2% ac-curacy on TEM-MCQ and 37.5% recall-oriented Answer Word Cover-age (AWC) on TEM-VQA, narrowing the gap to GPT-4o on AWC(40.4%) while enabling local deployment. On the TEM subset of MatCha,an independent external benchmark, ATOMIC-7B-Blend+FT outper-forms LLaVA-v1.5-7B+FT by 18.1 percentage points under identicalfine-tuning conditions. We release TEM-MCQ/TEM-VQA annotations,article URLs, figure IDs, normalized crop coordinates for sub-figure re-construction, the data-curation pipeline, and model weights to supportreproducibility within copyright constraints. These results show thatstructured literature-derived supervision offers an effective data-centricstrategy for adapting VLMs to materials-science TEM and may provide areproducible foundation for future studies of VLM adaptation in relateddata-scarce scientific-imaging domains.

Abstract:
Reinforcement learning (RL) has become indispensable forpushing Vision-Language-Action Models (VLA) beyond static imitationlearning. However, existing RL methods typically necessitate externalenvironmental feedback, relying on predefined success signals to guidepolicy updates. In this work, we demonstrate that VLA models possessstrong internal evaluative capabilities: in discrete-action VLAs, trajecto-ries with higher generation confidence are significantly more likely to suc-ceed. Based on the observation, we introduce T2 VLA (Test-time VLA),an architecture-agnostic test-time RL framework that enables VLA mod-els to achieve self-bootstrapping policy improvement. Instead of relyingon external rewards, T2 VLA leverages the trajectory-level similarityto high-confidence expert demonstrations as an intrinsic reward signal.In addition, we propose a Confidence-Driven Dual Expert Bootstrappingmechanism. By dynamically balancing a Local Pseudo-Expert for aggres-sive exploration and a Global Expert Pool for training stability, T2 VLAprevents policy collapse while discovering further breakthroughs. Exten-sive experiments on the LIBERO and RoboTwin benchmarks show thatT2 VLA consistently outperforms supervised baselines and approachesoracle RL performance with ground-truth rewards, achieving effectiveimprovement without external reward feedback. Furthermore, T2 VLAcan adapt to distinct VLA paradigms, including both OpenVLA-OFTand the π series.

Abstract:
Inspired by text prompts in large language models, visualprompts have been explored to enhance the perceptual capabilities oflarge vision-language models (LVLMs). However, performance tends tosaturate under single visual prompt designs, making further prompt engi-neering increasingly ineffective. To address this limitation, we shift fromprompt engineering to prompt retrieval and propose AutoV, a lightweightframework for instance-adaptive visual prompt identification. Given aninput image and a textual query, AutoV automatically locates the mostsuitable visual prompt from a diverse candidate pool. Training such a re-trieval framework requires prompt-level supervision, yet prompt qualityis inherently ambiguous and difficult to assess reliably, even for humans.To enable automatic supervision, we evaluate visual prompts using a pre-trained LVLM and label them according to their prediction losses. Usingthe loss-oriented ranking as a robust training signal, AutoV learns to re-trieve the query-aware optimal prompt for each instance without manualannotation. Experiments indicate that AutoV enhances the performanceof various LVLMs on image understanding, captioning, grounding, andclassification tasks. For example, AutoV improves LLaVA-OV by 10.2%on VizWiz and boosts Qwen2.5-VL by 3.8% on MMMU, respectively.

Abstract:
Large Vision-Language Models (LVLMs) remain bottleneckedby massive computational footprints, precluding their deployment onresource-constrained edge devices. While efforts to compress LVLMs fo-cus heavily on vision token reduction or smaller language models, thevision encoder is largely overlooked, typically deployed as a monolithic,computationally heavy feature extractor. Moreover, there is no previouseffort that designs a vision encoder for LVLMs directly optimized foron-device latency. In this paper, we present UltraViT, a vision encoderfor LVLMs, explicitly designed and optimized for on-device performance.Specifically, by taking into account real on-device latencies, we system-atically design a pyramidal architecture that strategically integrates andadapts heterogeneous spatial mixers at the macro-block level. Further-more, to pre-train UltraViT, we propose a novel two-stage generativepre-training strategy: cultivating rich spatial features via dense distil-lation, followed by direct generative supervision from a capacity-mixedfrozen LLM. Compared to standard contrastive and SSL, we show thatour pre-training is much more effective for achieving high-level seman-tic grounding for UltraViT needed for the subsequent generative multi-modal alignment of LVLM training. Extensive experiments demonstratethat our on-device latency-informed design combined with our tailoredtraining strategy establishes a new state-of-the-art for efficient LVLMencoding, significantly outperforming existing encoder-centric baselineswhile operating on-device at nearly 1.7× the speed.

Abstract:
Vision-Language Models (VLMs) exhibit strong visual rea-soning capabilities, yet they still struggle with 3D understanding. Inparticular, VLMs often fail to infer a text-consistent goal 6D pose of atarget object in a 3D scene. However, we find that with some inference-time techniques and iterative reasoning, VLMs can achieve dramatic per-formance gains. Concretely, given a 3D scene represented by an RGB-Dimage (or a compositional scene of 3D meshes) and a text instructionspecifying a desired state change, we repeat the following loop: observethe current scene; evaluate whether it is faithful to the instruction; pro-pose a pose update for the target object; apply the update; and ren-der the updated scene. Through this closed-loop interaction, the VLMeffectively acts as an agent. We further introduce three inference-timetechniques that are essential to this closed-loop process: (i) multi-viewreasoning with supporting view selection, (ii) object-centered coordinatesystem visualization, and (iii) single-axis rotation prediction. Withoutany additional fine-tuning or new modules, our approach surpasses priormethods at predicting the text-guided goal 6D pose of the target object.It works consistently across both closed-source and open-source VLMs.Moreover, when combining our 6D pose prediction with simple robot mo-tion planning, it enables more successful robot manipulation than recentVision-Language-Action models (VLAs). Finally, we conduct an ablationstudy to demonstrate the necessity of each proposed technique.

Abstract:
Vision–language models benefit from high-resolution images, but the increase in visual-token count incurs high compute overhead. Humans resolve this tension via foveation: a coarse view guides “where to look”, while selectively acquired high-acuity evidence refines “what to think”. We introduce Foveated Reasoner, an autoregressive visionlanguage framework that unifies foveation and reasoning within a single decoding trajectory. Starting from a low-resolution view, the model triggers foveation only when needed, retrieves high-resolution evidence from selected regions, and injects it back into the same decoding trajectory. We train the method with a two-stage pipeline: coldstart supervision to bootstrap foveation behavior, followed by reinforcement learning to jointly improve evidence acquisition and task accuracy while discouraging trivial “see-everything” solutions. Experiments show that the method learns effective foveation policies and achieves stronger accuracy under tight visual-token budgets across multiple vision–language benchmarks.

Abstract:
Object detection in remote sensing images (RSIs) is chal-lenged by the coexistence of geometric and spatial complexity: targetsmay appear with diverse aspect ratios, while spanning a wide range ofobject sizes under varied contexts. Existing RSI backbones address thetwo challenges separately, either by adopting anisotropic strip kernelsto model slender targets or by using isotropic large kernels to capturebroader context. However, such isolated treatments lead to complemen-tary drawbacks: the strip-only design can disrupt spatial coherence forregular-shaped objects and weaken tiny details, whereas isotropic largekernels often introduce severe background noise and geometric mismatchfor slender structures. In this paper, we extend PKINet, and presenta powerful and efficient backbone that jointly handles both challengeswithin a unified paradigm named Poly Kernel Inception Network v2(PKINet-v2). PKINet-v2 synergizes anisotropic axial-strip convolutionswith isotropic square kernels and builds a multi-scope receptive field, pre-serving fine-grained local textures while progressively aggregating long-range context across scales. To enable efficient deployment, we furtherintroduce a Heterogeneous Kernel Re-parameterization (HKR) Strategythat fuses all heterogeneous branches into a single depth-wise convolutionfor inference, eliminating fragmented kernel launches without accuracyloss. Extensive experiments on four widely-used benchmarks, includingDOTA-v1.0, DOTA-v1.5, HRSC2016, and DIOR-R, demonstrate thatPKINet-v2 achieves state-of-the-art accuracy while delivering a 3.9×FPS acceleration compared to PKINet-v1, surpassing previous remotesensing backbones in both effectiveness and efficiency.

Abstract:
Linear probing on frozen features is the standard evalua-tion protocol for vision foundation models, yet classical theory for d-dimensional classifiers demands Ω(d) labeled samples, far more thanpractitioners actually need. We resolve this discrepancy with the first non-vacuous PAC-Bayes generalization bounds for linear probing on frozenvision foundation-model features. The key insight is that foundation-modelfeatures do not fill Rd : covariance spectra decay steeply, concentratingvariance in a subspace of effective dimension deff ≪ d. A data-dependentGaussian prior aligned to this spectral structure yields a certificate whosecomplexity is governed by deff and the classification margin γ rather thanthe ambient dimension d. We validate the theory on ImageNet across 12vision encoders spanning discriminative, language-supervised, generative,and predictive pretraining: the bound is non-vacuous for all discrimi-natively pretrained models and ranks among the tightest PAC-Bayescertificates reported for any vision model. Three empirical predictionsfollow: generative pretraining yields markedly less certifiable features thandiscriminative pretraining, deff alone ranks foundation models withoutany labels, and larger models tend to be more certifiable because scalesteepens spectral decay.

Abstract:
Path-based attribution methods such as Integrated Gradi-ents (IG) are widely adopted for their strong axiomatic properties andeﬀectiveness in attributing model predictions to input features by inte-grating gradients along a path from a baseline to the input. However,the choice of the attribution path largely aﬀects the quality of explana-tions, and existing approaches rely on ﬁxed or hand-crafted paths thatoften produce noisy or distorted attributions. To address this limitation,we propose Diﬀusion Integrated Gradients (DiﬀIG), a novel method thatreformulates path generation as a conditional generative modeling prob-lem. DiﬀIG ﬁrst trains a diﬀusion model to learn a distribution overpaths generated from a Stick-Breaking Process, then employs guidedsampling to embed user guidance during the sampling procedure. Wedemonstrate that DiﬀIG quantitatively matches or outperforms existingpath-based methods, achieving perceptually aligned explanations. Thiswork introduces a new generative perspective for ﬂexible, inference-timecontrollable Explainable Artiﬁcial Intelligence (XAI) methods.

Abstract:
Proactive tamper localization embeds an imperceptible sig-nal into an image prior to distribution, enabling pixel-level manipulationdetection. Existing methods assume a spliced (SP) setting, where syn-thesized regions are composited onto the original background, leavingembedded signals intact. However, real-world diffusion-based inpaint-ing operates in a fully regenerated (FR) setting, where the entire im-age undergoes denoising, disrupting background signals and renderingexisting frameworks ineffective. We propose APT, a semi-fragile latent-space perturbation that embeds a dense, vector-wise localization signal.By aligning each spatial feature vector toward a fixed anchor direction,APT localizes tampering via the alignment disparity between synthesizedforeground and anchor-aligned background features after inpainting. Theproposed hard negative mining loss and noisy perturbation branch fur-ther enforce uniform alignment. Experiments on COCO demonstratethat APT achieves an FR IoU of 0.92, outperforming the strongest base-line (WAM, 0.84), while existing methods collapse to near-random per-formance (AUC ≈ 0.5), establishing APT as a practical forensic frame-work generalizable across tampering types unknown at test time.

Abstract:
In industrial environments, new product categories arrive se-quentially, requiring continual anomaly detection without access to pastdata. Normalizing Flows (NFs) provide exact density estimation but suf-fer from catastrophic forgetting as parameter updates across tasks dis-tort the density manifold. While parameter isolation can prevent inter-ference, it must preserve the strict invertibility and Jacobian validityof NFs. To satisfy these requirements, we exploit the inherent propertythat aﬃne coupling layers maintain transformation validity regardless ofsubnet parameterization. Based on this, we propose DeCoFlow, which de-composes subnets into a frozen universal base and task-speciﬁc low-rankadapters to isolate updates. We further introduce Task-Speciﬁc Align-ment, Auxiliary Coupling Layers, and Tail-Aware Loss to compensatefor frozen-base rigidity. DeCoFlow achieves state-of-the-art image-levelAUROCs of 98.40% on MVTec-AD and 93.00% on VisA, while maintain-ing parameter-level zero forgetting (0.00% FM under correct routing)with only 2.27M parameters per task.

Abstract:
Multi-objective masked image modeling (MIM) combines com-plementary learning signals (token distillation, CLS alignment, and pixelreconstruction) but existing methods weight these objectives with globalscalars, ignoring spatial heterogeneity across patches. We present Ex-PLoRe (Expert Patch-Level Loss Routing), which repurposes Soft Mix-ture of Experts (MoE) dispatch weights as learned, per-patch loss coef-ficients. The key mechanism is loss-coupling: allowing loss gradients toflow through dispatch weights to the router enables content-dependentspecialization, where different patches receive different emphases acrossobjectives. A detach ablation confirms loss-coupling as the core mecha-nism, degrading performance by 1.6% when gradients are blocked. OnImageNet-1K with ViT-Base, ExPLoRe improves over non-MoE base-lines on two objective combinations (Token+CLS: +0.5% k-NN, +4.4%linear probe; Token+Pixel: +2.2% k-NN), achieving 80.6% linear probeand 85.3% finetuning accuracy, competitive with published methods. Fordownstream transfer, we develop adaptation recipes (Freeze Routing, Ex-pert Dropout, and Freeze Attention) that improve MoE finetuning by+1.5% over the vanilla MoE, and close a 2.5–2.9 mIoU segmentation gapso that MoE models match or exceed non-MoE baselines on ADE20K.

Abstract:
Artistic image synthesis aims to recreate the expressive vi-sual identity of a target artist, yet existing methods often fail to cap-ture an artist’s global style. Conventional style transfer methods trans-fer the style of one or a few reference artworks to a content image ina One-to-One manner, making them effective for artwork-level styliza-tion but limited in representing the broader stylistic distribution of anartist. Text-to-image diffusion models conditioned on artist names, suchas ‘∼ in Van Gogh style’, offer greater flexibility, but they often sufferfrom text-induced bias and reproduce patterns from only a few iconicworks. To address these limitations, we introduce Global Style Transfer(GST), an artistic image synthesis paradigm, in a Many-to-One man-ner,that aggregates multiple artworks from a target artist and transferstheir shared global style to a single content image. For GST, we proposeGlobal Style Guidance (GSG), which learns a residual global style offset∆⟨t in the intermediate feature space, or h-space, of a diffusion modelunder a fixed prompt. By learning artist-level style semantics purely fromvisual statistics, GSG mitigates text-dependent artistic bias. We furtherpropose Content Alignment Guidance (CAG), a training-free perceptualguidance mechanism that preserves the semantic structure of the contentimage while allowing artist-specific geometric deformation. Experimentson WikiArt demonstrate that GST achieves superior stylistic fidelity,content preservation, and output diversity compared to existing styletransfer and diffusion-based artistic synthesis methods.

Abstract:
Autoregressive models have emerged as an effective paradigm for point cloud generation. However, most existing approaches rely on heuristic tokenization strategies, such as spatial sorting or stochastic downsampling, which often disrupt intrinsic point cloud topology and weaken the structural coherence of the generated shapes. In this paper, we present PointRSP, an autoregressive framework that reformulates point cloud generation as a topology-preserving tessellation process via recursive spectral partitioning. Instead of constructing token sequences heuristically, we introduce a topology-aware partitioning autoencoder that decomposes an unstructured point cloud into a nonbalanced binary tree through a hybrid recursive spectral partitioning strategy. This hierarchical representation provides a deterministic geometric blueprint that preserves topological relationships while capturing multiscale structural dependencies within a quantized latent space. To synthesize shapes in this space, we propose a dual-stream cascaded generator that jointly models structural evolution and feature synthesis. In addition, we design a geometry-calibrated positional encoding mechanism that anchors latent embeddings using multi-scale structural centers, which stabilizes cascaded generation during the early stages of structural formation. Extensive experiments show that PointRSP achieves state-ofthe-art performance in generation quality and diversity, demonstrating strong generalization across complex 3D topologies.

Abstract:
Diffusion models have demonstrated significant applicationsin the field of image generation. However, their high computational andmemory costs pose challenges for deployment. Model quantization hasemerged as a promising solution to reduce storage overhead and acceler-ate inference. Nevertheless, existing quantization methods for diffusionmodels struggle to mitigate outliers in activation matrices during in-ference, leading to substantial performance degradation under low-bitquantization scenarios. To address this, we propose HQ-DM, a novelQuantization-Aware Training framework that applies Single HadamardTransformation to activation matrices. This approach effectively reducesactivation outliers while preserving model performance under quanti-zation. Compared to traditional Double Hadamard Transformation, ourproposed scheme offers distinct advantages by seamlessly supporting INTconvolution operations while preventing the amplification of weight out-liers. For conditional generation on the ImageNet 256×256 dataset usingthe LDM-4 model, our W4A4 and W4A3 quantization schemes improvethe Inception Score by 12.8% and 467.73%, respectively, over the existingstate-of-the-art method.

Abstract:
The segmentation of thin linear structures is inherently topology-critical, where minor local errors can sever long-range connectivity. While recent State-Space Models (SSMs) offer efficient long-range modeling, their isotropic serialization (e.g., raster scanning) creates a geometry mismatch for anisotropic targets, causing state propagation across rather than along the structure trajectories. To address this, we propose FGOS-Net, a framework based on frequency-geometric disentanglement. We first decompose features into a stable topology carrier and directional high-frequency bands, leveraging the latter to explicitly correct spatial misalignments induced by downsampling. Building on this calibrated topology, we introduce frequency-aligned scanning that elevates serialization to a geometry-conditioned decision, preserving directionconsistent traces. Coupled with an active probing strategy to selectively inject high-frequency details and suppress texture ambiguity, FGOS-Net consistently outperforms strong baselines across four challenging benchmarks. Notably, it achieves 91.3% mIoU and 97.1% clDice on DeepCrack while running at 80 FPS with only 7.87 GFLOPs.

Abstract:
Modern Novel View Synthesis (NVS), such as 3D Gaussian Splatting (3DGS), introduces scene representations based on new parametrized primitives. These primitives are optimized through inverserendering optimization from color gradients, possibly with a depth prior. However, they fail to preserve geometric consistency and connectivity of the scene during the training phase. Furthermore, standard computer graphics (CG) pipelines do not easily handle these new primitives, thereby limiting rendering performance and integration into consumer applications. We propose a differentiable framework for Depth Image-Based Rendering (DIBR) using triangle primitives. Our method integrates seamlessly into traditional CG pipelines while remaining fully optimizable within modern NVS frameworks. Moreover, our primitives ensure geometric consistency and connectivity. Colors and depths are jointly optimized following a coarse-to-fine strategy. We compare our renderer to 3DGS, 2DGS, and Triangle Splatting across two forward-facing datasets. Although our approach yields slightly lower NVS quality (up to −3 dB), it achieves a 2× speed-up in rendering time (277 FPS) on a consumer-grade GPU, demonstrating its suitability for real-time or resource-constrained applications. Code: github.com/LISA-VR/ddibr.

Abstract:
Current feed-forward 3D reconstruction methods predict pixelaligned Gaussian primitives, resulting in highly redundant representa-tions. A natural solution is to prune the redundant Gaussians, but naivepruning introduces severe artifacts and often requires inference time fine-tuning, breaking the feed-forward paradigm. Based on previous works,high frequency regions require more Gaussian primitives, while low fre-quency regions can be represented with significantly fewer primitives.Motivated by this, we propose a novel approach to explicitly controlthe number of Gaussians by leveraging local texture information. Ourapproach achieves this through three key components: (1) texture es-timation to capture spatial variation in scene detail, (2) texture-awarepruning that removes redundant Gaussians from low frequency regions,and (3) an adaptive Gaussian head that predicts the modified attributesof the retained primitives without breaking the feed-forward paradigm.Experiments on RE10K, ACID, DL3DV, Tanks and Temples, and DTUdemonstrate the effectiveness of our approach, while ablation studiesvalidate the contributions of its key components.

Abstract:
Reconstructing 3D scene structures from sparse, low-overlapobservations remains a fundamental challenge in autonomous driving.Recent state-of-the-art frameworks achieve promising results by incor-porating voxel-based Gaussians, but incur substantial computational re-dundancy due to a uniform volumetric processing strategy. To bridgethe gap between the efficiency of pixel-based Gaussian methods and thestructural completeness of voxel-based Gaussian approaches, we proposeFocusGS, a simple yet effective framework that shifts the paradigmfrom global densification to targeted structural completion. Our centralinsight is that structural completion should be decoupled from determin-istic regions, with computation concentrated exclusively on areas exhibit-ing geometric ambiguity. Specifically, FocusGS addresses the localizationchallenge by deriving a 3D Geometric Ambiguity Manifold to accuratelyisolate localized areas prone to occlusion and high geometric uncertainty.To overcome the subsequent manifold completion challenge, we design alightweight targeted structure completion module that selectively instan-tiates and optimizes continuous Gaussian queries strictly within this un-structured, sparse topological subspace. Extensive experiments demon-strate that FocusGS achieves a superior efficiency-quality trade-off, ad-vancing state-of-the-art performance on driving-centric benchmarks whilenaturally reducing the total number of Gaussians by ∼74% and decreas-ing rendering time by ∼34%.

Abstract:
Reconstructing 3D scenes of objects displayed in glass enclo-sures remains a significant challenge for 3D Gaussian Splatting (3DGS),particularly in museum and exhibition hall settings where transparentprotective cases are ubiquitous. The high specular reflections (HSR) gen-erated by glass surfaces severely degrade reconstruction quality, yet theirunderlying properties and impact on 3DGS remain poorly understood.Through systematic investigation, we reveal two insights into HSR be-havior in 3DGS: (1) Geometry-inconsistency property: HSR causes 3Dpoints to exhibit drastically different appearances across viewpoints, fun-damentally violating the geometric consistency assumption that under-pins 3DGS; (2) Concept-preservation property: Despite corrupting lo-cal appearance, HSR has remarkably limited influence when learninghigh-level object concepts, as the true object structure remains intactbeneath the reflective layer. Motivated by these insights, we proposeGlassGS, a geometry and concept-aware 3DGS framework with an it-erative two-stage optimization. First, we compute geometry-consistencymaps for each input view to evaluate the appearance variations acrossviewpoints, explicitly identifying HSR-affected pixels by measuring theappearance variations of 3D points across views. Second, we introducea geometry-inconsistency-guided object concept learning module thatleverages these maps to learn robust object representations while activelysuppressing reflection interference. The two stages mutually reinforceeach other through iterative refinement, improving both inconsistencydetection and concept learning. For this new task, we collect the firstdataset specifically designed for 3DGS reconstruction of glass-enclosedobjects. Extensive experiments demonstrate that GlassGS significantlyoutperforms state-of-the-art methods, enabling high-fidelity digitizationof glass-enclosed scenes and opening new possibilities for virtual museumexperiences.

Abstract:
Geometric distortion breaks the projective mapping and ge-ometric relations in images, which degrades the performance and relia-bility of computer vision tasks. Although deep network-based methodsfor single-image distortion correction have achieved remarkable progress,most existing approaches optimize only for 2D pixel-level alignment, suchas optical flow losses or image reconstruction errors. Without explicit 3Dgeometric constraints, such models may produce visually plausible recti-fications yet fail to enforce 3D-consistent structure. To address this lim-itation, this paper proposes a 3D geometry guided framework for singleimage distortion correction, named Geometry-Grounded Distortion Cor-rection (G2DC). G2DC introduces a pretrained 3D foundation model toextract multi-dimensional geometric representations as supervision, andincorporates projective geometry priors into feature learning. Specifically,G2DC adds a geometric constraint scheme and jointly optimizes a struc-tural alignment loss and a physical alignment loss. The former minimizesfeature mismatch to ensure that the corrected output is structurally closeto the undistorted scene. The latter enforces strict 3D constraints usingthe predicted point map, depth topology, and camera parameter regular-ization, so the model preserves 3D structure consistency and plausibilitywhile restoring 2D appearance. Experiments on several widely used dis-tortion benchmark datasets show that G2DC outperforms state-of-the-art methods, especially on metrics that are sensitive to 3D geometricconsistency. Project webpage: https://gitee.com/VR_NAVE/g2dc.git.

Abstract:
Novel view synthesis has recently been revolutionized by 3DGaussian Splatting (3DGS), which enables real-time rendering throughexplicit primitive rasterization. However, existing methods tie visual fi-delity strictly to the number of primitives: quality downscaling is achievedonly through pruning primitives. We propose the first inherently scal-able primitive for radiance field rendering. Fourier Splatting employsscalable primitives with arbitrary closed shapes obtained by parameter-izing planar surfels with Fourier encoded descriptors. This formulationallows a single trained model to be rendered at varying levels of de-tail simply by truncating Fourier coefficients at runtime. To facilitatestable optimization, we employ a straight-through estimator for gradi-ent extension beyond the primitive boundary, and introduce HYDRA,a densification strategy that decomposes complex primitives into sim-pler constituents within the MCMC framework. Our method achievesstate-of-the-art rendering quality among planar-primitive frameworksand comparable perceptual metrics compared to leading volumetric rep-resentations on standard benchmarks, providing a versatile solution forbandwidth-constrained high-fidelity rendering.

Abstract:
3D Gaussian Splatting (3DGS) enables real-time novel viewsynthesis for static scenes. Extending it to dynamic scenes via deforma-tion fields has recently attracted significant attention, particularly fordynamic scene reconstruction and distractor-free reconstruction. How-ever, existing deformation networks lack explicit motion awareness: theyneither capture long-term motion intensity nor exploit short-term tempo-ral coherence, leading to inaccurate foreground deformation and pseudo-static residuals in the background. We present MVFusion-GS, a methodthat enhances deformation networks with two complementary motion-aware mechanisms. The Motion-Variance Guided Refinement ag-gregates per-Gaussian deformation statistics across time to estimate mo-tion variance and uses it to guide dynamic-static separation during defor-mation prediction. The MotionFormer Temporal Attention moduleapplies Transformer self-attention over neighboring timesteps to modellocal motion dependencies and improve temporal consistency. Extensiveexperiments on both dynamic scene reconstruction and distractor-free re-construction benchmarks demonstrate state-of-the-art performance, show-ing that explicit motion awareness improves both foreground motionmodeling and static background reconstruction.

Abstract:
Neural implicit representations have recently achieved im-pressive results in novel view synthesis and multi-view 3D reconstruc-tion, yet both NeRF- and Gaussian Splatting-based methods require per-scene optimization, which makes them inefficient. Generalizable NeuralSurface Reconstruction (GNSR) methods have been proposed to removethis need by learning feature representations directly predicted from in-put images. However, their typical reliance on dense feature volumesseverely limits achievable resolution and fidelity due to prohibitive mem-ory costs. We introduce Sparse Volumetric Reconstruction (SVRecon),a new GNSR framework that unlocks high-resolution, memory-efficientreconstruction through learned occupancy-driven sparsity, in a more ef-fective way than earlier approaches to introducing sparsity in GNSRs.Our approach uses a nested two-stage architecture: (1) an occupancyprediction network that identifies surface-containing voxels, and (2) ahigh-resolution sparse volume rendering framework defined only withinthese occupied regions, together with specialized sparsified algorithmsfor ray sampling, feature aggregation, and querying. This design enablesfine-grained surface reconstruction while avoiding the heavy memoryfootprint of dense grids. SVRecon operates at resolutions up to 5123on standard 32GB hardware—substantially higher than prior generaliz-able methods—and delivers smoother and more precise reconstructionsacross diverse datasets, particularly in sparse-view settings.

Abstract:
3D Gaussian Splatting (3DGS) has emerged as a powerfulrepresentation for high-fidelity rendering. However, existing assets of-ten suffer from quality bottlenecks such as missing details and texturenoise. Prior attempts to enhance these assets via 2D image processingintroduce multi-view inconsistencies and high computational costs. Inthis paper, we propose a novel 3D-native refinement paradigm namedAnchorSplat. AnchorSplat is an end-to-end deep network operatingdirectly on 3D structures, avoiding the expensive optimization overheadof traditional 3D-2D-3D pipelines. Crucially, AnchorSplat is a strictlysource-free solution requiring no original multi-view images. Central tothe proposed method is the Point Anchor Mechanism, which enforcesgeometric consistency via local offset constraints, mitigating ill-posedmapping and gradient confounding. Furthermore, AnchorSplat replacesiterative densification with a single-pass multiplication mechanism. Tofacilitate research, we construct 3DGS-SR, the first large-scale bench-mark for this task. Experiments demonstrate state-of-the-art results onthe 3DGS-SR dataset, with throughput up to 105 times faster thanoptimization methods. Notably, AnchorSplat exhibits robust zero-shotgeneralization across diverse data distributions, including generativemodel outputs and real-world scans. The repository is available at: github

Abstract:
Geometric reconstruction of indoor scenes poses unique challenges due to their inherent characteristics, such as large textureless surfaces, complex occlusions, and intricate geometric structures. While recent 2D/3D Gaussian Splatting-based methods have achieved impressive performance in general scene reconstruction, their direct application to indoor settings often leads to noisy, incomplete, or geometrically inconsistent results. In this paper, we propose a novel framework IndoorSplat to bridge this gap and enable high-quality indoor scene geometry reconstruction based on 2D Gaussian Splatting (2DGS). To this end, we first revisit the importance of Gaussian initialization in the indoor scene reconstruction process and introduce a carefully designed dense initialization strategy. This strategy leverages local geometric priors to densify point clouds estimated by an advanced feed-forward model and utilizes them to initialize the structure of Gaussian primitives. In addition, to better capture the rich structural details in indoor scenes, we introduce an iterative adaptive densification strategy that accelerates convergence in complex regions by applying more aggressive splitting and cloning policies to Gaussians with consistent activation. Extensive experiments demonstrate that our method achieves state-of-the-art performance by producing more geometrically accurate and complete surface reconstructions compared to existing approaches.

Abstract:
We present SharpGS, a differentiable blur-driven density con-trol, which enhances the quality of 3D Gaussian Splatting (3DGS). Stan-dard 3DGS often struggles to capture intricate details, particularly intextured patterns and object boundaries, due to the limited sensitivityof its density control. Simple finer densification can result in excessiveprimitive counts with marginal quality gain. To address this, we intro-duce blur as an effective perceptual cue, leveraging CUDA-based differ-entiable blur. Our density control estimates per-primitive blur levels bycomparing reconstructions to blurred ground-truth images, and identifieshigh-frequency regions where additional primitives are required. Whilethis naturally produces more primitives, we counterbalance this by sup-pressing redundant primitives. Specifically, we penalize the blur levelsof inherently smooth regions such as sky, and regularize the opacitiesof potential split/clone candidates. We experimentally demonstrate thatSharpGS greatly improves the state-of-the-art 3DGS methods in termsof quality, while keeping learned primitives compact.

Abstract:
Previous feed-forward 4D reconstruction methods either pre-dict per-frame static point clouds, ignoring foreground motion, or esti-mate point cloud trajectories while being limited to small camera mo-tions. This limits their ability to aggregate observations over time andreconstruct complete dynamic scenes under large viewpoint changes. Toaddress this, we propose OmniX, a feed-forward 4D reconstruction frame-work that predicts dense 3D point trajectories for every pixel fromvideos with large camera motion. 1) OmniX separates dynamic mo-tion modeling from static geometry prediction and represents motionwith a small set of dynamic tokens. Leveraging the sparse and low-rankstructure of 3D motion, these tokens generate trajectory fields for allpixels in all images while efficiently preserving global interactions. 2) Tofacilitate training, we build an automatic UE5-based 4D data engine andintroduce a dataset of 80k scenes and 1.28M multi-view videos with fullgeometric annotations. OmniX achieves state-of-the-art results on dense3D point trajectory prediction and 3D point tracking, with competitiveperformance on video depth estimation and camera pose estimation.

Abstract:
End-to-end (E2E) autonomous driving algorithms requirerigorous closed-loop validation in simulation environments offering highvisual fidelity, strong interactivity, and real-time performance. Exist-ing approaches, from game engines to static neural rendering, inher-ently trade off these requirements and struggle with the dynamic scenecomposition essential for E2E testing. To bridge this gap, we proposea novel decoupled 3D Gaussian Splatting (3DGS) framework tailoredfor large-scale E2E evaluation. We fundamentally decompose scenes intoa high-fidelity static background and manipulable dynamic agents us-ing an object-centric canonical representation. To resolve resulting rep-resentational conflicts, we introduce three targeted modules: (1) assetcompression via perceptual pruning and vector quantization for real-time traffic rendering; (2) map-guided geometric registration leveragingsemantic topology to strictly align trajectories; and (3) proxy-based re-lighting transferring ambient illumination for seamless photometric inte-gration. Extensive experiments demonstrate that DecoupleGS achievesa balanced fidelity-efficiency trade-off, improves metric and photomet-ric consistency, and provides a practical closed-loop sensor simulationplatform for E2E autonomous driving evaluation.

Abstract:
Sparse-view computed tomography (CT) is critical for re-ducing radiation exposure to patients. Recent advances in radiative 3DGaussian Splatting (3DGS) have enabled fast and accurate sparse-viewCT reconstruction. Despite these algorithmic advancements, practicalreconstruction fidelity remains fundamentally bounded by the qualityof the captured data, raising the crucial yet underexplored problem ofX-ray active view selection. Existing active view selection methods areprimarily designed for natural-light scenes and fail to capture the uniquegeometric ambiguities and physical attenuation properties inherent inX-ray imaging. In this paper, we present Perturbed Gaussian Ensemble,an active view selection framework that integrates uncertainty modelingwith sequential decision-making, tailored for X-ray Gaussian Splatting.Specifically, we identify low-density Gaussian primitives that are likely tobe uncertain and apply stochastic density scaling to construct an ensem-ble of plausible Gaussian density fields. For each candidate projection,we measure the structural variance of the ensemble predictions and selectthe one with the highest variance as the next best view. Extensive experi-mental results on arbitrary-trajectory CT benchmarks demonstrate thatour density-guided perturbation strategy effectively eliminates geometricartifacts and consistently outperforms existing baselines in progressivetomographic reconstruction under unified view selection protocols.

Abstract:
3D Gaussian Splatting (3DGS) delivers real-time and high-fidelity rendering but remains challenged by unconstrained in-the-wildscenes, where drastic appearance variations and transient objects violatemulti-view consistency. Existing methods are fundamentally limited byindependent and discrete embeddings that struggle to capture continuousenvironmental changes or model spatially-varying local illumination. Toaddress these limitations, we propose WilLaGS, a unified frameworkfor robust 3D scene reconstruction and generative appearance synthe-sis under unconstrained settings. Specifically, we introduce a generativeappearance model where a β-VAE learns a structured and continuousmanifold of global appearance. Conditioned on the latent code, we con-struct a 3D neural appearance field that generates dynamic Tri-Plane fea-tures to encode spatially-varying local illumination effects. Furthermore,to suppress transient artifacts, we present a self-supervised perceptualmasking mechanism that leverages a Teacher-Student (EMA) architec-ture to derive a stable scene consensus, robustly identifying inconsistentregions via perceptual discrepancies. Extensive experiments on multipledatasets demonstrate that WilLaGS achieves state-of-the-art perfor-mance in reconstruction quality and novel view appearance synthesis,while maintaining real-time rendering efficiency.

Abstract:
We propose SMP-UWGS, an underwater reconstruction frame-work based on 3D Gaussian Splatting (3DGS) that couples scalablemulti-partition geometry with physical light transport modeling. Ourmethod introduces two key components: SMP-GAUSSIAN, a multi-partitionarchitecture enabling distributed optimization for large-scale scenes, andDPR-Net (Differentiable Physical Rendering Network), which combinesWaterParamPredict module for estimating water optical parameters witha Dual-Branch Differential Refinement (DBDR) module to model atten-uation and backscattering. With tailored loss functions, the frameworkjointly estimates water optical parameters and improves color fidelityand geometric accuracy. Experiments on public datasets show that SMP-UWGS achieves state-of-the-art efficiency while enabling scalable, high-fidelity underwater reconstruction, benefiting applications such as ecolog-ical monitoring, habitat mapping, and autonomous underwater robotics.

Abstract:
3D Gaussian Splatting (3DGS) achieves state-of-the-art rendering quality at real-time speeds but suffers from “model bloat”—a large number of redundant, low-opacity Gaussians that inflate memory usage and training costs. This inefficiency stems from the standard “densifythen-prune” paradigm, which expands the model aggressively before relying on pruning to achieve compactness. To mitigate this problem, we present an efficient training framework that builds an intrinsically compact representation, replacing the conventional densify-then-prune cycle. Our method leverages a synergistic design: an L reconstruction loss to 2 provide error-proportional gradients that stabilize optimization, and a novel Polarized Opacity Prior (POP) to actively manage the Gaussian population. POP steers informative primitives toward full opacity and uninformative ones toward transparency, enabling natural pruning and accelerating rendering through Early Ray Termination. Experiments on three public datasets demonstrate that our approach consistently achieves accelerated 3DGS training with significantly fewer Gaussians while maintaining comparable visual reconstruction quality. These results show that the proposed framework provides a simple and effective path toward fast and inherently compact 3DGS training.

Abstract:
3D semantic occupancy prediction is crucial for fine-grainedscene understanding, yet its advancement in privacy-sensitive indoor en-vironments is fundamentally hindered by the scarcity of large-scale an-notated 3D data. To overcome this limitation, we explore learning indoor3D semantic occupancy prediction from abundant, uncalibrated in-the-wild internet videos while simultaneously bypassing the extensive manualannotation. Specifically, we introduce YouTube-Occ, including an auto-mated data pipeline that leverages 2D and 3D foundation models to pro-cess raw web videos, estimating camera geometry, reconstructing scenepoint clouds, and enriching them with dense semantic pseudo-labels.However, these plausible pseudo-labels fail to yield performance gains un-der naive supervision. To address this impasse, we further propose a pre-training framework driven by feature distillation with a dual-alignmentstrategy. Within it, an intra-frame alignment utilizes a voxel-anchoredGaussianization module to align 3D features with corresponding 2D pri-ors, whereas a cross-scene alignment achieves global semantic consistencyvia class-prototype distillation. Empirically, YouTube-Occ delivers con-sistent gains across three mainstream architectures on the NYUv2 andOcc-ScanNet benchmarks, especially under limited-data conditions. Wewill publicly release our code and data, hoping to inspire future research.

Abstract:
Collaborative perception extends single-agent perception by enabling multiple vehicles to exchange complementary perceptual information. However, it introduces an inherent trade-off between perception gain and communication overhead, which is particularly severe for 3D semantic occupancy prediction that relies on fine-grained spatial structures. Existing methods typically compress 3D features into 2D, causing severe spatial information loss, or transmit dense 3D representations, hindering real-world deployment. To overcome these limitations, we propose a bandwidth-efficient collaborative Vector Quantization Semantic Occupancy Prediction (VQSOP) framework. VQSOP employs a SparseAware Vector Quantization (SAVQ) mechanism that exploits 3D scene sparsity to compactly encode informative regions, drastically reducing communication overhead while preserving complete geometric context. Furthermore, to enhance structural consistency and feature continuity, we design a Dual-Branch Adaptive Spatial Refinement (ASR) module that dynamically fuses local high-frequency details with broad contextual semantics. Extensive experiments demonstrate that our approach achieves state-of-the-art performance while reducing communication volume by up to 82×.

Abstract:
We present DeWorldSG, a novel framework that generatesspatio-temporally robust 3D Semantic Scene Graphs from RGB-D se-quences. Existing methods often struggle to construct reliable 3D scenegraphs due to unstable 3D object representations and missing relationscaused by frame-wise inference. DeWorldSG addresses these issues byestimating instance-level geometric 3D Gaussian distributions throughdepth-guided filtering and representing each object as a probabilistic 3Dnode rather than a single projected point. To mitigate relational sparsityfrom frame-wise inference, our framework further aggregates spatiotem-poral evidence across object pairs and refines relations using contextualpriors derived from a world model (V-JEPA 2). Experiments on the3DSSG and ReplicaSSG datasets demonstrate state-of-the-art (SoTA)performance in both object and predicate prediction, while producingtemporally consistent scene structures. In particular, our method im-proves triplet recall by 77.4% and predicate recall by 23.2% over priorSoTA approaches, making it suitable for robotic manipulation and ARapplications. Our code and models are open-sourced.

Abstract:
Recent advancements in open-vocabulary 3D scene under-standing heavily rely on 3D Gaussian Splatting (3DGS) to register vision-language features into 3D space. However, we identify two critical limi-tations in these approaches: the spatial ambiguity arising from unstruc-tured, overlapping Gaussians which necessitates probabilistic feature reg-istration, and the multi-level semantic ambiguity caused by pooling fea-tures over object-level masks, which dilutes fine-grained details. To ad-dress these challenges, we present a novel framework that leverages SparseVoxel Rasterization (SVRaster) as a structured, disjoint geometry repre-sentation. By regularizing SVRaster with monocular depth and normalpriors, we establish a stable geometric foundation. This enables a de-terministic, confidence-aware feature registration process and suppressesthe semantic bleeding artifact common in 3DGS. Furthermore, we resolvemulti-level ambiguity by exploiting the emerging dense alignment proper-ties of the AM-RADIO foundation model, avoiding the computationaloverhead of hierarchical training methods. Our approach achieves state-of-the-art performance on Open Vocabulary Point Cloud Understanding,and highly competitive results on 3D Object Retrieval benchmarks.

Abstract:
Scene graph generation (SGG) approaches can be broadlyclassified into detector-based and query-based methods according to theirunderlying reasoning mechanisms. However, the discrepancy in their pre-dictive behaviors, induced by these distinct mechanisms, has not beensystematically analyzed. In this work, we design a controlled experimen-tal setup to examine prediction discrepancies from the perspective ofdetector-conditioned reachability. The results suggest clear complemen-tary clues. Motivated by this observation, we introduce a Dual-SGGmethod that consolidates both reasoning mechanisms via a dual-querydesign, thereby leveraging the complementary predictive behaviors ofboth detector-based and query-based methods. Extensive experimentson the Visual Genome, Open Images v6, and GQA-200 datasets demon-strate the effectiveness of the proposed method. Code is available at:Dual-SGG.

Abstract:
Autoregressive multimodal large language models (MLLMs)enable 3D generation but struggle to scale to high-resolution shapesdue to inadequate 3D tokenizations. Compact set-based representationsdiscard deterministic spatial ordering, leading to ambiguous sequenceprediction, while uniform or octree-based voxel grids preserve orderingat the cost of severe redundancy and excessively long sequences. Thisstructural trade-off limits stable and efficient autoregressive 3D gener-ation. We present SuperVoxelGPT, a representation-first frameworkthat resolves this tension through adaptive and deterministically orderedsupervoxel tokenization. Given a prompt, we first predict a coarse geo-metric saliency distribution and construct a shape-adaptive supervoxelpartition using saliency-guided centroidal Voronoi tessellation, allocatingfine-grained cells to complex regions and larger cells to smooth regions.Conditioned on this prompt and ordered supervoxel layout, we introducea SuperVoxelVAE and fine-tune a pretrained MLLM to autoregressivelygenerate supervoxel tokens. Experiments using Trellis-500K data showthat SuperVoxelGPT reduces token sequence length to 12.8% of uniformvoxel tokenization while achieving state-of-the-art generation quality andan average 10× speedup over prior methods.

Abstract:
3D point-cloud observations are inherently ambiguous in complex, cluttered manipulation scenes, where target objects may be partially occluded or tightly intermingled with visually similar distractors. As a result, standard 3D diffusion policies often struggle to localize and exploit task-relevant geometry as scene complexity grows. We propose Attention-DP3, a spatially object-aware 3D diffusion policy that injects object-level geometric cues via attention while keeping the DP3 diffusion backbone unchanged. Our pipeline performs open-vocabulary 2D segmentation on RGB images, then lifts predicted target masks into 3D using calibrated camera geometry to obtain object-centric geometric priors. We incorporate these cues through Tri-field Attentional Conditioning, which constructs three complementary fields: (i) a targetness field to anchor the target object, (ii) an intra-target saliency field to emphasize task-relevant geometry within the target, and (iii) a backgroundness field to suppress distractors and clutter. Experiments on Adroit, DexArt, MetaWorld, and the real-world SO101 platform show consistent improvements over DP3, achieving state-of-the-art performance across benchmarks. Notably, as distractor objects increase, DP3 drops sharply, whereas Attention-DP3 remains stable and outperforms DP3 by up to 31% under heavy clutter. The code is publicly available at https:// github.com/zhangzhongbo2213/Attention-DP3.

Abstract:
3D reconstruction from a lengthy video stream input poses a dilemma for feed-forward reconstruction models (FFRMs), that a whole-stream inference context cannot be retained under limited GPU memory. Recent studies seek to resolve this problem via a trade-off between the integrity of inference context and GPU memory usage, which either suffer from a rapid memory inflation or degraded context integrity due to artificially capping memory usage. Driven by our key observation that the initial saliency of a token reliably dictates its long-term importance across the stream, we propose RegVGGT, a training-free token regulation method which aggressively regulates the tokens of incoming frames. By admitting at most 1% of tokens per frame to update the context memory, our method dramatically suppresses memory inflation as the stream progresses. Equipped with a FlashAttention-compatible token saliency estimation scheme, RegVGGT is capable of processing thousands of frames on a consumer-grade GPU with negligible compromise to reconstruction quality. Extensive experiments demonstrate that RegVGGT achieves state-of-the-art performance on long-horizon benchmarks across diverse FFRM prediction tasks, surpassing prior FFRMbased stream reconstruction baselines by a large margin.

Abstract:
High-fidelity simulation of mmWave radar signals for dynamichuman motion is valuable for developing radar-based human sensing mod-els; yet collecting accurately labeled measurements for a specific deploy-ment site remains expensive. We present HybridSim, a physics–learninghybrid simulator that synthesizes mmWave radar signals from dynamichuman meshes under a fixed indoor room configuration, explicitly de-coupling propagation into two components. To parameterize the humansubject, we use a tri-plane representation to extract human features anda Graph Convolutional Network to stabilize optimization and mitigategradient instability. The direct signal path is modeled via an inverse-rendering formulation with a microfacet BRDF to capture primary surfacereflections. In parallel, the indirect path is approximated by combining3D Gaussian Splatting with a virtual-receiver geometry to fit and repro-duce site-specific multipath interference patterns, achieving substantiallylower computational cost than explicit full ray tracing. Experiments ina fixed-room setting show improved agreement with a physically basedreference and consistent gains on downstream radar-based human sensingtasks when using HybridSim for site-specific data augmentation.

Abstract:
Multi-exposure fusion (MEF) expands the luminance rangebeyond what a single exposure can capture. Combining images takenat different exposure levels requires handling geometric differences whilenaturally merging their complementary brightness information. It of-ten demands generative completion where details are missing. Diffusion-based generative methods address these challenges, however, they arecomputationally expensive and struggle to preserve fine structures in sat-urated regions. We propose LIIFusion, a coarse-to-fine framework thatbalances fusion quality and efficiency in generative MEF. The coarsestage performs low resolution generative fusion, enhanced by an adaptiveexposure correction that recovers structure lost in saturated over-exposedareas. The fine stage adapts a local implicit image function into a multi-exposure fusion function: conditioned on the HR OE/UE sources and thecoarse output, it queries arbitrary target coordinates and fuses sourceevidence regard- less of the HR input resolution. LIIFusion achieves upto 3.5× speed-up over existing generative methods while maintainingor improving structural fidelity and perceptual quality. We believe thisframework provides an effective pathway toward making generative MEFmore practical in real-world applications.

Abstract:
Magnetic resonance imaging comes in various modality con-trasts that provide complementary anatomical and pathological informa-tion. Complete multimodal acquisitions are often unavailable due to timeand protocol constraints. This leads to real-world datasets with missingmodalities, where conventional medical image translation methods aretypically limited to fixed source-target settings or require retraining foreach observed source-target pair. We propose a unified framework thatformulates missing-modality generation as a linear inverse problem un-der a joint distribution and solves it via posterior sampling with a flowmatching model. By learning a joint prior over the complete modalityset, our method can reconstruct arbitrary missing modalities at inferencetime by guiding the sampling trajectory to enforce measurement consis-tency with observed modalities. We further mitigate inter-modality er-ror propagation in multi-target generation by adopting a many-to-onesampling strategy. Experiments on BraTS and IXI datasets show thatour method achieves the best performance over baselines across mostmissing-modality scenarios. In downstream tumor segmentation, synthe-sized images from our method result in higher segmentation performance,indicating better preservation of clinically relevant structures. Our codeis available at github.com/jongdory/PS-MIT.

Abstract:
Despite significant advances in Medical Report Generation(MRG), the reliability remains constrained by the prevalence of factualerrors. While Direct Preference Optimization (DPO) has emerged as apromising post-training paradigm to enhance the performance of Super-vised Fine-Tuned (SFT) MRG models, existing DPO-based MRG meth-ods typically adopt a naive preference construction that directly pairsmodel-generated reports with ground truth reports. This strategy inad-vertently entangles critical clinical findings with clinically irrelevant lin-guistic characteristics, and fundamentally lacks explicit vision-languagealignment. To address these challenges, we propose DPO-Clin, a novelpost-training framework that focuses preference optimization on clinicalfindings and cross-modal alignment. First, we introduce the Entity-levelClinical Diagnostic (ECD) module to perform a precise entity-level fac-tual diagnosis. ECD guides the generation of linguistically-aligned re-port preference pairs, isolating clinical discrepancies from linguistic vari-ations. Second, to achieve fine-grained cross-modal alignment, we developM2 DPO, a retrieval-augmented multi-modal DPO variant that enforcestextual preference inversion triggered by visual context switches. Third,we locate correct yet highly uncertain predicted entities and apply coun-terfactual modifications to construct targeted preference data for latentrisk mitigation, thereby further enhancing the model reliability. Exten-sive experiments on two public chest X-ray datasets (MIMIC-CXR andIU X-Ray) and an in-house endoscopy dataset demonstrate that DPO-Clin significantly improves the SFT baselines on clinical-aware metrics.Furthermore, it achieves superior performance over existing DPO-basedMRG methods, exhibiting robust generalizability across distinct baselinearchitectures and diverse medical imaging modalities.

Abstract:
Functional maps are the cornerstone of recent non-rigid 3D shape matching methods due to their efficiency and performance. However, existing methods struggle with challenging scenarios, such as partiality, topological noise, and raw point clouds. A primary bottleneck is that significant intrinsic distortion prevents truncated spectral bases from being accurately aligned via linear transformations (i.e., functional maps). To address this, we introduce a hyper-network that predicts nonlinear neural functional maps (NFM), learned in an unsupervised manner, to better align spectral bases. Specifically, we model the NFM as an MLP with skip-connection to refine standard FM and employ a hypernetwork to predict its weights, conditioned on standard FM. Our framework is trained using a novel unsupervised spectral alignment loss. Experiments demonstrate that our approach can be seamlessly integrated into state-of-the-art unsupervised deep functional map pipelines, substantially improving matching accuracy in demanding scenarios.

Abstract:
Pathology whole slide image (WSI) analysis is crucial fordisease diagnosis and understanding. While pathology foundation modelshave advanced WSI analysis, their scalability remains limited by thelarge resolution of individual WSIs and the small number of availableslides. Existing methods often use the [CLS] token from tile-level ViTsfor efficiency, but this design overlooks spatially informative tokens thatencode fine-grained pathological patterns. Incorporating all spatial tokenscould enrich representation but is computationally prohibitive, revealinga fundamental trade-off between efficiency and representational richness.We observe high redundancy among spatial tokens, suggesting that se-lectively retaining the most informative ones can better balance thesegoals. To address this, we propose TokRet, a token retention and com-pression module that preserves the most informative spatial tokens whileeliminating redundancy. Built upon TokRet, we develop PathTokScale, aslide-level self-supervised pretraining framework that leverages efficientlong-contextual transformer modeling to scale training to the billion-tokenlevel—over 20× larger than previous WSI studies—without requiring mas-sive WSI collections. Experiments show that PathTokScale consistentlyoutperforms existing pathology foundation models across diagnostic, prog-nostic, and biomarker prediction tasks. Remarkably, using only 10% ofthe number of WSIs as used by TITAN, it achieves comparable per-formance through efficient token scaling, paving the way for scalable,high-performing pathology foundation models.

Abstract:
Precise medical image segmentation is crucial for clinical di-agnosis and treatment planning, yet relies heavily on expensive expertannotations. Semi-supervised medical image segmentation (SSMIS) of-fers a cost-effective solution but typically operates under the assump-tion of independent and identically distributed (i.i.d.) data, defaultingto random sampling. While statistically valid at scale, this strategy suf-fers from severe representation bias in low-data regimes, failing to cap-ture the heterogeneous medical data manifold. To address this, we pro-pose a highly data-efficient framework driven by distribution alignment.First, we introduce an offline Distribution-Aware Sample Selection strat-egy. By leveraging Vision Foundation Models (VFMs) and our designedDensity-K-Center algorithm, we explicitly identify representative struc-tural anchors, establishing a more representative labeled domain. Second,to bridge the remaining distribution gap, we propose the Memory-guidedCopy-Paste (MCP) module. Tailored for the inherent class imbalance inmedical scans, MCP leverages a semantic memory mechanism to retrievehistorically consistent priors for cross-domain alignment, encouragingsemantic consistency. Coupled with an easy-to-hard progressive sched-ule, this framework effectively mitigates early-stage pseudo-label noise.Extensive experiments on six diverse 2D and 3D datasets demonstratestrong segmentation performance, particularly in extremely low-labeledscenarios (e.g., 1/16 ratio).

Abstract:
Accurate 3D neuron segmentation in fluorescence microscopyis critical for neuroscience. However, the sparse and elongated morphol-ogy of neurons poses significant challenges to existing segmentation meth-ods. These methods struggle to preserve both local details and globaltopology, leading to fragmented results. To address this, we proposeNeuroRefiner, a multi-agent system that formalizes the human expertworkflow involving iterative global observation and local editing. Specifi-cally, NeuroRefiner comprises three collaborative agents dedicated to di-agnosing topological errors, generating correction instructions, and vali-dating refinement quality. To facilitate agent instruction-guided segmen-tation refinement, we propose TopoRefineNet, a dedicated 3D U-Net-based tool that leverages cross-modality feature fusion to generate refinedmasks. Through multi-round agent reasoning and voxel-level editing,NeuroRefiner produces topologically more accurate segmentations withenhanced interpretability. Experiments on the BigNeuron, CWMBS, andZBFWB datasets demonstrate that NeuroRefiner outperforms state-of-the-art methods, notably achieving a 3.02% improvement in F1 score onthe challenging ZBFWB dataset.

Abstract:
Conventional RGB cameras suffer from lighting artifacts suchas flare, glare, flicker, and overexposure, leading to irrecoverable informa-tion loss that necessitates computational restoration. However, existingapproaches treat these problems in isolation, failing to recover structuraldetails completely obscured by complex spatially discrete image degrada-tions. In this paper, we propose a novel cross-modal restoration paradigmand present DeLux, a modular proof-of-concept pipeline that leveragesneuromorphic event streams as a structural prior to guide the targeteddetection and inpainting of lighting artifacts in RGB video. Validation onsynthetic benchmarks and real-world automotive footage demonstratesthat DeLux effectively suppresses local artifacts and restores affected re-gions. The proposed approach outperforms existing RGB-only baselinesand event-guided HDR models, achieving an average MS-SSIM of over0.99 across all artifact types and demonstrating up to an 88% reductionin artifact severity in real-world automotive footage. The synthetic arti-fact generation tools and curated real-world evaluation datasets are madepublicly available to foster future research on cross-modal restoration.

Abstract:
We introduce Multi-Object Generative Perception (MultiGP),a generative inverse rendering method for stochastic sampling of all radio-metric constituents—reflectance, texture, and illumination—underlyingobject appearance from a single image. Our key idea to solve this in-herently ambiguous radiometric disentanglement is to leverage the factthat while their texture and reflectance may differ, objects in the samescene are all lit by the same illumination. MultiGP exploits this consen-sus to produce samples of reflectance, texture, and illumination from asingle image of known shapes based on four key technical contributions: acascaded end-to-end architecture that combines image-space and angular-space disentanglement; Coordinated Scheduling for diffusion convergenceto a single consistent illumination estimate; Axial Attention applied tofacilitate “cross-talk” between objects of different reflectance; and a Tex-ture Extraction ControlNet to preserve high-frequency texture detailswhile ensuring decoupling from estimated lighting. Experimental resultsdemonstrate that MultiGP effectively leverages the complementary spatialand frequency characteristics of multiple object appearances to recoverindividual texture and reflectance as well as the common illumination.

Abstract:
Modern high-resolution mobile sensors often adopt clusteredcolor filter array (CFA) layouts such as Quad and Hexadeca Bayer,while some premium sensors use quad-pixel phase-detection or 2 × 2 on-chip-lens (OCL) architectures with shared microlenses for dense phase-detection autofocus (PDAF). While effective for PDAF, this structurecan introduce grid-aligned phase artifacts during demosaicing, which aredifficult to model without calibrated sub-aperture streams, PSF super-vision, or sensor-specific optical calibration. We propose Synthetic Sub-Aperture Phase Augmentation (SPA), a physics-inspired data genera-tion framework for artifact-aware demosaicing. SPA uses compact phasekernels to approximate focus-dependent sub-aperture shifts in syntheticRAW, requiring no measured point spread functions (PSFs) or sensor-specific calibration. Experiments on synthetic and real RAW data fromHexadeca and Quad Bayer sensors show consistent suppression of zipper-ing, color bleeding, and grid-aligned artifacts. We also introduce spatiallattice imbalance (SLI) and Nyquist energy ratio (NER) for 2× 2 artifactanalysis and validate robustness to spatial phase variations.

Abstract:
Blood oxygen saturation (SpO2 ) is a vital indicator of respi-ratory and cardiovascular health, increasingly targeted by camera-based,contactless monitoring systems. Existing video-based approaches typi-cally rely on predefined frequency filtering to separate pulsatile (AC)and baseline (DC) components. Such spectral decomposition propagatesband-limited noise and assumes signal stationarity, limiting robustnessunder motion, illumination variation, and subject diversity.We propose FUSE, a filter-free, physics-guided framework that learnsAC and DC representations via structured differential constraints ratherthan static spectral filtering. The pulsatile component is regularized as aband-limited harmonic field through a second-order ordinary differentialequation, while spatial coherence is enforced by modeling it as a trans-ported density field under an advection-based partial differential con-straint. This coupled wave-transport formulation enforces physiologicallyconsistent spatiotemporal dynamics without explicit frequency-domain

Abstract:
Reliable detection of sharp features in point clouds—loci where the surface-normal field is discontinuous—remains challenging under increased local density variation and noise. SFD-Net couples a compact multi-scale Local Geometric Descriptor (LGD) with an enhanced PointNet++ backbone. LGD aggregates second-moment statistics of surface normal differences at three nested neighborhood scales, yielding rigid-motionand scale-invariant cues that remain stable under joint noise and spacing variability. On the ABC benchmark, SFD-Net achieves state-of-the-art F1-score and competitive False Positive Rate (FPR) across four noise conditions under a consistent evaluation setup. LGD further improves competitive detectors in a plug-and-play manner without architectural changes, and transfer to Stanford Large-Scale 3D Indoor Spaces (S3DIS) confirms generalization to real scans. Downstream applications potentially benefit from more reliable feature localization. Our source code and pretrained models are publicly available at https: //github.com/inyoungoh-cde/SFD-Net.

Abstract:
As web agents increasingly demonstrate capabilities in automated task execution, the development of robust evaluation frameworks for assessing their navigation and task completion performance has emerged as a critical research priority. However, existing benchmarks exhibit several fundamental limitations. First, they suffer from insufficient scale and limited domain diversity, thereby constraining comprehensive evaluation of cross-domain generalization. Second, prevailing LLM-asJudge evaluation methodologies inadequately capture fine-grained interaction semantics, particularly regarding precise query formulation and filtering operations. Third, current benchmarks predominantly emphasize navigation success metrics while neglecting critical requirements for realworld deployment scenarios. To address these limitations, we introduce WebRetriever, a large-scale benchmark encompassing 800 websites and 1,550 tasks across diverse domains, including consumer, professional, and enterprise sectors, with comprehensive coverage of user intent patterns. We propose NavEval (Navigation Evaluation), a novel LLM-as-Judge framework that leverages rich interaction context beyond visual screenshots, achieving state-of-the-art alignment with human judgment across multiple evaluation datasets. Furthermore, we establish three complementary evaluation protocols that collectively provide holistic assessment of web agent capabilities: navigation proficiency, knowledge-assisted interaction, and end-to-end task completion with information extraction. Extensive experimental analysis reveals substantial performance disparities across evaluation protocols, demonstrating that navigation success alone serves as an insufficient predictor of real-world application effectiveness. WebRetriever delivers fine-grained diagnostic insights into agent capabilities and establishes a rigorous foundation for advancing web agent research and development.

Abstract:
In intelligent transportation systems, roadside 3D object detection provides wide-area perception crucial for traffic understanding, cooperative early warning, and safe autonomous driving. However, existing methods suffer from high sensitivity to camera extrinsics; even slight deviations (whether manifesting as transient jitter or persistent drift) can be significantly amplified by projective geometry. This cascade results in severe feature misalignment and degraded localization. To mitigate this limitation, we propose RECO, a region-aware extrinsic compensation framework that corrects extrinsics using piecewise 6-DoF pose offsets. RECO predicts a learnable range boundary to partition the scene into near and far regions, estimating region-specific pose corrections. A differentiable sigmoid gate then smoothly blends the two compensated geometries to preserve continuous BEV sampling and facilitate stable optimization. To supervise the refinement of extrinsics, we introduce an auxiliary reprojection loss that compares 2D bounding boxes projected from 3D ground truth against 2D annotations, optimizing it jointly with the standard detection objective. Extensive experiments on the DAIR-V2X-I and Rope3D benchmarks under extrinsic perturbations demonstrate consistent improvements over state-of-the-art baselines across both yaw and z-axis deviations. RECO also generalizes from transient perturbations to persistent shifts, maintaining highly competitive performance under strict calibration uncertainty.

Abstract:
Action-conditioned robot world models generate future videoframes of the manipulated scene given a robot action sequence, o!eringa promising alternative for simulating tasks that are di"cult to modelwith traditional physics engines. However, these models are optimized forshort-term prediction and break down when deployed autoregressively:each predicted clip feeds back as context for the next, causing errors tocompound and visual quality to rapidly degrade. We address this throughthe following contributions. First, we introduce a reinforcement learning(RL) post-training scheme that trains the world model on its own au-toregressive rollouts rather than on ground-truth histories. We achievethis by adapting a recent contrastive RL objective for di!usion modelsto our setting and show that its convergence guarantees carry over ex-actly. Second, we design a training protocol that generates and comparesmultiple candidate variable-length futures from the same rollout state,reinforcing higher-fidelity predictions over lower-fidelity ones. Third, wedevelop e"cient, multi-view visual fidelity rewards that combine com-plementary perceptual metrics across camera views and are aggregatedat the clip level for dense, low-variance training signal. Fourth, we showthat our approach establishes a new state-of-the-art for rollout fidelityon the DROID dataset, outperforming the strongest baseline on all met-rics (e.g., LPIPS reduced by 14% on external cameras, SSIM improvedby 9.1% on the wrist camera), winning 98% of paired comparisons, andachieving an 80% preference rate in a blind human study.

Abstract:
End-to-end planning has emerged as a dominant paradigmfor autonomous driving, where recent models often adopt a scoring-selection framework to choose trajectories from a large set of candidates,with diffusion-based decoding showing strong promise. However, directlyselecting from the entire candidate space remains difficult to optimize,and Gaussian perturbations used in diffusion often introduce unrealis-tic trajectories that complicate the denoising process. In addition, fortraining these models, reinforcement learning (RL) has shown promise,but existing end-to-end RL approaches typically rely on a single coupledreward without structured signals, limiting optimization effectiveness.To address these challenges, we propose HAD, an end-to-end planningframework with a Hierarchical Diffusion Policy that decomposes planninginto a coarse-to-fine process. To improve trajectory generation, we intro-duce Structure-Preserved Trajectory Expansion, which produces realisticcandidates while maintaining kinematic structure. For policy learning, wedevelop Metric-Decoupled Policy Optimization (MDPO) to enable struc-tured RL optimization across multiple driving objectives. Extensive ex-periments show that HAD achieves new state-of-the-art performance onboth NAVSIM and HUGSIM, outperforming prior arts by a huge margin:+2.3 EPDMS on NAVSIM and +4.9 Route Completion on HUGSIM.

Abstract:
Forecasting future scene evolutions is crucial for autonomousdriving safety. Most existing driving world models focus on appearancegeneration and fail to model geometric and semantic evolutions. In thispaper, we propose a native 3D world model, Doe-2, to forecast the com-prehensive scene evolutions in a unified 3D latent representation space.This representation contains both appearance, geometry, and semanticinformation and can be decoded into multi-view RGB, depth, semantics,and 3D occupancy. For efficiency, we adopt an autoregressive transformerarchitecture to generate all latents for the next scene in one pass and thenuse a lightweight diffusion head to refine them for better fidelity. Operat-ing on the compact 3D latent space, our model supports efficient futurerollouts conditioned on different actions. Extensive experiments on thenuScenes dataset show the ability of Doe-2 to efficiently and comprehen-sively forecast future driving scenes. Despite being trained from scratchwith only 700 scenes, Doe-2 already shows high-quality scene generationand good generalization with action control ability.

Abstract:
The integration of Large Language Models (LLMs) into au-tonomous driving has attracted growing interest for their strong rea-soning and semantic understanding abilities, which are essential for han-dling complex decision-making and long-tail scenarios. However, existingmethods typically feed LLMs with tokens from multi-view and multi-frame images independently, leading to redundant computation and lim-ited spatial consistency. This separation in visual processing hinders ac-curate 3D spatial reasoning and fails to maintain geometric coherenceacross views. On the other hand, Bird’s-Eye View (BEV) representa-tions learned from geometrically annotated tasks (e.g., object detection)provide spatial structure but lack the semantic richness of foundationvision encoders. To bridge this gap, we propose BEVLM, a frameworkthat connects a spatially consistent and semantically distilled BEV rep-resentation with LLMs. Through extensive experiments, we show thatBEVLM enables LLMs to reason more effectively in cross-view driv-ing scenes, improving accuracy by 46.0 %, by leveraging BEV featuresas unified inputs. Furthermore, by distilling semantic knowledge fromLLMs into BEV representations, BEVLM significantly improves closed-loop end-to-end driving performance in safety-critical scenarios acrossUniAD and VAD, with gains of up to 28.2 %.

Abstract:
Predicting object dynamics (i.e., world modeling) is a fun-damental challenge for robotic manipulation, and modeling deformableobjects presents a particularly difficult case due to their high-dimensionalstate spaces and complex material properties. While current world mod-els approach this through two distinct paradigms: learning the dynamicsover the 2D pixel space or more explicit 3D geometric space. A systematicunderstanding of their relative strengths and limitations remains elusivedue to the lack of diverse, large-scale real-world data. To address this,we present Deform360, a large-scale visuotactile dataset featuring 198daily-life objects, 1,980 interaction sequences, and over 215 hours of ob-servations from 41 surround-view cameras and bimanual tactile grippersto capture both global motion and contact-induced local deformations.Leveraging a novel markerless visuotactile 3D tracking pipeline to ex-tract dense geometry and motion, we systematically evaluate currentstate-of-the-art world models, comparing 2D video models against 3Dparticle models. Finally, we provide a preliminary demonstration indi-cating the real-world applicability of our dataset by performing robotplanning tasks on deformable objects. Our analysis reveals key insightsinto the trade-offs between structural priors and scalability, providing asolid benchmark for future research in generalizable deformable object-centric world modeling. Project website: https://deform360.lhy.xyz

Abstract:
We present a novel implementation of Visual SimultaneousLocalisation and Mapping (VSLAM), whose computation is performedcollaboratively between a sensor-based front-end utilizing efficient in-pixel compute, and a PC-based back-end that sends pose estimationback to the sensor to improve feature matching. The front-end performsthe tasks of point-feature detection and tracking at up to 2000 Hz, whilethe PC back-end conducts the less immediate, higher level tasks, of map-ping and localisation at ≈ 200 Hz. This division of tasks is somewhatreminiscent of biological vision, in which the retina performs low levelcomputations, while the brain handles slower integrative functions. Onlysparse data is exchanged between these two ends, such as feature loca-tions, descriptor data, and sensor pose estimations. Essentially, each sideonly sends data which the other side requires to perform its respectivetask. This is a radical departure from the traditional visual pipeline,where dense image frames are streamed from sensor to PC. By compar-ison, our collaborative compute pipeline is extremely efficient, demon-strating a 10× improvement to speed and latency, > 1000× bandwidthand ∼ 20× reduction in power consumption per processed frame.

Abstract:
Human-human collaboration is a fundamental aspect of ev-eryday life, essential to success in a wide range of goal-directed activitiesfrom household tasks to professional teamwork. While much researchhas focused on modeling coordination and task execution, the cognitiveprocesses that support such collaboration, particularly Theory of Mind(the ability to infer others’ mental states), remain difficult to study innatural settings. To address this gap, we introduce a novel egocentric andexocentric video dataset capturing real-world collaboration in cookingscenarios. The dataset integrates multi-perspective video, high-qualityaudio, gaze tracking, and 3D scene and object scans, with annotations forshared gaze attention, social cues and interactions between agents, as wellas agent-object interactions. We establish benchmarks for Joint AttentionEstimation, Action Anticipation, and Collaborative Handover Prediction,enabling research on multimodal perception, proactive assistance, andcollaborative planning. By providing temporally aligned, richly annotatedmultimodal data, CoMind facilitates the development and evaluation ofAI systems capable of modeling complex social interactions and reasoningabout human behaviors in collaborative environments. Our dataset andbenchmarks are made available at https://comind.ethz.ch/.

Abstract:
What if a video generation model could not only imaginea plausible future, but the correct one – accurately reflecting how theworld changes with each action? We answer this by presenting the Ego-centric World Model (EgoWM), a simple, architecture-agnostic methodthat transforms any pre-trained video diffusion model into an action-conditioned world model, enabling precisely controllable future predic-tion. Rather than training from scratch, we repurpose the rich world pri-ors of Internet-scale video models by injecting appropriately compressedmotor commands through lightweight conditioning layers. This allowsour model to follow actions faithfully while preserving generalizationand realism. Our approach scales naturally across embodiments and ac-tion spaces – from 3-DoF mobile robots to 25-DoF humanoids, wherepredicting egocentric joint-angle driven dynamics is substantially morechallenging. The model produces coherent rollouts for both navigationand manipulation, requiring only modest fine-tuning. To evaluate phys-ical correctness independent of appearance, we introduce the StructuralConsistency Score (SCS), which measures whether stable scene elementsevolve consistently with the provided actions. Our method improves SCSby up to 65% over the prior state of the art, Navigation World Models;applies seamlessly to three different video diffusion model architectures;and effectively utilizes Internet priors to generalize to unseen environ-ments, including navigation and manipulation inside paintings. Finally,we demonstrate the applicability of EgoWM to robotic planning.

Abstract:
Embodied Instruction Following requires agents to executenatural-language tasks via holistic perception and sound planning. Cur-rent LLM-driven agents suffer from two primary bottlenecks: SpatialMyopia, where limited egocentric views hinder global topological under-standing, and Granularity Imbalance, where planning is polarized be-tween inefficient atomic actions that lack interpretability and ambiguoussubgoals that fail to align with low-level control. To address these is-sues, we propose STEP, a framework that empowers agents with SpatialThinking and Egocentric Pointing. For perception, STEP integrates Hy-brid Map-Egocentric Perception, fusing multi-view streams into Bird’s-Eye-View maps to maintain both global context and local visual cues.For planning, STEP introduces Point-Level Planning, which derives spa-tial waypoints through explicit reasoning chains, achieving highly inter-pretable, intermediate-granularity execution. We also design STEP-CoT,a scalable data engine, to generate a 400k Chain-of-Thought datasetthat aligns diverse modalities—views, maps, actions, and instructions—through reasoning chains. Results on ALFRED and AI2-THOR-Nav,along with real-world deployments, demonstrate STEP’s superior perfor-mance and generalization. Ultimately, STEP achieves a unified paradigm:Look at the View, Think with the Map, and Point to the Goal.

Abstract:
Video lane detection requires predictions that remain sta-ble across frames, yet severe vehicle occlusions can break temporal cues.In streaming recurrent models, corrupted observations may enter thehidden state and produce errors that persist into later frames. Existingocclusion-aware refinements usually provide obstacle masks as auxiliaryinputs, so the state-update path is only indirectly protected. We proposeSIGMA-Lane, which treats this failure mode as state contamination inState Space Model (SSM)-based temporal modeling. SIGMA-Lane placesocclusion-aware gates on the SSM write and residual-fusion paths, con-trolling how current observations enter temporal memory and are fusedback after temporal propagation. After coordinate-consistent affine align-ment, the model combines two complementary paths: SSM-consistentdual-gating for temporal filtering and Structural Spatial Retrieval (SSR)for recovering missing lane structure from aligned historical priors. Ex-periments on VIL-100 and OpenLane-V show improved temporal stabil-ity under heavy occlusion, with competitive F1 and mIoU scores.

Abstract:
VAR has gained widespread popularity due to its next-scaleprediction paradigm. However, it faces substantial performance bottle-necks when handling complex scenes with multiple objects and attributes.Existing diffusion-based enhancement methods fail to adequately addressthe unique challenge of cross-scale error propagation and accumulationin VAR. To this end, we propose SynVAR, the first training-free enhance-ment framework specifically tailored for the VAR paradigm, which intro-duces a spatial-semantic collaborative control strategy to effectively sup-press propagation error and improve generation quality. SynVAR com-prises three key components: (1) Global guidance to ensure reasonablespatial structure in the early stages, (2) Receptive field constraints tomitigate early-stage semantic confusion, (3) High-frequency compensa-tion to recover fine-grained details. Extensive quantitative and qualita-tive experiments demonstrate the significant improvements in the abilityof SynVAR to enhance the VAR’s capability for complex scene modeling.

Abstract:
Towards an embodied generalist for real-world interaction,Multimodal Large Language Model (MLLM) agents still suffer fromchallenging latency, sparse feedback, and irreversible mistakes. Videogames offer an ideal testbed with rich visual observations and closed-loopinteraction, demanding fine-grained perception, long-horizon planning,and precise control. However, systematically evaluating these capabilitiesis currently hindered by heterogeneous action interfaces and heuristicverification. To this end, we introduce GameWorld, a benchmarkdesigned for standardized and verifiable evaluation of MLLMs as generalistgame agents in browser environments. Two game agent interfaces arestudied: (i) Computer-use agents that directly emit keyboard and mousecontrols, and (ii) Generalist multimodal agents that act in a semanticaction space via deterministic Semantic Action Parsing. GameWorldcontains 34 diverse games and 170 tasks, each paired with state-verifiable metrics for outcome-based evaluation. The results across 18model-interface pairs suggest that even the best-performing agent isfar from achieving human capabilities on video games. Further studieson real-time interaction, context-memory sensitivity, and action validityexpose more challenges ahead for game agents. Together, by offering astandardized and verifiable evaluation framework, GameWorld laysa foundation for advancing research on multimodal game agents andbeyond.

Abstract:
End-to-End autonomous driving (E2E-AD) systems face chal-lenges in lifelong learning, including catastrophic forgetting, difficulty inknowledge transfer across diverse scenarios, and spurious correlationsbetween unobservable confounders and true driving intents. To addressthese issues, we propose DeLL, a Deconfounded Lifelong Learning frame-work that integrates a Dirichlet process mixture model (DPMM) withthe front-door adjustment mechanism from causal inference. The DPMMis employed to construct two dynamic knowledge spaces: a trajectoryknowledge space for clustering explicit driving behaviors and an implicitfeature knowledge space for discovering latent driving abilities. Leverag-ing the non-parametric Bayesian nature of DPMM, our framework en-ables adaptive expansion and incremental updating of knowledge withoutpredefining the number of clusters, thereby mitigating catastrophic for-getting. Meanwhile, the front-door adjustment mechanism utilizes theDPMM-derived knowledge as mediators to deconfound spurious correla-tions, such as those induced by sensor noise or environmental changes,and enhances the causal expressiveness of the learned representations.Additionally, we introduce an evolutionary trajectory decoder that en-ables non-autoregressive planning. To evaluate the lifelong learning per-formance of E2E-AD, we propose new evaluation protocols and metricsbased on Bench2Drive. Extensive evaluations in the closed-loop CARLAsimulator demonstrate that our framework significantly improves adapt-ability to new driving scenarios and overall driving performance, whileeffectively retaining previously acquired knowledge.

Abstract:
Imitation learning enables robots to acquire manipulationskills from demonstrations by mapping observations to actions. Existingapproaches predict either short-horizon continuous action sequences ordiscrete keyposes. However, continuous prediction methods suffer fromcompounding errors due to short prediction horizons and struggle withmulti-modal action distributions, whereas keypose-based methods ne-cessitate an external planner, constraining real-time applicability. Toaddress these challenges, we introduce SegDiff, a closed-loop visuomo-tor policy that integrates the strengths of both paradigms. SegDiff de-composes demonstrations into motion segments between keyposes andlearns to predict the continuous trajectory from the current state tothe next keypose, enabling long-horizon prediction with real-time refine-ment. Furthermore, we leverage the capability of diffusion models andDDIM inversion to propose a Dynamic Temporal Ensembling mecha-nism, which allows the policy to efficiently respond to dynamic environ-ments and mitigate discontinuities caused by inconsistent multi-modalsampling. SegDiff demonstrates significant performance gains over exist-ing approaches across various simulated and real-world scenarios, indi-cating its strong ability to reason over extended temporal dependencieswhile maintaining real-time adaptability and control stability.

Abstract:
Existing world model-based planners for visual navigationtypically follow a verification-centric paradigm, decoupling goal intentfrom trajectory synthesis. This approach suffers from candidate depen-dence, heavy computational overhead, and inconsistencies between sam-pled actions and predicted visuals. To address these issues, we proposeSWAM (Spatial-perceiving World Action Model), a task-centric jointobservation-action generation framework. Given start and goal RGB ob-servations, SWAM performs single-pass inference to simultaneously gen-erate intermediate RGB-D sequences and corresponding action trajecto-ries, promoting goal-consistent trajectory generation and improved spa-tial feasibility. SWAM leverages depth pseudo-labels during training tointernalize spatial priors, but it requires only monocular RGB input atinference time by using depth estimation model. We further introducea visual-guided action refinement module and a trajectory-scale regular-ization loss to enforce fine-grained alignment between motion and visualcues while stabilizing predictions across varying distances. Extensive ex-periments show that SWAM significantly outperforms state-of-the-arttwo-stage planners in success rate, trajectory accuracy, and inference ef-ficiency, while demonstrating robust zero-shot generalization to unseenenvironments.

Abstract:
Human driving behavior embodies two core cognitive mechanisms: experiential reasoning and functional specialization. However, existing end-to-end autonomous driving (E2E-AD) frameworks typically model these two processes separately, often resulting in inconsistent decisions under long-horizon or dynamic conditions. To address this issue, we propose TEX-Drive, a unified E2E framework that integrates temporal perception with an experience-driven Mixture-of-Experts, achieving coordinated temporal perception–guided experience routing. Specifically, the temporal perception unit employs explicit key-frame selection to fuse critical information from both historical and current inputs, forming a temporally coherent scene representation. Building upon this, the experience-driven Mixture-of-Experts planning retrieves expert modules from a long-term memory space according to contextual similarity, dynamically routing the most relevant experts to adaptively generate future trajectories. This design enables the decision-making process to maintain temporal consistency while being guided by accumulated experiential knowledge. Extensive experiments on the Bench2Drive benchmark show that TEX-Drive consistently improves driving score, route completion, and behavioral robustness, substantially outperforming existing state-ofthe-art methods.

Abstract:
We propose Sen-Cap, a Sensor-Flexible and Noise-Resilient3D human motion Capture framework that integrates multi-modal datafrom LiDAR and camera. While multi-modal sensors provide richer infor-mation than single-modal sensors, existing approaches still su x001B er fromtwo core challenges. First, multi-modal alignment/matching across ar-bitrarily deployed sensors is typically handled by explicit calibration,which propagates errors under changing viewpoints and in turn con-strains deployment to x001C xed, highly overlapped layouts. Second, priormethods degrade under severe noise or partial sensor failures, which areSen-common in real-world environments. To address these challenges,Cap introduces a Uni x001C ed Across-Sensor Motion Estimator that recon-structs local pose and shape in a human-centric space without calibra-tions between sensors, supporting a x001D exible number of sensors, as well asa Noise-Resistant Trajectory Tracker that maintains robustness under se-vere point cloud noise through iterative re x001C nement. These sensor- x001D exibleand noise-resilient features make Sen-Cap more practical in real-worlddeployment. Notably, operating in real time, Sen-Cap achieves state-of-the-art performance on major metrics on Human-M3 and FreeMotion,as well as strong cross-domain performance on LiDARHuman26M andRELI11D. This combination of x001D exibility and robustness opens new op-portunities for motion capture in real-world scenarios, e.g. sports ana-lytics, x001C eld robotics, and large-scale immersive environments.

Abstract:
Vision-and-Language Navigation in Continuous Environments(VLN-CE) requires agents to follow natural-language instructions in un-seen scenes. While Large Models (LMs) have advanced VLN-CE, theirperformance remains severely degraded by real-world visual corruptions,a critical yet underexplored domain constraint. We introduce Tempo-ral Conditional Flow Decorruptor (FlowDec), a novel image restorationframework tailored for LM-based VLN-CE. FlowDec integrates a hybridtemporal conditioning strategy to align the generative flow path withhistorical context and employs action-centroid guided filtering to dy-namically assess and integrate outputs. Extensive experiments demon-strate that FlowDec outperforms state-of-the-art decorruption methodsin both navigation accuracy and generation latency. Our approach es-tablishes a robust, efficient paradigm for resilient embodied navigationin unpredictable real-world conditions.

Abstract:
On-device learning is essential for personalization, privacy,and long-term adaptation in resource-constrained environments. Achiev-ing this requires efficient learning, both fine-tuning existing models andcontinually acquiring new tasks without catastrophic forgetting. Yet bothsettings are constrained by high memory cost of storing activations dur-ing backpropagation. Existing activation compression methods reducethis cost but rely on repeated low-rank decompositions, introducing com-putational overhead. Also, such methods have not been explored forcontinual learning. We propose LANCE (Low-rank Activation Compres-sion), a framework that performs one-shot higher-order Singular ValueDecomposition (SVD) to obtain a reusable low-rank subspace for ac-tivation projection. This eliminates repeated decompositions, reducingboth memory and computation. Moreover, fixed low-rank subspaces fur-ther enable on-device continual learning by allocating tasks to orthogo-nal subspaces without storing large task-specific matrices. Experimentsshow that LANCE reduces activation storage by 25× to 380× depend-ing on the architecture, while maintaining accuracy comparable to fullbackpropagation on CIFAR-10/100, Oxford-IIIT Pets, Flowers102, andCUB-200 datasets. On continual learning benchmarks (Split CIFAR-100,Split MiniImageNet, 5-Datasets), it performs competitively with orthog-onal gradient projection methods at a fraction of the memory cost. Theseresults position LANCE as a practical and scalable solution for efficientfine-tuning and continual learning on edge devices.3

Abstract:
Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling diffusion models in visual generation. Recent advancements have focused on adaptively allocating computational resources across diverse tokens to improve efficiency and performance. However, we identify a routing assignment problem in existing diffusion MoE frameworks: the router fails to accurately allocate more computational resources to salient tokens. Our analysis attributes this failure to the router’s reliance on noise-corrupted latent features throughout the denoising process. Such stochastic noise obscures the critical structural and textural information, thereby preventing the router from effectively distinguishing salient tokens. To address this, we propose SharpMoE, a post-training framework with a saliency-harnessing accurate routing mechanism, which utilizes clean latent features as a noise-free guidance signal for routing. By bypassing the noise-distorted inputs, SharpMoE provides the router with clear saliency guidance, enabling the identification of salient tokens even in high-noise stages. Furthermore, we introduce a trajectory routing loss to constrain the compute allocation throughout the multi-step denoising trajectory, ensuring precise resource allocation along the generation rollout. Extensive experiments demonstrate that SharpMoE serves as a versatile, plug-and-play solution that further enhances the pretrained, converged MoE models, achieving stateof-the-art performance in visual generation.

Abstract:
Object detection for Unmanned Aerial Vehicles (UAVs) work-ing in open and dynamic environments is a highly challenging task.While Vision-Language Models (VLMs) have offered a powerful solutionfor universal object detection, adapting them to UAV scenarios remainsnon-trivial due to a substantial domain gap between VLM pre-trainingdata and aerial imagery. The prevailing Parameter-Efficient Fine-Tuning(PEFT) methods prove ineffective in bridging this gap, as VLMs’ “natural-scene, foreground-dominant” visual priors misalign with the “bird’s-eye-view, background-dominant, small-object” characteristics of UAV data.To address this issue, we propose DroneFINE, a novel PEFT paradigmcomprising two domain-aware complementary modules tailored for VLM-based drone image detectors. Specifically, a data-dependent, foreground-aware, and multi-path adaptation mechanism named HyperAdapter isdesigned, which overcomes the static structural constraints of PEFT. Inaddition, a background suppression algorithm named SemanticGate isdeveloped, which is a text-conditioned guidance strategy that employsbackground vocabulary to actively guide the model in suppressing re-sponses from irrelevant regions. Extensive experiments on VisDrone andUAVDT demonstrate that DroneFINE significantly outperforms existingPEFT methods and achieves performance comparable to full fine-tuningwhile substantially reducing the fine-tuned parameters.

Abstract:
Vision-language models (VLMs) achieve remarkable multi-modal comprehension, yet their massive parameter counts impede de-ployment on resource-limited hardware. Traditional pruning techniquesrely on manual heuristics that frequently disrupt delicate cross-modalsynergies, leading to substantial performance declines. To overcome this,we introduce KATANA (Knowledge-Aligned Topology-Aware NeuralAgents), a reinforcement learning framework that autonomously discoversexecutable pruning algorithms. Guided by a synthesized multi-objectivereward signal and safely evaluated within an isolated sandbox, an LLM-driven agent iteratively evolves innovative compression strategies. Ourflagship discovered algorithm, KIRI (Kernel-Integrated ReconstructionIterator), utilizes a cubic sparsity scheduler alongside a novel Dual-NormActivation (DNA) importance metric that dynamically fuses weight mag-nitudes, structural regularizations, and data-dependent activation profiles.Extensive evaluations across four diverse VLM architectures (LLaVA-1.5,BLIP-2, Qwen2.5, and Llama-3.2-Vision) on MSCOCO, Flickr30k, andNoCaps benchmarks demonstrate KATANA’s superiority. At an aggres-sive 70% sparsity, KIRI delivers a measured 2.8× inference speedup whileconsistently outperforming 16 state-of-the-art baselines, including recentadaptive and hybrid methods such as SCOPE and GSOP. KATANA thusintroduces an automated and extensible framework for VLM compressionthat preserves vision-language coherence for practical deployment.

Abstract:
Pretrained vision models are widely used as backbones fordownstream tasks, yet adapting them to multi-task learning (MTL) re-mains challenging. Naive full fine-tuning incurs substantial overhead andoften exacerbates task interference, while existing parameter-efficientfine-tuning (PEFT) methods are largely designed for single-task adap-tation. We propose Residual Upcycling with Task Ladder (RUTaL), aparameter-efficient multi-task vision adaptation framework. To accom-modate the diverse and heterogeneous representation demands in MTL,We proposed Task-General Residual Upcycling (TGRU) to transforma pre-trained vision Transformer backbone into a Mixture-of-Expertsarchitecture through low-rank residual weight reparameterization, en-abling efficient and task-scalable capacity expansion. Built upon this up-cycled representation paradigm, we further introduce Task-Specific Lad-der Adaptation (TSLA), which extracts task-relevant features from theshared upcycled representations in a decoupled manner to accommodatethe unique requirements of each task. Experiments on multi-task densescene understanding benchmarks show that RUTaL achieves state-of-the-art performance while demonstrating superior computational efficiency.

Abstract:
Vision-language agents that orchestrate specialized tools forimage restoration (IR) have emerged as a promising method, yet most ex-isting frameworks operate in a training-free manner. They rely on heuris-tic task scheduling and exhaustive tool traversal, resulting in sub-optimalrestoration paths and prohibitive computational cost. We argue that thecore bottleneck lies in the absence of a learned policy to make decision,as a vision-language model cannot efficiently handle degradation-awaretask ordering and tool composition. To this end, we propose TIR-Agent, atrainable image restoration agent that performs a direct tool-calling pol-icy through a two-stage training pipeline of supervised fine-tuning (SFT)followed by reinforcement learning (RL). Two key designs underpin effec-tive RL training: (i) a random perturbation strategy applied to the SFTdata, which broadens the policy’s exploration over task schedules andtool compositions, and (ii) a multi-dimensional adaptive reward mech-anism that dynamically re-weights heterogeneous image quality metricsto mitigate reward hacking. To support high-throughput, asynchronousGPU-based tool invocation during training, we further develop a glob-ally shared model-call pool. Experiments on both in-domain and out-of-domain degradations show that TIR-Agent outperforms 12 baselines,including 6 all-in-one models, 3 training-free agents, and 3 proprietarymodels, and achieves over 2.5× inference speedup by eliminating redun-dant tool executions.

Abstract:
Knowledge distillation (KD) aims to transfer useful informa-tion from a large-scale model (teacher) to a lightweight model (student).Classical KD focuses on leveraging the teacher’s predictions as soft labelsto regularize student training. However, the exact match of predictionsin Kullback-Leibler (KL) divergence could be somewhat in conflict withthe classification objective, given that the distribution discrepancies be-tween teacher-generated predictions and ground-truth annotations tendto be fairly severe. In this paper, we rethink the role of teacher predic-tions from a Mixture-of-Experts (MoE) perspective and transfer knowl-edge by introducing teacher predictions as latent variables to reformulatethe classification objective. This MoE strategy results in breaking downthe vanilla classification task into a mixture of easier subtasks with theteacher classifier as a gating function to weigh the importance of sub-tasks. Each subtask is efficiently conquered by distinct experts that areeffectively implemented by resorting to multi-level teacher outputs. Wefurther develop a theoretical framework to formulate our method, termedMoE-KD, as an Expectation-Maximization (EM) algorithm and provideproof of the convergence. Extensive experiments manifest that MoE-KDoutperforms advanced knowledge distillers on mainstream benchmarks.

Abstract:
Weight-only post-training quantization (PTQ) has proven highly effective for deploying large language models under memory-bound inference constraints. However, extending PTQ to Multimodal LLMs at ultra-low bit-widths (2–3 bits) presents a significant challenge. Existing uniform-precision methods suffer severe accuracy collapse due to their implicit assumption that all output channels within a layer are equally sensitive to quantization noise. This uniform bit-width assignment fails to account for the heterogeneous sensitivity of different channels. By lifting the analysis to the Transformer block level, we reveal a two-level anisotropy—inter-layer and intra-layer inter-channel. This anisotropy is further amplified by an outlier–sensitivity resonance mechanism, causing different output channels to contribute vastly different amounts of quantization error to the block output. Together, these findings provide the rigorous foundation for per-output-channel mixed-precision quantization. Building on the derived optimality condition, we propose GloBitQ, a framework that unfolds the anisotropic loss into principled bit allocation. It integrates three intermediate proxies: power-law balanced layer weighting, rank–value hybrid channel scoring, and global top-τ bit assignment. The resulting allocation provably approximates the optimum of the block-level quantization objective. On six multimodal benchmarks spanning Qwen2-VL-7B, Qwen2.5-VL-7B, and LLaVA-OneVision, GloBitQ consistently surpasses GPTQ, GPTAQ, and VLMQ: the 3-bit model performs within 2.1% of full precision(81.70 vs. 83.76), while the 2-bit model remains stable where prior methods collapse—all within a calibration-only, finetuning-free, hardware-compatible pipeline. Code is available in the supplementary material.

Abstract:
Vision-language models (VLMs) excel at zero-shot recogni-tion, yet their performance degrades under distribution shifts during in-ference. Unsupervised test-time adaptation (TTA) often relies on noisypseudo-labels or heuristics, offering limited guidance for where decisionboundaries should move; existing active TTA (ATTA) methods typi-cally treat annotations as local fixes rather than structured updates tothe shared semantics of VLMs. We introduce PAA (Probe, Anchor, andAmend), an ATTA framework that treats visual and textual prototypesas an evolving state and updates this state through a three-step cycle. Ineach cycle, the Probe step performs prototype-guided querying under aclass-balanced budget, selecting both central samples to preserve sourceknowledge and boundary samples to track distribution shifts. Using onlybinary verifications, the Anchor step applies a prototype-regularized up-date that couples cross-modal alignment with intra-class compactnessand inter-class separation, yielding a decision space that is stable yetplastic. As a slow controller, the Amend step revisits past hard casesstored in memory and converts temporal discrepancies into memory-driven self-refinement. Across VLM test-time benchmarks, PAA deliversconsistent gains over strong TTA and ATTA baselines under comparableannotation budgets and compute, turning sparse, delayed feedback intostructured evolution of the prototype-anchored decision space.

Abstract:
Object detection models deployed in safety-critical applica-tions remain vulnerable to backdoor attacks that cause targeted misbe-haviors when a hidden trigger is present. Existing detection methods ei-ther rely on trigger inversion or exploit architecture-specific assumptions,and critically, representative existing methods fail to generalize reliablyto scene-level attacks, where a single trigger induces anomalous behavioracross all objects in the scene simultaneously. We present DistScan, abackdoor detection framework based on a simple but previously unex-ploited observation: backdoor injection systematically shifts a model’spre-NMS prediction class distribution away from its training class fre-quencies, even on clean inputs without any trigger present. DistScanaggregates intermediate class predictions over a clean validation set andflags a model as backdoored if the resulting distribution deviates sig-nificantly from the training class frequencies, requiring no model weightaccess, no trigger knowledge, and no additional training. Extensive exper-iments on MS-COCO and PASCAL VOC across two architectures andthree scene-level attack scenarios demonstrate that DistScan substan-tially outperforms existing methods, improving average detection accu-racy over the best-performing applicable baseline by 27.32 percentagepoints.

Abstract:
Graphical User Interface (GUI) grounding is essential for au-tonomous agents to map natural language instructions to precise screencoordinates. However, existing supervised fine-tuning and reinforcementlearning methods are constrained by the high cost of annotation, creatinga scalability bottleneck. In this paper, we introduce a label-free test-timetraining paradigm driven by two key insights: (1) confidence patterns incoordinate tokens are a better indicator than full-sequence confidence,and (2) in sparse GUI coordinate spaces, negative samples offer more re-liable learning signals than potentially noisy positive ones. We first pro-pose Confidence-Anchored Learning (CAL), which utilizes coordinate-token confidence to filter pseudo-labels and assign distance-based binaryrewards. Building on this, we develop Confidence-Anchored NegativeLearning (CANL), which exclusively optimizes the model using negativesamples to bypass the risks of incorrect positive samples. Experimen-tal results demonstrate that CANL-7B achieves 92.1% on ScreenSpot-V2. On more challenging ScreenSpot-Pro, CANL-7B reaches 33.8%, an8.9% absolute improvement over the base model. Our findings establishcoordinate-token confidence as a powerful alternative to manual annota-tions for scalable GUI agent development.

Abstract:
End-to-end autonomous driving increasingly relies on gener-ative planners, broadly classified into continuous diffusion policies anddiscrete token-based vision-language-action models (VLAs). While diffu-sion policies exploit parallel iterative noise prediction to efficiently gener-ate accurate trajectories, they suffer from cross-modal misalignment, lim-ited training efficiency, and poor generalization. Conversely, token-basedVLAs generate discrete trajectory tokens autoregressively, offering bettergeneralization but being constrained by inefficient causal reasoning, cu-mulative error, and irreversible decoding. To overcome these limitations,we propose DriveFine, a vision-language-action model that exploresmasked diffusion LLMs for trajectory planning, which significantly im-proves the efficiency, flexibility, and adaptability of trajectory generation.To address the inherent irreversibility of token decoding and improve thetrajectory quality, we introduce a plug-and-play block Mixture-of-Expert(block MoE) module, which seamlessly injects the refinement capa-bility into the dLLM at minimal cost. Through explicit expert routingand gradient isolation, DriveFine decouples generation and refinement,preventing cross-task interference. We further devise a tailored hybridreinforcement learning strategy to facilitate the effective exploration ofthe refinement expert and further explore the performance ceiling. Ex-tensive experiments on the NAVSIM v1, v2, and Navhard benchmarksdemonstrate that DriveFine exhibits strong performance, robustness andefficiency. The code will be released.

Abstract:
Learned image compression (LIC) is bottlenecked by theneed to store independent models for each rate-distortion operating point.Existing variable bit-rate (VBR) methods aim to reduce this overhead viadense parameter modulation, but forcing a shared backbone to approx-imate divergent mappings causes severe feature entanglement. Specifi-cally, low-rate smoothing gradients inherently conflict with the preser-vation of high-frequency textural details, leading to sub-optimal perfor-mance. To resolve this, we propose MixCompress, a unified VBR frame-work based on sparse structural specialization. While sparsely gatedMixture-of-Experts (MoE) routing successfully mitigates gradient con-flict, it operates on a fixed computational budget. To address the in-creased representational demands of higher bit-rates we introduce a Mixture-of-Depths (MoD) extension to dynamically scale model capacity. Com-bined with Conditional Auxiliary Transforms (CAT) for dynamic sub-band energy modulation, our hierarchical framework effectively dynam-ically scales capacity. Extensive evaluations demonstrate that MixCom-press not only matches individually optimized single-rate baselines butcan even surpass them, establishing a new Pareto frontier for computa-tionally efficient image coding.

Abstract:
Real-time object detection has achieved substantial progress through meticulously designed architectures and optimization strategies. However, the pursuit of high-speed inference via lightweight network designs often leads to degraded feature representation, which hinders further performance improvements and practical on-device deployment. In this paper, we propose a cost-effective and highly adaptable distillation framework that harnesses the rapidly evolving capabilities of Vision Foundation Models (VFMs) to enhance lightweight object detectors. Given the significant architectural and learning objective disparities between VFMs and resource-constrained detectors, achieving stable and task-aligned semantic transfer is challenging. To address this, on one hand, we introduce a Deep Semantic Injector (DSI) module that facilitates the integration of high-level representations from VFMs into the deep layers of the detector. On the other hand, we devise a Gradient-guided Adaptive Modulation (GAM) strategy, which dynamically adjusts the intensity of semantic transfer based on gradient norm ratios. Without increasing deployment and inference overhead, our approach painlessly delivers striking and consistent performance gains across diverse DETR-based models, underscoring its practical utility for real-time detection. Our new model family, RT-DETRv4, achieves state-of-the-art results on COCO, attaining AP scores of 49.8/53.7/55.4/57.0 at corresponding speeds of 273/169/124/78 FPS.

Abstract:
One-shot Federated Learning (OFL) reduces communication costs by requiring only a single round of model exchange. However, when clients train independently on heterogeneous data, their learned representations develop different means and variances, creating distributional mismatch that prevents effective server-side aggregation. Existing methods attempt post-calibration at the server, but struggle to reconcile representations that have already diverged into incompatible statistical spaces. We propose FACE (Federated Aligned-Consistent Ensemble) to prevent distributional divergence during client training rather than correcting it afterward. FACE uses a shared fixed reference matrix to guide representation means toward consistent directions, applies a variance stabilizer to maintain consistent feature variance, and performs featurefusion ensemble inference. Extensive experiments on CIFAR-10/100 and Tiny-ImageNet demonstrate that FACE achieves state-of-the-art performance, with particularly strong improvements under extreme data heterogeneity. Comprehensive ablation studies validate the necessity of both components for achieving distributional alignment.

Abstract:
Deploying 3D point cloud analysis in privacy-sensitive and resource-constrained settings faces two coupled barriers: data cannot be centralized for training, and the trained model must run on limited edge hardware. We present a multi-seed benchmark that jointly evaluates federated learning (FL) and knowledge distillation (KD) for 3D point cloud classification. It spans thirteen FL algorithms and ten KD objectives, supporting their full 130-pair teacher–objective cross-product per dataset; every standardized configuration is repeated over three random seeds for 504 training runs in total, with the complete combined grid evaluated at multi-seed scale on the clinical dataset. We characterize federated degradation and the combined-pipeline pitfall on ModelNet40, then validate them on a real-world clinical craniosynostosis dataset of patient head shapes, where the privacy and edge-deployment stakes are concrete. We report three findings. First, under extreme non-independent and identically distributed (non-IID) label skew, standalone FL degrades sharply: on ModelNet40 the strongest method reaches only 76.32% against a 92.26% centralized reference, on the clinical data the best reaches 75.83% against 100%, and the four server-side optimizers collapse to near the chance level; the best algorithm differs by dataset, so none is universally robust. Second, distillation compresses the teacher into a student 74.51% smaller and roughly twice as fast at inference, with five of the seven objectives evaluated on ModelNet40 matching or surpassing the 92.44% teacher. Third, the combined pipeline exposes an evaluation pitfall: when distillation keeps a hard-label cross-entropy term on a labeled proxy split, a collapsed federated teacher at 8.50% paired with Logit-MSE still yields a 92.94% student. This 84.4-point gap reflects the proxy labels rather than the federated model, and the hard-label term reuses the very labels whose privacy motivated federation. Objectives without a hard-label term instead track teacher quality (r ≈ 0.99 on the clinical grid) and collapse when the teacher does. We therefore recommend evaluating FL-KD pipelines with label-free distillation, so that the reported accuracy reflects the federated teacher rather than the proxy.

Abstract:
When LiDAR scans a scene with reflective materials, it of-ten produces erroneous virtual points, which resemble real structuresand degrade downstream 3D perception. Conventional methods rely onmulti-stage pipelines that combine 3D surface reconstruction with heuris-tic symmetry detection, making them highly sensitive to surface noise,reflection irregularities, and accumulated estimation errors across stages.To address this, we propose an end-to-end framework that reformu-lates virtual point removal as ray-aligned geometric reasoning in the 2Dequirectangular projection domain, eliminating the dependency on ex-plicit surface modeling. Specifically, we introduce a Layer-Wise NearestPoint Map, which organizes multi-return LiDAR points by layer corre-spondence rather than fixed distance intervals, preserving geometric con-tinuity between real and reflected surfaces. Building on this representa-tion, we propose a Ray-Path-Aware Attention mechanism that constrainsfeature aggregation along physically plausible reflection trajectories de-rived from surface normals and incident ray directions. This physicallyguided attention enables the network to learn reflection-induced symme-try between real and reflected regions without stage-wise surface estima-tion. Extensive experiments on real-world LiDAR datasets demonstratethat our method achieves more accurate and stable virtual point removalthan prior symmetry-based approaches.

Abstract:
Accurate predictions with well-calibrated uncertainties are vital for safety-critical LiDAR scene understanding in autonomous driving and robotics. While Bayesian Neural Networks and deep ensembles are effective, they are computationally expensive, requiring multiple forward passes or independently trained models. We propose GMM-NIG, a Bayesian generative classification head that extends the GMMSeg framework by placing conjugate Normal-Inverse-Gamma (NIG) priors over class-conditional GMM parameters to obtain posterior-predictive uncertainty. While the backbone remains deterministic, GMM-NIG enables analytic closed-form posterior updates in a single training run. In inference, GMM-NIG yields a mixture of Student-t distributions, providing wellcalibrated predictive uncertainty without extra computational overhead. On SemanticKITTI and nuScenes, GMM-NIG achieves state-of-the-art calibration with an Adaptive Calibration Error of 2.01%, outperforming deep ensembles, Monte Carlo Dropout, evidential models and post-hoc calibration methods. Beyond calibration, it increases segmentation mIoU by nearly 6% and yields the strongest failure detection performance.

Abstract:
Existing zero-shot 6D object pose estimation methods predominantly rely on multi-stage pipelines with iterative render-and-compare refinement or coarse-to-fine hypothesis selection, leading to substantial computational overhead and implementation complexity. Generative modeling on geometric manifolds offers an attractive alternative by directly representing pose posteriors. In this work, we present UF0-6D, a Unified Flow-based Zero-shot framework for 6D object pose estimation without refinement. UF0-6D reformulates pose estimation as conditional Riemannian flow matching on SE(3), learning an instance-conditioned posterior via geodesic-consistent bridge velocities and symmetry-aware Riemannian optimal transport supervision. A single ODE integration pass replaces coarse-to-refine cascades and iterative alignment, unifying modelbased and model-free settings via a geometry-conditioning design that maps both CAD renders and reference-view renders into a shared pointtoken representation. Our RGB-D variant achieves a mean AR of 81.2 on BOP benchmark datasets, surpassing the previous state-of-the-art by +6.0 AR, while our RGB variant reaches 70.6 AR at only 0.379s per image, establishing a new accuracy–speed Pareto frontier.

Abstract:
Conformal prediction provides distribution-free reliability guar-antees for vision systems, but these guarantees depend on how predictionerrors are measured in the output space. Many vision tasks produce out-puts on curved spaces (e.g. gaze directions on the sphere or 3D headrotations), yet intermediate prediction heads, residuals, uncertainty es-timates, or conformal scores are often defined in flat coordinate chartssuch as yaw–pitch or Euler angles. We show that this scoring choice intro-duces systematic geometric distortion near coordinate singularities (largepitch angles on the sphere and poses approaching gimbal lock in 3D rota-tions). Across four datasets (ETH-XGaze, Gaze360, BIWI, AFLW2000-3D), slice-conditional coverage at a nominal 90% target drops by 30–50percentage points in these regions, falling to 38.9% on ETH-XGaze and42.0% on Gaze360 at gaze pitch above 70◦ , and to 57.5% on BIWI and55.2% on AFLW2000-3D at head pose pitch above 60◦ near gimbal lock,despite marginal coverage remaining near 90%. We prove that this isstructural. Scalar thresholding changes the size of chart-coordinate pre-diction sets but leaves their distorted axis ratios unchanged. To diagnosethis hidden failure mode, we show that a simple geometric quantity,the Riemannian volume density, strongly correlates with where cover-age collapse occurs. Finally, we show that coordinate-free geodesic scor-ing removes this distortion. It requires no retraining and adds negligiblecomputational cost.

Abstract:
Spatiotemporal prediction of complex dynamical systems de-pends not only on historical observations but also on evolving exter-nal control parameters. Crucially, for ionospheric Total Electron Con-tent, existing concatenation-based conditioning fails to capture region-dependent responses driven by space weather.We propose PMGC-SimVP, a spatiotemporal prediction framework withParametric Multi-scale Gated Convolution. PMGC injects external con-trol parameters into local dynamics via latitudinally structured mod-ulation, enabling explicit modeling of non-stationary evolution and re-gional heterogeneity. Built upon a purely convolutional backbone anda low-rank temporal adapter, PMGC-SimVP remains computationallye x001E cient.We evaluate PMGC-SimVP on global TEC forecasting, where dynam-ics are strongly driven by space weather and exhibit pronounced lati-tudinal variability. The proposed method achieves consistent improve-ments over recent state-of-the-art models, including Transformer-basedand frequency-domain approaches, across overall evaluation settings andduring geomagnetic disturbances. Experiments on generic video predic-tion benchmarks further verify the robustness of the proposed backboneeven without parameter injection.

Abstract:
Continuous 6-DoF pose estimation is essential for au-tonomous UAV operations. Yet, existing visual odometry and SLAMmethods accumulate drift and yield only relative, up-to-scale trajecto-ries. Single-frame geo-localization, in turn, discards temporal continu-ity and remains too slow for real-time use. We present OrthoTrack, atraining-free system that estimates continuous 6-DoF UAV trajectoriesusing only publicly available orthophotos and surface models as a mapprior. OrthoTrack matches keyframes against the orthophoto and liftscorrespondences to metric 3D via the surface model. It then propagatesthese map-anchored correspondences to intermediate frames with opticalflow, producing absolute, metrically scaled poses at every frame with-out GPS or post-hoc alignment. We also introduce the MovingDroneDataset, a large-scale benchmark pairing photorealistic UAV sequenceswith dense 6-DoF ground truth and co-registered multi-modal geodataincluding multi-temporal orthophotos. On MovingDrone and real-worldbenchmarks, OrthoTrack runs in real time on a single GPU. It outper-forms all baselines by a large margin, even those receiving oracle scaleand alignment. By relying on publicly available geodata, OrthoTrackenables deployment to new regions without site-specific adaptation.

Abstract:
Modern Visual Place Recognition (VPR) methods excel onstandard benchmarks yet remain brittle in feature-poor environments.By treating each query image in isolation, they discard the sequentialcontext in any real trajectory. We formalize a task that exploits thiscontext: given a query sequence, localize the final image against an un-ordered reference database—which, unlike sequence-to-sequence meth-ods, requires no sequential structure in the database. We propose TRAIL(TRajectory-Aware Image Localization), a principled framework basedon Conditional Random Fields (CRF) that combines learned functionsfor visual similarity and for camera-motion consistency, refining a dis-tribution over candidate references as each query arrives. A lightweightpost-processing layer atop any pre-trained VPR backbone, TRAIL im-proves a state-of-the-art baseline by up to 8.3 percentage points on ourprimary benchmark, transfers to unseen datasets without retraining, anddelivers its largest gains where visual cues are scarce.

Abstract:
Rolling-shutter cameras introduce characteristic distortionswhen imaging fast moving objects, and these effects are typically treatedas artifacts to be corrected. In this work, we instead leverage rolling-shutter distortions as a valuable source of temporal information to es-timate the 3D translational and angular velocities of rapidly movingspherical objects from a single rolling-shutter frame. We design a robustand easily detectable spherical pattern and propose a correspondence-free formulation that recovers motion by enforcing geometric consis-tency in a back-projection framework. By exploiting the geometry ofthe sphere, translational and rotational motions are decoupled and esti-mated through a two-stage optimization process, enabling reliable veloc-ity recovery even for textureless objects. Extensive experiments on bothsynthetic and real datasets demonstrate accurate and robust estimationof motion parameters under challenging high-speed conditions.

Abstract:
Decomposing CAD models into sweepable sub-blocks is acritical preprocessing step for high-quality hexahedral mesh generation.Existing block decomposition methods fall into two main categories: tra-ditional geometry-driven approaches and reinforcement learning (RL)-based methods. Traditional methods rely heavily on manual expertiseand interactive operations, resulting in intensive manual labor and ex-tremely low automation efficiency. Existing RL-based methods are ei-ther only applicable to simple 2D models, or dependent on large-scaleoffline datasets and pre-training, leading to high training costs and diffi-cult industrial deployment. To address these challenges, we propose thefirst pure online RL framework for 3D CAD model block decomposition.Specifically, we formulate the task as a tree-structured Markov Deci-sion Process (MDP), and adopt two core designs: a decoupled frozengeometric-topological feature extraction architecture for lightweight RLnetwork design, and a continuous-to-discrete action projection mecha-nism to ensure geometric validity of decomposition operations. Withoutrelying on any pre-training or offline datasets, our agent learns optimalcutting strategies purely through online interaction with a single targetCAD model. Experiments on our self-built dataset and public datasetsshow that our method outperforms the state-of-the-art offline RL base-line, and achieves competitive decomposition quality comparable to thehybrid fine-tuning paradigm with no pre-training dependency.

Abstract:
We propose UniDynamics, a diffusion-based framework forfuture 4D dynamic scenes (RGB, depth, and optical flow) generationfrom a single event-RGB pair, without requiring long histories or con-trol priors as in existing methods, while explicitly modeling future mo-tion fields. The core idea is to leverage event streams to offer an al-ternative motion prior for single-RGB extrapolation, and to enforce ge-ometric and motion constraints throughout generation via multimodalmodeling. Specifically, we design an Event Latent Enhancement (ELE)module to align and enhance event latents into diffusion-injectable con-ditioning features, providing robust initial motion priors and reliabletexture/structure cues. We further introduce a Perceptual DynamicsSpace (PDS) embedded in the multi-scale U-Net, which decouples andadaptively interacts depth and flow while continuously feeding back con-straints to appearance features, improving geometric-motion consistencyfor physically plausible and spatiotemporally coherent prediction. Exper-iments on VKitti2 and DSEC demonstrate state-of-the-art performance,producing high-quality, temporally coherent, and 4D-consistent futurepredictions, especially under challenging high-speed motion blur.

Abstract:
Multi-modal image matching is essential for visual localiza-tion and multi-sensor fusion, but it is hindered by the scarcity of large-scale training data with precise geometric annotations. Existing real-world datasets suffer from prohibitive costs, limited scene diversity, anderrors in SfM-MVS pipelines, while synthetic methods struggle to main-tain 3D geometric consistency or achieve photorealistic appearance. Toaddress this, we propose AnyMatch, a novel framework that leveragesabundant, easily accessible single-view images at minimal cost to gen-erate rich multi-modal training data. AnyMatch integrates monoculardepth estimation, 3D reprojection, diffusion-based inpainting, and cross-modal image translation to synthesize multi-view, multi-modal imagepairs with 3D geometric fidelity. Crucially, our method provides annota-tions that strictly adhere to 3D geometric consistency through explicit3D reprojection, avoiding SfM-MVS error accumulation. Furthermore,AnyMatch offers strong scalability, enabling controllable scene diversityand annotation difficulty via adjustable input and camera parameters.We construct Any-syn, a large-scale synthetic multi-modal dataset us-ing AnyMatch. Experimental results show that matching networks (e.g.,LoFTR, EDM, RoMa) fine-tuned on Any-syn achieve substantial perfor-mance gains on multi-modal benchmarks, exhibiting superior generaliza-tion and robustness compared to models trained on existing data.

Abstract:
Recent advances in diffusion models have shown impressiveperformance in controllable image generation and dense prediction tasks.However, existing approaches typically treat diffusion-based controllablegeneration and dense prediction as separate tasks, overlooking the po-tential benefits of jointly modeling the heterogeneous distributions. Inthis work, we introduce UniGP, a framework built upon MMDiT, whichunifies controllable generation and dense prediction through simple jointtraining, without the need for complex task-specific designs or losses,while preserving the backbone’s versatile priors. By learning controllablegeneration and prediction under different conditions, our model effectivelycaptures the joint distribution of image-geometry pairs. UniGP is capableof versatile controllable generation, dense prediction, and joint generation.Specifically, the proposed UniGP consists of DUGP and a unified datasettraining strategy. The former, following the principle of Occam’s razor,uses only a copied image branch of MMDiT to model dense distributionsbeyond RGB, while the latter integrates heterogeneous datasets into aunified training framework to jointly model generation and perceptiontasks. Extensive experiments demonstrate that our unified model sur-passes prior unified approaches and performs on par with specializedmethods. Furthermore, we demonstrate that multi-task joint training pro-vides complementary benefits: generative priors enrich perceptual details,while perceptual learning improves structural alignment in generation.Project page: guoqincode.github.io/UniGP.

Abstract:
Group Relative Policy Optimization (GRPO) enables stable and preference-oriented updates via group-wise comparisons for posttraining video generation. However, GRPO directly optimizes rewardinduced advantages. Under sustained optimization, the reward score can lose fidelity as a proxy for true video quality, consistent with the phenomenon described by Goodhart’s Law. This leads to two recurring issues: (i) shortcut-driven optimization under composite objectives and (ii) reward saturation within prompt groups. To address these issues, we introduce TaRoS, a Target-Robust Reward Signaling framework for Video generation GRPO. TaRoS leverages component level performance assessment together with intra-group sparsity to organize multi-aspect rewards towards optimization objectives. In addition, it adaptively downweights components that exhibit saturation, thereby preserving effective optimization directions and mitigating redundancy. This maintains meaningful optimization directions and preserves within-group ranking separation, thereby preventing reward hacking and leading to more reliable policy updates. Extensive experiments show consistent improvements in visual fidelity, motion coherence, and text-video alignment over strong baselines.

Abstract:
Text-to-image (T2I) diffusion models have achieved strik-ing progress but still struggle to synthesize rare concepts involving un-usual attribute-object pairings, often resulting in concept omission orsemantic drift where a dominant entity overwhelms the generation. Trac-ing these failures to a lack of compositional balance during the denois-ing trajectory, we propose RADIANCE, a training-free framework thattreats inference as a closed-loop feedback process. RADIANCE augmentspretrained backbones with three modular components: (1) a Composi-tional Similarity Monitor (CSM) that tracks the emergence of objectsand attributes in intermediate latents via CLIP-based feedback; (2) aBidirectional Scale Controller (BSC) that applies a reactive "restoringforce" using positive and negative IP-Adapter scales to rebalance biasedtrajectories; and (3) a Feedback Guidance Scheduler (FGS) that coordi-nates these updates across timesteps without additional training. We fur-ther extend the framework to multi-object prompts via Delayed AdapterActivation (DAA) and Layer-wise Alternating Guidance (LAG) to pre-vent premature concept fusion. By overlapping monitoring and denoisingthrough pipelined execution, RADIANCE maintains competitive latencywhile significantly enhancing the per-sample success rate and effectivethroughput. Experiments on RareBench and T2I-CompBench demon-strate that RADIANCE consistently enhances compositional alignmentand perceptual quality over state-of-the-art baselines.

Abstract:
The rapid advancement of diffusion models has enabled thegeneration of high-fidelity images from textual prompts, yet achievingprecise, disentangled control over specific attributes remains a significantchallenge. A fundamental limitation arises because visual differences be-tween images are often far more descriptive and nuanced than what canbe captured through human-crafted text descriptions, which frequentlyfail to convey fine-grained semantic details. To address this, we intro-duce ViDiT (Visual Direction Transfer for Diffusion), a frameworkthat expands the editing vocabulary by capturing latent semantics di-rectly from image-edit pairs. ViDiT learns the underlying transformationby optimizing a single, global, and continuous editing direction from asmall set of “before-and-after” examples. This optimization process trans-fers visual changes into the diffusion model’s conditioning space, allowingfor detailed edits that text alone cannot easily describe. ViDiT operateson a “Learn Once” principle, which completely eliminates the need formodel fine-tuning or expensive per-image optimization during inference.Once learned, these continuous directions enable “Edit Anywhere” capa-bilities, allowing users to apply highly disentangled manipulations, suchas changes in facial features, animal attributes, or artistic styles, to anyimage in a zero-shot manner with granular control over the edit inten-sity. Quantitative and qualitative evaluations demonstrate that ViDiToutperforms existing text-based editing methods in maintaining inputfaithfulness while achieving precise, scalable attribute control.

Abstract:
Tone style transfer for photo retouching aims to adapt thestylistic tone of the reference image to a given content image. However,the lack of high-quality large-scale triplet datasets with stylized groundtruth forces existing methods to rely on self-supervised or proxy objec-tives, which limits model capability. To mitigate this gap, we design adata construction pipeline to build TST100K, a large-scale dataset of100,000 content-reference-stylized triplets. At the core of this pipeline,we train a tone style scorer to ensure strict stylistic consistency for eachtriplet. In addition, existing methods typically extract content and ref-erence features independently and then fuse them in a decoder, whichmay cause semantic loss and lead to inappropriate color transfer and de-graded visual aesthetics. Instead, we propose ICTone, a diffusion-basedframework that performs tone transfer in an in-context manner by jointlyconditioning on both images, leveraging the semantic priors of generativemodels for semantic-aware transfer. Reward feedback learning using thetone style scorer is further incorporated to improve stylistic fidelity andvisual quality. Experiments demonstrate the effectiveness of TST100K,and ICTone achieves state-of-the-art performance on both quantitativemetrics and human evaluations. The project page is available online.

Abstract:
While Multimodal Large Language Models (MLLMs) showremarkable capabilities, their safety alignments are susceptible to jail-break attacks. Existing attack methods typically focus on text-imageinterplay, treating the visual modality as a secondary prompt. This ap-proach underutilizes the unique potential of images to carry complex,contextual information. To address this gap, we propose a new image-centric attack method, Contextual Image Attack (CIA), which employsa multi-agent system to subtly embed harmful queries into seeminglybenign visual contexts using four distinct visualization strategies. Tofurther enhance the attack’s efficacy, the system incorporates contextualelement enhancement and automatic toxicity obfuscation techniques. Ex-perimental results on the MMSafetyBench-tiny dataset show that CIAachieves high toxicity scores of 4.62 and 4.83 against the GPT-4o andQwen2.5-VL-72B models, respectively, with Attack Success Rates (ASR)reaching 86.83% and 91.02%. Our method significantly outperforms priorwork, demonstrating that the visual modality itself is a potent vector forjailbreaking advanced MLLMs. WARNING: This paper may contain ex-amples of harmful content for research purposes.

Abstract:
We present ScaleMoGen, a scale-wise autoregressive frame-work for text-driven human motion generation. Unlike conventional au-toregressive approaches that rely on standard next-token prediction, Scale-MoGen frames motion generation as a coarse-to-fine process. We quantize3D motions into compositional discrete tokens across multiple skeletal-temporal scales of increasing granularity, learning to generate motionby autoregressively predicting next-scale token maps. To maintain struc-tural integrity, our motion tokenizers and quantizers are explicitly de-signed so that discrete tokens at every scale strictly preserve the skeletalhierarchy. Additionally, we employ bitwise quantization and prediction,which efficiently scale up the tokenizer vocabulary to preserve motiondetails and stabilize optimization. Extensive experiments demonstratethat ScaleMoGen achieves state-of-the-art performance, establishing anFID of 0.030 (vs. 0.045 for MoMask) on HumanML3D and a CLIP Scoreof 0.693 (vs. 0.685 for MoMask++) on the SnapMoGen dataset. Further-more, we demonstrate that our skeletal-temporal multi-scale representa-tion naturally facilitates training-free, text-guided motion editing.

Abstract:
Diffusion model advances have enabled powerful text-guidedimage editing, but also raise ethical and legal risks such as deepfakesand unauthorized use. To prevent these risks, adversarial attack-basedimage immunization has emerged as a promising defense against AI-driven semantic manipulation. Yet, most existing approaches requireimage-specific optimization or additional neural networks at inferencetime, hindering scalability and practicality. In this paper, we proposethe first universal adversarial perturbation-based image immunizationframework that generates a single, image-agnostic adversarial perturbationspecifically designed for diffusion-based editing pipelines. Inspired by UAPused in targeted attacks, our method aims to generate a UAP that inducesdiffusion models to misinterpret the input image as a specific semantictarget. Simultaneously, it suppresses original content to misdirect themodel’s attention during editing, thereby effectively blocking unauthorizededits by overwriting the image’s original semantics via the UAP. Extensiveexperiments show that our method, as the first universal immunizationapproach, significantly outperforms several baselines in the UAP setting.Notably, despite the inherent difficulty of universal perturbations, ourmethod achieves competitive or superior performance compared to image-specific methods under a more restricted perturbation budget, while alsoexhibiting strong black-box transferability across diverse diffusion models.

Abstract:
Automated layout generation powers a wide range of designs, such as posters, banners, and user interfaces. However, current single-shot generators overlook the iterative nature of human design and often produce aesthetically subpar compositions. We present i-Design, a unified framework that models layout generation as a progressive process, placing k elements at a time, conditioned on previously rendered partial canvases. Our approach combines Progressive Imitation Learning (PIL) on partial canvases, followed by Progressive Aesthetic Policy Optimization (PAPO), a reinforcement learning algorithm that aligns generation with aesthetic preferences. In PAPO, a frozen vision judge evaluates rendered layout pairs, constructs a directed comparison graph, and computes a global aesthetic consensus to derive trajectory-level rewards reflective of visual appeal rather than spatial accuracy. During training, multiple rollouts are rendered through a non-differentiable Skia engine and scored in real time by the judge to enable adaptive aesthetic learning. Experiments on Crello and WebUI benchmarks demonstrate that i-Design surpasses SoTA methods, achieving a 13% improvement in win rate and a 7% gain in mIoU, producing layouts with superior balance, alignment, and visual harmony. Our findings demonstrate that iterative generation and progressive aesthetic optimization offer a powerful new paradigm for human-aligned graphic layout generation.

Abstract:
Virtual Try-On (VTON) synthesizes realistic images of aperson wearing a target garment, with broad applications in e-commerceand fashion. Diffusion-based dual-UNet methods achieve strong resultsbut double the parameters by dedicating a separate network to garmentconditioning. Spatial concatenation offers a simpler single-network alter-native, yet both UNet- and DiT-based instantiations report that full fine-tuning is ineffective, and the community has settled for attention-onlytraining. We ask: why does full fine-tuning fail, and can this be resolved?Through what is, to our knowledge, the first visualization study of dual-UNet reference network behavior, we identify a unifying insight: garmentconditioning must be decoupled from the denoising process. Spatial con-catenation violates this by embedding the garment within the denoisingtarget, causing three conflicts: guidance leakage, gradient competition,and train-test discrepancy. We derive three design principles to restorethis decoupling and implement them as a pure recipe atop a standardarchitecture with no modification. The resulting model, DeCo-VTON(860M params), achieves single-network state of the art, matching thedual-UNet state of the art at half the cost while being preferred in humanevaluation.

Abstract:
Diffusion models have recently achieved remarkable successin high-fidelity image synthesis, yet their application to visual text gener-ation and editing remains relatively underexplored. Unlike general imagegeneration, visual text tasks demand precise structural regularity andlegibility, which may pose additional challenges for small-scale text andnon-Latin scripts such as Chinese. Existing UNet-based models oftenstruggle to produce clear and coherent text, while DiT-based models,though more expressive, are typically limited to a single task, whichmay lead to redundant training pipelines, inconsistent visual styles, andreduced cross-task generalization. To address these challenges, we pro-pose InnoText, a unified DiT-based framework capable of performingboth text generation and editing within a single model. We introduce aFont Size-Aware Modulation (FSAM) module to enhance representationsacross font scales, a Small-Character Aware Augmentation strategy toimprove fine-grained fidelity, and a Task-Specific Region Weighted Lossfor adaptive optimization. To support training and evaluation, we alsoconstruct a high-quality bilingual (English-Chinese) visual text datasetcovering diverse fonts, sizes, and backgrounds. Experimental results demon-strate that our method achieves superior generation accuracy and editingquality, producing visually appealing and realistic text images.

Abstract:
Unified Multimodal Models (UMMs) excel in text-to-image generation and editing but often entangle multimodal generative reasoning with high-fidelity visual synthesis. We introduce Query-Kontext, a novel approach that bridges a Vision-Language Model (VLM) and a diffusion model via multimodal “kontext” tokens. This design cleanly decouples the complex reasoning delegated to the VLM, including instruction understanding, grounding, and identity preservation, from the high-quality visual rendering executed by the diffusion model. We propose a three-stage progressive training strategy: (1) connecting the VLM to a lightweight diffusion head to activate generative reasoning; (2) scaling to a large, pre-trained diffusion model to enhance visual realism; and (3) incorporating a low-level image encoder for fine-grained instruction tuning. Supported by a comprehensive multi-task dataset, extensive experiments demonstrate that Query-Kontext matches or outperforms state-of-theart task-specific and unified methods across diverse reference-to-image scenarios.

Abstract:
Text-to-image diffusion models are increasingly aligned with human preferences through preference optimization (PO) with learned reward models. We observe that the CLIP-based reward models used in this pipeline encode demographic biases, and that PO amplifies them: after alignment, the demographic distribution of generated images narrows significantly compared to the base model. We show that this degradation is progressive—race entropy drops steadily throughout training— and that existing diversity-preservation methods such as DRIFT and DiverseGRPO do not resolve it, because visual diversity and demographic fairness are distinct properties. To address this, we propose DRPO, which identifies demographic directions in the reward feature space via linear probing and removes them through orthogonal projection before reward computation. DRPO requires no demographic annotations during training and is compatible with any PO method. Experiments on Stable Diffusion and SDXL with six baselines demonstrate that DRPO maintains generation quality while substantially restoring the demographic balance lost during alignment.

Abstract:
Makeup transfer applies a reference cosmetic style to a source face while preserving its identity and geometry. However, this task is severely hindered by the lack of real paired training data. Current methods rely on either weak priors or synthetic pseudo-targets from largescale editing models. These paradigms provide suboptimal guidance, often leading to degraded fine-grained details, synthetic artifacts, and identity drift. To this end, we propose Anchoring on Reality Makeup Transfer (ART), a two-stage framework with a reality-anchored refinement cycle. In Stage I, the model is initialized with pseudo-targets to establish basic semantic alignment and global makeup placement. Crucially, Stage II shifts supervision from pseudo-targets to the real reference, reconstructing it from its bare-skin counterpart through a differentiable cycle that penalizes any omitted detail and overrides synthetic artifacts. Furthermore, we introduce MakeupFaces2K (MF2K), the first 2K-resolution in-the-wild makeup portrait dataset comprising 8,573 images. Extensive experiments demonstrate that our method achieves superior makeup fidelity, strong background stability, and robust identity preservation, especially for complex makeup styles.

Abstract:
We introduce DiverseVAR, a test-time framework that enhances the output diversity of text-conditioned visual autoregressive models (VAR) without additional training or substantial computational overhead. While VAR models have recently emerged as strong competitors of diffusion and flow models for image and video generation, they suffer from a critical diversity limitation: even simple prompts often produce nearly identical outputs. This issue has largely gone unnoticed amid the field’s predominant focus on image quality. We address this limitation in two stages. First, we systematically explore diversity enhancement techniques by injecting noise into different components of VAR at test time, finding that noise injection into the text embedding yields the best diversity gains. However, this comes at the cost of sharp degradation in image quality. To recover quality, we propose scale-travel: a latent refinement technique that leverages a multi-scale autoencoder to extract coarsescale tokens to resume generation from intermediate stages. Extensive experiments demonstrate that combining text-embedding noise injection with scale-travel refinement substantially improves diversity while minimizing quality degradation, advancing the diversity-quality Pareto front. Project page: https://diverse-var.github.io/

Abstract:
Diffusion models are powerful deep generative models, butunlike classical models, they lack an explicit low-dimensional latent spacethat parameterizes the data manifold. This absence makes it difficultto perform manifold-aware operations, such as geometrically faithful in-terpolation or conditional guidance that respects the learned manifold.We propose a training-free Riemannian metric on the noise space, de-rived from the Jacobian of the score function. The key insight is thatthe spectral structure of this Jacobian separates tangent and normaldirections of the data manifold; our metric leverages this separation to en-courage paths to stay tangential to the manifold rather than drift towardhigh-density regions. To validate that our metric faithfully captures themanifold geometry, we examine it from two complementary angles. First,geodesics under our metric yield perceptually more natural interpolationsthan existing methods on synthetic, image, and video frame datasets.Second, the tangent–normal decomposition induced by our metric pre-vents classifier-free guidance from deviating off the manifold, improvinggeneration quality while preserving text-image alignment.

Abstract:
Thermal-to-visible face translation presents fundamental challenges including geometric discontinuities, semantic attribute mismatches, and identity degradation. We propose MTVDiff, a novel multimodal latent diffusion framework that synergistically integrates depth and textual information to address these limitations while preserving identity characteristics. The MTVDiff framework presents three core technical contributions: (1) a Dual-Branch Cross-Attention Fusion (DBCAF) module for multi-scale thermal-depth feature extraction and fusion; (2) a Gated Text-to-Visual Feature Alignment mechanism for semantically-guided generation; and (3) Spatial Feature Transformations (SFT) for adaptive multimodal prior integration. Extensive experiments on the MCXFace and SpeakingFaces datasets demonstrate that our multimodal approach significantly outperforms existing GAN-based and diffusion-based approaches across multiple metrics, achieving substantial improvements in both image quality and face verification performance, with FID reductions of up to 48.3% and Rank-1 accuracy improvements of up to 8.9%. Our work provides a robust solution for face recognition systems operating under varying illumination conditions and advances the state-of-theart in cross-spectral facial image translation through effective multimodal integration.

Abstract:
Achieving precise, versatile whole-body character control inphysics-based animation remains challenging. Recent diffusion-based poli-cies generate rich and expressive motions but typically rely on gradient-based test-time guidance to satisfy task objectives, which is slow andcan reduce robustness. We introduce NaP-Control (Navigating Diffu-sion Prior for Versatile and Fast Character Control), abbreviated asNaP. Our method uses reinforcement learning to manipulate the latentnoise of a task-agnostic diffusion policy prior, steering it toward task-specific behaviors for fast, robust control with high motion fidelity. Incontrast to methods that rely solely on offline training, NaP interactswith the environment during training to correct motions and optimizetask rewards, improving success rates and enabling adaptation to chal-lenging scenarios. By directly predicting task-optimized diffusion noise,NaP eliminates iterative guidance during denoising and enables efficientinference. Experiments show that NaP attains higher success rates andfaster inference while preserving natural motion across diverse tasks.

Abstract:
Color transfer aims to align the color distribution of a sourceimage with that of a reference image while preserving structural andsemantic consistency. However, existing methods often suffer from inac-curate global mapping, semantic misalignment, and visual artifacts. Toaddress these issues, we propose ColorFM, an optimization-to-learningframework. ColorFM connects online optimization to offline inference byreformulating color transfer as the transport of pixel distributions alongvelocity fields via Flow Matching. Specifically, we introduce ColorFM-O, an instance-specific optimization scheme that fits the velocity fieldthrough hierarchical color coupling guided by semantic priors. By nu-merically integrating the induced flow trajectories, ColorFM-O producesprecise and semantically consistent color transfer results, while generat-ing high-quality paired data as pseudo-supervision. Building upon this,we design ColorFM-L, an efficient feed-forward model trained on thegenerated pairs. Through implicit state modeling, ColorFM-L extractsdeep semantic features to predict flow parameters for bidirectional lin-earized transport, ensuring accurate color transfer. Extensive experi-ments demonstrate that ColorFM-L outperforms state-of-the-art meth-ods in visual quality, structural fidelity, and semantic consistency, suc-cessfully combining the accuracy of optimization with the speed of feed-forward inference.

Abstract:
Consistency distillation has significantly accelerated diffusion-model inference, but its sampling dynamics remain underexplored. Wereveal an asymmetry: although Logit-Normal sampling priors work well forstandard iterative generation, consistency distillation exhibits a differentdifficulty profile (e.g., U-shaped), with optimization bottlenecks concen-trated at the boundary stages rather than intermediate steps. To addressthe limitations of static sampling under evolving learning demands, wepropose Curvature-Adaptive Consistency Flow Matching (CACFM). Byformulating distillation as a dynamic decision process, CACFM usesa lightweight reinforcement learning agent to probe Probability FlowODE trajectories and construct an efficiency-oriented curriculum thatprioritizes critical regions without manual scheduling. Combined withFlow-adapted DMD and adversarial consistency objectives, our RL-basedscheduler achieves state-of-the-art results on large-scale models suchas FLUX and SDXL, mitigating structural deformities and preservinghigh-frequency details in extreme few-step regimes.

Abstract:
Stereo image compression fundamentally relies on exploitingcross-view redundancies. We observe that the performance of conven-tional correspondence-driven methodologies degrades severely in out-of-view (OOV) regions or when reference features are heavily quantized,resulting in blurred textures and prediction failure. In this paper, we pro-pose a novel generative stereo image compression framework driven bya controllable generative reference. Instead of relying on finding explicitcorrespondences from a degraded reference, we leverage the powerful pri-ors of a pre-trained diffusion model to synthesize a high-quality target-view reference. To bridge the gap between stochastic generative processesand strict stereo consistency, we introduce Joint Semantic-Spatial Con-trol for reference generation. This mechanism synergistically steers thesynthesis using a robust geometric anchor from the compressed left viewand an ultra-compact semantic condition extracted from the target view.Furthermore, to minimize the impact of structural inconsistency duringreference generation on the quality of the reconstructed image, we de-sign a Reliability-Aware Gating module. By dynamically evaluating thesynthesized prior in the feature space, this module adaptively regulatesgenerative information flow within the entropy model to improve thereconstructed image quality. Extensive experiments on standard bench-marks demonstrate that our framework achieves state-of-the-art rate-distortion performance, delivering remarkable bitrate savings and highvisual quality at low bitrates.

Abstract:
Recent advances in generative medical models are often con-strained by modality-specific scenarios that hinder the integration ofcomplementary evidence, such as imaging, pathology, and clinical notes.This fragmentation limits their development to true foundation modelsthat empower medical AI agents to learn from and predict across thefull spectrum of biomedical knowledge. To address these challenges, wepropose MeDiM, the first medical discrete diffusion model that learnsshared distributions across different medical modalities without requiringmodality-specific components. MeDiM unifies multiple generative tasks:it flexibly translates between images and text or jointly produces im-age–report pairs across domains in response to user prompts. It builds ona discrete diffusion framework that unifies vision and language represen-tations by modeling their shared probabilistic distribution. To empowerthe diffusion process to support unified and versatile medical generation,we employ a multimodal large language model (MLLM) as the diffusionbackbone, leveraging its rich prior knowledge and cross-modal reason-ing abilities. Because MLLMs are trained with causal (autoregressive)masking while diffusion denoising benefits from bidirectional context,MeDiM introduces two adaptation designs: 1) removing the causal at-tention mask to enable a fully bidirectional information flow essentialfor mutual alignment, and 2) injecting continuous timestep embeddingsto make the MLLM aware of the diffusion steps. Extensive experimentsvalidate MeDiM as a unified foundation model capable of high-fidelitymedical generation across various modalities, including medical imagegeneration (16.60 FID on MIMIC-CXR; 24.19 FID on PathGen) and re-port generation (0.2650 METEOR on MIMIC-CXR; 0.2580 METEOR onPathGen). In addition, the jointly generated medical image-report pairsimprove the downstream task performance (+6.43% BLEU-1, +18.57%BLEU-2, +31.58% BLEU-3, and +4.80% METEOR in PathGen), en-abling the use of multimodal inputs and the production of coherent,clinically grounded outputs.

Abstract:
Conditional diffusion models frequently suffer from language-image misalignments. Due to the ambiguity of intermediate noise cor-rupted latents, assessing prompt adherence currently requires complet-ing the entire sampling trajectory. This late-stage evaluation incurs evenhigher computational costs during test-time scaling strategies, such asBest-of-N (BoN) sampling, as all misaligned trajectories must finish gen-eration before being discarded. To tackle this, we propose NoisyCLIP, anoise-aware twin-tower model that enables early language-to-latent align-ment estimation. By learning a vision encoder on noise-corrupted latents,we allow the model to "see" through the ambiguity of intermediate diffu-sion steps. To facilitate this training, we investigate noise-data augmen-tation sampling strategies and introduce two new benchmark datasets:Noisy-Conceptual-Captions and Noisy-GenAI-Bench. When applied asan early-stopping criterion for BoN, NoisyCLIP at half cost matches orbeats frozen CLIP at full cost. Ultimately, this transforms alignment as-sessment from an expensive final check into a continuous monitoring tool,drastically reducing compute costs without sacrificing semantic fidelity.

Abstract:
Despite decades of work, surveillance still struggles in searching and reasoning about specific targets across long, multi-camera videos. Existing methods—tracking, retrieval, and video LLMs—require heavy manual filtering, capture only shallow attributes, and fail at temporal understanding. Prior benchmarks are also limited to basic retrieval and question answering, without addressing real-world challenges that often involve multimodal queries and temporal grounding (e.g., “When did this person join the fight?” with the person’s image). To address this gap, we introduce ForeSeaQA, a new benchmark specifically designed for video QA with image-and-text queries and timestamped annotations of key events. The dataset consists of long-horizon surveillance footage paired with diverse multimodal questions, enabling systematic evaluation of retrieval, temporal grounding, and multimodal reasoning in realistic forensic conditions. Not limited to this benchmark, we propose ForeSea, an AI forensic search system with a 3-stage, plug-and-play pipeline. (1) A tracking module filters irrelevant footage; (2) a multimodal embedding module indexes the remaining clips; and (3) during inference, the system retrieves top-K candidate clips for a video LLM to answer queries and localize events. On ForeSeaQA benchmark, ForeSea improves accuracy by 3.1 points and temporal IoU by 10.1 points over prior retrieval-augmented baselines. To our knowledge, ForeSeaQA is the first benchmark to support complex multimodal queries with precise temporal grounding, and ForeSea is the first VideoRAG system built to excel in this setting.

Abstract:
Text Image Machine Translation (TIMT) aims to translatetext embedded in images in the source-language into target-language,requiring synergistic integration of visual perception and linguistic un-derstanding. Existing TIMT methods, whether cascaded pipelines orend-to-end multimodal large language models (MLLMs), struggle withhigh-resolution text-rich images due to cluttered layouts, diverse fonts,and non-textual distractions, resulting in text omission, semantic drift,and contextual inconsistency. To address these challenges, we proposeGLoTran, a global-local dual visual perception framework for MLLM-based TIMT. GLoTran integrates a low-resolution global image withmulti-scale region-level text image slices through pre-local region trans-lation replay and an instruction-guided alignment strategy, conditioningMLLMs to maintain scene-level contextual consistency while faithfullycapturing fine-grained textual details. Moreover, to realize this dual-perception paradigm, we construct GLoD, a large-scale text-rich TIMTdataset comprising 510K high-resolution global-local image-text pairscovering diverse real-world scenarios. Extensive experiments demonstratethat GLoTran substantially improves translation completeness and ac-curacy over state-of-the-art MLLMs.

Abstract:
Natural-language queries about 3D environments become actionable when responses are verifiable and metric. Verifiability requires explicit grounding to the referred 3D region, while metric answers report physical measurements in real-world units (e.g., size, thickness, clearance, and distance). Existing 3D large multimodal models (LMMs) approaches remain limited: conversational systems typically respond without explicit 3D grounding, while 3D grounding models are not designed for interactive, metric-aware dialogue. In this paper, we present Ground3DLMM, a unified model that takes a point cloud and an optional RGB image as input and supports 3D spatial conversation with (i) pointgrounded responses and (ii) metric numeric outputs at both object and part granularity, including multi-object queries. To evaluate this intersection of grounding and measurement, we define the 3D Grounded Measurement task, which requires predicting the referred 3D region and the corresponding metric quantities in real-world units. We introduce a large-scale dataset built on ScanNet and ScanNet++ datasets with dense object and part annotations and roughly 2.5M question-answer pairs spanning eight tasks, along with a manually verified test set. Extensive experiments on multiple datasets and tasks show that our proposed Ground3D-LMM model provides a strong baseline for grounded, metric-aware 3D conversational understanding. Our dataset and model are publicly available.

Abstract:
Touch supplies the physical grounding needed to perceive intrinsic material properties, such as friction and compliance, that vision alone often cannot resolve. Recent efforts for equipping multimodal LLMs with this tactile sense, however, expose a zero-sum trade-off: the limited parameter budget of compact models forces a choice between acquiring the new sensory modality and preserving the established visionlanguage reasoning. We present Splash, a mask-isolated tactile alignment learning framework for MLLMs. Splash quantifies the significance of each pretrained parameter, and partitions the parameter space into a dormant and critical subspace. While the frozen critical subspace acts as a stable anchor to safeguard general visual knowledge, Splash updates the isolated dormant subspace to internalize tactile alignment towards LLMs. This selective, non-destructive expansion effectively prevents catastrophic forgetting and ensures non-destructive modality expansion. Extensive experiments show that Splash effectively achieves tactile reasoning without additional inference overhead in the LLM part, demonstrating state-of-the-art performance on visuo-tactile benchmarks, including SSVTP, TVL, and TacQuad, while preserving its original generalpurpose capabilities.

Abstract:
Document comprehension is a challenging yet impactful taskfor Multimodal Large Language Models, especially as these systems seegrowing adoption in real-world, human-centric applications. However,this adoption is limited for low-resource languages such as Bangla dueto the scarcity of high-quality annotated data. To address this gap, weintroduce BaFCo, a benchmark dataset for Bangla form comprehensionwith a focus on Document Layout Analysis (DLA) and Key InformationExtraction (KIE). BaFCo curates 200 multi-page complex Bangladeshigovernment forms, sourced from across diverse sectors including agricul-ture, education, banking, and land management. To accurately capturethe structural and contextual complexity of these forms, we define a fine-grained annotation schema comprising 26 types of form entities, alongwith a separate coarse form entity set consisting of 5 types. We evaluatethe latest MLLMs from the ChatGPT, Gemini, Claude, Qwen, and Kimiseries using zero-shot and chain-of-thought prompts under both low andhigh reasoning setups. Our results reveal limitations in current MLLMs’ability in comprehending Bangla forms, particularly in accurately local-izing highly granular form entities. Our dataset and code is available at:https://huggingface.co/datasets/Mausul/bafco

Abstract:
GUI agents powered by vision-language models show strongpotential for automating digital tasks, yet frequently fail in long-horizonscenarios due to the absence of step-level verification. Existing process re-ward models verify actions through textual reasoning alone, missing thevisual nature of GUI state changes. We introduce VisCritic, a visualprocess reward framework that verifies agent actions by directly com-paring pre-action and post-action screenshots in visual feature space.VisCritic employs a Siamese vision transformer to extract change-awarerepresentations, coupled with an Action-Aware Critic Head that jointlyevaluates action success, task progress, and error type. A critic-trainingdata construction pipeline generates weakly supervised samples from ex-isting trajectories without additional human labels for critic training.Experiments and offline analyses across five benchmarks demonstratethat VisCritic serves as a plug-and-play enhancement for diverse GUIagents, generally improving benchmark metrics while providing visualdiagnostic cues.

Abstract:
We propose MotionAtlas, a system for detailed captioningof motion-centric videos, comprising (1) a dedicated human-annotatedbenchmark, (2) a scalable, high-quality pipeline to construct trainingsamples, and (3) a family of powerful Video-MLLMs. Unlike conventionalglobal motion captioning datasets, we focus on region-aware motion cap-tioning: given a video and a spatiotemporal mask, the model generatesprecise descriptions of motion within the target region, thereby alleviatingvisual clutter and motion entanglement and enabling reliable, quantiﬁ-able evaluation. Concretely, we ﬁrst build MotionAtlas-Bench, a compre-hensive benchmark comprising 2,073 multiple-choice questions, meticu-lously annotated for a curated set of high-quality, motion-centric videos,to evaluate ﬁne-grained motion understanding of the objects in question.Second, we design a rigorous and scalable data pipeline that leveragesself-bootstrap reﬁnement to suppress ﬁne-grained hallucinations, yield-ing 159k high-quality motion captioning data. Third, we design a tai-lored training data composition strategy, which achieves consistent andsubstantial performance gains across diverse baseline Video-MLLMs, in-cluding Molmo2 and Qwen3-VL. For instance, MotionAtlas-4B surpassesQwen3-VL-4B by an average of 5.2 percentage points across general mo-tion benchmarks. The benchmark, dataset, and code have been released.

Abstract:
Interleaved text-and-image generation represents a signifi-cant frontier for Multimodal Large Language Models (MLLMs), offeringa more intuitive way to convey complex information. Current paradigmsrely on either image generation or retrieval augmentation, yet they typi-cally treat the two as mutually exclusive paths, failing to unify factualitywith creativity. We argue that the next milestone in this field is AgenticTool Planning, where the model serves as a central controller that au-tonomously determines when, where, and which tools to invoke to pro-duce interleaved responses for visual-critical queries. To systematicallyevaluate this paradigm, we introduce ATP-Bench, a novel benchmarkcomprising 7,702 QA pairs (including 1,592 VQA pairs) across eight cat-egories and 25 visual-critical intents, featuring human-verified queriesand ground truths. Furthermore, to evaluate agentic planning indepen-dent of end-to-end execution and changing tool backends, we proposea Multi-Agent MLLM-as-a-Judge (MAM) system. MAM evaluates tool-call precision, identifies missed opportunities for tool use, and assessesoverall response quality without requiring ground-truth references. Ourextensive experiments on 10 state-of-the-art MLLMs reveal that modelsstruggle with coherent interleaved planning and exhibit significant varia-tions in tool-use behavior, highlighting substantial room for improvementand providing actionable guidance for advancing interleaved generation.

Abstract:
At the heart of composed visual data retrieval is the fusionof a reference visual input and a textual modification into a single query.While current state-of-the-art methods utilize multimodal large languagemodels for this fusion, their complexity introduces prohibitive query-time latency, limiting their scalability. We instead revisit the efficacy ofsimple linear interpolation within an embedding space, and introduceSRAIN, the first framework that dynamically predicts query-specific in-terpolation weights. The key challenge lies in the fact that the qualityof an interpolation weight should be measured by the interpolated em-bedding’s discriminability from negatives as well as its proximity to truetargets; this makes collecting and predicting optimal weights intractable.We overcome this bottleneck through two key innovations: batch-wiserank-aware weight estimation during training, and a compact memorybank that synthesizes hard negatives during inference. SRAIN achievesthe best in composed video retrieval and matches the current state of theart in composed image retrieval, all while substantially reducing query-time latency compared to MLLM-based alternatives.

Abstract:
Conventional vision-language models are largely object-centric, focusing on detecting and describing individual entities. In safety-critical X-ray baggage screening, however, threat often emerges not from a single object but from the functional compatibility of spatially dispersed components, such as batteries, detonators, and explosive charges. We formalize this setting as compositional threat reasoning, where risk is modeled as a relational property of grounded regions rather than an independent detection outcome. We introduce Falcon, a multimodal framework that abstracts segmentation-aware region features into a structured safety state capturing component presence, pairwise functional compatibility, and scene-level risk. This structured representation is injected into the language model as an explicit intermediate interface, encouraging relationally consistent and safety-aware reasoning. To evaluate this problem, we present Falcon-X, a benchmark that unifies dense grounding with structured supervision over component completeness and risk inference in cluttered X-ray imagery. Experiments show that while existing multimodal models adapt to appearance, they struggle with compositional safety reasoning. Falcon improves functional grounding and produces more coherent threat assessments, establishing compositional safety reasoning as a distinct evaluation paradigm for multimodal systems.

Abstract:
Recent advances in Reinforcement Learning with VerifiableRewards (RLVR) for multimodal large language models (MLLMs) havemainly focused on improving final answer correctness and strengthen-ing visual grounding. However, a critical bottleneck remains: althoughmodels can attend to relevant visual regions, they often fail to effectivelyincorporate visual evidence into subsequent reasoning, leading to reason-ing chains that are weakly grounded in visual facts. To address this issue,we propose Trajectory-Guided Reinforcement Learning (TGRL), whichguides the policy model to integrate visual evidence into fine-grained rea-soning processes using expert reasoning trajectories from stronger mod-els. We further introduce token-level reweighting and trajectory filteringto ensure stable and effective policy optimization. Extensive experimentson multiple multimodal reasoning benchmarks demonstrate that TGRLconsistently improves reasoning performance and effectively bridges thegap between visual perception and logical reasoning.

Abstract:
Visual markups such as highlights, underlines, and bold textare common in table-centric documents. Although multimodal large lan-guage models (MLLMs) have made substantial progress in document un-derstanding, their ability to treat such cues as explicit logical directivesremains under-explored. More importantly, existing evaluations cannotdistinguish whether a model fails to see the markup or fails to reason withit. This creates a key blind spot in assessing markup-conditioned behaviorover tables. To address this gap, we introduce HighlightBench, a diagnos-tic benchmark for markup-driven table understanding that decomposesevaluation into five task families: Markup Grounding, Constrained Re-trieval, Local Relations, Aggregation & Comparison, and Consistency &Missingness. We further provide a reference pipeline that makes interme-diate decisions explicit, enabling reproducible baselines and finer-grainedattribution of errors along the perception-to-execution chain. Experi-ments show that even strong models remain unstable when visual cuesmust be consistently aligned with symbolic reasoning under structuredoutput constraints.

Abstract:
Pixel-space reasoning enables Vision-Language Models to perform active perception: instead of answering from a single global view, the model can zoom into high-resolution regions to gather finegrained evidence. This mirrors human visual problem solving, which involves broad exploration (finding relevant cues) and local verification (confirming details). However, most pixel-space reasoning systems are trained with Group Relative Policy Optimization (GRPO), whose groupwise advantage normalization directly compares sampled rollouts that follow different perception strategies. Rollouts with different zoom depths and different visual intents (explore vs. verify) are normalized together, making exploratory multi-zoom behavior appear disadvantageous: the multi-zoom strategies newly discovered during training typically yield lower mean rewards than already-mastered single-zoom or no-zoom strategies. This leads to visual laziness: the policy does not explore active multi-zoom perception strategies and collapses into a zoom-averse state. To address this, we propose Visual-Intent Stratified GRPO (VIS-GRPO), a drop-in replacement for GRPO that restores fair learning signals for active perception. VIS-GRPO computes advantages only among strategically comparable rollouts by stratifying trajectories along (i) zoom depth and (ii) visual intent, separating broad search over diverse regions from local verification over overlapping regions. This alignment prevents exploratory trajectories from being overshadowed and encourages the learning of active perception. Extensive experiments demonstrate that VIS-GRPO enables effective multi-zoom active perception strategies and consistently improves performance across challenging visual understanding benchmarks like HR-Bench and MME-RealWorld.

Abstract:
Neural rendering techniques allow for accurate reconstruc-tion of the geometry and color appearance of 3D scenes. Some methodshave extended their use to additional imaging modalities, such as multi-spectral, infrared, or polarimetric data. However, all of these approachesrequire expensive sensors and calibrated setups to capture new multi-modal frames for each new scene. We propose Spectral and PolarimetricImplicit Learned Representation (SPoILeR), a novel method to obtainmulti-view consistent renderings of unconventional modalities for sceneswhere either only RGB frames or very few of the additional modali-ties are available. Thanks to a multimodal pre-training phase, the modellearns the mutual correlation between different modalities. This step al-lows predicting accurate renderings of unconventional modalities duringa fine-tuning phase supervised only by RGB images. Experimental resultsshow that the approach can accurately render infrared, polarimetric, andmultispectral frames for scenes where no input sample captured by thesetypes of sensors is provided.

Abstract:
Credentials, such as corporate qualifications and personalidentification, serve as common forms of evidentiary materials. The forgeryof such credentials poses significant risks to security, making its detectiona long-standing area of widespread concern. With the advancement of AI-Generated Content technologies, forgery techniques can now largely pre-serve visual characteristics, making purely vision-based detection meth-ods insufficient to meet practical demands. Instead, a growing number offorgeries necessitate cognitive reasoning for detection. While the devel-opment of Multimodal Large Language Models (MLLMs) presents newopportunities for complex credential forgery detection, the scarcity ofdatasets and benchmarks severely impedes progress in this critical do-main. To bridge this gap, we introduce CogniCred — the first large-scale,multimodal dataset built upon complex, real-world credentials with care-fully crafted cognitive-level forgeries. Along with this dataset, we proposea dedicated benchmark, namely CogniCredBench, that evaluates modelperformance across five distinct types of cognitive forgeries. We conductcomprehensive evaluations across 21 mainstream MLLMs, revealing sub-stantial performance disparities and a strong bias toward text-based rea-soning, highlighting that current models still fall short in balanced multi-modal reasoning for cognitive forgery detection. Our datasets are publiclyavailable at https://huggingface.co/datasets/sjs0606/CogniCred.

Abstract:
Recent Vision-Language Models (VLMs) have achieved re-markable success in visual understanding, driven by the growing avail-ability of high-quality image-text pairs. However, the performance ofVLMs often degrades in the video domain due to the essential needfor temporal understanding and the scarcity of large-scale annotatedvideo data. In this work, we focus on marine video understanding, whichbrings further challenges: first, it requires substantial domain expertise;and video VLMs usually struggle with localizing and interpreting criti-cal information from marine videos, as the informative events are typ-ically sparse, unpredictable, and unevenly distributed. To address thesechallenges, we carefully curate the first event-centric marine video un-derstanding dataset called MarineEVT, which features 20K multi-task,video-level visual question-answering pairs spanning multiple dimensionsof marine understanding and analysis. Meanwhile, based on MarineEVT,we decompose marine video understanding as an Event-centric VisualTool-integrated Reasoning process (EVT-R1 for short), where we lever-age powerful visual tools to drive the model to localize and interpretcritical information aligned with visual questions and human intent. Todemonstrate its effectiveness, we compare EVT-R1 against 11 SOTAVLMs in different settings. EVT-R1 outperforms the top open-sourceand top commercial models by 5.22 and 11.09, respectively. MarineEVTand EVT-R1 lay the foundation for ecological discovery and marine ed-ucation, fostering the development of VLMs capable of interpreting ma-rine dynamics, reasoning about ecological interactions, and supportingsustainable ocean video understanding and analysis.

Abstract:
Precise spatial understanding in Earth Observation is essen-tial for translating raw aerial imagery into actionable insights for criti-cal applications like urban planning, environmental monitoring and dis-aster management. However, Multimodal Large Language Models ex-hibit critical deficiencies in fine-grained spatial understanding withinRemote Sensing, primarily due to a reliance on limited or repurposedlegacy datasets. To bridge this gap, we introduce a large-scale datasetgrounded in verifiable cadastral vector data, comprising 3.8 million an-notated objects across 510k high-resolution images with 135 granularsemantic categories. We validate this resource through a comprehensiveinstruction-tuning benchmark spanning seven spatial grounding tasks.Our evaluation establishes a robust baseline using a standard LLaVAarchitecture. We show that while current RS-specialized and commer-cial models (e.g., Gemini) struggle in zero-shot settings, high-fidelity su-pervision effectively bridges this gap, enabling standard architecturesto master fine-grained spatial grounding without complex architecturalmodifications. Data, pretrained model and code are available at: https://huggingface.co/datasets/RogerFerrod/GroundSet

Abstract:
While Unified Multimodal Models (UMMs) show remarkablereasoning capabilities, their spatial intelligence remains limited to pas-sive 2D Question-Answering (QA). In this paper, we argue that true spa-tial intelligence demands active construction: not only recognizing a 3Dstructure in pixel space, but also building and modifying it. We introduceMindBlock, a benchmark that challenges models’ active generative con-struction in pixel space across two primary axes: Spatial Assembly,which evaluates step-by-step compositional and causal reasoning, andSpatial Structure, which probes spatial equivariance through localsub-component rotations and global viewpoint transformations. To movebeyond pixel-level metrics, we propose 3DGS-Eval, a novel validationprotocol using 3D Gaussian Splatting to reconstruct implicit scenes frommodel-generated multi-view images. This allows us to quantify structuralconsistency, verifying for the first time whether a model’s generative out-put admits a coherent internal 3D world model. Furthermore, we con-duct a deep-dive diagnostic analysis into the representational groundingof spatial logic, disentangling whether structural consistency relies ontextual Chain-of-Thought (CoT) as a symbolic scaffold, or emerges as anative spatial intuition within the generative latent space. Our findingsreveal a significant “perception-execution” gap: while current models cor-rectly identify the intended spatial state, they fail at active construction.They struggle to maintain spatial equivariance without explicit symbolicscaffolding. MindBlock provides a rigorous foundation for the next gen-eration of embodied, physically-grounded multimodal AI.

Abstract:
Multimodal large language models often generate reasoningchains containing subtle errors that lead to incorrect answers. Currentverification approaches have notable limitations. Existing approaches ei-ther require expensive labelled supervision with inconsistent cross-taskperformance or aggregate scores from multiple sources by simple ag-gregations, missing a key insight: when these scores disagree, that dis-agreement itself carries important information about whether a reasoningstep is truly valid or not. We formalise this as a coupled scoring prob-lem among disparate, frozen verifiers, interpretable as a coordinationgame with a unique closed-form equilibrium where agreement signalsvalid steps while disagreement reveals instability. Towards this end, wepropose a training-free domain-agnostic step-wise verification approachwe call VERDICT: VERification via Disagreement-Informed CoupledThresholding. To our knowledge, VERDICT is the first training-freeverifier that makes the structure of cross-modal disagreement explicit andactionable. It computes consensus scores through a closed-form solution,enabling both disagreement-aware filtering and stability-conscious rank-ing of reasoning steps. Evaluated across six benchmarks, VERDICTconsistently improves over the base model by up to +5.95%, and per-forms competitively with domain-specific critics that demand extensivesupervision, demonstrating that cross-modal agreement provides robustverification signals without task-specific adaptation.

Abstract:
Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. Despite inheriting strong reasoning capabilities from LLMs, they remain prone to hallucinations that contradict their visual inputs. Mechanistic studies indicate that this weakness stems from visual laziness: MLLMs encode the correct visual evidence internally, but overly rely on strong language priors during response. Existing alignment methods, such as direct preference optimization, primarily optimize outcome-level rewards based on text. This introduces an optimization bias toward linguistic shortcuts, leading to responses that often contradict the visual evidence. To address this, we propose Visual Information Gain In aLignment (VIGIL), a reinforcement-learning (RL) post-training framework that shifts the focus from numerical reward fitting to causal visual grounding. VIGIL introduces a geometric constraint that explicitly maximizes the mutual information between the visual input and the generated response. We achieve this by penalizing “blind confidence” instances where the model remains improperly certain even when textual-visual attention is masked to create a counterfactual blind state. Extensive experiments show that VIGIL consistently outperforms recent alignment methods across hallucination and reasoning benchmarks without compromising text-only capabilities. Our approach matches the full-data performance of state-of-the-art methods using only 25% of the preference data and even demonstrates emergent spatial grounding capabilities without explicit bounding box supervision.

Abstract:
Modern multimodal large language models (MLLMs) achieve impressive results on vision-language benchmarks, yet existing evaluations judge only final answers, making shortcuts indistinguishable from genuine understanding. We introduce CRYSTAL (Clear Reasoning via Yielded Steps, Traceability and Logic), a diagnostic benchmark with 6,372 instances that evaluates multimodal reasoning through verifiable intermediate steps. We propose two complementary metrics: Match F1, which scores step-level precision and recall via semantic similarity matching, and Ordered Match F1, which further penalizes disordered reasoning chains. References are constructed through a Delphi-inspired pipeline where four independent MLLMs generate trajectories, aggregated via semantic clustering and validated through human quality gates. Evaluation of 20 MLLMs, including commercial frontier systems not used during benchmark construction, reveals systematic failures invisible to accuracy: universal cherry-picking (precision far exceeds recall), non-monotonic scaling trade-offs, and disordered reasoning, where even the strongest reasoners preserve under 60% of matched steps in correct order. Beyond evaluation, we propose the Causal Process Reward (CPR), a multiplicative reward that couples answer correctness with step-level alignment, and CPR-Curriculum, which progressively increases reasoning difficulty during training. CPR-Curriculum achieves +32% Match F1 via GRPO where additive reward strategies fail, improving reasoning without manual step annotation.

Abstract:
Advanced Large Multimodal Models (LMMs) have demonstrated impressive performance in K-12 reasoning tasks, exhibiting great promise as intelligent tutors. Realizing this potential requires models to navigate real-world examinations effectively, yet most existing benchmarks fail to capture the complexity of authentic testing environments. Specifically, most datasets are static, prone to data contamination, and are often confined to restricted modalities, disciplines, and evaluation criteria. To address these issues, we introduce LiveK12Bench, a dynamic, holistic, multi-disciplinary benchmark designed to evaluate the reasoning abilities of LMMs in realistic examination scenarios. LiveK12Bench comprises 2K+ verified questions spanning Mathematics, Physics, Chemistry, and Biology, sourced from the latest real-world exam papers and designed to grow over time. Our framework features several core innovations: 1) featuring an automated pipeline that continuously ingests and parses latest examination papers to mitigate data leakage; and 2) proposing a novel ’Mock Exam’ evaluation scheme, which assesses the ability to complete end-to-end exams autonomously with accurate and efficient reasoning paths. Extensive experiments on 12 LMMs reveal that advanced models suffer substantial performance degradation under exam-realistic constraints: GPT-5’s score drops from 79 to 53 (out of 100) when process rigor and efficiency are jointly evaluated. Our findings expose critical vulnerabilities, such as sensitivity to complex visual layouts, highlighting the gap between idealized reasoning capabilities and true educational readiness. Both code and dataset are publicly available.

Abstract:
3D sequential affordance reasoning requires a model to locate a logical series of functional parts based on language instructions. Current Multimodal Large Language Models (MLLMs) tackle this by generating a special token to guide the final mask prediction. However, compressing complex reasoning into a single token loses crucial low-level structural details. This leads to severe geometric ambiguity, causing the predicted masks to have inaccurate boundaries. To solve this, we propose the Geo-DPO framework, which addresses the ambiguity from two directions. First, we introduce the Hierarchical Geometry Adapter (HGA) to structurally inject multi-scale 3D physical features back into the token. Second, we propose Contrastive Affordance Preference Optimization (CAPO), a reward-based training objective that forces the segmentation to align with real geometric boundaries. Extensive experiments on the standard SeqAfford dataset demonstrate that our method achieves state-of-the-art performance, generating highly precise masks and effectively resolving geometric ambiguity. Code is available here.

Abstract:
Visual token pruning is a crucial strategy for accelerating VLMs by compressing redundant image patches, yet existing methods often fail to preserve critical cues under dense instructions and fine-grained queries. In this paper, we investigate this failure and identify two underlying bottlenecks: the widespread dispersion of textual noise that corrupts dense cross-modal scoring, and the feature fragmentation inherent to standard token selection. To address these issues, we propose Entropy-Aware Dense Pruning (EADP), a framework that reformulates pruning as a structured compression problem. EADP first leverages statistical entropy to quantify and filter out textual noise, yielding a robust, fine-grained instruction relevance score. Subsequently, instead of naive Top-K selection, EADP casts token selection as a submodular maximization problem with a spatial prior, explicitly ensuring a holistic and non-redundant visual representation. Extensive experiments demonstrate that EADP improves the accuracy-efficiency trade-off of VLMs, robustly preserving fine-grained visual cues under strict token budgets while achieving SoTA performance on challenging multimodal benchmarks.

Abstract:
Open-vocabulary 3D Scene Graph (3DSG) can enhance various downstream tasks in robotics by leveraging structured semantic representations, yet current 3DSG construction methods suffer from semantic inconsistencies caused by noisy cross-image aggregation under occlusions and constrained viewpoints. To mitigate the impact of such inconsistency, we propose RAG-3DSG, which introduces re-shot guided uncertainty estimation. By measuring the semantic consistency between original limited viewpoints and re-shot optimal viewpoints, this method quantifies the underlying semantic ambiguity of each graph object. Based on this quantification, we devise an Object-level Retrieval-Augmented Generation (RAG) that leverages low-uncertainty objects as semantic anchors to retrieve more reliable contextual knowledge, enabling a VisionLanguage Model to rectify the predictions of uncertain objects and optimize the final 3DSG. Extensive evaluations across three challenging benchmarks and real-world robot trials demonstrate that RAG-3DSG achieves superior recall and precision, effectively mitigating semantic noise to provide more reliable scene representations for robotics tasks.

Abstract:
Multimodal Sentiment Analysis (MSA) seeks to infer humanemotions by integrating textual, acoustic, and visual cues. However, ex-isting approaches often rely on all modalities are completeness, whereasreal-world applications frequently encounter noise, hardware failures, orprivacy restrictions that result in missing modalities. There exists a sig-nificant feature misalignment between incomplete and complete modali-ties, and directly fusing them may even distort the well-learned represen-tations of the intact modalities. To this end, we propose PRLF, a Pro-gressive Representation Learning Framework designed for MSA underuncertain missing-modality conditions. PRLF introduces an AdaptiveModality Reliability Estimator (AMRE), which dynamically quantifiesthe reliability of each modality using recognition confidence and Fisherinformation to determine the dominant modality. In addition, the Pro-gressive Interaction (ProgInteract) module iteratively aligns the othermodalities with the dominant one, thereby enhancing cross-modal con-sistency while suppressing noise. Extensive experiments on CMU-MOSI,CMU-MOSEI, and SIMS verify that PRLF outperforms state-of-the-artmethods across both inter- and intra-modality missing scenarios, demon-strating its robustness and generalization capability.

Abstract:
Hyperspectral-panchromatic fusion reconstructs high-resolutionhyperspectral (HRHS) images by combining low-resolution hyperspectral(LRHS) observations with high-resolution panchromatic (PAN) imagery.Although recent deep architectures achieve strong spectral–spatial re-construction performance, most models rely on stacked layers in whichglobal cross-modal interactions and local spatial refinement are updatedjointly at every stage. Consequently, the influence of PAN information isimplicitly coupled to network depth, limiting independent control overcross-modal interaction and reducing flexibility in balancing spectralpreservation and spatial enhancement. We propose the Leader-GuidedDynamics Network (LGD-Net), a framework that explicitly decouplesglobal cross-modal interaction from local multi-scale reconstruction. Aglobal leader representation first aggregates modality-specific features tosummarize global context. Instead of propagating interactions throughrepeated stacking, only the leader state evolves through a controlled,continuous-depth dynamic process, while local feature nodes focus onspatial reconstruction. The refined leader is subsequently injected backinto the multi-scale feature hierarchy via structured residual modula-tion, providing global guidance without increasing architectural depth.By separating global state evolution from local reconstruction, LGD-Net enables explicit control over cross-modal interactions while main-taining computational efficiency. Experiments on multiple hyperspectralbenchmarks demonstrate improved spectral–spatial reconstruction qual-ity compared to conventional stacked fusion architectures.

Abstract:
Semi-supervised medical image segmentation (SSMIS) aimsto achieve accurate segmentation with limited labeled data. However,existing methods still struggle in multi-organ scenarios. Limited anno-tations exacerbate class imbalance and feature entanglement among co-occurring organs, leading to false activations and performance degrada-tion. To overcome these challenges, we propose a dual-space patch-drivencomplementary learning framework for semi-supervised multi-organ seg-mentation (DPCL). Our framework addresses these problems from bothimage and feature space. In the image space, we combine uncertaintyand class frequency to form a probabilistic sampling strategy for im-age decomposition, which generates informative patches to enhance localfeature perception and promote balanced learning across organs. In thefeature space, we design two complementary objectives to achieve ro-bust feature disentanglement. The patch-based contrastive loss focuseson decoupling features of co-occurring neighboring organs within lo-cal patches, mitigating semantic confusion among adjacent structures.Meanwhile, the global-based contrastive loss minimizes the gap betweenglobal and local semantics, improving model sensitivity to fine-graineddetails and enhancing consistency across different regions. These com-plementary losses collaboratively enhance inter-organ feature discrimi-nation while mitigating false activations. Extensive evaluations on fourpublic multi-organ medical segmentation datasets (Synapse, FeTA2021,SegTHOR, and ACDC) demonstrate that DPCL outperforms state-of-the-art methods, achieving substantial improvements in segmentationperformance.

Abstract:
Few-shot classification performance of Contrastive Language-Image Pre-training (CLIP) varies widely across datasets, especially whenclass names provide weak or ambiguous semantic priors. This issue is alle-viated in vision-only fine-grained methods, as they identify discriminativelocal features but require training on the full dataset. We thus introduce atraining-free approach that integrates discriminative patches into CLIPto reduce reliance on textual prompts. Our method identifies patcheswith high intra-class consistency and low inter-class ambiguity, forminga Class-Discriminative Patch Set (CDPS) for each category. Using CDPS,we enhance the recognition ability of CLIP through a hybrid classifiercombining global image-text alignment with local patch-based similarity.Extensive experiments on diverse benchmarks show that CDPS injectsfine-grained discriminatory power into CLIP and yields more robust few-shot recognition performance against ambiguous class names.

Abstract:
Federated remote sensing (RS) semantic segmentation en-ables privacy-preserving collaborative training of dense prediction mod-els. However, its performance is severely hindered by the structured het-erogeneity inherent in RS image: sensor diversity causes distinct spec-tral shifts, while geographic variability induces spatial shifts. Existingmethods treat client heterogeneity as an unstructured distributional mis-match, failing to accommodate these coupled, RS-specific variations.To address this, we propose FedDap, a federated prototype adapta-tion framework that disentangles domain-conditioned variations from in-variant class semantics via Domain-Adaptive Prototypes (DAPs). DAPstransform globally shared semantic anchors through two specialized mod-ulation branches: a spectral branch for radiometric alignment and a spa-tial branch for multi-scale structural adaptation. A shift-aware gatingmechanism dynamically fuses these branches, producing class-conditionalprototypes tailored to local sensing and regional contexts. By employ-ing a pixel-to-prototype contrastive objective, we align local representa-tions with both the shared semantic references and the DAPs, effectivelymitigating cross-client mismatch. Extensive experiments across diversefederated RS benchmarks demonstrate that our method consistently out-performs state-of-the-art baselines.

Abstract:
3D object detection is a core task in autonomous driving per-ception, with mainstream methods primarily based on LiDAR. However,its robustness deteriorates significantly in adverse weather conditions.While 4D radar offers all-weather robustness and unique Doppler veloc-ity, it is hindered by its inherent sparsity and noise interference. There-fore, fusing LiDAR and 4D radar is highly promising. However, due to thesignificant differences in semantics and density between the data of thesetwo modalities, single feature space fusion cannot fully leverage the ad-vantages of each modality and the complementarity between modalities.To address this, we propose a two-stage fusion detection framework—HVA-Fusion. Specifically, motion velocity is first estimated through aMotion Velocity Estimation and Encoding module to enhance dynamicperception. Then, in the pillar feature space, a Cross-Modal Progres-sive Adaptation module is introduced for local feature alignment andfusion (Stage-I fusion) to enrich representations and mitigate semanticdifferences between modalities. After that, to alleviate the sparsity of4D radar point cloud, a Radar Cross-Section (RCS) Guided GaussianGeneration module is developed to densify the feature representations.Subsequently, in the Bird’s Eye View (BEV) space, the enhanced multi-modal features are adaptively fused through a Multi-Scale Bi-DirectionalDeformable Attention Gate module (Stage-II fusion) to address featuremisalignment and sensor degradation caused by adverse weather. Exten-sive experiments demonstrate that HVA-Fusion achieves highly compet-itive performance across the K-Radar, VoD, and TJ4DRadSet datasets,while exhibiting robustness in adverse weather conditions.

Abstract:
Face anti-spoofing (FAS) is critical for secure deploymentof face recognition in high-stakes applications. However, existing multi-modal FAS methods still generalize poorly to unseen domains. We arguethat a key reason is that they directly align noisy multimodal featuresin which spoof-relevant cues are entangled with modality- and domain-specific variations, so both useful and nuisance patterns are forced tomatch across domains. Motivated by this, we design Multimodal Denoisingand Alignment (MMDA), a CLIP-based framework that (i) purifiesmultimodal features, (ii) softly guides them into a semantic space, and(iii) preserves the pre-trained geometry during task adaptation. To de-couple spoof cues from these variations before alignment, the Modality-Domain Joint Differential Attention (MD2A) module contrasts same-domain cross-sample features to down-weight patterns shared withineach domain/modality, which helps reduce domain- and modality-relatedbiases while better preserving discriminative spoof information. To avoiddestructive, over-rigid alignment, the Representation Space Soft (RS2)strategy aligns visual features to real/spoof subspaces spanned by mul-tiple prompts in the CLIP space, providing a soft semantic pull insteadof collapsing features onto a single text embedding. To prevent task-specific fine-tuning from destroying CLIP’s generalizable structure, theU-shaped Dual Space Adaptation (U-DSA) module introduces deepyet parameter-efficient adaptation and remaps adapted features backto shallower, more domain-invariant layers. Extensive experiments onWMCA, CeFA, PADISI, and SURF under complete-modality, missing-modality, and limited-source protocols show that MMDA consistentlysurpasses state-of-the-art methods, reducing average HTER by up to9.63% and improving AUC by up to 5.98% in cross-domain evaluation.

Abstract:
Text-to-motion generation aims to synthesize realistic andsemantically aligned human motions from natural language descriptions.Recent diffusion-based approaches predominantly adopt Transformer orCNN-based denoisers; However, the former suffers from quadratic com-plexity with respect to sequence length, while the latter is fundamen-tally constrained by limited receptive fields. To bridge this gap, we pro-pose SFM, a state-space-enhanced diffusion framework that introducesa structured and efficient long-range modeling scheme tailored for mo-tion generation. At its core, SFM integrates two novel components into aUNet backbone: a Frequency State-Space Module (F-SSM), which trans-forms motion features into the frequency domain with text-gated multi-scale modulation, and a Spatial State-Space Module (S-SSM), whichcaptures bidirectional spatial dependencies through parallel state tran-sitions. This dual-axis decomposition enables SFM to model both theglobal structure and fine-grained variations of human motion while pre-serving alignment with linguistic prompts. Extensive experiments onHumanML3D and KIT-ML demonstrate that SFM outperforms strongTransformer- and CNN-based baselines in both fidelity and text-motionalignment, achieving superior R-Precision and FID with optimized speed.

Abstract:
3D scene graphs (3DSGs) provide a compact and structuredabstraction of 3D environments. Although advances in foundation modelshave enabled open-vocabulary 3DSG generation, existing approaches re-main object-centric and encode limited relational information—restrictingtheir applicability in real-world scenarios that require fine-grained under-standing. We propose OP3DSG, an open-vocabulary part-aware 3DSGgeneration framework that constructs unified graphs that jointly modelobjects, interactive parts, spatial relations, functional relations, and af-fordances. OP3DSG integrates object-part knowledge-guided detectionwith part-aware 3D fusion to preserve small and interaction-relevantcomponents, and employs a geometry-initialized prior graph with LLM-based refinement to reduce spurious relational predictions while enablingefficient graph construction. To systematically evaluate unified 3D scenegraph construction, we introduce UniGraph3D, a benchmark designedfor part-aware perception and multi-level relational reasoning. Experi-mental results show that OP3DSG achieves state-of-the-art performanceand demonstrates its effectiveness as a perception backbone in diversereal-world robotics tasks.

Abstract:
Reasoning segmentation aims to predict pixel-wise masks fortargets given complex language queries. Existing approaches leverageMultimodal Large Language Models (MLLMs) for vision-language rea-soning and generate intermediate target cues (e.g., points or boxes) toguide a segmentation model. However, compressing rich reasoning intosparse cues often introduces ambiguity and noise, preventing these cuesfrom accurately preserving the reasoning intent. While multiple comple-mentary cues can enrich target information, existing methods typicallyfeed them jointly into a single segmentation process, allowing ambiguousor erroneous cues to affect the entire prediction. Therefore, we proposeDGSeg, a reasoning segmentation framework that learns to fuse pre-dictions guided by semantic and spatial cues. Specifically, the MLLMjointly reasons about both target identity and spatial location, produc-ing complementary semantic and spatial cues that are fed into separatesegmentation branches. Their predictions are adaptively integrated by alightweight dynamic gating module trained with relative branch-qualitysupervision to suppress noisy or conflicting regions. Extensive experi-ments demonstrate that DGSeg consistently outperforms strong base-lines on multiple benchmarks and achieves 69.6% and 67.3% gIoU on thechallenging ReasonSeg validation and test splits.

Abstract:
Real-world semi-supervised learning (SSL) often encounterssignificant challenges with long-tailed label distributions and noisy pseudo-labels, which hinder generalization and amplify confirmation bias. In thiswork, we introduce a novel framework, Gaussian Bridge Consistency(GBC), to address these challenges by constructing semantic interpo-lation paths between unlabeled samples and high-quality class anchors.Our method maintains a dynamic Prototype Atlas that stores a diverseand evolving set of labeled and pseudo-labeled exemplars per class. Foreach unlabeled instance, GBC forms a class-conditional Gaussian Fea-ture Bridge in the latent space, enabling the student model to traversea smooth trajectory from uncertain predictions to reliable class proto-types. A bridge consistency loss is applied along this path to enforcealignment with a geometrically interpolated target distribution. Further-more, we propose BridgeMix, a confidence-aware feature mixing strategythat interpolates both sample and anchor pairs to amplify cross-samplegeneralization. Extensive experiments on CIFAR10-LT and ImageNet-LT (USB benchmarks) validate the robustness and effectiveness of GBCunder realistic long-tailed SSL settings, consistently improving long tail-class performance without sacrificing scalability.

Abstract:
Self-supervised learning (SSL) has become a leading paradigmfor skeleton-based action recognition, yet the choice of prediction targetremains a central limitation. Existing masked modeling methods typi-cally reconstruct raw joint coordinates, emphasizing low-level detail andremaining sensitive to noise, while recent non-reconstruction variantspredict learned latent features that improve semantics but capture lit-tle explicit motion structure. We propose Path-JEPA, a self-supervisedpredictive learning framework that adopts path signatures as the predic-tion target for skeleton sequences. Rather than predicting coordinatesor abstract embeddings, Path-JEPA predicts latent representations ofcontinuous-time, multi-scale geometric descriptors computed over joint,edge, and chain paths, capturing motion across multiple spatial and tem-poral scales. These targets encode displacement, signed area, and higher-order interactions, yielding a geometry-grounded representation of hu-man motion that is naturally robust to temporal resampling and frame-rate variation. To make such targets effective within a JEPA framework,we introduce signature-augmented masking, which propagates joint-spacemasks to all dependent signature tokens, forcing the model to infer themissing motion geometry from broader anatomical and temporal context.Because signatures summarize motion over intervals rather than indi-vidual frames, they also yield greater computational efficiency on longsequences. Extensive experiments on NTU RGB+D 60, NTU RGB+D120, and PKU-MMD show that Path-JEPA learns stronger representa-tions than prior masked prediction methods, achieving state-of-the-artperformance across diverse downstream tasks while exhibiting improvedrobustness to irregular sampling. Project website:Path-JEPA.

Abstract:
The advancement of Video Object Segmentation (VOS) isimpeded by its heavy reliance on expensive annotations. This has led toa landscape of compromised alternatives: unsupervised methods provideweak information, few-shot learners are plagued by referential ambiguityfrom distractors, and referring techniques are limited by a lack of visionpriors. We revisit Few-Shot Referring Video Object Segmentation (FSR-VOS) under a streamlined and practical formulation that segments tar-get objects using only query-level textual descriptions and a small set ofannotated support images. This dual conditioning enables coarse-to-finedisambiguation and precise boundary refinement with minimal annota-tion cost. To address the challenge of complex multi-source informationfusion, we propose the QST-SAM framework. Specifically, a SupportInstruction Compressor is introduced to refine and condense the sup-port information into compact representations. The Q-S-T Transformerfurther facilitates comprehensive integration of multi-modal supervisorycues from query, support, and text branches. The Multi-Priors Prompt-ing module provides the SAM2 prompt encoder with enriched guidancefor precise, context-aware segmentation. We further establish a new FS-RVOS benchmark derived from Ref-Youtube-VOS and Ref-DAVIS17, onwhich QST-SAM achieves state-of-the-art performance, validating its ro-bustness and effectiveness in segmentation.

Abstract:
Industrial Video Anomaly Detection (IVAD) aims to identifyanomalous objects and events in an industrial process, which is crucial formodern manufacturing and quality control systems. Existing VLM-basedanomaly reasoning methods are capable of detecting open-ended anoma-lies in general domains. However, their performance declines in industrialsettings characterized by intricate object transformations, strict physics,and procedural constraints. To tackle the complexity of such interaction-intensive detection, we introduce a training-free agentic framework O-VAD for anomaly detection free of domain-specific knowledge, empha-sizing object state evolution like humans inspectors. It is designed totrack spatial-temporal dynamics and underlying transformations of de-tected objects over time, and then reason over the object-wise temporalstate trajectories to identify abnormal objects in grounded frames. Ourmethod overcomes limitations of prior approaches that rely on retrainingon normal clips or injecting domain knowledge as context for test-timeinference. Extensive experiments on three IVAD datasets demonstratethat our method outperforms frontier VLMs, agentic frameworks, andtraditional VAD methods fine-tuned on the respective datasets, whileproviding interpretable reports over anomaly processes and types.

Abstract:
Deploying video anomaly detection (VAD) in the real worldis often constrained by the scarcity, privacy, and cost of collecting realabnormal footage. We propose PA-VAD, a novel pseudo-only frame-work that trains an anomaly detector without using any real abnormalvideos, by pairing real normal videos with diffusion-synthesized pseudo-abnormal videos generated from a small set of real normal images. Be-yond proposing a generation-driven training pipeline, we make a keyempirical discovery: pseudo anomalies exhibit a characteristic spatiotem-poral magnitude bias in feature space, which can dominate MultipleInstance Learning and degrade generalization if left unaddressed. Tocounter this pseudo-induced bias, we introduce the Domain-Aligned Reg-ularized Module (DARM), which combines domain alignment with usage-aware memory updates to balance prototype coverage and stabilize opti-mization under biased pseudo supervision. Extensive experiments demon-strate that PA-VAD achieves 98.2% AUC on ShanghaiTech, 82.5% onUCF-Crime, and 95.1% on XD-Violence, and further improves general-ization to unseen anomaly classes in open-set evaluations. Notably, PA-VAD surpasses the best real-abnormal WVAD baselines on ShanghaiTechand XD-Violence by +0.6% and +0.9% , respectively, and improves overthe UVAD state of the art on UCF-Crime by +1.9% —showing that high-accuracy VAD is attainable without collecting real abnormal videos.

Abstract:
Remote sensing change detection is caught in a long-standingtrade-off: reliable suppression of pseudo changes requires global contex-tual reasoning, while attention-based models incur prohibitive quadraticcomplexity, hindering deployment on resource-constrained UAV and satel-lite platforms. This paper proposes ChangeGCC, a fully convolutional,linear-time framework that delivers efficient global context for change de-tection. ChangeGCC core features two key components: Globally Condi-tioned Convolution that enable convolutional global routing, and a dual-temporal fusion scheme that enhances semantic consistency across scaleswhile highlighting genuine structural changes. Extensive experiments onmultiple benchmarks show that ChangeGCC achieves state-of-the-artperformance, reaching 85.28% IoU and 92.06% F1 on the LEVIR-CD benchmark, while substantially reducing parameters, FLOPs, andinference latency compared to attention-based counterparts.

Abstract:
Systematic failures of vision models on semantically coherentsubsets, known as error slices, reveal limitations in robustness and evalu-ation. Existing slice discovery approaches largely model slices as clustersin representation space or combinations of predefined attributes. Whileeffective for image-level classification, such formulations are insufficientfor instance-level tasks such as object detection and segmentation, wherefailures often arise from contextual, relational, and spatially groundedvisual patterns. We propose GH-ESD (Grounded Hypothesis-Driven ErrorSlice Discovery), a generate-and-verify framework that reformulates slicediscovery as grounded hypothesis generation and statistical verification.GH-ESD constructs relational failure hypotheses using LLM priors andgrounded visual evidence, discovers hypothesis slices at the instance levelvia Vision-Language Models, and verifies them through statistical trendanalysis over instance-level errors. We also introduce GESD (GroundedError Slice Dataset), a new benchmark for instance-level error slice discov-ery, providing expert-defined and spatially grounded slices derived fromdetection and segmentation failures. Extensive experiments demonstratethat GH-ESD consistently outperforms baselines, improving Precision@10by 0.10 (0.73 vs. 0.63) on the GESD benchmark for detection tasks, whilealso supporting segmentation scenarios. GH-ESD identifies interpretableslices that facilitate actionable model improvements.

Abstract:
Event cameras provide asynchronous visual sensing, making them a compelling modality for agile edge AI systems. For practical deployment, event-based vision algorithms must sustain sufficiently high throughput to keep pace with the incoming event stream. Yet existing approaches often fail to do so, as they are rarely designed in close consideration of the underlying sensor and compute hardware. We propose a new event-based feature detection and tracking algorithm that operates at high throughput on an embedded CPU, without GPU acceleration. Our pipeline is motion-defined: it activates only when sufficient motion is detected and dynamically adapts its computational load to the observed motion. At its core is a novel slice-parallel formulation of event-based corner detection and tracking that operates directly on live event streams. The pipeline is enabled by our Motion-Defined Concurrent Slices architecture, which efficiently processes event data and maximizes utilization of available compute resources. On event streams generated by handheld motion in typical scenes, our method achieves throughput above the average event rate, supporting responsive event-based vision on the edge. Extensive benchmarking on standard datasets and live demonstrations confirms the effectiveness of the proposed method. 1

Abstract:
Quantitative microstructural characterization is fundamen-tal to materials science, and electron micrographs (EMs) provide indis-pensable high-resolution insights. However, progress in deep learning-based analysis of EMs has been hampered by the scarcity of large-scale,expert-annotated public datasets. To address this issue, we introduceEM3M, a large-scale and multimodal dataset for instance-level under-standing of EMs. EM3M comprises 5,091 high-quality EMs, approxi-mately 3 million instance segmentation annotations, and image-leveltextual descriptions with disentangled attributes. The dataset is con-structed through a rigorous multi-stage curation and validation pipeline,with comprehensive statistical analyses to ensure reliability and repro-ducibility. Building upon these curated image-text pairs, we further pro-vide a text-to-image diffusion model that serves as a controllable dataaugmentation engine, demonstrating that synthetic augmentation con-sistently improves downstream segmentation performance. To establish asystematic benchmark, we evaluate representative instance segmentationmethods on EM3M. Our results reveal that conventional detection-basedand query-based methods struggle with the extreme instance densitiesand textural complexities inherent in EMs. We additionally provide anoptimized flow-based baseline to facilitate fair comparison and future re-search. EM3M1 , the generative engine2 , and an online demo3 are publiclyavailable to support future research in automated materials analysis.

Abstract:
Modern object detectors systematically fail on concealed, camouflaged, or strategically hidden objects, not due to insufficient capacity, but because they reason solely from visual appearance with no mechanism to consider where objects might be hidden. We propose IACD (Iterative Adversarial Collaborative Detection), a dual-perspective framework that pairs a frozen pretrained YOLOv11m (Seeker) with a lightweight trainable module (Hider) that reasons inversely: given the environment, where would a target be hidden? The Hider analyzes environmental concealment suitability across texture, edge, and semantic dimensions, then identifies the Seeker’s blind spots by contrasting suitability against detection coverage. The resulting blind spot maps residually amplify feature responses via attention gates before a second detection pass. The two branches interact across multiple rounds, with the Hider’s blind spot estimates informing the attention gates that guide the second detection pass. Crucially, the Hider is trained under weak supervision derived solely from the detector’s own false negatives, requiring no manual concealment annotations. We frame this as detector blind-region recovery—recovering a frozen detector’s false negatives—rather than as standard camouflaged object detection. Experiments on the Poppy illegal crop detection dataset and the VisDrone aerial surveillance benchmark demonstrate consistent improvements over the strong YOLOv11m baseline, while introducing only ∼9M trainable parameters atop the frozen detector.

Abstract:
Referring Multi-Object Tracking has attracted increasing at-tention due to its human-friendly interactive characteristics, yet it ex-hibits limitations in low-visibility conditions, such as nighttime, smoke,and other challenging scenarios. To overcome this limitation, we proposea new RGB-Thermal RMOT task, named RT-RMOT, which aimsto fuse RGB appearance features with the illumination robustness ofthe thermal modality to enable all-day referring multi-object tracking.To promote research on RT-RMOT, we construct the first Ref erringMulti-Object Tracking dataset under RGB-Thermal modality, namedRefRT. It contains 388 language descriptions, 1,250 tracked targets,and 166,147 Language-RGB-Thermal (L-RGB-T) triplets. Furthermore,we propose RTrack, a framework built upon a multimodal large lan-guage model (MLLM) that integrates RGB, thermal, and textual fea-tures. Since the initial framework still leaves room for improvement, weintroduce a Group Sequence Policy Optimization (GSPO) strategy tofurther exploit the model’s potential. To alleviate training instabilityduring RL fine-tuning, we introduce a Clipped Advantage Scaling (CAS)strategy to suppress gradient explosion. In addition, we design StructuredOutput Reward and Comprehensive Detection Reward to balance explo-ration and exploitation, thereby improving the completeness and accu-racy of target perception. Extensive experiments on the RefRT datasetdemonstrate the effectiveness of the proposed RTrack framework.

Abstract:
Event cameras enable high-frequency visual perception withmicrosecond latency, offering advantages for dynamic scenes. However,event-based small object detection remains challenging due to sparseasynchronous measurements and weak object responses that are easilydisrupted by noise. Limited spatial support causes small-object signalsto lose temporal continuity, resulting in fragmented and unstable pre-dictions. To address this issue, we propose a physics-guided advection-consistent modeling framework, termed PACT, which formulates eventevolution as a motion-driven feature transport process. Instead of rely-ing solely on local spatio-temporal aggregation, PACT propagates fea-tures along estimated velocity fields and enforces trajectory-level consis-tency through advection constraints. This design preserves weak eventresponses over time and prevents their degradation under complex back-ground interference. Technically, PACT integrates motion-aware featureextraction with a differentiable advection-based transport operator, en-abling coherent motion representation and effective noise suppressionduring temporal evolution. Extensive experiments on benchmark event-based datasets demonstrate that PACT consistently outperforms state-of-the-art methods, achieving improvements of 20.72% in IoU and 15.03%in accuracy while maintaining comparable computational efficiency. Thecode will be made publicly available.

Abstract:
Existing person search methods assume access to complete visual queries or exhaustive tracking, yet real-world witness accounts are vague, partial, and spread across cameras and time. We introduce ARGOS (Agentic Retrieval with Grounded Observational Search), a benchmark and agent framework that recasts multi-camera person search from one-shot retrieval on a complete query into interactive reasoning from partial clues. To our knowledge, ARGOS is the first interactive benchmark to couple witness dialogue with camera-network topology, requiring an agent to plan, question, and eliminate under information asymmetry. An ARGOS agent receives a vague witness statement and must decide what to ask, when to invoke spatial or temporal tools, and how to interpret ambiguous natural-language responses, all within a limited turn budget. To ground reasoning in physical constraints, the agent accesses a Spatio-Temporal Topology Graph (STTG) encoding camera connectivity and empirically validated transition times. The benchmark comprises 2,691 tasks across 14 real-world scenarios in three progressive tracks: semantic perception (Who, 989 tasks), spatial reasoning (Where, 550 tasks), and temporal reasoning (When, 1,152 tasks). We propose TurnWeighted Success (TWS) as the primary metric, jointly measuring correctness and turn efficiency. Experiments with four LLM backbones show the benchmark is far from solved: the best agent achieves TWS of 0.383 (Track 2) and 0.590 (Track 3). Ablations confirm each component is essential: removing domain-specific tools drops Top-1 accuracy by up to 49.6 percentage points, and removing strategic reasoning halves TWS while barely affecting Top-1.

Abstract:
Eye tracking (ET) plays a critical role in augmented andvirtual reality applications. However, rapidly deploying high-accuracy,on-device gaze estimation for new products remains challenging becausehardware configurations (e.g., camera placement, camera pose, and il-lumination) often change across device generations. Visual foundationmodels (VFMs) excel on natural-image benchmarks and offer a promis-ing path to rapid training and deployment; yet, we find that off-the-shelfVFMs still struggle to reach high accuracy on specialized near-eye in-frared images. To close this gap, we introduce DistillGaze, a frameworkthat distills a VFM using labeled synthetic data and unlabeled real datafor rapid, high-accuracy on-device gaze estimation. DistillGaze proceedsin two stages. First, we adapt a VFM into a domain-specialized teacherusing synthetic gaze labels and unlabeled real images. Synthetic dataprovide scalable, high-quality gaze supervision, while unlabeled real databridges the synthetic-to-real domain gap. Second, we train an on-devicestudent from both teacher guidance and self-training. Evaluated on alarge-scale crowd-sourced dataset spanning more than 2,000 participants,DistillGaze reduces median gaze error by 58.6% relative to synthetic-only baselines while maintaining a lightweight 256K-parameter modelsuitable for real-time on-device deployment. More broadly, DistillGazeoffers an efficient path to training and deploying ET models that adaptto hardware changes, and a recipe for combining synthetic supervisionwith unlabeled real data in on-device regression tasks.

Abstract:
AI models are increasingly trained on personal images scraped from social media and public platforms, often without consent, leading to serious privacy violations, such as unauthorized facial recognition and targeted advertising. To counter this, researchers have developed unlearnable examples (UEs), images modified with imperceptible noise to prevent AI models from extracting meaningful information. However, existing UE methods primarily rely on pixel-space noise, which can be bypassed by relearning strategies such as adversarial training, image transformation, and compression. While some techniques improve robustness, they often come at the expense of significant degradation in image utility and perceptual quality. In this paper, we introduce DiffUE to overcome these limitations by injecting noise into the semantic space of images instead of the pixel space. Instead of corrupting pixel values, DiffUE modifies high-level semantic features of images, ensuring robust unlearnability while preserving visual quality and utility. By leveraging a diffusion-based autoencoder framework to manipulate semantic features, DiffUE generates purposeful, natural-looking modifications that effectively resist advanced relearning strategies. Extensive experiments on four datasets, CIFAR-10, CIFAR-100, CelebA-HQ, and ImageNet, as well as a subjective user study, demonstrate that DiffUE significantly enhances the trade-off between image quality and unlearnability, offering a more robust and effective solution for safeguarding personal data in an increasingly exploitative AI landscape.

Abstract:
Multi-site functional MRI (fMRI) studies are essential forrobust neuropsychiatric diagnosis yet suffer severe domain shifts fromscanner heterogeneity, demographics, and site-specific acquisition pro-tocols. Traditional domain adaptation requires concurrent source andtarget data access, violating clinical privacy regulations. Moreover, func-tional connectivity matrices lie on the Symmetric Positive Definite (SPD)manifold, where Euclidean operations cause geometric distortions cor-rupting diagnostic patterns. We propose BrainRiem, a source-free do-main adaptation framework learning compact Riemannian brain pro-totypes via manifold-aware bi-level optimization. It employs the Log-Euclidean Metric to ensure prototypes remain valid SPD matrices, whileDirichlet Energy spectral calibration aligns their frequency characteris-tics with real brain networks. Only anonymized prototypes are trans-mitted to target sites, serving as stable anchors for training local mod-els without source data access and reducing leakage under the evalu-ated attacks. Comprehensive experiments on ABIDE and REST-meta-MDD show BrainRiem consistently outperforms state-of-the-art source-free, traditional, and graph domain adaptation methods across diversescanners and demographics. Notably, learned prototypes exhibit biolog-ically interpretable connectivity patterns aligning with established neu-roscience findings, validating the necessity of Riemannian geometry forbrain network analysis.

Abstract:
Red-teaming Text-to-Image (T2I) models is essential for safedeployment, yet it remains particularly challenging against implicit ad-versarial prompts. Unlike explicit adversarial prompts that can be read-ily identified and blocked, implicit ones are much harder to detect: theprompts appear benign on the text surface yet still lead to inappropri-ate visual content. To address this, we propose Adversarial Probing forImplicit VulnErabilities (AdvPIE), a multimodal agentic framework toexpose implicit vulnerabilities without requiring access to the parametersof target models. AdvPIE adopts a policy agent to generate and refineimplicit adversarial prompts based on the feedback from a judge agent.To construct informative feedback, the judge agent provides modality-specific safety evaluation at both global and relative levels across itera-tions. To effectively leverage the feedback, we propose a novel CumulativeAdversarial Decoding strategy for the policy agent, which dynamicallyreweights token distributions to favor tokens that lead to more harmfulimages while preserving sampling diversity. Extensive experiments onstandard and safety-aligned T2I models show that AdvPIE1 effectivelyuncovers implicit vulnerabilities, outperforming various baseline meth-ods. Warning: This paper includes content that may be disturbing.

Abstract:
Robustness to natural corruptions remains a fundamentalchallenge for deep neural networks. In this paper, we identify a robustnessfading phenomenon where shallow layers spontaneously develop robustrepresentations and flat loss landscapes in early training, yet these prop-erties are not preserved during standard convergence. To address this,we propose a framework that performs strategic interventions on train-ing dynamics to stabilize the empirically identified early-emergent robustpriors. Our approach includes two parameter-free strategies: Early-PhaseStabilization (EPS) and Asymmetric Weight Reversion (AWR), whichstabilize or recover robust shallow configurations without modifying themodel architecture or introducing learnable parameters. Extensive exper-iments demonstrate the efficacy of our framework across various bench-marks and architectures, yielding significant gains in downstream trans-fer, dynamic adaptation, and diverse computer vision applications.

Abstract:
Text-driven hand–object interaction (HOI) generation is gain-ing attention for immersive applications and robotics, yet producingphysically plausible interactions remains challenging. Even when individ-ual motions appear natural, small contact errors can cause conspicuousartifacts such as floating and interpenetration. Prior methods mitigatethese issues using explicit contact cues or implicit grasp priors, but typ-ically rely on multi-stage pipelines and fail to model temporally evolvingcontact. We present JointHOI, a single-stage diffusion framework thatjointly generates 3D hand–object motion and dynamic, distance-basedcontact maps from text. By treating contact as an auxiliary inner modal-ity, joint generation enables the model to learn contact–motion couplingduring training. At inference, contact-guided sampling enforces consis-tency between generated contact maps and motion-implied geometry,improving temporal stability and reducing penetration and floating. Ex-periments on GRAB and ARCTIC demonstrate consistent improvementsin text adherence and physical plausibility over prior methods.

Abstract:
Controlled video generation has seen drastic improvementsin recent years. However, editing actions and dynamic events, or insert-ing contents that should affect the behaviors of other objects in real-world videos, remains a major challenge. Existing trained models strug-gle with complex edits, likely due to the difficulty of collecting relevanttraining data. Similarly, existing training-free methods are inherently re-stricted to structure- and motion-preserving edits and do not supportmodification of motion or interactions. Here, we introduce DynaEdit,a training-free editing method that unlocks versatile video editing ca-pabilities with pretrained text-to-video flow models. Our method relieson the recently introduced inversion-free approach, which does not in-tervene in the model internals, and is thus model-agnostic. We showthat naively attempting to adapt this approach to general unconstrainedediting results in severe low-frequency misalignment and high-frequencyjitter. We explain the sources of these phenomena and introduce novelmechanisms for overcoming them. Through extensive experiments, weshow that DynaEdit achieves state-of-the-art results on complex text-based video editing tasks, including modifying actions, inserting objectsthat interact with the scene, and introducing global effects (see website).

Abstract:
Sign language models are typically trained on datasets cap-tured under constrained conditions, with limited viewpoint, background,and signer-identity diversity, leading to poor robustness under real-worlddistribution shifts. We introduce SignNet-1M, a large-scale augmenteddataset spanning ASL, CSL, and German Sign Language (DGS).SignNet-1M synthesizes realistic variations along three axes: (i) novel-view rendering (rotation and zoom) via 3D Gaussian Splatting (3DGS),(ii) scene/ identity editing via diffusion models for background replace-ment and signer substitution while preserving sign motion and linguisticcontent, and (iii) post-rendering augmentations that emulate captureand compression artifacts (e.g., geometric transforms, photometric shifts,mild temporal resampling, and compression) to better match in-the-wildrecordings. Beyond data release, we provide a unified benchmark suiteacross downstream tasks (e.g., translation and recognition) and abla-tions that isolate each augmentation component. Experiments acrossbackbones show that training with SignNet-1M consistently improvesgeneralization under cross-view, cross-background, cross-identity, andpost-rendering shifts, while maintaining strong in-distribution perfor-mance. The dataset, released augmentation components, metadata, andbenchmark resources are available at https://signnet.chatsign.ai/.

Abstract:
Recent advancements in audio-video joint generation mod-els have demonstrated impressive capabilities in content creation. How-ever, generating high-fidelity human-centric videos in complex, real-worldphysical scenes remains a significant challenge. We identify that the rootcause lies in the structural deficiencies of existing datasets across threedimensions: limited global scene and camera diversity, sparse interac-tion modeling, and insufficient individual attribute alignment. To bridgethese gaps, we present OmniHuman, a large-scale, multi-scene datasetdesigned for fine-grained human modeling. OmniHuman provides a hier-archical annotation covering video-level scenes, frame-level interactions,and individual-level attributes. To facilitate this, we develop a fully au-tomated pipeline for high-quality data collection and multi-modal an-notation. Complementary to the dataset, we establish the OmniHumanBenchmark (OHBench), a three-level evaluation system that providesa scientific diagnosis for human-centric audio-video synthesis. Crucially,OHBench introduces metrics that are highly consistent with human per-ception, filling the gaps in existing benchmarks by providing a compre-hensive diagnosis across global scenes, relational interactions, and in-dividual attributes. Experiments show that fine-tuning on only 20% ofOmniHuman significantly boosts performance, validating its effective-ness in advancing complex scenario modeling. The dataset is available athttps://huggingface.co/datasets/julia527/OmniHuman

Abstract:
Recent advances in video generation have made AI-synthesizedcontent increasingly difficult to distinguish from real footage. We pro-pose a physics-based authentication signature that real cameras producenaturally, but that generative models cannot faithfully reproduce. Ourapproach exploits the Moiré effect: the interference fringes formed whena camera views a compact two-layer grating structure. We utilize theMoiré motion invariant as a verification criterion: under real image for-mation, fringe phase and grating image displacement are linearly cou-pled by optical geometry, independent of viewing distance and gratingstructure. A verifier extracts both signals from video and tests theircorrelation. We validate the invariant on both real-captured and AI-generated videos from multiple state-of-the-art generators, and find thatreal and AI-generated videos produce significantly different correlationsignatures, suggesting a robust means of differentiating them. Our workdemonstrates that deterministic optical phenomena can serve as physi-cally grounded, verifiable signatures against AI-generated video.

Abstract:
Reconstructing animatable 3D human avatars from monocularvideo is a fundamental problem in computer vision with broad applicationsin AR/VR and digital content creation. Existing approaches typically coupleparametric body models with neural rendering or 3D Gaussian splattingand optimize all body regions jointly from short videos, which often de-grades fidelity in the visible areas. To overcome this limitation, we introduceFlexiAvatar, a unified framework that explicitly optimizes only the visiblebody regions, effectively eliminating artifacts arising from unobserved limbs.Our method integrates occlusion-robust SMPL-X tracking with part-specificresidual refinement to capture high-frequency geometric and appearancedetails. To complete entirely unseen regions (e.g., back views), we leverage adiffusion-based approach to generate texture consistent with the observedappearance. Experiments on full-body (NeuMan, ZJU-MoCap, WildAvatar),upper/half-body (talk-show clips), and head-only (INSTA) inputs show thatFlexiAvatar delivers consistently higher reconstruction quality, outperformingstate-of-the-art methods by an average PSNR improvement of approximately3% across datasets. Finally, by restricting optimization to observed regions,our method reduces the effective number of Gaussians that must be opti-mized and rendered, leading to reduced runtime and memory overhead inpartial-visibility scenarios. The project page can be found here.

Abstract:
Generative world models increasingly rely on 4D occupancyfor realistic autonomous driving simulation. However, existing genera-tion frameworks are limited in two respects: geometric-condition-basedmethods depend on explicit trajectories and cannot generalize to free-form instructions, while text-based methods operate at a coarse attributelevel and fail to orchestrate complex, sequential multi-agent interactions.We propose OccDirector, a framework that generates 4D occupancy dy-namics conditioned solely on natural language scripts, without requir-ing any geometric priors. OccDirector encodes free-form scripts via afrozen VLM and processes occupancy tokens through a Spatio-TemporalMMDiT backbone. A history-prefix anchoring strategy further ensuresthat generated sequences remain consistent with any provided contextover long horizons. To support training and evaluation, we introduceOccInteract-85k, a dataset of 85k clips annotated with multi-level lan-guage instructions spanning static layouts to intricate multi-agent be-haviors, alongside a novel VLM-based evaluation benchmark. Extensiveexperiments demonstrate state-of-the-art generation quality and stronginstruction-following fidelity across diverse driving scenarios.

Abstract:
Choreography is a structured creative process grounded inmusical form, rather than a mere sequence of improvised movements.Professional choreographers design choreography following a set of chore-ographic primitives: (1) organizing movements at the phrase level, (2)refining movements by aligning them with musical beats and energy,and (3) stylizing phrases through semantic text descriptions. However,existing dance generation methods overlook these primitives, leadingto dances that feel structurally incomplete and resemble improvisationrather than professional choreography. Here, we present HiChor: Hi-erarchical Choreography generation from pop music, a framework in-spired by the structured workflow of professional choreographers, whichis grounded in choreographic primitives. HiChor hierarchically generateschoreography from pop music by (1) generating phrase-level choreogra-phy aligned with beats, (2) enhancing choreography by aligning withmusic’s energy, and (3) concatenating all phrase-level choreography andenhancing it through plausibility enhancement to ensure smooth transi-tions between phrases. Furthermore, we introduce a semantic text styl-ization module that converts semantic text descriptions into low-levelchoreography feature captions using an LLM, enabling semantic control.To assess how well the generated choreography adheres to choreographicprinciples, we introduce new metrics—Phrase Diversity, Feature Align-ment, and Text Stylization Score. Experiments demonstrate that HiChoroutperforms existing methods in both choreographic quality and seman-tic stylization.

Abstract:
Conversational talking face generation has recently attractedincreasing attention, aiming to synthesize interactive talking videos wherecharacters speak, listen, and respond dynamically to each other. Thistask presents three core challenges: 1) Flexibility: enabling multi-rounddialogues with an arbitrary number of participants; 2) Naturalness: main-taining coherent motion and appropriate non-verbal feedback through-out the interaction; and 3) Efficiency: achieving real-time generation andlow computation overhead for long-term continuous online conversation.Despite recent advances, existing methods still fall short in balancingall three requirements. To bridge this gap, we introduce InterTalk, anovel and efficient framework designed for highly interactive conver-sational talking face generation. Built upon a motion-based architec-ture, InterTalk supports real-time conversation synthesis. Our methodachieves strong flexibility by explicitly modeling multi-round conversa-tional dynamics among each participant, eliminating constraints on theirnumbers. To enhance interactivity, we incorporate motion feedback frommultiple participants and introduce an iterative generation strategy formore natural behaviors. Besides, we disentangle motion into several fa-cial components, enabling targeted refinements for natural response suchas precise lip-sync and realistic eye-blinking. Finally, we construct a newmulti-person conversational dataset and enrich it with 3D face-baseddata augmentation. Extensive experiments demonstrate that InterTalkachieves superior interaction quality while maintaining real-time perfor-mance at 30 FPS.

Abstract:
Recent diffusion-based models have enabled realistic audio-driven avatar generation in real-time streaming. However, existing ap-proaches struggle to maintain visual temporal consistency and fail toexplicitly perceive user intent in complex interactive streaming scenar-ios. To address these challenges, we propose InteractiveAvatar, a real-time infinite-streaming video generation framework that supports vi-sually consistent avatar video generation and intent-aware interactions.With autoregressive distillation, InteractiveAvatar achieves real-time str-eaming generation of human avatars over arbitrarily long durations. Forvisual consistency, we introduce a Long-Short Visual Memory (LSVM)mechanism that flexibly compresses historical visual information intocompact tokens, preserving both short-range coherence and long-termconsistency. To generate avatars with speeches and actions aligned withuser intent, we propose a Reasoning-Reaction Module (RRM), which in-corporates a State-Cycling strategy and a Cache-Switching mechanism.Extensive experimental results over diverse scenarios demonstrate thatour method achieves state-of-the-art visual consistency in long-durationgeneration, while enabling complex user-avatar interaction in real time.

Abstract:
Generating realistic human avatars in complex motions—suchas clothing dynamics—requires modeling of global and local deformationswhich remains challenging in monocular settings. We address this prob-lem by leveraging neural Jacobian fields (NJFs) for representing semi-rigid deformations. We train self-supervised neural networks for predict-ing Jacobian matrices that give the pose-dependent deformations, bysolving a Poisson equation. However, monocular input presents severaldifficulties such as self-occluded regions and invisible surfaces. To addressthese issues, we introduce three key components: a constrained Poissonsolver, signed distance-based Jacobian regularization, and a deformation-guided residual flow loss, which together suppress boundary artifacts, re-cover frequently occluded regions such as armpits and thighs, and enforcetemporal consistency during motion. Experiments on benchmark and in-the-wild videos demonstrate that our method generates temporally sta-ble and geometrically coherent avatars, outperforming state-of-the-artapproaches.

Abstract:
Monocular 3D object detection is crucial for scalable per-ception across fields like autonomous driving, robotics, and surveillance.However, progress is hindered by limited 3D annotations and the in-herent ambiguity of single-image geometry. Existing methods often relyon strong geometric assumptions or carefully curated datasets, whichlimit their applicability to real-world scenarios. In this paper, we presentPLOT (Pseudo-Labeling via Object Tracking), a framework that gen-erates 3D annotations from monocular videos without auxiliary sen-sors or model retraining. PLOT tracks object and background trajecto-ries to estimate camera motion and perform object association in pose-unknown settings. These trajectories provide point correspondences thatalign frame-wise pseudo-LiDARs, which are then fused via simple opti-mization into a unified object shape robust to occlusion and viewpointshifts. Recognizing temporal coherence as a fundamental requirementfor reliable shape fusion and video perception, we design a global ob-ject memory that preserves consistent object identities across frames.PLOT achieves robust annotation quality and strong generalization onboth M3OD video benchmarks and in-the-wild videos, proving its effec-tiveness across diverse and unconstrained domains. Project page: https://plot-eccv.github.io.

Abstract:
A major challenge in autonomous driving is the “long tail” ofsafety-critical edge cases, which often emerge from unusual combinationsof common traffic elements. Synthesizing these scenarios is crucial, yetcurrent controllable generative models provide incomplete or entangledguidance, preventing the independent manipulation of scene structure,object identity, and ego actions. We introduce CompoSIA, a composi-tional driving video simulator that disentangles these traffic factors, en-abling fine-grained control over diverse adversarial driving scenarios. Tosupport controllable identity replacement of scene elements, we proposea noise-level identity injection, allowing pose-agnostic identity generationacross diverse element poses, all from a single reference image. Further-more, a hierarchical dual-branch action control mechanism is introducedto improve action controllability. Such disentangled control enables ad-versarial scenario synthesis—systematically combining safe elements intodangerous configurations that entangled generators cannot produce. Ex-tensive comparisons demonstrate superior controllable generation qualityover state-of-the-art baselines, with a 17% improvement in FVD for iden-tity editing and reductions of 30% and 47% in rotation and translationerrors for action control. Furthermore, downstream stress-testing revealssubstantial planner failures: across editing modalities, the average colli-sion rate of 3s increases by 173%.

Abstract:
Identity preserving face synthesis (IPFS) aims to generate large-scale virtual face datasets to train robust face recognition (FR) models while mitigating privacy and bias issues of web-crawled data. While diffusion-based IPFS methods have made huge progress, FR models trained on synthetic data still underperform those trained on real images. This gap stems from limited style diversity and insufficient intraidentity variation. Through analysis, we find that identity embeddings from real images primarily encode core facial features and inherently contain intra-identity variation. To generate more realistic virtual images, we propose TextFace, a diffusion-based IPFS framework that combines compositional text control for overall style diversity and a principled score-space identity blending strategy to enhance intra-identity variation. The identity-irrelevant condition factors are modeled by compositional language attributes, while identity-inherent factors are diversified by identity blending, which injects small, stable structural variations during denoising without sacrificing identity consistency. Extensive experiments show that FR models trained on TextFace synthetic data consistently outperform prior IPFS pipelines and substantially narrow the gap to real-data training.

Abstract:
Character swapping aims to faithfully transfer a target iden-tity while preserving the source scene layout and pose, yielding visuallyseamless composites. However, existing general image editing methodsoften struggle to simultaneously achieve identity consistency, backgroundpreservation, and pose or structural stability. This challenge is further ex-acerbated by the lack of high-quality character swapping datasets. To ad-dress these challenges, we propose Spatio-Temporal Identity PreferenceAlignment (SIPA) framework, that systematizes diffusion-based pref-erence optimization from both spatial and temporal perspectives. Spa-tially, we introduce Region-Weighted Preference (RWP), which increasesthe weighting of critical regions (e.g., the face or body) during DPOtraining, focusing preference signals on the desired local differences andthereby substantially reducing data dependence while improving train-ing stability. Temporally, we propose Time-Injected Identity Preference(TIP): during low-noise steps, we explicitly inject face-difference cues intothe DPO preference term to further enhance facial identity consistency.In addition, we build and plan to release a two-stage dataset tailoredfor character swapping. Extensive quantitative and qualitative exper-iments demonstrate that SIPA consistently outperforms prior methodsacross identity fidelity, structure preservation, and visual quality, achiev-ing state-of-the-art performance.

Abstract:
Human-object interaction (HOI) video generation has gar-nered increasing attention due to its promising applications in digitalhumans, e-commerce, advertising, and robotics imitation learning. How-ever, existing methods face two critical limitations: (1) a lack of effec-tive mechanisms to inject multi-view information of the object into themodel, leading to poor cross-view consistency, and (2) heavy relianceon fine-grained annotations including hand mesh and body templatesfor modeling interaction occlusions, due to limited training data. To ad-dress these challenges, we introduce ByteLOOM, a Diffusion Transformer(DiT)-based framework that generates realistic human-object interactionvideo with geometrically consistent object illustration, using simplifiedhuman conditioning and 3D object inputs. We first propose an RCM-cache mechanism that leverages Relative Coordinate Maps (RCM) as auniversal representation to maintain object’s geometry consistency andprecisely control 6-DoF object transformations in the meantime. To com-pensate HOI dataset scarcity and leverage existing datasets, we furtherdesign a training curriculum that enhances model capabilities in a pro-gressive style and relaxes the demand of hand mesh. Extensive experi-ments demonstrate that our method faithfully preserves human identityand the object’s multi-view geometry, while maintaining smooth motionand object manipulation. Project page: neutrinoliu.github.io/byteloom/

Abstract:
Accurately reconstructing complex full multi-object scenesfrom sparse observations remains a core challenge in computer vision anda key step toward scalable and reliable simulation for robotics. In thiswork, we introduce RecGen, a generative framework for probabilisticjoint estimation of object and part shapes, as well as their pose underocclusions and partial visibility from one or multiple RGB-D images. Byleveraging compositional synthetic scene generation and strong 3D shapepriors, RecGen generalizes across diverse object types and real-worldenvironments. RecGen achieves state-of-the-art performance on complex,heavily occluded datasets, robustly handling severe occlusions, symmetricobjects, objects parts, and intricate geometry and texture. Despite usingnearly 80% fewer training meshes than the previous state of the artSAM3D, RecGen outperforms it by 30.1% in geometric shape quality,9.1% in texture reconstruction, and 33.9% in pose estimation. We willrelease our code, training data and evaluation benchmark at our website.

Abstract:
Controllable video generation for complex hand-object interactions is a critical step toward building visual world models. However, existing methods often struggle to achieve fine-grained, 3D-consistent hand articulation in generated videos. By relying on dense 2D trajectories or implicit pose representations, they collapse crucial geometric structures into spatially ambiguous signals, leading to severe motion inconsistencies and hallucinated artifacts under egocentric occlusions. To address this, we propose leveraging sparse 3D hand joints as explicit control signals with three key advantages: explicit geometry to resolve occlusions, an intuitive interface for interactive editing, and cross-embodiment generalization to robotic hands. Built upon this, our efficient control module extracts occlusion-aware features from the source reference frame by penalizing unreliable visual features from hidden joints, and employs a 3Dbased weighting mechanism to handle dynamically occluded target joints during motion propagation. Meanwhile, it directly injects 3D geometric embeddings into the latent space to enforce structural consistency. To facilitate robust training and evaluation, we develop an automated annotation pipeline, yielding 1M high-quality egocentric video clips paired with precise hand trajectories. Experiments demonstrate that our approach outperforms state-of-the-art baselines, generating high-fidelity egocentric videos with realistic hand-object interactions.

Abstract:
Despite rapid progress in video diffusion models (VDMs),ensuring semantic adherence and physical commonsense remains a fun-damental challenge, as even state-of-the-art systems frequently violatereal-world physical laws, such as dynamics, thermodynamics, and op-tics. While Direct Preference Optimization (DPO) has become a popu-lar post-training strategy for aligning generative models, existing videoDPO methods to improve physical commonsense suffer from high compu-tational costs, poorly matched preference pairs, and ambiguous global su-pervision that fails to localize physical violations. We propose PhysPO,a physics-aware local preference optimization framework for physicallyconsistent video generation. We first introduce CounterPhyPipe, a physics-aware counterfactual data construction pipeline that forms video pref-erence pairs with consistent global semantics and local physical viola-tions, enabling meaningful comparisons for preference learning. Thenwe leverage high-frequency decomposition to introduce a physical mask,which localizes regions where physical state transitions may occur. Fi-nally, we develop PhysPO, a physics-aware DPO framework that con-centrates supervision on physically relevant regions while enforcing neu-trality on background regions. This mechanism reduces gradient noiseand mitigates shortcut optimization, encouraging the model to focus ongenuine physical discrepancies rather than superficial cues. Extensiveexperiments demonstrate that PhysPO significantly improves physicalcommonsense without compromising semantic adherence. Our projectpage is here3 .

Abstract:
Camera intrinsics play a vital role in recovering 3D structurefrom 2D video. However, most 3D algorithms assume that intrinsics re-main fixed throughout input video, an assumption that frequently failsfor real-world in-the-wild videos. Consequently, estimating per-frame in-trinsics from RGB images is critical for enabling 3D methods to operaterobustly on dynamic intrinsics videos. Previously, InFlux has contributedto this research direction by establishing the first real-world benchmarkwith per-frame ground truth intrinsics for dynamic intrinsics videos, en-abling systematic evaluation of algorithms for this task. Nevertheless,existing methods remain inaccurate due to two key obstacles: (i) train-ing data for this task is scarce and lacks diversity in camera intrinsics;and (ii) existing benchmarks, including InFlux, are limited in scene andcamera motion diversity, making it difficult to properly evaluate methodperformance. To address both gaps, we present InFlux++, consisting oftwo components. InFlux++ Synth is a large-scale procedurally generatedsynthetic video dataset with 441K+ annotated frames from 1841 high-resolution videos, providing accurate per-frame ground truth intrinsicsfor training dynamic intrinsics prediction models; a subset additionallyincludes per-frame camera pose, depth, and surface normals. The videosfeature rich intrinsics diversity through changes in camera zoom andfocus over time, as well as dynamic objects and realistic rendering ef-fects such as lens distortion and defocus blur. InFlux++ Real is a large-scale real-world benchmark that extends InFlux with 514K+ newly cap-tured frames across 334 high-resolution videos, spanning a wider rangeof scenes and camera motions. Finetuning existing intrinsics predictionmethods on InFlux++ Synth consistently improves focal length estima-tion across both InFlux++ Real and InFlux, suggesting that syntheticsupervision is a promising direction for RGB-based intrinsics prediction.For the dataset, benchmark, code, videos, submission instructions, andlive leaderboard, please visit https://influx.cs.princeton.edu/.

Abstract:
World models aim to capture environmental dynamics andpredict future trajectories, showing growing potential for embodied in-telligence. Physics-informed 4D generation integrates physical simulationto predict 3D object interactions, offering a promising pathway towardworld models. However, this paradigm relies on manually imposed dy-namical assumptions rather than internalizing world dynamics, and thusstill leaves a gap toward a true world model. To bridge this gap, we pro-pose NeuIDO, a novel world dynamics modeling framework that learnsa unified intrinsic dynamics representation from visual observations, ad-vancing physics-informed 4D generation toward a world model. Specifi-cally, we formulate world modeling as a neural operator learning problemand introduce a two-stage training strategy to learn a generalizable map-ping from the visual observation distribution to the intrinsic dynamicsdistribution. Building on this observation-dynamics mapping, NeuIDOenables zero-shot dynamics inference directly from videos and can be fur-ther aligned with complex real-world dynamics via few-shot adaptation.Extensive experiments demonstrate that NeuIDO effectively unifies theintrinsic dynamics underlying diverse visual observations into a sharedrepresentation and rapidly infers dynamics in novel scenes.

Abstract:
The widespread adoption of consumer 360° cameras has created a growing demand for robust in-the-wild panoramic 3D reconstruction. Existing methods remain fragile on long-horizon trajectories with strong ego-motion and dynamic interference. Quantifying this gap at scale is challenging because available benchmarks are often short, constrained, or captured in controlled settings, limiting evaluation and progress. To address this, we introduce OmniLife360, a benchmark for 3D reconstruction from in-the-wild 360° captures. It spans diverse scenes, environments, and motion dynamics captured by individuals across real-world activities, enabling rigorous evaluation of reconstruction robustness in complex natural conditions. Extensive evaluations reveal that state-ofthe-art methods excel in controlled settings but suffer significant degradation in dynamic scenarios. Motivated by these findings, we propose Omni4DGS, a motion-aware Gaussian Splatting framework that combines motion decomposition with lightweight supervision to better handle motion variations, dynamic distractors, and long-horizon pose drift. Experiments show that Omni4DGS consistently improves reconstruction quality and stability in dynamic 360° scenarios, reducing motion-induced artifacts and producing cleaner renderings under strong scene motion. The benchmark is available at https://huggingface.co/datasets/ AutoLab-SJTU/OmniLife360.

Abstract:
High-fidelity removal of eyeglasses from video is a major challenge in facial attribute editing, as the underlying facial geometry is often obscured by complex refractive distortions and view-dependent specular reflections. While large-scale generative priors have shown promise in eyeglasses removal via static image inpainting, they often lack the structural constraints necessary to maintain identity, expression, and pose, leading to visible “identity drift” in both static images and dynamic sequences. In this paper, we propose a novel transfer framework that addresses the stochastic nature of generative priors. Our pipeline first extracts highfidelity synthetic face images from a commercial-grade generative model (Nano Banana, Gemini 3 Pro Image), regularizes them via a three-stage structural filtering process to preserve identity, expression, and pose, and finally applies physically-based simulation of lens optics during training to provide diverse, paired data. This process transfers Nano Banana’s photo-realistic, multi-view knowledge into a specialized restoration architecture, JFSnet (Joint Feature-Spatial network). JFSnet integrates DINOv2-based semantic features with a convolutional decoder for spatial reconstruction, leveraging translation equivariance constraints to improve temporal consistency and high-frequency detail preservation. Evaluations on the curated Flickr-Faces-HQ (FFHQ) subset (12,163 images) show that our approach achieves high fidelity and structural accuracy, while maintaining inference speed of 27.68 FPS. In perceptual studies on CelebV-Text video sequences, our results are consistently preferred over diffusion and GAN-based baselines for ocular consistency, temporal stability, and overall restoration quality.

Abstract:
Subject-driven image generation faces an “Identity-DiversityParadox”, where strong identity preservation often leads to rigid and low-diversity outputs. We propose a post-training framework called DivRLthat jointly optimizes identity consistency and structural diversity si-multaneously by leveraging disentangled visual features from a robustsimilarity model. Specifically, we introduce a Negative Self-SimilarityMeasure (nSSM) to quantify structural diversity, and Visual Seman-tic Matching (VSM) to evaluate identity consistency. We propose an“Explore-and-Suppress” strategy that treats VSM as a gated constraint:the model freely explores structurally diverse configurations, and onlysamples that violate the identity threshold are penalized via a quadratichinge loss. This converts identity preservation from a competing objectiveinto a feasibility constraint, allowing nSSM and VSM to improve jointly.Experiments demonstrate that our method effectively pushes the modelto generate both consistent and diverse images and improves structuraldiversity while maintaining comparable identity consistency through agated optimization formulation.

Abstract:
Recent advances in text-to-image (T2I) generation have led to models capable of producing highly realistic images. Yet, reliably evaluating their outputs remains challenging, especially at scale. Existing automatic evaluators, often relying on a static prompt set, struggle to capture subtle failure modes such as partial prompt misalignment, compositional errors or visually plausible but semantically incorrect generations. In this work, we introduce DynEval, a Dynamic Evaluation framework designed to jointly assess text-to-image alignment and image quality of T2I models. To support scalable training beyond limited humanannotated data, we construct two large datasets. First, we build GenDB, a collection of 500K prompt-image pairs generated from human-written prompts drawn from DiffusionDB using a tiered prompt-model generation strategy. Second, building upon GenDB, we construct DynEvalInstruct, a 250K instruction dataset comprising prompt-image-response triplets distilled from a structured evaluation pipeline that decomposes evaluation into text-image alignment and visual quality reasoning. Using this dataset, we perform full fine-tuning of a compact evaluator through a curriculum learning strategy to effectively distill the superior evaluation capabilities of a larger teacher vision-language model, resulting in DynEval-2B and DynEval-4B. In extensive comparisons against existing evaluators across 11 benchmarks, our evaluator achieves a higher overall correlation with human judgments. Furthermore, it provides finegrained analysis of the capabilities and failure modes of 36 T2I models across 42 subcategories and 9 semantic dimensions.

Abstract:
Long video understanding is fundamentally constrained by massive data volume and high information density. While agent-based methods mitigate context length bottlenecks, they predominantly rely on static, "one-size-fits-all" multi-agent systems (MAS). These rigid architectures are computationally redundant and lack the flexibility to handle diverse, instance-level reasoning demands. To address this, we propose DyMAC (Dynamic MAS Construction), a novel framework that dynamically generates instance-specific MAS for each video-question pair. By logically abstracting the MAS into a directed computational graph via automated code generation, DyMAC exhibits exceptional adaptability, seamlessly tailoring its collaborative architecture to the specific complexity of each input. Furthermore, we employ a streamlined two-stage training strategy—Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR)—to enhance the model’s dynamic construction and decision-making capabilities. Extensive experiments across four mainstream benchmarks demonstrate that DyMAC achieves a new state-of-the-art, outperforming the second-best method by 3.3% on average. Notably, it significantly improves inference efficiency compared to existing static approaches, further highlighting its superior adaptability and robustness in complex video scenarios.

Abstract:
Video Variational Auto-Encoders (Video VAEs) compress video data from the highly redundant pixel space into a compact latent representation, playing an important role in state-of-the-art video generation models. However, existing methods typically learn inter-frame correlations implicitly, overlooking the potential of breaking down video compression into two separate parts: keyframe encoding and inter-frame dynamic encoding, which is a fundamental design of traditional video codecs. To address this, we incorporate traditional video codec standard design into the Video VAE and introduce VC-VAE, a model that explicitly separates keyframe and inter-frame dynamic compression. We start by establishing a high-fidelity static keyframe anchor through initialization from a powerful pre-trained image VAE. Then, to explicitly model dynamic relative to this anchor, we introduce the Temporal Dynamic Difference Convolution (TDC), an operator designed to learn sparse motion residuals from inter-frame differences while maintaining a separate pathway for static content. Qualitative and quantitative experiments show that our proposed VC-VAE significantly outperforms baseline models in reconstruction quality, dynamic modelling, and training efficiency.

Abstract:
The surging demand for adapting long-form cinematic content into short videos has motivated the need for versatile automatic video compilation systems. However, existing compilation methods are limited to predefined tasks, and the community lacks a comprehensive benchmark to evaluate the cinematic compilation. To address this, we introduce CineBench, the first benchmark for instruction-driven cinematic video compilation, featuring diverse user instructions and highquality ground-truth compilations annotated by professional editors. To overcome contextual collapse and temporal fragmentation, we present CineAgents, a multi-agent system that reformulates cinematic video compilation into “design-and-compose” paradigm. CineAgents performs script reverse-engineering to construct a hierarchical narrative memory to provide multi-level context and employs an iterative narrative planning process that refines a creative blueprint into a final compiled script. Extensive experiments demonstrate that CineAgents significantly outperforms existing methods, generating compilations with superior narrative coherence and logical coherence.

Abstract:
Emerging multi-modal world models attempt to jointly generate videos across diverse modalities (e.g., RGB, depth, and mask), yet they fail to fully exploit the priors of existing foundation models. We propose M2-REPA, the first representation alignment method tailored for multi-modal video generation. Our key insight is that foundation models trained on different modality spaces naturally capture distinct domain-specific priors, acting as complementary “experts.” Specifically, we first decouple modality-specific features from the diffusion model’s intermediate representations, then align each with its corresponding expert foundation model. To this end, we design two synergistic objectives: a multi-modal representation alignment loss that enforces feature-toexpert matching, and a modality-specific decoupling regularization that encourages complementarity across different modalities. This design enables joint optimization, exploiting priors from multiple foundation models. Extensive experiments demonstrate that our method outperforms baselines in visual quality and long-term consistency.

Abstract:
Existing Multimodal Large Language Models (MLLMs) of-ten suffer from hallucinations in long video understanding (LVU), pri-marily due to the imbalance between textual and visual tokens. Ob-serving that MLLMs handle shorter but more accurate visual inputswell, recent LVU works alleviate hallucinations by automatically parsingthe vast visual data into manageable segments that can be effectivelyprocessed by MLLMs. SFT-based tool-calling methods can serve thispurpose, but they typically require vast amounts of fine-grained, high-quality data and suffer from constrained tool-calling trajectories. Wepropose a novel VideoTIR that leverages Reinforcement Learning (RL)to encourage proper usage of comprehensive multi-level toolkits for ef-ficient long video understanding. VideoTIR explores both Zero-RL andSFT cold-starting to enable MLLMs to retrieve and focus on meaning-ful video segments/images/regions, enhancing long video understandingboth accurately and efficiently. To reduce redundant tool-calling in theearly RL-stage and accelerate convergence, we propose Toolkit ActionGrouped Policy Optimization (TAGPO), which enhances the efficiencyof the calling process through the finer stepwise reward assignment. Ad-ditionally, we develop a sandbox-based trajectory synthesis frameworkto generate high-quality trajectory data. Extensive experiments on threelong-video QA benchmarks demonstrate the effectiveness and efficiencyof our method.

Abstract:
Skeleton-based action recognition classifies human actionsfrom sequences of joint coordinates and is widely studied using graph con-volutional networks (GCNs). GCNs typically rely on repeated graph nodefeature aggregation via one-hop message passing to capture long-rangedependencies across entire joint sequences, while progressively refiningrepresentations from multiple perspectives through block-wise learnedtopology. However, this repeated aggregation can often degrade repre-sentations, making the node features in the final embedding increasinglyhomogenized and causing the final embedding to fail to capture local orfine-grained representations formed in early blocks. To address these is-sues, we propose a novel topology-conditioned block-progressive memoryfor skeleton GCNs. Along the GCN block axis, memory stores comple-mentary block-wise representations and fuses the accumulated memorystate into the final embedding, thereby mitigating representation degra-dation caused by repeated aggregation. At each block, a sample-specificmemory state is updated by minimizing a topology-conditioned recon-struction loss, where the reconstruction error quantifies how well theprevious memory explains the current block representation while captur-ing newly emerging, discriminative cues. We support two inference-timemodes: memory-free, which uses memory only during training, and anoptional memory-enabled mode, which also applies the same memoryupdate rule at test time for modest gains. Both variants achieve state-of-the-art results on large-scale benchmarks, including NTU RGB+D,NTU RGB+D 120, and Kinetics-Skeleton.

Abstract:
Zero-shot video moment retrieval (ZMR) aims to overcomethe limitations of traditional approaches that require large-scale datasetsannotated with text and its relevant temporal spans. Despite advances inpre-trained vision–language models (VLMs) and multimodal large lan-guage models (MLLMs), existing ZMR methods still heavily depend onquery-to-video content similarity, making them vulnerable to modal-ity and language-style gaps. These gaps lead to unreliable span pro-posals and unstable moment retrieval results. To address this issue, wepropose Self-Similarity-based Moment proposal and Scoring (Self-SiMS)that instead exploits intrinsic relationships within videos, enabling ro-bust span generation and scoring. By deriving self-similarity only fromthe video content, we circumvent the noisy and mismatched patternsof query–frame or query–caption similarities, thereby mitigating bothmodality and language-style gaps. Furthermore, we introduce a query-aware MLLM-based reasoning stage to further sharpen alignment be-tween text and video. Extensive experiments demonstrate that Self-SiMSachieves the state-of-the-art performance across ZMR benchmarks.

Abstract:
Multimodal Large Language Models (MLLMs) suffer fromsevere memory bottlenecks in streaming video understanding due to ac-cumulated Key-Value (KV) cache with the growth of video length. Exist-ing training-free compression methods employ the same strategy acrossall attention heads in one layer, but ignore their diverse functional roles intemporal representation. In this paper, we reveal the inherent temporalheterogeneity of attention heads in MLLMs through systematic Spatio-temporal Attention Profiling (SAP), and discover that heads naturallyspecialize into instantaneous, short-term, and episodic roles agnostic toinput video frames and their queries. Motivated by this finding, we pro-pose the Head-aware Heterogeneous Streaming Video Cache (H2 SVC),a training-free KV cache compression framework for efficient streamingvideo understanding. H2 SVC features an offline routing strategy that ex-ploits the distinct temporal behavior of heads in each layer and adapttheir KV cache to dedicated memory banks tailored for varying temporalreceptive fields. Furthermore, we develop Semantic Trajectory CurvatureEviction (STCE) for the long-term episodic bank to enhance semanticdiversity under strict streaming budget. STCE models the Value statesas a geometric trajectory to continuously evict predictable frames onsmooth paths, while preserving sharp semantic inflection points. Exten-sive experiments on widely adopted streaming benchmarks demonstratethat H2 SVC achieves state-of-the-art performance, and is comparableor superior to full-context offline models with significantly reduced KVcache memory and accelerated inference speed.

Abstract:
Agentic multimodal large language models (MLLMs) (e.g.,OpenAI o3 [41] and Gemini Agentic Vision [10]) achieve remarkablereasoning capabilities through the iterative invocation of visual tools.However, the cascaded perception, reasoning, and tool-calling loops intro-duce significant sequential overhead. This overhead, termed agentic depth,incurs prohibitive latency and seriously limits system-level concurrency.To this end, we propose SpecEyes, an agentic-level speculative accelera-tion framework that breaks this sequential bottleneck. Our key insight isthat a lightweight MLLM can plan a tool-free execution path to directlyanswer many queries, bypassing the expensive tool-use loop. To regulatethis speculative planning, we introduce a cognitive gating mechanismbased on answer separability, which quantifies the model’s confidence inself-verification without requiring oracle labels. Furthermore, we designa heterogeneous parallel funnel that exploits the small model’s statelessconcurrency to mask the large model’s stateful serial execution, therebymaximizing system throughput. Extensive experiments on V Bench,HR-Bench, and POPE demonstrate that SpecEyes achieves 1.1 − 3.35×speedup over the baseline while preserving or even improving accuracy,thereby boosting serving throughput under concurrent workloads.

Abstract:
Continuous variational autoencoders (VAEs) are widely usedas visual tokenizers for latent diffusion models. While CNN-based VAEsreliably capture local details, they model long-range dependencies ineffi-ciently. Vision Transformers (ViTs) provide global context, but existingViT-VAEs exhibit three practical limitations in this setting: (i) weakresolution extrapolation when trained at a single resolution, (ii) slowconvergence with suboptimal fine-grained color and texture fidelity, and(iii) inconsistent scaling gains when increasing model size. To addressthese challenges, we propose TransVAE, a hybrid CNN-ViT VAE thatcombines a shallow CNN front-end for local feature extraction with adeep Transformer backbone for global modeling. We apply minimal yetprincipled architectural modifications and systematically analyze their ef-fectiveness, including a pure Rotary Position Embedding (RoPE) strategy,a multi-stage design, and a convolutional feed-forward network. Acrossmodel sizes from 44M to 2.3B parameters, TransVAE shows learning-efficient training and predictable scaling improvements, and it supportssingle-resolution training (256×256) with robust inference at higher resolu-tions (512 × 512/1024 × 1024). Moreover, TransVAE achieves competitivereconstruction and downstream generation performance compared tostrong tokenizers, such as FLUX-VAE and VA-VAE, offering a favorabletrade-off between reconstruction fidelity and generation-friendly latents.

Abstract:
Multimodal Large Language Models (MLLMs) have achievedremarkable progress but still struggle with complex visual reasoningtasks requiring multi-step perception and logical deduction. While ex-plicit visual generation incurs prohibitive computational costs, existinglatent approaches often rely on external experts or lack rigorous cogni-tive logic. In this paper, we introduce ProLaViT (Progressive LatentVisual Thought), a framework empowering MLLMs to perform struc-tured visual derivation in the continuous latent space. Unlike worksdependent on heterogeneous external models, ProLaViT leverages anendogenous self-distillation mechanism, utilizing the model’s own vi-sual encoder to supervise latent thoughts. To facilitate this, we con-struct a scalable programmatic synthesis pipeline enabling the modelto internalize algorithmic precision without inference-time tools. We de-sign two reasoning paradigms: (1) Coarse-to-Fine Causal Chain for spa-tial tasks, guiding attention from global context to local targets. (2)Dialectical Reasoning Chain for logical tasks, incorporating counter-factual thinking for verification. Furthermore, we propose a Distance-Weighted Diversity Loss to impose topology-aware constraints, prevent-ing feature degeneration by enforcing semantic distinctiveness. Exten-sive experiments demonstrate that ProLaViT outperforms baselines onvision-centric benchmarks, achieving superior accuracy and interpretabil-ity with high efficiency.

Abstract:
Humans learn locomotion through visual observation, inter-preting visual content first before imitating actions. However, state-of-the-art humanoid locomotion systems rely on either curated motion capturetrajectories or sparse text commands, leaving a critical gap between visualunderstanding and control. Text-to-motion methods suffer from semanticsparsity and staged pipeline errors, while video-based approaches onlyperform mechanical pose mimicry without genuine visual understanding.We propose RoboMirror, the first retargeting-free video-to-locomotionframework embodying “understand before you imitate”. Leveraging VLMs,it distills raw egocentric/third-person videos into visual motion intents,which directly condition a diffusion-based policy to generate physicallyplausible, semantically aligned locomotion without explicit pose recon-struction or retargeting. Extensive experiments validate RoboMirror’seffectiveness, it enables telepresence via egocentric videos, drasticallyreduces third-person control latency by 80%, and achieves a 3.7% highertask success rate than baselines. By reframing humanoid control aroundvideo understanding, we bridge the visual understanding and action gap.

Abstract:
Audio–Visual Segmentation (AVS) aims to localize sound-producing objects at the pixel level by integrating auditory and visualcues. However, existing methods often struggle with multi-source en-tanglement and audio–visual misalignment, leading to a dominance biastoward acoustically or visually salient objects (i.e., louder or larger ones)at the expense of subtler or co-occurring sources. To address these chal-lenges, we propose DDAVS: Delayed Bidirectional Alignment via Dis-entangled Audio Semantics for Audio-Visual Segmentation. To mitigatemulti-source entanglement, DDAVS employs learnable queries to extractaudio semantics and anchor them within a structured semantic spacederived from an audio prototype memory bank. This process is furtheroptimized through contrastive learning to enhance discriminability androbustness. To alleviate audio–visual misalignment, DDAVS introducesdual cross-attention with delayed modality interaction, improving therobustness of multimodal alignment. Extensive experiments on the AVS-Objects and VPO benchmarks demonstrate that DDAVS achieves state-of-the-art performance across single-source, multi-source, and multi-classmulti-instance scenarios. These results validate the effectiveness and gen-eralization ability of our framework under challenging real-world au-dio–visual segmentation conditions. Project page.

Abstract:
Localising multiple sound sources in visual scenes remains afundamental challenge in multimodal perception due to an inherent cir-cular dependency: separating mixed audio requires knowing source loca-tions, while identifying sound-producing regions requires separated audiosignals. In this paper, we focus on the dual-source setting and discover aselective convergence in self-supervised audio-visual learning: when pre-sented with multiple sound sources, contrastive models naturally con-verge to the most salient audio-visual correspondence rather than at-tempting to represent all sources equally. This emergent phenomenon,analogous to human selective auditory attention, enables us to breakthe above circular dependency through a progressive two-stage frame-work: first, leveraging selective convergence to identify dominant sources,and then exploiting these learned priors to uncover remaining sources.Our self-supervised approach achieves the best performance among self-supervised methods on dual-source benchmarks without requiring anymanual annotations, and even surpasses some weakly-supervised ap-proaches on certain metrics. Furthermore, we identify a fundamentalevaluation inconsistency in existing benchmarks: comparing continuouslocalisation heatmaps against bounding-box annotations creates system-atic biases, particularly for non-axis-aligned objects where the boundingbox includes substantial background regions. To address this, we intro-duce pixel-level segmentation masks to the existing benchmark, enablingspatially-aligned evaluation. Together, these results suggest that embrac-ing rather than suppressing selectivity offers a scalable, annotation-freeroute to multi-source localisation.

Abstract:
Egocentric video understanding requires procedural reasoning under partial observability and continuously shifting viewpoints. Current multimodal large language models (MLLMs) struggle with this setting, often generating plausible but visually inconsistent or weakly grounded responses. We introduce EgoVITA, a framework that decomposes egocentric video reasoning into a structured plan-then-verify process. The model first generates an egocentric plan: a causal sequence of anticipated actions from a first-person perspective. This plan is then evaluated by an exocentric verification stage that uses third-person reasoning over the same video to verify its spatiotemporal and logical consistency, without exocentric video input. This decomposition enables cross-perspective feedback without requiring paired ego-exo supervision. To train this reasoning process, we adopt Group Relative Policy Optimization (GRPO) with two dense reward signals: one that grounds anticipated actions in subsequent visual observations and another that reinforces consistent third-person verification. EgoVITA achieves state-of-the-art performance on egocentric reasoning benchmarks, outperforming Qwen2.5-VL-7B by +7.7 on EgoBlind and +4.4 on EgoOrient, while maintaining strong generalization on exocentric video tasks with only 52k training samples.

Abstract:
Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLIP, resulting in entangled representations. These challenges are severely exacerbated by two fundamental properties in the video domain: Temporal Misalignment, where textual descriptions often correlate only to specific, constrained temporal windows, leaving other frames text-irrelevant; and Semantic Asymmetry, which dictates a sparse, bidirectional, and non-equivalent relevance between frame-level visual details and caption-level concepts. This failure persists whether captions are short and temporally disjoint, creating ambiguity, or long and detailed, fostering entanglement between static objects and their temporal evolution. In this paper, we establish theoretical conditions that enable flexible alignment between video and text representations across the temporal dimension and at varying levels of granularity. Building on these theoretical insights, we introduce MoVA—Modular Long Video–Text Alignment—which learns dual asymmetric projections: a text-side projection that adaptively selects frame-aware subspaces of the caption, and a video-side projection that disentangles text-relevant visual concepts. Our framework ensures that the model can preserve global cross-modal semantics while disentangling evolving, frame-specific concepts and scale naturally to long captions and videos. Empirical evaluations show that MoVA outperforms existing methods in multiple video-text alignment tasks, demonstrating the effectiveness of our method.

Abstract:
Weakly-supervised video scene graph generation (WS-VSGG)aims to parse video content into structured relational triplets withoutbounding box annotations and with only sparse temporal labeling, sig-nificantly reducing annotation costs. Without ground-truth boundingboxes, these methods rely on off-the-shelf detectors to generate objectproposals, yet largely overlook a fundamental discrepancy from fully-supervised pipelines. Fully-supervised detectors implicitly filter out non-interactive objects, while off-the-shelf detectors indiscriminately detectall visible objects, overwhelming relation models with noisy pairs. We ad-dress this by introducing a learnable pair affinity that estimates the like-lihood of interaction between subject–object pairs. Through Pair AffinityLearning and Scoring (PALS), pair affinity is incorporated into inference-time ranking and further integrated into contextual reasoning throughPair Affinity Modulation (PAM), enabling the model to suppress non-interactive pairs and focus on relationally meaningful ones. To providecleaner supervision for pair affinity learning, we further propose Relation-Aware Matching (RAM), which leverages vision-language grounding toresolve class-level ambiguity in pseudo-label generation. Extensive exper-iments on Action Genome demonstrate that our approach consistentlyyields substantial improvements across different baselines and backbones,achieving state-of-the-art WS-VSGG performance.

Abstract:
Multi-modal Large Language Models (MLLMs) have signifi-cantly advanced video reasoning, yet Video Question Answering (VideoQA)remains challenging due to its demand for temporal causal reasoningand evidence-grounded answer generation. Prevailing end-to-end MLLMframeworks lack explicit structured reasoning between visual percep-tion and answer derivation, causing severe hallucinations and poor in-terpretability. Existing methods also fail to address three core gaps:faithful visual clue extraction, utility-aware clue filtering, and end-to-end clue-answer alignment. Inspired by hierarchical human visual cogni-tion, we propose ClueNet, a clue-aware video reasoning framework with atwo-stage supervised fine-tuning paradigm without extensive base modelmodifications. Decoupled supervision aligns clue extraction and chain-based reasoning, while inference supervision with an adaptive clue filterrefines high-order reasoning, alongside lightweight modules for efficientinference. Experiments on NExT-QA, STAR, and MVBench show thatClueNet outperforms state-of-the-art methods by ≥ 1.1%, with strongbenchmark generalization, hallucination mitigation, and improved infer-ence efficiency. This work bridges the perception-to-generation gap inMLLM video understanding, providing an interpretable, faithful reason-ing paradigm for real-world VideoQA applications.

Abstract:
Active Speaker Detection determines whether a visible per-son in a video is speaking at each moment. While recent audio–visualfusion methods perform well on clean data, they degrade under real-world corruptions such as background noise, occlusion, or simultaneousmodality degradation. We attribute this limitation to the absence of ex-plicit consistency constraints that promote robust, semantically alignedrepresentations across modalities. Without such guidance, models tendto learn fragile modality-specific shortcuts that fail under corrupted con-ditions. We propose C 3 ASD, a multi-level consistency-driven frameworkwith three complementary constraints: embedding-level inter-modalityconsistency aligns audio-visual representations during speech; sequence-level intra-modality consistency separates speaking and non-speakingclusters via track-aware contrastive learning; and prediction-level con-sistency stabilizes fusion through knowledge distillation. Extensive ex-periments demonstrate significant improvements under diverse audio, vi-sual and joint corruptions, while maintaining competitive performanceon clean data.

Abstract:
Real-world image restoration (IR) remains challenging dueto complex and coupled degradations. While recent agentic IR frame-works leverage Large Language Models for flexible tool planning, theyface two critical limitations. First, from a search scheme perspective,excessive reliance on greedy strategies fails to balance exploration andexploitation. Second, existing agentic systems underutilize information,exhibiting episodic amnesia. To address these challenges, we proposeSelf-Evolving Agentic Image Restoration (SEAR), which formu-lates restoration as a sequential decision-making problem. Inspired by thedual-process theory, SEAR comprises an Intuitive Executor and a Delib-erate Planner, respectively following the fast-thinking System 1 and slow-thinking System 2 principles. The Deliberate Planner employs Pruning-Aware Monte Carlo Tree Search for long-horizon reasoning, utilizing ahybrid no-reference reward and a Multimodal Large Language Model(MLLM)-based tournament to prevent metric exploitation. Complemen-tarily, the Intuitive Executor leverages a self-evolving episodic memoryindexed by degradation-aware state fingerprints. This mechanism dis-tills expensive search trajectories into adaptive expertise, overcomingepisodic amnesia while progressively amortizing cold-start explorationcosts through memory reuse. Extensive experiments on synthetic andreal-world benchmarks demonstrate its strong perceptual and quantita-tive performance.

Abstract:
Multimodal Large Language Models (MLLMs) have achievedremarkable success in visual understanding but remain constrained invisual generation due to the fundamental feature discrepancy betweensemantic perception and pixel-level reconstruction. Bridging this gap re-quires overcoming two core challenges: endowing semantic encoders withhigh-fidelity reconstruction capabilities, and effectively aligning gener-ative models with semantic spaces without relying on external teach-ers. To this end, we propose a novel unified multimodal framework fea-turing Semantic-Pixel self-alignment and Adaptive Routing (SPAR).First, to reconcile semantic perception with pixel-level reconstruction, weintroduce an asymmetric dual-stream unified tokenizer. A lightweightsemantic stream anchors discriminative features, while a Transformer-augmented pixel stream recovers fine-grained visual details into a uni-fied compact latent space. Second, to eliminate external dependencies, wepropose a self-aligned generation paradigm that natively leverages thisoptimized tokenizer as an internal alignment teacher for the diffusionmodel. Furthermore, to facilitate flexible multimodal interaction withinthis unified space, we introduce Dynamic Token Routing, which enableseach token to adaptively aggregate multi-layer MLLM features based onits distinct semantic demands. Extensive experiments demonstrate thatSPAR establishes the state-of-the-art for unified architectures, achiev-ing exceptional generation and reconstruction quality while preservingfoundational visual understanding capabilities.

Abstract:
Video-LLMs often attend to irrelevant frames, which is espe-cially detrimental for sports coaching tasks requiring precise temporal lo-calization. Yet obtaining frame-level supervision is challenging: expensiveto collect from humans and unreliable from other models. We improvetemporal grounding without additional annotations during training byexploiting the observation that related tasks, such as generation andverification, must attend to the same frames. We enforce this via a self-consistency objective over select visual attention maps of tightly-relatedtasks. Using VidDiffBench, which provides ground-truth keyframe an-notations, we first validate that attention misallocation is a meaningfulbottleneck. We then show that training with our objective yields gains of+3.0%, +14.1% accuracy and +0.9 BERTScore over supervised finetun-ing across three sports coaching tasks: ExAct, FitnessQA, and ExpertAF.

Abstract:
As video corpora continue to expand in both scale and task complexity, there is increasing demand for approaches that retrieve relevant videos from large-scale corpora (inter-video reasoning) and subsequently perform fine-grained, query-conditioned tasks (intra-video reasoning) within the retrieved content, such as temporal grounding. However, existing approaches typically treat retrieval as a preprocessing step, and consequently, when the initial retrieval fails, there is no mechanism to refine the search, leading to the failure of subsequent fine-grained intra-video reasoning. Moreover, while recent agentic frameworks have advanced video understanding, they typically assume that the queryrelevant video is already given, focusing exclusively on intra-video reasoning tasks. To address these limitations, we propose VideoSearch-R1, an agentic framework for iterative video retrieval and reasoning through multi-turn interaction with a video search engine. Specifically, we introduce Soft Query Refinement (SQR) to refine search query tokens in a continuous latent space rather than rewriting queries in the discrete text space, enabling more efficient and fine-grained adjustments. SQR and its reasoning process are trained using Group Relative Policy Optimization (GRPO), guided by task-level reward signals derived from retrieval and downstream tasks. Building upon this, VideoSearch-R1 achieves stateof-the-art performance across three datasets on Video Corpus Moment Retrieval (VCMR), iteratively retrieving videos from large-scale corpora, refining search queries, and performing precise query-conditioned temporal grounding within the retrieved content. Our analyses show that SQR effectively refines the original query, requiring significantly fewer generated tokens than explicit text-level query refinement. Code and model checkpoints are publicly available at mlvlab.github.io/VideoSearch-R1.

Abstract:
Motion, speech, and sound effects are fundamental elementsof human-centric videos, yet their heterogeneous temporal characteris-tics make joint generation highly challenging. Existing audio-video gen-eration models often fail to maintain consistent alignment across thesemodalities, leading to noticeable mismatches between motion, speech,and environmental sounds. We present Unison, a unified framework thatexplicitly promotes coherence across the motion, speech, and sound modal-ities. Within the audio stream, Unison employs a semantic-guided har-monization strategy that decouples the generation of speech and sound-effect components. Leveraging bidirectional audio cross-attention andsemantic-conditioned gating for semantic-driven adaptive recomposition,this approach effectively mitigates speech dominance and enhances acous-tic clarity. For audio–motion synchronization, we propose a bidirectionalcross-modal forcing strategy where the cleaner modality guides the nois-ier one through decoupled denoising schedules, reinforced by a progres-sive stabilization strategy. Extensive experiments demonstrate that Uni-son achieves state-of-the-art performance in both audio perceptual qual-ity and cross-modal synchronization, highlighting the importance of ex-plicit multimodal harmonization in human-centric video generation.

Abstract:
Tree-based speculative decoding accelerates autoregressive generation by verifying a branching tree of draft tokens in a single targetmodel forward pass. However, existing methods prioritize maximizing token-level likelihood or the number of accepted tokens while ignoring a critical “efficiency paradox”: the computational overhead of drafting and verifying big trees can grow super-linearly, particularly at scale. This often leads to negative wall-clock speedup when batch sizes increase or hardware saturation limits are reached. To address this, we propose SMART, a System-aware Marginal Analysis framework for Runtime Tree construction. SMART reformulates tree expansion as a hardware-aware optimization problem that directly maximizes end-toend speedup. By applying a principled marginal benefit–cost rule at inference time, SMART expands a node only when its marginal benefit– cost ratio exceeds the tree-level speedup. SMART is training-free and serves as a plug-and-play controller for existing frameworks like MSD and EAGLE. Extensive evaluations across three MLLMs (e.g., LLaVA, Qwen2-VL) and four LLMs (e.g., Llama-3.1, DeepSeek-R1) demonstrate that SMART consistently outperforms state-of-the-art baselines. It delivers an average additional speedup of 20.0% for MLLMs and 15.4% for LLMs across compute-bound batching regimes and diverse GPU architectures without performance loss.

Abstract:
Temporal Action Localization (TAL) typically relies on seg-ment annotations or offline access to full videos, limiting scalability andonline use. We introduce Point-Supervised Online TAL (POTAL), whichlocalizes actions in streaming videos using only one temporal point perinstance. To solve POTAL, we propose OnPoint, an offline-to-onlinemulti-level distillation framework that transfers knowledge from a point-supervised offline teacher to an online student via (i) pseudo-segmentinstance distillation, (ii) class-activation sequence distillation, and (iii)anticipatory window-level distillation. We further improve robustness byincorporating the original point labels into student training and by re-fining anchor decoding with actionness-guided attention calibration. Ex-periments on five datasets show OnPoint consistently outperforms strongbaselines, establishing a solid foundation for POTAL. †

Abstract:
Although VLMs are making fast progress in recognizing non-Latin scripts, they still face feasibility problems for less common scriptswith extremely low resources. Current Open-set text recognition methodsshow some good qualitative results, although they still lack quantitativeevidence. Specifically, the majority of open-set and zero-shot text recog-nition methods are confined to the CJK family, and to our knowledge,none handle more than one script family at the same time. In this work,we fill this gap with a unified, script-agnostic open-set text recognitionframework that can handle not just multiple scripts, but multiple scriptfamilies - Indic, Latin, and CJK scripts - in one set of partially sharedmodules. In this framework, we introduce a robust network structurethat can handle the complex character layout across a diverse collectionof scripts of different families. Furthermore, we introduce a heteroge-neous co-training method and tasks to enhance the model performanceon unseen scripts, and discuss the impacts from different data sourcesand task formats (character and word recognition). Experiments demon-strate that the proposed framework shows zero-shot learning capabilityon natural scene word images of Bengali, Gujarati, Japanese, Korean,and synthetic Yi samples. Specifically on the rare, yet actively used, Yiscript, our framework can achieve a word accuracy (ACR) of 16.10%,while the GPT5.2 shows an ACR of only 0, indicating a fundamentalcapability gap.

Abstract:
Scaling video generation to long durations reveals a criti-cal bottleneck: current models lack robust long-term memory. This defi-ciency can be studied along two critical aspects: object permanence, theability to precisely reproduce the appearance of objects upon re-entry;and memory capacity, the ability to process ultra-long context and use in-formation from distant history. Robust long-term memory requires both:object permanence without sufficient context handling limits the tempo-ral scope, while long context length without permanence fails to maintainidentity. To address this, we present Ring Forcing, an autoregressivevideo diffusion framework designed to robustly construct and preciselyutilize long-term memory. Our ring-structured training strategy enforcesretrieval from distant history, effectively reconciling the trade-off betweenstrict historical adherence and generative diversity. To expand memorycapacity, we introduce a compression and timestep composition strat-egy. Under fixed sequence length constraints, this method extends theeffective historical span to minutes-long durations and achieves a compre-hensive receptive field over the entire history. Furthermore, we present asparse RoPE mechanism to enable flexible, scalable memory adaptationwhile fully exploiting pre-trained priors. Extensive experiments demon-strate that Ring Forcing achieves superior minutes-long coherence andobject permanence, significantly outperforming state-of-the-art methods.

Abstract:
Existing Video Detailed Captioning (VDC) methods pre-dominantly rely on costly human annotations or distillation from pow-erful proprietary models, creating a dependency on external supervision.In this paper, we propose VDC-Agent, an autonomous self-evolvingframework that empowers a single Multimodal Large Language Model(MLLM) to generate and refine high-quality captions through principle-guided self-reflection. To overcome the inference latency inherent in it-erative refinement, we further propose to internalize this reflective ca-pability into the model. Specifically, we construct VDC-Agent-19K, apreference dataset derived from the agent’s self-scored trajectories, andintroduce a Curriculum Direct Preference Optimization (DPO) strategy.This strategy leverages the quality gap between generated candidates toprogressively align the model from easy to hard samples. Extensive ex-periments demonstrate that VDC-Agent achieves state-of-the-art perfor-mance on VDC and DREAM-1K benchmarks, generating captions withsuperior detail and faithfulness. Crucially, our internalization strategyretains the inference efficiency of the base model while significantly en-hancing its generalization capabilities, as validated by both quantitativemetrics and human evaluation.

Abstract:
Optical character recognition (OCR) has evolved from line-level transcription to structured document parsing, requiring models torecover long-form sequences containing layout, tables, and formulas. De-spite recent advances in vision-language models, most existing systemsrely on autoregressive decoding, which introduces sequential latency andamplifies error propagation in long documents. In this work, we revisitdocument OCR from an inverse rendering perspective, arguing that left-to-right causal generation is an artifact of serialization rather than anintrinsic property of the task. Motivated by this insight, we proposeMinerU-Diffusion, a unified diffusion-based framework that replaces au-toregressive sequential decoding with parallel diffusion denoising undervisual conditioning. MinerU-Diffusion employs a block-wise diffusion de-coder and an uncertainty-driven curriculum learning strategy to enablestable training and efficient long-sequence inference. Extensive experi-ments demonstrate that MinerU-Diffusion consistently improves robust-ness while achieving up to 3.2× faster decoding compared to autoregres-sive baselines. Evaluations on the proposed Semantic Shuffle benchmarkfurther confirm its reduced dependence on linguistic priors and strongervisual OCR capability.

Abstract:
Vision-language models (VLMs) excel at many tasks, yetcontinue to struggle with spatial reasoning, problems where the key in-formation is not directly observable in the input. Many spatial questionsrequire imaginative perception: simulating an unseen viewpoint, tracinga trajectory through an occluded space, or integrating partial views intoa coherent spatial map. Humans naturally support this kind of reason-ing through imagination. Prior work has introduced intermediate visualrepresentations (e.g., visual thoughts, depth, or box tokens), but theseintermediates often refine structure already visible rather than predict-ing the missing spatial structure implied by the evidence. We introduceImaginative Perception Tokens (IPT), intermediate perceptual rep-resentations that externalize what a VLM would perceive under an alter-native spatial configuration while remaining consistent with the observedinput. To study this capability, we formulate three tasks that requireimaginative perception: Perspective Taking (PET), Path Tracing(PT), and Multiview Counting (MVC). For each task, we constructdatasets of →20K examples spanning simulated and real-world settings,paired with ground-truth intermediate imaginations, final answers, andcurated evaluation benchmarks. Using the unified VLM BAGEL [12] asour backbone, IPT supervision improves spatial reasoning across severalsettings and often outperforms textual chain-of-thought training, evenwhen no image is generated at inference time. For example, on MVC,IPT improves accuracy by 3.4% and achieves performance competitivewith strong closed-source models on Path Tracing. We also find thatmixed training with IPT and label-only data can further improve perfor-mance. In contrast, textual chain-of-thought can be detrimental on thesetasks, substantially degrading performance in some cases, highlighting amodality mismatch when forcing spatial computation through language.Overall, IPT provides a principled supervision signal for reasoning overunobserved structure, yielding stronger spatial generalization and a moreinterpretable intermediate aligned with the underlying geometry of thetask. Code will be released at the project page.Path Tracing (11k)Question: As you move from waypoint 1 to 2. This is what you see looking forward from Question: Which object can you seepoint 1: . This is what you see looking forward from point 2: . on your right side at waypoint M?Which object can you see on your left side at waypoint M?Options: Imagination1 M 2A. A dining table 2 22B. A fridge MC. A countertop 1D. A sofa 1 13Perspective Taking (55k)Question: After moving to the marked ‘X’ position on the ground while keeping my Question: Will the sideboard appearcurrent orientation, then turning left for 90 degrees, will the sideboard appear closer closer or further away?or further away?Options:A. CloserB. Further XX1 ImaginationMultiview Counting (17k)Question: What is the total number of office chairs in this scene? Question: How many refrigerator(s)are in this area?Answers: 8 Imagination1 23 4Fig. 1: Overview of the three spatial imagination tasks. The left columns showtraining examples with ground-truth imaginative perception; the right columns showevaluation examples.

Abstract:
Multimodal Large Language Models (MLLMs) have recentlydemonstrated strong performance across vision-language tasks. However,their high inference cost, arising from both the large number of inputvisual tokens and the heavy computation of the large language model(LLM), remains a key barrier to practical deployment. Recent work at-tempts to reduce the cost by adaptively optimizing individual dimen-sions, e.g., pruning redundant visual tokens or skipping LLM layers andheads. Nonetheless, prior approaches typically treat these dimensions in-dependently and overlook a fundamental coupling: the available computeresources must be dynamically allocated across all dimensions based onthe input content. To bridge the gap, we propose SmartVL, a unifiedadaptive inference framework that jointly controls vision token numberand model compute capability in response to varying input contents andcompute budgets. SmartVL introduces a vision-side token controllerthat dynamically selects informative visual tokens and an LLM-side com-pute controller that adaptively adjusts LLM computation. Importantly,these controllers are trained to coordinate with each other so that theoverall inference cost satisfies a target budget. To allow this joint schedul-ing, we connect the controllers using a shared budget encoding and lever-age a differentiable latency estimator for end-to-end training. This de-sign enables SmartVL to learn cross-stage allocation strategies thatadapt to both input complexity and runtime compute constraints. Exper-iments across multiple MLLM benchmarks demonstrate that, with jointscheduling, SmartVL consistently outperforms prior adaptive methodsand achieves superior accuracy–efficiency Pareto frontiers. Project page:https://www.schaterji.io/publications/2026/jointtokencompute.

Abstract:
Multimodal Large Language Models (MLLMs) inherit richrelational priors from their language backbones, yet often fail when askedto apply these relationships in visual contexts. We trace this failure to astructural blind spot: projection-based alignment trains each visual to-ken to carry the right semantics, but never asks whether the relationshipsbetween concepts survive the crossing from language to vision. To ad-dress this, we propose MIRROR (Mapping Inter-concept Relationsfrom language to visual Representation via Optimal-transport-basedRegularization), a geometric regularization framework that transfers re-lational priors from language to vision by exploiting the rich relationalstructure encoded in language representations. Specifically, we derive asurrogate loss from the proposed Semi-Inverse Gromov–Wasserstein(SI-GW) problem, an inverse geometric problem that aligns visual rep-resentations with language-derived relational priors. We show that thisformulation admits a unique closed-form solution that prescribes theideal visual relational structure implied by language geometry and cross-modal coupling. The structure of the formulation also enables efficientcomputation, making it applicable to long token sequences. ApplyingSI-GW inside decoder-only Transformers requires careful design. We in-troduce targeted strategies at the layer, head, and token levels to ensurestable extraction without additional parameters or inference cost. MIR-ROR improves relational consistency while preserving performance ongeneral vision-language tasks.

Abstract:
4D understanding and reasoning is a fundamental capabil-ity for embodied AI agents operating in dynamic physical environments.However, existing vision encoders are largely limited to static 2D im-ages or 3D point clouds without temporal modeling, or to 2D videosthat lack accurate geometric depth reasoning. Consequently, current ap-proaches fail to jointly capture spatial structure and motion evolutionin dynamic scenes. We present CL4D, the first foundational 4D visionencoder that directly operates on dynamic point clouds, trained witha contrastive learning objective to align spatio-temporal geometric rep-resentations with natural language descriptions. By learning a shared1 ∗These authors contributed equally. † These authors jointly supervised this work.embedding space between text and 4D scene dynamics, CL4D enableszero-shot motion-to-text and text-to-motion retrieval in dynamic en-vironments and serves as a foundational 4D vision encoder for down-stream 4D vision–language tasks. Building on this encoder, we intro-duce 4DVLM, a 4D vision–language model that conditions languagegeneration on dynamic geometric representations. 4DVLM is the firstVLM designed to operate directly on 4D point clouds without relyingon 2D images, 2D videos, or static 3D point clouds. We train CL4D andsubsequently 4DVLM on a newly constructed dataset termed DynAc-tion4D capturing diverse human motions across varying object inter-actions and scene environments. Extensive experiments across multiple4D human action benchmarks demonstrate that CL4D achieves state-of-the-art performance, with improvements of approximately ∼16.75% overprior methods. Furthermore, 4DVLM outperforms frontier video VLMssuch as Gemini and GPT-5 even when these models are provided withRGB video sequences corresponding to the same scenes represented as4D point clouds for 4DVLM.

Abstract:
Integrated circuit (IC) footprint geometry labeling refers tothe process of converting pin diagrams in IC datasheets into machine-readable geometric parameters. This task is critical in printed circuitboard (PCB) design and component assembly, as accurate labeling en-sures proper IC placement and reliable connectivity. The process is chal-lenged by unstructured annotations, complex footprint arrangements,and abstract geometric diagrams, making fully automated labeling meth-ods inadequate. Traditional EDA tools require heavy manual input. Ex-isting automation methods, such as OCR or object detection, fail tocapture the implicit geometric relationships in IC diagrams, leaving thelabeling task incomplete. Recent work has shown that end-to-end largemultimodal models (LMMs) can perform IC geometry labeling. How-ever, by treating the task as a black box, it is prone to shortcut learningand lack interpretability. In this work, we introduce ICLAgent, thefirst multi-agent framework for fully automated IC footprint geometrylabeling that explicitly models the workflow of expert engineers to pro-duce more interpretable and reliable labeling outcomes. Furthermore, wepresent ICAgent-Instruct, the first dynamic planning and reasoningdataset tailored for IC footprint geometry labeling. Extensive experi-ments show that ICLAgent improves overall accuracy by 10.3% com-pared to the previous SOTA method and by 79.5% compared to man-ual annotation. Despite using only simple supervised fine-tuning on a7B model (Qwen2-VL-7B), ICLAgent surpasses general-purpose LMMssuch as GPT-5 (by 94.6%) and Gemini-3 Pro (by 15.2%).

Abstract:
Personalized Text-to-Image (PT2I) generation aims to pro-duce customized images based on reference images. A prominent interestpertains to the integration of an image prompt adapter to facilitate zero-shot PT2I without test-time fine-tuning. However, current methods grap-ple with three fundamental challenges: 1. the elusive equilibrium betweenConcept Preservation (CP) and Prompt Following (PF), 2. the difficultyin retaining fine-grained concept details in reference images, and 3. therestricted scalability to extend to multi-subject personalization. To tacklethese challenges, we present Dynamic Image Prompt Adapter (DynaIP),a cutting-edge plugin to enhance the fine-grained concept fidelity, CP·PFbalance, and subject scalability of state-of-the-art T2I multimodal diffu-sion transformers (MM-DiT) for PT2I generation. Our key finding is thatMM-DiT inherently exhibit decoupling learning behavior when injectingZ. Wang and T. Chu—Equal contribution. ∗ Corresponding author.reference image features into its dual branches via cross attentions. Basedon this, we design an innovative Dynamic Decoupling Strategy that re-moves the interference of concept-agnostic information during inference,significantly enhancing the CP·PF balance and further bolstering thescalability of multi-subject compositions. Moreover, we identify the vi-sual encoder as a key factor affecting fine-grained CP and reveal thatthe hierarchical features of commonly used CLIP can capture visual in-formation at diverse granularity levels. Therefore, we introduce a novelHierarchical Mixture-of-Experts Feature Fusion Module to fully leveragethe hierarchical features of CLIP, remarkably elevating the fine-grainedconcept fidelity while also providing flexible control of visual granularity.Extensive experiments across single- and multi-subject PT2I tasks verifythat our DynaIP outperforms existing approaches, while requiring onlysingle-subject training datasets.

Abstract:
Human perception of surface slant from texture exhibits systematic, graded biases that emerge reliably in psychophysical experiments. Prior work showed that unsupervised CNNs reproduce several human-like biases,whilesupervisedCNNsdonot.DoVision-LanguageModels(VLMs) exhibit similar competences? Across multiple VLM families and model scales,zero-shotandin-contextpromptingbothproducedistinctivefailures: slantispredictedatonlyasmallsetofanchors(e.g.,0°,±25°,±45°)withlittle dependence on stimulus field of view, optical slant, or surface curvature. Supervised fine-tuning partially remediates the failure, but residual anchoring persists. While success in high-level vision-language benchmarks might notrequiresensitivitytolow-levelgeometriccues,weinterpretanchoringas a failure at the representation-to-output language interface: not necessarily anabsenceofgeometricencoding,butafailuretoexpressitinagradedform.

Abstract:
Humans can effortlessly reason about scenes across different viewpoints, yet it remains unclear whether Vision–Language Models (VLMs) possess similar cross-view spatial abilities. Satellite-street scene pairs, with their complex contexts and extreme viewpoint variations, provide an ideal testbed. Motivated by this, we introduce CVSBench, a large-scale benchmark for evaluating cross-view spatial reasoning through satellite-street pairs. This benchmark supports multiple tasks, including cross-view VQA, cross-view grounding, and viewpoint identification. CVSBench comprises 3,297 cross-view image groups with 9,468 object-level annotations and 40,679 question–answer (QA) pairs, enabling systematic and controlled evaluation of cross-view spatial reasoning. Extensive evaluations reveal that advanced VLMs struggle to maintain object-level and layout consistency under drastic viewpoint changes. To bridge this gap towards human-like spatial cognition, we investigate two categories of approaches: spatially grounded reasoning and the incorporation of cognitive map inputs. Our findings demonstrate that language-only reasoning yields marginal improvements, while incorporating visual spatial imagination via a 3D scene imagination pipeline substantially improves cross-view reasoning. These results highlight the necessity of explicit visual-spatial representations for robust spatial cognition in VLMs. Our data and code are released at https://huggingface.co/datasets/zlyzlyzly/CVSBench.

Abstract:
Multimodal large language models (MLLMs) often halluci-nate, producing fluent but visually ungrounded outputs, partly becausedirect preference optimization (DPO) overfits to superficial linguisticcues under static preference supervision. We propose TARS, a token-adaptive preference strategy that reformulates DPO as a min-max op-timization problem: the inner maximization perturbs visual-agnostic to-kens to induce worst-case distributional shifts, while the outer minimiza-tion enforces alignment with causal visual signals rather than surface-level patterns. A spectral alignment loss further regularizes hidden rep-resentations in the frequency domain via the Fast Fourier Transform(FFT). With only 4.8k preference samples and no expert feedback, TARShalves the hallucination rate (26.4% → 13.2%) and reduces the cognitionscore from 2.5 to 0.4, outperforming standard DPO. It also surpasses 5×larger LLM-based data augmentation (28.8k samples; Hal-Rate 16.0% vs.13.2%) and narrows the gap to GPT-4o, suggesting that token-adaptiveoptimization improves data efficiency beyond simply scaling augmentedpreference data.

Abstract:
A common way to improve vision-language model outputsis Best-of-N (BoN) selection: generate N candidate answers, score eachone, return the best. When the candidate pool and compute budget areheld fixed, the selected answer should be reproducible. We show it often isnot. Slightly adjusting the softmax temperature, toggling a probability-truncation option, or enabling dropout noise during the scoring passis enough to change the winning answer on 41–47% of test items —even though the candidate pool is identical. This failure is specific tolikelihood-based selection, where candidates are ranked by policy likeli-hoods or policy/reference likelihood ratios; a deterministic reward-modelor rule-based (e.g. RLVR) reranker assigns identical scores to identicaltext and is stable on a fixed pool by construction.Standard scoring conflates answer quality with two confounding factors:when token-level scores are summed, low-value suffixes can change thetotal without improving answer quality, and score magnitudes shift when-ever the model is updated. We propose DSPA, which separates two con-cerns into a test-time selector and a training-time filter. At test time,we score each candidate by comparing it token-by-token against a fixedreference model and averaging the result, which removes the additivelength accumulation of summed scores and keeps all scores on a fixedreference scale; this is the component responsible for selection stability.At training time, we filter preference pairs to prevent shortcuts based onanswer length, verbatim prompt copying, or hallucinated objects; thiscomponent improves the factual quality of the candidate pool, not thestability of selection. We evaluate stability by replaying the scoring stepon the same frozen pool under controlled perturbations (a protocol wecall SRP), where the anchored selector reduces the answer-flip rate from44.3% to 24.9%. Manual inspection shows roughly 70% of remainingflips are near-ties that differ only in phrasing. On identical pools, the an-chored selector also reduces object hallucination (POPE) from 9.6% to6.2%; the stability gain holds at 13B scale, while the utility gain transfersto Qwen2.5-VL-7B-Instruct.

Abstract:
Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans. Vision-language models (VLMs) have made tremendous progress in the last few years for many visual tasks, potentially o!ering a promising solution for understanding emotions. However, it is surprising that even the most sophisticated contemporary VLMs struggle to recognize human emotions or to outperform even specialized vision-only classifiers. In this paper we ask the question ‘Why do VLMs struggle to recognize human emotions?’, and observe that the inherently continuous and dynamic task of facial expression recognition (DFER) exposes two critical VLM vulnerabilities. First, emotion datasets are naturally long-tailed, and the web-scale data used to pre-train VLMs exacerbates this head-class bias, causing them to systematically collapse rare, under-represented emotions into common categories. We propose alternative sampling strategies that prevent favoring common concepts. Second, temporal information is critical for understanding emotions. However, VLMs are unable to represent temporal information over dense frame sequences, as they are limited by context size and the number of tokens that can fit in memory, which poses a clear challenge for emotion recognition. We demonstrate that the sparse temporal sampling strategy used in VLMs is inherently misaligned with the fleeting nature of micro-expressions (0.25–0.5 seconds), which are often the most critical a!ective signal. As a diagnostic probe, we propose a multi-stage context enrichment strategy that utilizes the information from ‘in-between’ frames by first converting them into natural language summaries. This enriched textual context is provided as input to the VLM alongside sparse keyframes, preventing attentional dilution from excessive visual data while preserving the emotional trajectory.

Abstract:
Real-time inference of vision–language–action (VLA) models is essential for robotic control. While visual token pruning has shown strong potential for accelerating inference, most existing methods mainly base pruning decisions on shallow-layer cues and risk discarding visual information required by deep layers. To address this issue, we propose SAFE-Pruner, a plug-and-play pruning framework that incorporates attention cues of future layers into pruning decisions. Specifically, we identify semantic attention consistency, the tendency that VLA models concentrate their attention probability mass on the same semantic entity across control timesteps. Based on this observation, we design a forwardlooking strategy to forecast the token saliency in deep layers, which prevents the premature removal of critical tokens and leads to more stable acceleration. We further introduce a reference timestep refresh strategy that triggers updates upon attention shifts, thereby improving forecasting accuracy and pruning reliability. Extensive experiments across diverse evaluation settings demonstrate that our method achieves up to 1.89× speedup with a minimal degradation in success rate of less than 1.5%, while outperforming state-of-the-art methods by up to 1.9%.

Abstract:
Visual encoding followed by token condensing has becomethe standard architectural paradigm in multi-modal large language mod-els (MLLMs). Many recent MLLMs increasingly favor global native-resolution visual encoding over slice-based methods. To investigate thistrend, we systematically compare their behavior on vision-language un-derstanding and attention patterns, revealing that global encoding en-hances overall capability but at the expense of greater computationaloverhead. To address this issue, we present NaVLM-PVC, an MLLM cen-tered upon our proposed Progressive Visual Compression (PVC) method,which can be seamlessly integrated into standard Vision Transformer(ViT) to enable efficient native-resolution encoding. The PVC approachconsists of two key modules: (i) refined patch embedding, which supportsflexible patch-size scaling for fine-grained visual modeling, (ii) windowedtoken compression, hierarchically deployed across ViT layers to progres-sively aggregate local token representations. Jointly modulated by thesetwo modules, a widely pretrained ViT can be reconfigured into an effi-cient architecture while largely preserving generality. Evaluated acrossextensive benchmarks, the transformed ViT, termed ViT-PVC, demon-strates competitive performance with MoonViT while reducing TTFT(time-to-first-token) by 2.4×, when developed within an identical MLLMarchitecture. Building upon ViT-PVC, NaVLM-PVC also achieves com-petitive performance to Qwen2-VL, while further reducing TTFT by1.9×. We will release all code and checkpoints to support future researchon efficient MLLMs.

Abstract:
Images shared on social media often expose geographic cues.While early geolocation methods required expert effort and lacked gen-eralization, the rise of Large Vision Language Models (LVLMs) now en-ables accurate geolocation even for ordinary users. However, existingapproaches are not optimized for this task. To explore the full potentialand associated privacy risks, we present GEO-Detective, an agent thatmimics human reasoning and tool use for image geolocation inference.It follows a procedure with four steps that adaptively selects strategiesbased on image difficulty and is equipped with specialized tools suchas visual reverse search, which emulates how humans gather externalgeographic clues. Experimental results show that GEO-Detective out-performs baseline large vision language models (LVLMs) overall, par-ticularly on images lacking visible geographic features. In country levelgeolocation tasks, it achieves an improvement of over 11.1% compared tobaseline LLMs, and even at finer grained levels, it still provides around a5.2% performance gain. Meanwhile, when equipped with external clues,GEO-Detective becomes more likely to produce accurate predictions, re-ducing the “unknown” prediction rate by more than 50.6%. We furtherexplore multiple defense strategies and find that GEO-Detective exhibitsstronger robustness, highlighting the need for more effective privacy safe-guards.

Abstract:
Recent work explores new opportunities at the intersectionof vision-language-action models (VLAs) and geometric foundation mod-els (GFMs) for 3D reconstruction, such as VGGT. While the resultinggeometric VLAs often show improved performance, it remains unclear(i) if modern VLAs already have sufficient geometric understanding tostart with, (ii) what is the best architecture to inject geometric under-standing into a VLA, and (iii) what is the effect of other design choicesthat affect geometric VLAs. In this paper we provide a rigorous exper-imental analysis to shed light on these questions, for a specific choiceof VLA (GR00T-N1.5) and GFM (VGGT). Our first contribution is toformalize prior work’s intuition that current VLAs lack geometric un-derstanding, by providing a rigorous analysis based on linear probing.The analysis quantifies, for the first time, the “geometric gap” betweenVLAs and GFMs. Our second contribution is to identify and comparedifferent strategies to bridge GFMs with VLAs. We implement threedifferent architectures, which differ in the way they inject geometry inthe VLA, while keeping low-level implementation details as similar aspossible, to ensure a fair comparison. Finally, we analyze the impact ofnon-architectural choices (e.g., training data, number of cameras, recon-struction quality) on the performance of the geometric VLAs.

Abstract:
Counting serves as a simple but powerful test of a Large Vision-Language Model’s (LVLM) reasoning; it forces the model to identify each individual object and then add them all up. In this study, we investigate how LVLMs implement counting using controlled synthetic and real-world benchmarks, combined with mechanistic analyses. Our results show that LVLMs display a human-like counting behavior, with precise performance on small numerosities and noisy estimation for larger quantities. We introduce two novel interpretability methods, Visual Activation Patching and HeadLens, and use them to uncover a structured “counting circuit” that is largely shared across a variety of visual reasoning tasks. Building on these insights, we propose a lightweight intervention strategy that exploits simple and abundantly available synthetic images to fine-tune arbitrary pretrained LVLMs exclusively on counting. Despite the narrow scope of this fine-tuning, the intervention not only enhances counting accuracy on in-distribution synthetic data, but also yields an average improvement of +8.36% on out-of-distribution counting benchmarks and an average gain of +1.54% on complex, general visual reasoning tasks for Qwen2.5-VL. These findings highlight the central, influential role of counting in visual reasoning and suggest a potential pathway for improving overall visual reasoning capabilities through targeted enhancement of counting mechanisms.

Abstract:
Vision-language models (VLMs) are increasingly deployedin high-stakes settings where reliable uncertainty quantification (UQ)is as important as predictive accuracy. Extended reasoning via chain-of-thought (CoT) prompting or reasoning-trained models has becomeubiquitous in modern VLM pipelines, yet its effect on UQ reliabilityremains poorly understood. Our results show that reasoning tends todegrade the quality of many uncertainty estimates, even when it im-proves task accuracy. We identify implicit answer conditioning as theprimary mechanism: as reasoning traces converge on a conclusion beforethe final answer is generated, token probabilities increasingly reflect con-sistency with the model’s own reasoning trace rather than uncertaintyabout correctness. In effect, the model becomes overconfident in its an-swer. In contrast, agreement-based consistency remains robust and oftenimproves under reasoning, making it a practical choice for uncertaintyestimation in reasoning-enabled VLMs.

Abstract:
Vision-Language-Action (VLA) models enable robots to per-form manipulation tasks directly from natural language instructions andare increasingly viewed as a foundation for generalist robotic policies.However, their reliability under Out-Of-Distribution (OOD) instructionsremains underexplored. In this paper, we reveal a critical failure modein which VLA policies continue executing visually plausible actions evenwhen the language instruction contradicts the scene. We refer to this phe-nomenon as linguistic blindness, where VLA policies prioritize visualpriors over instruction semantics during action generation. To systemati-cally analyze this issue, we introduce ICBench, a diagnostic benchmarkconstructed from the LIBERO dataset that probes language–action cou-pling by injecting controlled OOD instruction contradictions while keep-ing the visual environment unchanged. Evaluations on three represen-tative VLA architectures, including π0 , π0.5 , and OpenVLA-OFT, showthat these models frequently succeed at tasks despite logically impossi-ble instructions, revealing a strong visual bias in action generation. Tomitigate this issue, we propose Instruction-Guided Attention Re-calibration (IGAR), a train-free inference-time mechanism that rebal-ances attention distributions to restore the influence of language instruc-tions. IGAR operates without retraining or architectural modificationand can be directly applied to existing VLA models. Experiments across30 LIBERO tasks demonstrate that IGAR substantially reduces erro-neous execution under OOD contradictory instructions while preservingbaseline task performance. We additionally validate the approach on areal Franka robotic arm, where IGAR effectively prevents manipulationtriggered by inconsistent instructions.

Abstract:
Diffusion models produce high-quality images, but their de-ployment remains expensive because generation requires many denoisingsteps through large transformer or U-Net backbones. Existing methodsimprove efficiency through post-training quantization or timestep reduc-tion, but they optimize these two axes separately. Quantization methodsusually assume a fixed precision, timestep reduction methods assume afixed schedule, and both often rely on proxy costs such as BitOps insteadof real hardware measurements. We introduce DiffPro, a post-trainingframework that jointly optimizes per-layer quantization precision and thedenoising schedule under a unified budget defined by measured memoryand latency on real hardware. DiffPro combines three key ideas: (i) acomposite sensitivity metric that combines PCA dimensionality withdiagonal Hessian curvature for bit allocation, (ii) Dynamic ActivationQuantization (DAQ), which adapts activation scales across timesteps tohandle temporal activation drift, and (iii) a drift-guided timestep selectorthat removes redundant steps while preserving the late refinement phase.On DiT-XL/2 (ImageNet 256 × 256), DiffPro achieves 50% step reduction,7.9× compression, and 2.7× speedup at FID 5.89 (∆FID 3.62), improvingover uniform thinning by 2.5 FID at matched precision. On SDXL U-Net(COCO 2014 1024 × 1024), DiffPro achieves 5.6× compression with FID26.50 (∆FID 2.0) and 45% energy reduction, all without retraining.

Abstract:
Diffusion Transformers (DiTs) have achieved state-of-the-art(SOTA) performance in visual generative modeling, yet their training re-mains computationally prohibitive. While the recently proposed Momen-tum Orthogonalization (Muon) optimizer offers a promising alternativeto AdamW, its direct application to DiTs yields suboptimal late-stageconvergence. In this paper, we identify the root cause of this bottleneck:standard DiT architectures fuse functionally distinct weights (e.g., withinAdaLN and QKV layers) into unified tensors for computational efficiency.Applying Muon to these fused tensors inadvertently induces implicit sub-space coupling, which distorts update directions and degrades global op-timization. To address this, we introduce Chunked Muon (CMuon), asimple yet highly effective strategy that partitions these matrices intoindependent sub-components prior to orthogonalization. Extensive ex-periments demonstrate that a 675M-parameter DiT trained with CMuonachieves a FID of 1.18 on ImageNet 256 in just 200 epochs. This rep-resents more than a 2x training speedup over AdamW, while effectivelyovercoming the late-stage convergence plateaus of vanilla Muon.

Abstract:
High definition map generation is essential for autonomousdriving, yet remains a labor-intensive process at scale. We present Map-Dreamer, a generative diffusion model that synthesizes lane-level vec-tor maps with explicit topology directly from a single aerial image.MapDreamer learns a compact latent representation of lane centerlinesand their topological relations using a variational autoencoder and pre-dicts graphs with a transformer-based latent diffusion model. To aligngenerated maps with the observed scene, we condition each denoisingstep on dense aerial features injected through cross-attention. To handlethe varying number of lanes across scenes, we propose a lane cardinal-ity module paired with background ghost lane latents, a learned bufferthat prevents slot collapse during diffusion. Furthermore, we introduce asliding-window global graph aggregation strategy that stitches local tilesinto city-scale maps while preserving connectivity through encoded laneboundaries. Experiments on UrbanLaneGraph derived from Argoverse2 show improved geometric and topological fidelity over non-generativebaselines.

Abstract:
Given the remarkable ability of 2D foundation image modelsto generate high-fidelity outputs, we investigate a fundamental question:do 2D foundation image models inherently possess 3D world model ca-pabilities? To answer this, we systematically evaluate multiple state-of-the-art image generation models and Vision-Language Models (VLMs)on the task of 3D world synthesis. To harness and benchmark their po-tential implicit 3D capability, we propose an agentic framing to facilitate3D world generation. Our approach employs a multi-agent architecture:a VLM-based director that formulates prompts to guide image synthesis,a generator that synthesizes new image views, and a VLM-backed two-step verifier that evaluates and selectively curates generated frames fromboth 2D image and 3D reconstruction space. Crucially, we demonstratethat our agentic approach provides coherent and robust 3D reconstruc-tion, producing output scenes that can be explored by rendering novelviews. Through extensive experiments across various foundation mod-els, we demonstrate that 2D models do indeed encapsulate a grasp of3D worlds. By exploiting this understanding, our method successfullysynthesizes expansive, realistic, and 3D-consistent worlds.

Abstract:
Designing urban spaces that provide pedestrian wind comfort and safety requires time-resolved Computational Fluid Dynamics (CFD) simulations, but their current computational cost makes extensive design exploration impractical. We introduce WinDiNet (Wind Diffusion Network), a pretrained video diffusion model that is repurposed as a fast, differentiable surrogate for this task. Starting from LTX-Video, a 2Bparameter latent video transformer, we fine-tune on a dataset of 13,000 2D incompressible CFD simulations over procedurally generated building layouts. A systematic study of training regimes, conditioning mechanisms, and VAE adaptation strategies, including a physics-informed decoder loss, identifies a configuration that outperforms purpose-built neural PDE solvers. The resulting model generates full 112-frame rollouts in under a second. As the surrogate is end-to-end differentiable, it doubles as a physics simulator for gradient-based inverse optimization: given an urban footprint layout, we optimize building positions directly through backpropagation to improve wind safety as well as pedestrian wind comfort. Experiments on singleand multi-inlet layouts show that the optimizer discovers effective layouts even under challenging multi-objective configurations, with all improvements confirmed by ground-truth CFD simulations.

Abstract:
Recovering an editable design file from a raster image is a common and costly bottleneck in modern design workflows, yet remains challenging since editability depends on recovering multi-modal attributes, such as typography, vector geometry, colors, grouping, and layer ordering. We present ReDesign, an agentic framework that grows an editable layer hierarchy by selecting and composing specialized tools across modalities. To keep this long decision process reliable despite imperfect tool outputs, we introduce graceful verification at each expansion, which provides local accept, prune, or retry feedback that prevents error accumulation and avoids large scale reruns. To evaluate editability at scale, we introduce the Figma Edit Replay Benchmark, consisting of 909 raw Figma files and 14,796 controlled edit instructions that replay edits on reconstructed outputs. Across this benchmark and standard reconstruction metrics, ReDesign achieves strong visual fidelity while delivering the highest editability across layout, color, and text edits, outperforming layered decomposition baselines and serial tool use pipelines.

Abstract:
Generating high-fidelity and controllable 3D assets from a single image remains a significant challenge. We propose a novel framework that integrates preference optimization into a rectified flow-based diffusion model operating on a latent set representation. This approach enhances geometric fidelity and user alignment, enabling the efficient synthesis of diverse 3D formats. Our core contribution is a versatile reward-based optimization strategy for aligning generation with human-centric criteria like surface quality. We introduce Direct Reward Optimization (DRO), which fine-tunes the model using only absolute quality feedback, removing the need for pairwise data. The framework also seamlessly incorporates Direct Preference Optimization (DPO) when paired data is available. Parameter-efficient control is achieved via Low-Rank Adaptation (LoRA). To ensure data quality, we also develop a robust preprocessing pipeline using a neural SDF reconstruction method with Gaussian curvature constraints to produce high-quality watertight meshes. Extensive experiments show our framework achieves state-of-the-art performance. Quantitative evaluations demonstrate significant improvements in Chamfer Distance and F-Score, while qualitative results confirm superior geometric fidelity and user preference alignment compared to existing methods.

Abstract:
We present the Identity-Preserving Large Human Recon-struction Model (IPRM), a feed-forward framework that reconstructsphotorealistic, clothed 3D humans from a single in-the-wild image whilepreserving 3D identity. Recent works predominantly infer 3D structurefrom 2D features, making it challenging to achieve 3D consistency andpreserve human identity in 3D space. To alleviate these challenges, IPRManchors the single-view 3D human reconstruction by constructing a human-based 3D feature space and explicitly preserves the human 3D identityfeatures during inference. Specifically, we introduce an efficient and ro-bust SMPL-based sparse voxel representation to transform 2D imageidentity features into 3D space, categorizing them into 3D visible iden-tity tokens and 3D invisible tokens to be inferred. Using these 3D tokens,an identity-aware 3D token inference module is proposed to propagateprojected 3D identity features from visible to invisible tokens, ensuringthat only unobserved regions are predicted while the observed identityremains intact. We further design an encoder–decoder architecture thatdecodes SMPL-based 3D features into either a 3D Gaussian Splatting ormesh representation, and augment it with a 3D ID Adapter for identitypreservation. Instead of conventional conditioning on image tokens (2DID Adapter), this adapter utilizes 3D identity tokens extracted from aparallel identity branch as guidance to inject token-wise identity infor-mation. Comprehensive experiments on existing benchmarks and in-the-wild data show IPRM surpasses state-of-the-art methods in reconstruc-tion performance, efficiency, 3D consistency and identity consistency.

Abstract:
While 3D Gaussian Splatting (3DGS) has enabled real-time high-fidelity novel view synthesis, maintaining already-deployed static 3DGS assets remains difficult. When a local region is missing or blurred, retraining the whole scene from scratch is costly, while directly finetuning the asset on newly captured repair images causes severe degradation of unobserved views, and geometric stitching introduces boundary and depth conflicts. We propose Flash-Refine, a practical in-situ repair pipeline for static 3DGS assets that operates directly on a native PLY model without access to historical training images. Given newly registered repair views, Flash-Refine uses Point-Wise Consensus Masking and an Adaptive Depth-Bounded Prior to identify a bounded active region, applies Gradient Locking to frozen background Gaussians, propagates the mask through native densification, and protects frozen Gaussians from close-up pruning. Under this constrained active set, gradient-gated densification naturally allocates new parameters to the defective region, enabling rapid local completion from peripheral seeds. Extensive experiments on Deep Blending and Mip-NeRF 360 show that Flash-Refine achieves the best Dropped and Kept PSNR among practical repair methods across four scenes, while requiring only 2–4 minutes on an RTX 4090. We further analyze the method’s scope and limitations: it assumes static scenes and preregistered repair views, and although frozen background parameters remain unchanged, newly densified active Gaussians may still induce scene-dependent visibility interactions in unobserved views.

Abstract:
Though Gaussian splatting has achieved impressive resultsin novel view synthesis, it requires millions of primitives to representhighly textured scenes — even when the underlying geometry is simple.We propose a representation that decouples geometry and appearanceto achieve a compact and computationally efficient representation. Ourmethod uses surfels for geometry and combines a global neural field withper-primitive colors for appearance. While existing approaches that tex-ture primitives incur significant overhead in either memory or renderingspeed, our method strikes a better balance by leveraging the neural fieldto implicitly compress texture information. We bound the computationsrequired in the rendering pass by querying the neural field for a fixednumber of primitives per pixel, enabling real-time rendering speeds. Ourrepresentation matches the perceptual quality of 3D Gaussian splattingwhile using 9.7× fewer primitives and 5.5× less memory on outdoorscenes, and 31× fewer primitives and 3.7× less memory on indoor scenes.

Abstract:
Focus and depth of field (DoF) define where attention falls and how much of a scene appears sharp. Adjusting both after capture provides creators two-dimensional control over visual attention. However, existing focus and DoF editing tools fail when applied to scenes with complex geometries, such as reflections and fine features. This failure is a result of fundamental limitations in existing training data generation pipelines: current pipelines either sacrifice geometric fidelity for scalability, or sacrifice scalability for geometric fidelity. To address this, we present Vid2Bokeh, a scalable bokeh data acquisition pipeline that reconstructs dense 25×25 light fields from casual videos via feed-forward 3D reconstruction, bypassing depth estimation entirely and avoiding the geometric errors it introduces. The resulting Vid2Bokeh Dataset comprises over 100K light fields on diverse real-world scenes, simultaneously achieving scene diversity, optical density, and geometric fidelity that no existing bokeh dataset or bokeh data acquisition pipeline provides. By training on this geometrically faithful bokeh data, we introduce a diffusion model for full bidirectional focus–DoF editing that outperforms depth-based baselines on complex geometries. This result demonstrates that geometric fidelity in large-scale training data is the key to geometrically consistent bokeh editing.

Abstract:
Recent multi-view 3D human pose estimation methods de-compose the problem into per-image 2D pose detection and 3D lift-ing stages, which can naturally scale to multi-frame input and achievestrong results by leveraging temporal context. However, this 2D-3D lift-ing paradigm introduces two levels of geometric inconsistency: (i) projec-tion inconsistency, i.e., the detected 2D keypoints may disagree with the2D projections of the inferred 3D pose, and (ii) cross-view inconsistency,i.e., 2D poses across views are mutually inconsistent because off-the-shelfdetectors operate independently per camera, so there may exist no sin-gle 3D pose whose projections match all detections. We observe that 2Ddetection errors closely follow a Gaussian distribution, meaning that de-tector outputs can be naturally viewed as noisy samples from a diffusionforward process. Based on this insight, we propose CoDePose, a coupled2D-3D denoising diffusion framework that jointly performs multi-viewdenoising of the detected 2D poses and estimates the underlying 3Dpose. As training proceeds, the generated joint distribution progressivelyapproaches the real data distribution, leading to increased projection andcross-view consistency and more reliable 3D estimation. Unlike existingdiffusion-based methods that condition on fixed 2D detections, CoDe-Pose refines the 2D observations during the reverse process, preventing2D detection errors and cross-view inconsistencies from being reinforcedthroughout the whole 3D pose generation. Experimental results on Hu-man3.6M show that CoDePose achieves 12.1 mm MPJPE, a 19.9% im-provement over the previous state-of-the-art method. Cross-dataset eval-uation on HumanEva and MPI-INF-3DHP further demonstrates stronggeneralization.

Abstract:
Recent point-based intrinsic decomposition and inverse ren-dering methods have advanced the modelling of the shading and albedoof 3D scenes. However, we identify a fundamental limitation: these meth-ods suffer from a misattribution issue, where individual primitives learnincorrect appearance features despite producing correct aggregated ren-derings. We show that the root cause lies in volume rendering, whichaggregates translucent primitives along each ray and only supervises thefinal colour, preventing direct supervision of individual primitive fea-tures. To address this, we propose Intrinsic PAPR, a robust intrinsic de-composition framework which leverages Proximity Attention Point Ren-dering (PAPR) to enable direct per-point supervision. Unlike volumerendering approaches, PAPR eliminates translucent primitives and di-rectly predicts appearance at ray-surface intersections, enabling accuratesupervision to the feature of each individual point. Our method incor-porates a 2D albedo prior adapted with conditional Implicit MaximumLikelihood Estimation (cIMLE) to handle monocular ambiguities, andemploys a space carving loss to ensure multi-view consistency. Exten-sive evaluations on synthetic and real-world datasets demonstrate thatIntrinsic PAPR outperforms point-based inverse rendering, NeRF-basedintrinsic decomposition, and diffusion-based PBR methods in novel viewsynthesis and albedo estimation while resolving the misattribution issue.

Abstract:
Scaling monocular 3D Gaussian Splatting (3DGS) SLAM to kilometer-level outdoor environments poses two tightly coupled challenges: fragile long-term pose tracking and excessive memory overhead during large-scale mapping. In this paper, we propose KiloGS-SLAM, a highly efficient and robust monocular 3DGS-SLAM system that jointly addresses both bottlenecks. Since high-fidelity scene reconstruction fundamentally relies on drift-free camera poses, we first introduce a motionadaptive hybrid tracking module. This module features a conditiontriggered three-tier solving pipeline. It dynamically switches between Essential matrix and PnP models to handle geometric degeneracies. An ondemand foundation model can also be activated to rescue the trajectory from catastrophic drift. To ensure the system can sustain these long trajectories without memory exhaustion, we subsequently design a lifecyclemanaged Gaussian mapping strategy. By integrating probabilistic initialization with chunk-based multi-view densification and pruning, this full-pipeline optimization effectively reduces primitive redundancy while preserving high-frequency details. Together, the robust tracking guarantees the geometric foundation required for accurate mapping, while the memory-efficient lifecycle-managed mapping enables large-scale operation. Extensive experiments across three challenging outdoor datasets demonstrate that our approach achieves state-of-the-art tracking accuracy and rendering quality, successfully scaling to sequences of over 10,000 frames on a single GPU. Project Page: https://3dagentworld. github.io/KiloGS-SLAM/

Abstract:
Feed-forward 3D reconstruction for autonomous driving hasdemonstrated strong performance and promising generalization capabil-ities. However, pixel-level methods often suffer from multi-view inconsis-tencies and content duplication. While voxel-level approaches alleviatethese issues by predicting 3D Gaussian primitives in voxel space, theyremain limited by resolution and computational overhead. We introducean efficient feed-forward framework for scene reconstruction from uncal-ibrated images. The key insight is the explicit decoupling of instanta-neous generation necessity and long-term geometric persistence duringGaussian generation. Specifically, we introduce Generation Confidencedriven by reprojection errors to enable demand-aware Gaussian expan-sion, and Reuse Confidence constructed from depth uncertainty and tem-poral decay to model the long-term stability. To balance fidelity andsparsity, we employ differentiable voxelization to aggregate neighboringfeatures and suppress spatial redundancy, followed by confidence-awaresoft-gating pruning. Gaussian distillation is further introduced to enablea compact set of backbone Gaussians to approximate the full scene repre-sentation. Extensive experiments on multiple datasets demonstrate thatour method achieves strong reconstruction quality and robustness whilemaintaining a highly compact scene representation.

Abstract:
3D Gaussian Splatting (3DGS) has emerged as a powerfulparadigm for real-time and high-fidelity 3D reconstruction from posedimages. However, recent studies reveal its vulnerability to adversarialcorruptions in input views, where imperceptible yet consistent pertur-bations can drastically degrade rendering quality, increase training andrendering time, and inflate memory usage, even leading to server denial-of-service. In our work, to mitigate this issue, we begin by analyzingthe distinct behaviors of adversarial perturbations in the low- and high-frequency components of input images using wavelet transforms. Basedon this observation, we design a simple yet effective frequency-aware de-fense strategy that reconstructs training views by filtering high-frequencynoise while preserving low-frequency content. This approach effectivelysuppresses adversarial artifacts while maintaining the authenticity ofthe original scene. Notably, it does not significantly impair training onclean data, achieving a desirable trade-off between robustness and per-formance on clean inputs. Through extensive experiments under a widerange of attack intensities on multiple benchmarks, we demonstrate thatour method substantially enhances the robustness of 3DGS without ac-cess to clean ground-truth supervision. By highlighting and addressingthe overlooked vulnerabilities of 3D Gaussian Splatting, our work pavesthe way for more robust and secure 3D reconstructions.

Abstract:
Monocular depth foundation models have demonstrated re-markable generalization capabilities across diverse environments. How-ever, they continue to struggle with metric depth estimation in diverseenvironments. This limitation stems from the inherent scale ambiguity ofsingle-view inference, leading to misaligned scale predictions even whenthe relative geometry is accurate. Conversely, recent multi-view founda-tion models leverage cross-view cues to learn robust scene-level geometryand consistent scale. Yet, these benefits typically vanish during single-image inference, as the absence of explicit geometric constraints causesperformance to degrade. To bridge this gap, we propose a novel frame-work that transfers the scale-aware geometric priors of multi-view modelsinto monocular depth foundation models. Specifically, we introduce anEpipolar Distillation (EpiDistill), an approach utilizing Rectified StereoTokens, which enables the single-view prediction model to retain epipolarattention patterns and maintain geometric consistency without requiringmulti-view inputs at inference. Experimental results demonstrate thatour method significantly improves zero-shot metric depth estimation,particularly on challenging datasets like ETH3D and DIODE where scalealignment is critical. Furthermore, our approach is model-agnostic, con-sistently boosting the performance of state-of-the-art ViT-based models,including UniDepthV2 and DepthPro. Project Link

Abstract:
Prevailing 2D-centric visuomotor policies exhibit a pronounceddeficiency in novel view generalization, as their reliance on static observa-tions hinders consistent action mapping across unseen views. In response,we introduce GenSplat, a feed-forward 3D Gaussian Splatting frameworkthat facilitates view-generalized policy learning through novel view ren-dering. GenSplat employs a permutation-equivariant architecture to re-construct high-fidelity 3D scenes from sparse, uncalibrated inputs in asingle forward pass. To ensure structural integrity, we design a 3D-priordistillation strategy that regularizes the 3DGS optimization, preventingthe geometric collapse typical of purely photometric supervision. By ren-dering diverse synthetic views from these stable 3D representations, wesystematically augment the observational manifold during training. Thisaugmentation forces the policy to ground its decisions in underlying 3Dstructures, thereby ensuring robust execution under severe spatial per-turbations where baselines severely degrade.

Abstract:
Achieving truly immersive large-scale scene digitization ne-cessitates consistent and visually pleasing rendering across all possibleviewing perspectives. However, collecting multi-view images covering ev-ery fine detail of a large-scale scene is prohibitive due to scene complexity,capture cost, negligence, or accessibility constraints. As a result, the sam-pled views tend to be highly unstructured – the majority of the sceneis well covered yet certain regions inevitably lack sufficient observations.Existing reconstruction based methods are vulnerable to view scarcitywhile generation based approaches suffer from generalization, controlla-bility, and 3D consistency issues. To address this challenge, we proposeInceptionGS, which bootstraps Gaussian splatting by subtly balancingreconstruction and generation. Starting from an initial Gaussian splat-ting, InceptionGS reasonably rethinks and repairs problematic regionscaused by view scarcity while preserving the quality elsewhere, by softlyincorporating scene- and view-adaptive generative priors. Extensive ex-periments on real-world large-scale scenes demonstrate the superiorityand broad applicability of our approach in handling unstructured im-agery and boosting high-fidelity Gaussian splatting.

Abstract:
Existing pruning methods for 3D Gaussian splatting (3DGS)suffer from either severe quality degradation or prohibitive computa-tional overhead. In this paper, we propose REFINE, a highly acceler-ated 3DGS pruning framework centered on a novel rendering-free primi-tive importance metric. Our approach leverages an analytically approx-imated, rendering-aware Hessian field to quantify the expected percep-tual error induced by the removal of individual primitives. By modelingthe joint modulation of visibility, projection geometry and the contentadaptive hyperparameter, we entirely bypass costly forward renderingpasses and derive an anisotropic perceptual weight field that serves asa high-fidelity proxy for primitive importance. Extensive experimentsacross multiple benchmark datasets demonstrate that REFINE main-tains highly competitive rendering quality while achieving a 3, 000× re-duction in pruning-related computational complexity, translating to apractical ∼ 20× speedup in device latency compared to state-of-the-artpruning methods.

Abstract:
Multiresolution hash encodings have recently enabled fast and high-fidelity implicit neural representations by storing multi-scale features in fixed-size hash tables along a geometric resolution schedule. However, the standard design is data-agnostic: different resolution levels receive identical hash-table capacity despite large differences in image frequency content. As a result, some levels experience severe hash collisions while others underutilize parameters, leading to inefficient capacity allocation. To address this issue, we propose Collision-Aware Resolution Adaptation (CARA), a method that assigns per-level resolutions by balancing the effective information load across hash levels. This adaptive allocation reduces capacity bottlenecks and improves parameter efficiency. In addition, we introduce an invertible pixel-shuffle transform that reduces hash load factors by redistributing spatial information, thereby mitigating collision-induced information loss without enlarging the hash tables. To support evaluation on extremely high-resolution data, we also curate, to the best of our knowledge, the first uncompressed wholeslide image dataset for academic research. Experiments on Kodak images, gigapixel natural images, and raw whole-slide images demonstrate that CARA consistently improves the fidelity–parameter trade-off. Our method matches state-of-the-art performance while using only 27.76% of the parameters, and achieves up to 6.11dB PSNR improvement at comparable parameter counts. Code is provided in the supplementary.

Abstract:
Camera-controllable view synthesis aims to synthesize novelviews of a given scene under varying camera poses while strictly preservingcross-view geometric consistency. However, existing methods typicallyrely on fragmented geometric guidance, such as only injecting point cloudsat the representation level despite models containing multiple levels, andare mainly based on image diffusion models that operate on discreteview mappings. These two limitations jointly lead to geometric drift andstructural degradation under continuous camera motion. We observethat while leveraging video models provides continuous viewpoint priorsfor camera-controllable view synthesis, they still struggle to form stablegeometric understanding if geometric guidance remains fragmented. Tosystematically address this, we inject unified geometric guidance across thethree levels that jointly determine the generative output: representation,architecture, and loss function. To this end, we propose UniGeo, a novelcamera-controllable editing framework. Specifically, at the representationlevel, UniGeo incorporates a frame-decoupled geometric reference injectionmechanism to provide robust cross-view geometry context. Furthermore,at the architecture level, it introduces a geometric anchor attention toalign multi-view features, and at the loss function level, it proposes atrajectory-endpoint geometric supervision strategy to explicitly reinforcethe structural fidelity of target views. Experiments across multiple publicbenchmarks, encompassing both extensive and limited camera motionsettings, demonstrate that UniGeo significantly outperforms existingmethods in visual quality and geometric consistency.

Abstract:
Generating realistic 3D city environments from remote sens-ing data is important for simulation, urban planning, and mixed reality,yet existing point cloud generation methods are limited to single ob-jects or bounded indoor scenes and cannot handle the scale, seamlesstiling, and partial observability challenges of city-scale generation. Wepresent GridFlow, a multi-stage framework that generates dense, col-ored point clouds (105 points per 150m×150m tile) at city scale, con-ditioned on satellite imagery, semantic segmentation maps, and digitalsurface models (DSM). A Grid-Aligned VAE encodes each tile into atopology-preserving latent grid where tokens correspond to fixed spatialregions, enabling spatially coherent multi-modal conditioning and com-pact latent-space edge consistency that implicitly aligns thousands ofboundary points for seamless cross-tile generation. A conditional rectifiedflow model synthesizes geometry latents from the fused multi-modal con-ditions, and an orientation-aware diffusion colorizer separately handlessatellite-visible horizontal surfaces and occluded vertical façades. To sup-port standardized evaluation, we build on public 3D data sources to in-troduce City3D-MultiGen, a benchmark of 163K densely annotated tilesfrom Melbourne and London with aligned point clouds, satellite images,semantic maps, and elevation data. Experiments show that GridFlowoutperforms adapted point cloud generation baselines across all geometrymetrics and produces visually coherent colored point clouds with seam-less boundaries over arbitrarily large urban extents. Our benchmark de-tails are available at https://huggingface.co/datasets/e32/City3D-MultiGen.

Abstract:
Recent agentic frameworks for 3D scene synthesis have ad-vanced realism and diversity by integrating heterogeneous generationand editing tools. These tools are organized into workflows orchestratedby an off-the-shelf LLM. Current approaches typically adopt an exe-cute–review–reflect loop: at each step, the orchestrator executes a tool,renders intermediate results for review, and then decides on the tooland its parameters for the next step. However, this design has two keylimitations. First, next-step tool selection and parameter configurationare driven by heuristic rules, which can lead to suboptimal executionflows, unnecessary tool invocations, degraded output quality, and in-creased runtime. Second, rendering and reviewing intermediate resultsafter each step introduces additional latency. To address these issues, wepropose SceneOrchestra, a trainable orchestration framework that opti-mizes the tool-call execution flow and eliminates the step-by-step reviewloop, improving both efficiency and output quality. SceneOrchestra con-sists of an orchestrator and a discriminator, which we fine-tune witha two-phase training strategy. In the first phase, the orchestrator learnscontext-aware tool selection and complete tool-call trajectory generation,while the discriminator is trained to assess the quality of full trajectories,enabling it to select the best trajectory from multiple candidates. In thesecond phase, we perform interleaved training, where the discriminatoradapts to the orchestrator’s evolving trajectory distribution and distillsits discriminative capability back into the orchestrator. At inference, weonly use the orchestrator to generate and execute full tool-call trajec-tories from instructions, without requiring the discriminator. Extensiveexperiments show that our method achieves state-of-the-art scene qualitycompared to previous work.

Abstract:
We present DRS-VPT, a feed-forward transformer archi-tecture for foundational image-to-scan registration. Given query imagesand a reference 3D point cloud, the model predicts the scan pose andpoint map alongside the poses and point maps of each camera, all ex-pressed in the first camera’s frame. It additionally predicts a coarse-to-fine pyramid of per-point and per-pixel features for direct reprojectivealignment of the scan to the first image. This formulation unifies down-stream tasks such as camera–LiDAR calibration in autonomous driv-ing and indoor camera-to-map relocalization. A single DRS-VPT modelachieves state-of-the-art performance for image-to-LiDAR registration inautonomous driving, competitive indoor relocalization without trainingmap-specific weights, and strong zero-shot transfer to unseen environ-ments. We also show qualitatively that the model learns complex scan-to-image projection properties such as occlusion of back-facing points.

Abstract:
The rapid evolution of 3D scene understanding relies heav-ily on high-resolution LiDAR, which generates massive data volumesthat impose significant bottlenecks on transmission and storage. WhileRange Image Compression (RIC) provides a more structured and com-putationally efficient alternative to conventional Point Cloud Compres-sion (PCC), existing RIC frameworks frequently fail to achieve an op-timal trade-off between coding efficiency and real-time performance. Tobridge this gap, we propose BitRIC, a novel learning-based frameworktailored for high-performance LiDAR range image compression. BitRICdecomposes raw range images into hierarchical bitplanes and processesthem through a coarse-to-fine autoregressive entropy model. At its core, ashared contextual backbone leverages cross-bitplane dependencies to in-form two specialized pathways: a Probability Estimation Module (PEM)that predicts probability maps for lossless entropy coding, and a One-Step Refinement Module (ORM) that reconstructs residual details dur-ing bitplane truncation for lossy compression. Extensive experiments onthe Waymo Open Dataset and SemanticKITTI demonstrate that BitRICachieves superior rate-distortion performance, yielding over 60% BD-BRsavings compared to standard image codecs, while offering higher down-stream task accuracy. Furthermore, BitRIC supports real-time lossy andnear-real-time lossless processing, making it a highly practical candidatefor real-world applications.

Abstract:
Feed-forward 3D reconstruction models such as DUSt3R, VGGT, and Depth Anything 3 (DA3) are transformer–based foundation models that infer camera geometry and dense scene structure in a single forward pass. Trained at scale in a supervised fashion, they raise a central question: do these models build upon geometric principles akin to traditional multi–view pipelines, or do they primarily rely on learned priors arising from the large–scale training setup? We find that epipolar geometry emerges within the intermediate layers of all three models and is causally linked to correspondence patterns in attention heads. To study this, we perform a systematic analysis of their internal representations across three real–world datasets and a controlled synthetic dataset. We quantify geometric understanding by probing intermediate features, analyzing attention patterns to identify correspondence matching patterns, and performing targeted interventions at the attention level. Further, we assess the role of learned priors by applying challenging input–level perturbations, such as occlusions, scene ambiguities, and varying camera configurations, and compare them against classical multi–stage reconstruction pipelines.

Abstract:
Autonomous robotic systems require spatio-temporal understanding of dynamic environments to ensure reliable navigation and interaction. While Vision-Language Models (VLMs) provide open-world semantic priors, they lack grounding in 3D geometry and temporal dynamics. Conversely, geometric perception captures structure and motion but remains semantically sparse. We propose SNOW (Scene Understanding with Open-World Knowledge), a training-free and backboneagnostic framework for unified 4D scene understanding that integrates VLM-derived semantics with point cloud geometry and temporal consistency. SNOW processes synchronized RGB images and 3D point clouds, using HDBSCAN clustering to generate object-level proposals that guide SAM2-based segmentation. Each segmented region is encoded through our proposed Spatio-Temporal Tokenized Patch Encoding (STEP), producing multimodal tokens that capture localized semantic, geometric, and temporal attributes. These tokens are incrementally integrated into a 4D Scene Graph (4DSG), which serves as 4D prior for downstream reasoning. A lightweight SLAM backend anchors all STEP tokens spatially, providing the global reference alignment, and ensuring unambiguous spatial grounding across time. The resulting 4DSG forms a queryable, unified world model through which VLMs can directly interpret spatial scene structure and temporal dynamics. Experiments on a diverse set of benchmarks demonstrate that SNOW enables precise 4D scene understanding and spatially grounded inference, setting new stateof-the-art performance in several settings, highlighting the importance of structured 4D priors for embodied reasoning and autonomous robotics.

Abstract:
Image registration is an ill-posed dense vision task, wheremultiple solutions achieve similar loss values, motivating probabilisticinference. Variational inference has previously been employed to capturethese distributions, however restrictive assumptions about the posteriorform can lead to poor characterisation, overconfidence and low-qualitysamples. More flexible posteriors are typically bottlenecked by the com-plexity of high-dimensional covariance matrices required for dense 3Dimage registration.In this work, we present a memory and computationally e!cient in-ference method, Structured SIR, that enables expressive, multi-modal,characterisation of uncertainty with high quality samples. We proposethe use of a Sampled Importance Resampling (SIR) algorithm witha novel memory-e!cient high-dimensional covariance parameterisationas the sum of a low-rank covariance and a sparse, spatially structuredCholesky precision factor. This structure enables capturing complex spa-tial correlations while remaining computationally tractable.We evaluate the e!cacy of this approach in 3D dense image registrationof brain MRI data, which is a very high-dimensional problem. We demon-strate that our proposed method produces uncertainty estimates that aresignificantly better calibrated than those produced by variational meth-ods, achieving equivalent or better accuracy. Crucially, we show thatthe model yields highly structured multi-modal posterior distributions,enable e"ective and e!cient uncertainty quantification.

Abstract:
3D occupancy prediction provides dense semantic scene rep-resentations for autonomous driving, yet camera-only methods sufferfrom occlusion and adverse weather, while existing fusion approachestreat 4D radar as a generic point cloud and neglect its distinct physicalmeasurements. We propose RIGS, a radar-informed Gaussian splattingframework that systematically exploits 4D radar across three stages ofthe pipeline. In scene modeling, we extract range and power from the 4Dradar tensor for depth initialization and fuse them with images throughimage–radar–Gaussian tri-modal cross-attention. In occupancy refine-ment, we introduce evidential ellipsoidal BKI with anisotropic kernelsand use RCS power as physical existence evidence for adaptive false-positive filtering, yielding uncertainty-aware predictions with Dirichletepistemic and semantic uncertainty. In velocity estimation, we proposetemporal displacement-guided Doppler de-aliasing to recover high-speedradial velocities and complement tangential components, supervised bya multi-level loss over sparse radar points, dense voxels, and connectedcomponents. Experiments on K-Radar show that RIGS achieves stronglong-range occupancy among camera–radar methods with uncertainty-aware refinement and competitive velocity estimation, validated by sys-tematic incremental ablations.

Abstract:
Large foundation models trained on extensive datasets demon-strate strong zero-shot capabilities in various domains. Knowledge dis-tillation has become an established tool for transferring knowledge fromfoundation models to small student networks when data and model sizeare constrained. However, the efficacy of distillation is often hampered bylimited training data coverage. This can result in a covariate shift betweentraining and test data, which in turn can lead the student to exploitspurious features or even shortcut learning. We address this problemby introducing a novel diffusion-based data augmentation strategy thatgenerates images by maximizing the disagreement between the teacherand the student, effectively creating challenging samples that the studentstruggles with, thus mitigating the problem of covariate shift. Experi-ments demonstrate that, compared to state-of-the-art diffusion-based dataaugmentation baselines, our approach is best or second-best in samplemean accuracy and improves the worst group and mean group accuracyon CelebA-HQ, SpuCo Birds and BAR as well as the spurious score onSpurious ImageNet under covariate shift.

Abstract:
Robust egocentric hand-gesture recognition remains chal-lenging under the rapidly changing illumination typical of intelligent re-ality environments. Existing first-person gesture datasets rarely quantifylighting, limiting controlled analysis of illumination robustness. Unlikeprior egocentric datasets that describe lighting qualitatively (e.g., “in-door”, “bright”), BiCE-HG is the first to provide calibrated lux measure-ments at 80 spatial points, enabling reproducible illumination robustnessbenchmarking. BiCE-HG (Bi-Conditional Egocentric Hand Gestures)dataset addresses this gap as the first egocentric gesture dataset withmeasured bi-conditional lighting and spatial illuminance documentation.BiCE-HG contains 19 gestures performed by 23 participants across sixconditions combining sitting, standing, and walking with full and lowlighting setups. Walking trials capture realistic motion through a 4 mlighting corridor with transitions between bright and dim zones. Spatialilluminance is recorded every 25 cm using a calibrated meter, enablingprecise modelling of lighting gradients and reproducible experimentalconditions. Baseline evaluation reaches 81.24% validation accuracy (F1= 0.80) on the static (single-frame) release with a no-attention ST-GCN,the dynamic (temporal) release lifts accuracy to 88.57% with the identicalarchitecture, isolating a 7.3-percentage-point gain from temporal infor-mation alone, while adding tri-attention contributes a further marginalgain to 89.24% (F1 = 0.88). Lighting-robustness analysis shows stablespatial measurements, with a coefficient of variation below 5% for mostconditions across a 4.95× illuminance range (29-523 lux). BiCE-HG pro-vides quantified environmental metadata and realistic mobility lightinginteractions, supporting the development of illumination-robust egocen-tric gesture-recognition systems for AR/VR applications.The dataset ispublicly available at https://doi.org/10.15131/shef.data.32374137

Abstract:
Curvilinear object segmentation, including vessels and cracks, is challenging due to extreme spatial sparsity and topological fragility, where small local errors can cause severe structural disconnections. Meanwhile, modern segmentation pipelines increasingly rely on strong but hard-to-modify foundation encoders whose heavy downsampling limits fine structural recovery. Motivated by this, we focus on the post-encoder stage and study two recurring and actionable failure modes: a reconstruction bottleneck in high-resolution feature restoration and a decision bottleneck in binarization. We present PEPA, a lightweight Post-Encoder Plug-in Adapter for 2D curvilinear segmentation pipelines with accessible decoder/head features and target, query, or class descriptors. PEPA couples (i) Target-Conditioned Snake Upsampling (TCSU), which uses target-conditioned continuous snake-like sampling to better recover thin and tortuous structures during upsampling, and (ii) Target-Adaptive Differentiable Thresholding (TADT), which predicts target-specific thresholds and optimizes a soft-threshold surrogate with explicit safeguards against trivial bias shifting. Under this post-encoder interface, PEPA can be attached to both prompt-based decoders and conventional dense predictors. Experiments on five medical and industrial benchmarks show that adding PEPA to frozen-encoder baselines yields consistent improvements, with gains in topological connectivity (clDice) typically exceeding those in region overlap (IoU), indicating improved structural continuity. With only ∼0.26M additional parameters, PEPA offers a practical postencoder enhancement for structure-centric segmentation.

Abstract:
Progress in dental computer vision is limited by the absence of large-scale multimodal datasets that jointly capture 3D intraoral geometry and 2Dappearance across diverse clinical settings. Existing resources are typically uni-modal, which hinders robust cross-modal learning and generalization. We as-semble and release a multi-center dataset of 1,000 patients comprising 2,000registered upper/lower intraoral scans, 5,000 paired intraoral photographs, and2,403 clinician-authored reports. This combination links detailed 3D dental ge-ometry with complementary 2D evidence, supporting occlusal and orthodonticanalysis. Moreover, to enable scalable and privacy-preserving acquisition andannotation across distributed centers, we introduce an open platform that sup-ports multimodal ingestion and structured labeling. Experiments indicate thatstate-of-the-art multimodal models fail to generate clinically faithful reports, moti-vating geometry-aware adaptation. We therefore propose IOS-Qwen, which fuses aPointTransformer 3D encoder with Qwen3-VL to generate structured, point-cloud-conditioned reports. Together, the dataset, the platform, and the baselines establisha foundation for multimodal dental AI research. Code is publicly released.3

Abstract:
Event cameras provide several unique advantages over standard frame-based sensors, including high temporal resolution, low latency, and robustness to extreme lighting. However, existing learningbased approaches for event processing are typically confined to narrow, task-specific silos and lack the ability to generalize across modalities. We address this gap with REALM, a cross-modal framework that learns an RGBand Event-Aligned Latent Manifold by projecting event representations into the pretrained latent space of RGB foundation models. Instead of task-specific training, we leverage low-rank adaptation (LoRA) to bridge the modality gap, effectively unlocking the geometric and semantic priors of frozen RGB backbones for asynchronous event streams. We demonstrate that REALM effectively maps events into the ViT-based foundation latent space. Our method performs downstream tasks, such as depth estimation and semantic segmentation, by simply transferring linear heads trained on the RGB teacher. Most significantly, REALM enables the direct, zero-shot application of complex, frozen image-trained decoders, such as MASt3R, to raw event data. We demonstrate stateof-the-art performance in wide-baseline feature matching, significantly outperforming specialized architectures. Code and models are available at https://papers.starslab.ca/realm/.

Abstract:
Topological data analysis (TDA), particularly persistent ho-mology (PH), captures geometric structural properties in medical im-ages (e.g., connected components, loops, shape characteristics), whichconventional pixel-level deep learning approaches often neglect. Whilemany topological descriptors are known for converting persistence di-agrams (PDs) or raw images into topological feature vectors, existingmethods mostly default to a single fixed descriptor (e.g., persistenceimages), leaving the diversity of topological representations largely un-explored. To the best of our knowledge, there is no known large lan-guage model (LLM)-based agentic framework that can automaticallydetermine the most suitable topological descriptors for a given imagedataset and produce the corresponding topological feature vectors fordownstream tasks. To fill this gap, we propose TopoAgent, an LLM-based agentic framework that automates topology learning for medicalimage analysis. TopoAgent operates through a Perception–Reasoning–Action–Reflection loop supported by 21 domain-specific tools and dualmemory that accumulates experience across runs. Its skill set is dis-tilled from systematic evaluation of 15 topological descriptors across 26datasets with six classifiers. TopoAgent analyzes input images and theirtopological characteristics, reasons about which topological descriptorsbest suit the input, and determines the optimal descriptor and its con-figuration, all without task-specific training. To evaluate TopoAgent, weintroduce TopoBenchmark, a frozen benchmark of 113,182 samplesfrom 26 medical image datasets spanning five object types: cells, glandsand lumens, organ shapes, vessel trees, and surface lesions. Experimentsshow that TopoAgent obtains 68.21% average balanced accuracy, out-performing the strongest baseline by 9.32% and general-purpose LLMsequipped with the same tools by over 21%.

Abstract:
Accurate monocular metric depth estimation remains inher-ently ill-posed due to scale ambiguity. Light field cameras mitigate thisambiguity by capturing micro-baseline disparities within a single lensto recover metric depth, but current learning-based solutions are lim-ited by scarce training data and often overfit Lambertian appearancepatterns, restricting real-world robustness. To address this, we proposeSynLF, which alleviates data scarcity via physics-grounded light field(PG-LF) synthesis and performs robust real-world metric depth esti-mation through VisDepth, a prior-initialized iterative estimator with ex-plicit multi-view occlusion reasoning. During training, we synthesize lightfield supervision on-the-fly from RGB-D via physics-grounded light fieldsynthesis, including geometry-consistent view synthesis, depth-dependentdefocus, and non-Lambertian perturbations. At inference, the model isdirectly evaluated on real light field captures without fine-tuning. Ex-tensive experiments demonstrate that SynLF generalizes zero-shot toour custom real-world light field dataset, which features highly chal-lenging conditions such as transparency, reflections, specular highlights,and textureless regions. It achieves 30 mm absolute and 1.4% relativeerrors across 0.5–2.5 m, demonstrating that physics-grounded LF syn-thesis from large-scale RGB-D provides a scalable and storage-efficientalternative to conventional light field acquisition for accurate single-lensmetric depth estimation.

Abstract:
This paper introduces an inverse-rendering framework fornatural-light, uncalibrated photometric stereo (PS) that leverages back-ground cues captured alongside the target object. Natural-light PS (NaPS)acquires shading variations by moving or rotating the camera and objectunder fixed, uncontrolled illumination, such as indoor lighting, whilemaintaining their relative geometry. However, uncalibrated NaPS, inwhich the lighting conditions are unknown, remains inherently ill-posed.To tackle this challenge, we propose explicitly reconstructing the lightingenvironment from the image background, which is typically masked outin prior work, thereby converting uncalibrated NaPS into a tractableinverse-rendering problem. Specifically, we move and rotate the camera-object pair while keeping their relative pose fixed, and reconstruct thesurrounding illumination directly from the observed background usinga 3D Gaussian Splatting (3DGS) representation. We then optimize thetarget object’s shape and reflectance via inverse rendering under thereconstructed illumination. Experimental results demonstrate that ourinverse-rendering-based approach yields more accurate estimates of bothgeometry and reflectance than existing learning-based PS methods, espe-cially under realistic near-field indoor conditions.

Abstract:
The ability to remotely measure an animal’s size and shape(i.e. its morphology) in its natural habitat is of key importance for ap-plications such as conservation, re-identification, and biomechanical un-derstanding. Monocular depth estimation (MDE) and 3D reconstructionapproaches can be used. This is useful to generate accurate deformablemesh models or to characterize biologically important parameters such assexual dimorphism or the size distribution of populations. Whilst depthestimation and 3D reconstruction have been extensively studied as coretopics in computer vision, ranging from early work on simple, rigid ob-jects, to more recent work on human and animal reconstruction, themajority of existing animal models are trained on video/image baseddatasets that lack metric scale and ground truth e.g. from camera trapimages or from public videos. In addition, they do not fully reflect thechallenges of accurately estimating the focal animal’s scale when it is dis-tant from a camera. To address this limitation, we present WildDepth,a multimodal dataset and benchmark suite for depth estimation, behav-ior detection, and 3D reconstruction from diverse categories of animalsranging from domestic to wild environments with synchronized RGB andLiDAR. We provide three focused benchmarks: (1) monocular depth es-timation with per-distance and temporal stability analysis, (2) behav-ior detection, (3) 3D reconstruction and densification. Our results showthat large-scale MDE models degrade significantly at long range, whileLiDAR-anchored fusion reduces metric error by up to 25% – 30% RMSEin mid-range scenarios. Our aim is to enable camera trap data to bemore accurately ‘lifted’ to 3D morphometrics, unlocking a new era ofscale-aware animal modelling.⋆ M. Aamir, N. Muramatsu, and S. Shin contributed equally and are listed alpha-betically by surname.

Abstract:
While most image deblurring techniques directly restore thespatial image variable, we propose an amplitude and phase decomposi-tion recognizing the importance of accurate phase estimation in recover-ing sharp image details. To that end, we first develop novel linear min-imum mean squared (LMMSE) estimators of the amplitude and phaseof the blurred, noisy image observation. An iterative optimization algo-rithm follows that recovers the sharp image using the aforementionedLMMSE estimators. Finally, matrix parameters that are statistically de-termined and fixed in the iterative algorithm are now learned using atraining dataset of clean and degraded observations. Our deblurring en-gine is dubbed UPADNet – Unrolled Phase and Amplitude Decompo-sition Network, such that each iteration of the underlying phase andamplitude recovery algorithm is parameterized and trained end-to-end.Experiments over benchmark evaluation datasets such as GoPro, Real-Blur and COCO datasets confirm that UPADNet outperforms state ofthe art deep networks including those based on algorithm unrolling inthe image domain. The benefits of UPADNet are even more pronouncedin high noise and limited training data regimes.

Abstract:
Autonomous personal mobility benefits greatly from the abil-ity to predict executable spatial goals from language and front view imagealone. In this study, we focus on absolute target position prediction froma language instruction and front view image, which requires referring ex-pression disambiguation and absolute target localization without depthcues. To address this, we propose AiSCREAM, a language-conditionedtarget localization model based on cross-view vision-language reason-ing. AiSCREAM constructs cross-view geometric semantic alignment be-tween the front and an Aerial Semantic view, which is an overhead-viewimage synthesized from the front image via a text-conditioned image-to-image generation model. By preserving appearance-level scene charac-teristics, the alignment can leverage language-conditioned semantic cuesand enable reliable absolute target localization from a single image. Toevaluate AiSCREAM, we constructed DRAMATiST, a benchmark thatintroduces road and traffic diversity. The experimental results demon-strated that AiSCREAM achieved a mean absolute position error of4.03 m on DRAMATiST, which outperformed baseline methods and hu-man performance. The project page is available at https://ai-scream-project-page.vercel.app/ .

Abstract:
Diffuser-based imaging systems replace conventional lenses with optical diffusers to enable compact lensless cameras, but their performance critically depends on computational reconstruction from compressed measurements. Deep unrolled networks have emerged as a leading framework by incorporating physical forward models into iterative optimization. However, existing methods typically rely on simplified assumptions, most notably shift-invariant point spread functions (PSFs), which fail to capture spatially-varying aberrations and geometric distortions in real hardware. In this paper, we propose learning physics-based forward model corrections for diffuser-based imaging. We construct a physically interpretable forward model with learnable correction terms based on Eigen-Kernel decomposition to handle spatially-varying aberrations. This design preserves the structure of the physical model while improving its fidelity to real measurements. The corrected forward model is integrated into an unrolled reconstruction framework (EK-DUN), leading to consistent improvements in reconstruction accuracy. Experimental results on real-world data demonstrate that our method achieves stateof-the-art performance compared to existing unrolled baselines.

Abstract:
Embodied agents operating in the real world require multi-scale reasoning and knowledge adaptation as conditions change. We iden-tify two challenges in applying Mixture of Experts (MoE) to this setting:routing lacks an explicit notion of scale, preventing targeted updates atspecific scales, and a uniform update policy cannot accommodate thedifferent rates at which knowledge at each scale becomes outdated. Wepresent MuSix, a framework that addresses both challenges throughscale-aware world model mixture and evolution. A two-stage routingmechanism first maps experiential distance, a measure of situational nov-elty inspired by Construal Level Theory, to a weight over continuous scalespace via a meta-router, then selects world models within the identifiedscale. For adaptation, scale-dependent forgetting rates allow low-scaleknowledge to refresh rapidly while high-scale abstractions persist, andgated inter-scale transfer maintains coherence across the hierarchy. Ex-periments on EmbodiedBench and HAZARD show that MuSix improvesover state-of-the-art baselines.

Abstract:
Existing vision-and-language navigation (VLN) models pri-marily reason over past and current visual observations, while largelyignoring the future visual dynamics induced by actions. As a result,they often lack an effective understanding of the causal relationship be-tween actions and how the visual world changes, limiting robust decision-making. Humans, in contrast, can “imagine” the near future by leveragingaction–dynamics causality, which improves both environmental under-standing and navigation choices. Inspired by this capability, we proposeLatentPilot, a new paradigm that exploits future observations duringtraining as a valuable data source to learn action-conditioned visual dy-namics, while requiring no access to future frames at inference. Con-cretely, we propose a flywheel-style training mechanism that iterativelycollects on-policy trajectories and retrains the model to better match theagent’s behavior distribution, with an expert takeover triggered whenthe agent deviates excessively. LatentPilot further learns visual latenttokens without explicit supervision; these latent tokens attend globallyin a continuous latent space and are carried across steps, serving asboth the current output and the next input, which enabling the agentto “dream ahead” and reason about how actions will affect subsequentobservations. Experiments on R2R-CE, RxR-CE, and R2R-PE bench-marks achieve new SOTA results, and real-robot tests across diverseenvironments demonstrate LatentPilot’s superior understanding of envi-ronment–action dynamics in scene.

Abstract:
Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions. While most driv-ing world models forecast the external environment, in-cabin intelligenceremains strictly recognition-oriented and lacks multi-step rollout capa-bilities for driver dynamics. We introduce Driver-WM, a driver-centriclatent world model that rolls out in-cabin dynamics causally conditionedon out-cabin traffic context. This formulation unifies physical kinemat-ics forecasting with auxiliary behavioral and emotional semantic recog-nition. Operating in a compact latent space constructed from frozenvision-language features, Driver-WM adopts a dual-stream architectureto separately encode external traffic and internal driver states. Thesestreams are directionally coupled via a gated causal injection mechanism,which uses a learned vector gate to modulate external contextual per-turbations while strictly enforcing temporal causality. Experiments onAIDE show robust long-horizon forecasting on reactive high-motion clips,improved driver/traffic semantic alignment, and controlled interventionsthat expose the external-to-internal mechanism.

Abstract:
While current 6D pose estimation benchmarks have reached near-saturation on household objects, they often fail to capture the stochastic and optical complexities of industrial environments. We introduce XYZ-IBD, a high-precision benchmark for object detection and 6D pose estimation specifically designed for industrial bin-picking. XYZ-IBD addresses the domain gap by providing 75 multi-view real-world scenes containing approximately 273k annotated instances of metallic, symmetrical, and specular objects. Unlike existing datasets, our benchmark features high-density stochastic stacking and multi-instance ambiguity, reflecting authentic robotic manipulation challenges. We employ a rigorous multistage and semi-automatic annotation pipeline, ensuring sub-millimeter annotation accuracy. The annotations are validated through our designed error quantification scheme, securing the reliability of the annotation quality. In addition to real-world evaluation data, we provide a large-scale complementary synthetic training set that is rendered under realistic bin-picking simulation. Benchmarking state-of-the-art (SOTA) methods for 2D detection and 6D pose estimation reveals a significant performance degradation compared to standard household benchmarks, highlighting the unsolved challenges of industrial vision. XYZ-IBD establishes a new frontier for robust pose estimation in complex, high-occlusion, and reflective scenarios. The dataset and benchmark are publicly available at https://xyz-ibd.github.io.

Abstract:
LiDAR-based 3D object detection is essential for autonomousdriving systems. However, traditional Ego-only Perception (Eo-Perception)suffers from limited perspective and occlusions in a complex outdoor en-vironment, leading to performance bottlenecks. Recently, research onmulti-agent Collaborative Perception (Co-Perception) has demonstratedexcellent performance, but high communication costs and accumulatedpose error hinder its application. To address this, we explore a novelC2E (Co-Perception to Eo-Perception) paradigm through the Multi-to-Single (M2S) agent contrastive knowledge distillation framework. OurM2S framework first designs Multi-Level Feature Enhancement moduleto provide more stable features, and introduces Auxiliary Point CloudReconstruction and Multi-Teacher Contrastive Distillation mechanismsto mitigate domain gaps in point cloud and feature distributions withinthe C2E paradigm. Benefiting from this, our M2S can retain the ex-cellent performance of collaborative perception while effectively avoidingthe drawbacks, such as communication delays and positioning errors. Ex-tensive experiments on the V2XSet, V2V4Real and DAIR-V2X datasetsshow the effectiveness and generalizability of our M2S framework whencombined with the state-of-the-art CoSDH model and other excellent 3Ddetectors. Our M2S framework can deliver up to a 8.64% improvementin 3D mAP performance without introducing any communication costs.

Abstract:
Latent world models roll observations forward accurately onfactual transitions, but their predictive latents are typically entangledand weakly aligned with the underlying causal factors; as a result, theydegrade under interventions and on counterfactual queries. We addressthis with CausalVAE, a plug-in structural module that attaches to di-verse encoder–transition backbones and organizes their latents througha learned directed acyclic graph (DAG) over causal factors, withoutaltering the base prediction architecture. Across four benchmarks andeight backbones, the plug-in largely preserves factual retrieval while im-proving intervention-aware counterfactual retrieval on most backbone–benchmark pairs; the gains are benchmark- and backbone-dependentrather than universal. Improvements are largest on the Physics bench-mark: for a graph-network backbone trained with a negative-log-likelihoodobjective, counterfactual H@1 (CF-H@1) rises from 11.0 to 41.0, withseveral other backbones gaining +10 to +30 CF-H@1 points. A causalanalysis further shows that the learned structure recovers meaningfulfirst-order physical interaction trends, supporting the interpretability ofthe latent causal structure.

Abstract:
Imitation learning enables robots to acquire complex manipulation skills from expert demonstrations, but offline-trained policies struggle to generalize beyond their training distribution, where small errors compound into severe distribution shift. Dataset Aggregation (DAgger) mitigates this by collecting human corrections during deployment, yet conventional DAgger operates entirely in the real world with a oneto-one ratio between expert effort and training trajectories makes comprehensive out-of-distribution (OOD) coverage impractical. Digital Twin (DT) techniques can scale data generation, but when applied to DAgger data without modification, they ignore the spatial and temporal failure patterns that DAgger rollouts reveal, leaving policies vulnerable precisely where and when they fail most. We propose Twin-DAgger, which treats each real-world intervention as simultaneously a spatial probe identifying failure-prone workspace regions and a temporal prior marking critical deviation moments. Twin-DAgger exploits these signals through two targeted augmentation mechanisms: difficulty-based stratified sampling concentrates synthetic data generation on high-failure-rate regions, and intervention-focused trajectory warping perturbs trajectories at each intervention onset to simulate a distribution of near-failure states while preserving the expert’s corrective recovery. On real-world manipulation tasks, Twin-DAgger matches conventional DAgger performance using only 15% human interventions and outperforms failure-agnostic DT baselines by 20% success rate under equal data budgets, demonstrating that generating data where and when the policy fails, rather than generating more data uniformly, is the critical driver of sample-efficient robustness.

Abstract:
Recent world-model-based Vision-Language-Action (VLA)architectures have improved robotic manipulation through predictive vi-sual foresight. However, dense future prediction introduces visual redun-dancy and accumulates errors, causing long-horizon plan drift. Mean-while, recent sparse methods typically represent visual foresight usinghigh-level semantic subtasks or implicit latent states. These representa-tions often lack explicit kinematic grounding, weakening the alignmentbetween planning and low-level execution. To address this, we proposeStructVLA, which reformulates a generative world model into an explicitstructured planner for reliable control. Instead of dense rollouts or seman-tic goals, StructVLA predicts sparse, physically meaningful structuredframes. Derived from intrinsic kinematic cues (e.g., gripper transitionsand kinematic turning points), these frames capture spatiotemporal mile-stones closely aligned with task progress. We implement this approachthrough a two-stage training paradigm with a unified discrete token vo-cabulary: the world model is first trained to predict structured frames andsubsequently optimized to map the structured foresight into low-level ac-tions. This approach provides clear physical guidance and bridges visualplanning and motion control. In our experiments, StructVLA achievesstrong average success rates of 75.0% on SimplerEnv-WidowX and 94.8%on LIBERO. Real-world deployments further demonstrate reliable taskcompletion and robust generalization across both basic pick-and-placeand complex long-horizon tasks.

Abstract:
Recent years have witnessed remarkable progress in autonomousdriving, yet generalization to long-tail and open-world scenarios remainsthe primary bottleneck for large-scale deployment. To address this, oneline of research explores LLMs and VLMs for their vision-language un-derstanding and reasoning capabilities, equipping AVs with the abilityto interpret rare and safety-critical situations when generating drivingactions. In parallel, another line investigates generative world models tocapture the spatio-temporal evolution of driving scenes, enabling agentsto imagine and evaluate possible futures before acting. Inspired by hu-man intelligence, which seamlessly unites understanding and imaginationas a hallmark of AGI, this work explores a unified model that bringsthese two capabilities together for autonomous driving. We present LM-GenDrive, the first framework that unifies LLM-based multimodal un-derstanding with generative world models for end-to-end closed-loop au-tonomous driving. Given multi-view camera inputs and natural-languageinstructions, our model generates both realistic future driving videosand corresponding control signals. By coupling an LLM with genera-tive video capabilities, LMGenDrive gains complementary benefits: fu-ture video prediction enhances spatio-temporal scene modeling, while theLLM provides strong semantic priors and instruction grounding learnedfrom large-scale pretraining. A progressive three-stage training strat-egy—ranging from vision pretraining to multi-step long-horizon driv-ing—is proposed to further improve stability and performance. The re-sulting model can also operate in two complementary modes: low-latencyonline planning and autoregressive offline video generation. Experimentsshow that LMGenDrive significantly outperforms state-of-the-art meth-ods on challenging closed-loop driving benchmarks, improving instruc-tion following, spatio-temporal understanding, and robustness to rarescenarios. Our work not only sets a new state-of-the-art in autonomousdriving, but also demonstrates that unifying multimodal understandingand generation provides a promising direction for building more gener-alizable and robust embodied decision-making systems.

Abstract:
Recent progress in Vision–Language–Action (VLA) modelshas enabled embodied agents to interpret multimodal instructions andperform complex tasks. However, existing VLAs are mostly confined toshort-horizon, table-top manipulation, lacking the memory and reasoningcapability required for mobile manipulation, where agents must coordi-nate navigation and manipulation under changing spatial contexts. Inthis work, we present EchoVLA, a memory-aware VLA model for mobilemanipulation. EchoVLA incorporates a synergistic declarative memoryinspired by the human brain, consisting of a scene memory that main-tains a collection of spatial–semantic maps and an episodic memory thatstores task-level experiences with multimodal contextual features. Thetwo memories are individually stored, updated, and retrieved based oncurrent observations, task history, and instructions, and their retrievedrepresentations are fused via coarse- and fine-grained attention to guidebase–arm diffusion policies. To support large-scale training, we furtherintroduce MoMani, an automated benchmark that generates expert-leveltrajectories through multimodal large language model (MLLM)–guidedplanning and feedback-driven refinement, supplemented with real-robotdemonstrations. Comprehensive simulated and real-world results demon-strate that EchoVLA substantially improves overall performance, e.g., itachieves the highest success rates of 0.52 on manipulation/navigationtasks and 0.31 on mobile manipulation tasks in simulation, exceedingthe strong baseline π0.5 by +0.20 and +0.11, respectively.

Abstract:
Human driving behavior is inherently diverse, yet most end-to-end autonomous driving (E2E-AD) systems learn a single averagedriving style, neglecting individual differences. Achieving personalizedE2E-AD faces challenges across three levels: limited real-world datasetswith individual-level annotations, a lack of quantitative metrics for eval-uating personal driving styles, and the absence of algorithms that canlearn stylized representations from users’ trajectories. To address thesegaps, we propose Person2Drive, a comprehensive personalized E2E-ADplatform and benchmark. It includes an open-source, flexible data col-lection system that simulates realistic scenarios to generate scalable, di-verse personalized driving datasets; style vector–based evaluation metricswith Maximum Mean Discrepancy and KL divergence to comprehen-sively quantify individual driving behaviors; and a personalized E2E-ADframework with a style reward model that efficiently adapts E2E mod-els for safe and individualized driving. Crucially, our framework enablesplug-and-play personalization by fine-tuning only the trajectory predic-tion head, preserving the pretrained base model and ensuring safety. Ex-tensive experiments demonstrate that Person2Drive enables fine-grainedanalysis and effective personalization, while preserving driving perfor-mance and success rate even in challenging scenarios.

Abstract:
Building robust 3D perception for self-driving still relies heav-ily on large-scale data collection and manual annotation, yet this paradigmbecomes impractical as deployment expands across diverse cities andregions. Meanwhile, modern cities are increasingly instrumented withroadside units (RSUs), static sensors deployed along roads and at inter-sections to monitor traffic. This raises a natural question: can the cityitself help train the vehicle? We propose infrastructure-taught, label-free3D perception, a paradigm in which RSUs act as stationary, unsuper-vised teachers for ego vehicles. Leveraging their fixed viewpoints and re-peated observations, RSUs learn local 3D detectors from unlabeled dataand broadcast predictions to passing vehicles, which are aggregated aspseudo-label supervision for training a standalone ego detector. The re-sulting model requires no infrastructure or communication at test time.We instantiate this idea as a fully label-free three-stage pipeline and con-duct a concept-and-feasibility study in a CARLA-based multi-agent en-vironment. With CenterPoint, our pipeline achieves 82.3% AP for detect-ing vehicles, compared to a fully supervised ego upper bound of 94.4%.We further systematically analyze each stage, evaluate its scalability, anddemonstrate complementarity with existing ego-centric label-free meth-ods. Together, these results suggest that city infrastructure itself canpotentially provide a scalable supervisory signal for autonomous vehi-cles, positioning infrastructure-taught learning as a promising orthogonalparadigm for reducing annotation cost in 3D perception.

Abstract:
Robust human-body understanding benefits from fusing het-erogeneous sensors—WiFi CSI, depth, LiDAR, mmWave, and RGB—yeteach modality carries distinct noise characteristics and may be intermit-tently unavailable. Existing fusion methods often assume fixed sensorsets and can collapse onto dominant modalities, reducing robustness un-der missing-modality conditions. We propose LEO-Fuse, a task-general,modality-agnostic Local Expert Orchestration (LEO) framework for uni-versal multimodal human sensing. LEO-Fuse freezes unimodal encoders,aligns modality features in a shared token space, fuses available modal-ity tokens with a mask-aware transformer, and applies task-specific lo-cal routing for downstream tasks including 3D human pose estimation(HPE) and human activity recognition (HAR). For each target task, asingle trained model handles all 31 non-empty subsets of five modalitieswithout retraining. On MM-Fi, LEO-Fuse obtains 75.5 mm mean per-joint position error (MPJPE), averaged over all 31 subsets. For HAR,HPE-trained encoders yield 93.4% average top-1 accuracy, improving to95.7% with HAR-trained encoders. On XRF55, LEO-Fuse reaches up to95.5% HAR accuracy. These results demonstrate scalable and robust uni-versal multimodal human sensing under realistic missing-modality set-tings.

Abstract:
Understanding human visual attention on a scene over timehas applications in domains such as autonomous driving, interface de-sign, inferring cognitive states. Modeling human visual scanpaths has his-torically relied on specialized architectures with hand-crafted geometricpriors. While these architectures successfully model x001C xation sequences,their rigid structural biases restrict easy extendability and x001D exible con-ditioning. For instance, integrating task-speci x001C c instructions or adaptingto distinct viewer identities traditionally requires custom, disjoint ar-chitectural additions. We address this limitation by framing scanpathprediction purely as a discrete sequence modeling task. By mapping spa-tial coordinates into an autoregressive vocabulary, we leverage the pre-trained representations of Large Vision-Language Models (LVLMs). Thisformulation naturally absorbs diverse factors of variation: simple prompt-ing modi x001C cations allow for global conditioning, such as providing vieweridentities to capture personalized biases, or task-speci x001C c objectives (likefree-viewing versus visual search). The framework can also easily inte-grate the modeling of per- x001C xation attributes, such as individual x001C xa-tion durations alongside spatial locations. Crucially, this autoregressivealignment enables the scalable, exact computation of per- x001C xation log-likelihoods, directly equivalent to the commonly used Information Gain(IG) metric. Our model, DeepGaze3.5-VL, establishes a new state-of-the-art across multiple datasets, achieving 2.18 bits of IG on MIT1003,a 46% improvement over matched-backbone DeepGaze III. This advan-tage persists even when baselines use identical high-capacity vision en-coders. Beyond predictive performance, our generative framework servesas a powerful computational tool for direct behavioral interventions, al-lowing for controlled in-silico simulations that would be experimentallydi x001E cult or impossible to conduct in vivo. We demonstrate this abilityby performing controlled interventions on the durations of pre-saccadic x001C xations, recovering known oculomotor phenomena purely from data.

Abstract:
Enabling reliable long-horizon robotic manipulation is a cru-cial step toward open-world embodied intelligence. However, VLM-basedplanners treat each step as an isolated observation-to-action mapping,forcing them to reinfer scene geometry from raw pixels at every decisionstep while remaining unaware of how prior actions have reshaped the en-vironment. Despite strong short-horizon performance, these systems lackthe spatio-temporal reasoning required for persistent geometric anchoringand memory of action-triggered state transitions. Without persistentstate tracking, perceptual errors accumulate across the execution horizon,temporarily occluded objects are catastrophically forgotten, and com-pounding failures lead to precondition violations that cascade throughsubsequent steps. In contrast, humans maintain a persistent mental modelthat continuously tracks spatial relations and action consequences acrossinteractions rather than reconstructing them at each instant. Inspiredby this human capacity for causal spatio-temporal reasoning with persis-tent memory, we propose RoboStream, a training-free framework thatachieves geometric anchoring through Spatio-Temporal Fusion Tokens(STF-Tokens), which bind visual evidence to 3D geometric attributesfor persistent object grounding, and maintains causal continuity via aCausal Spatio-Temporal Graph (CSTG) that records action-triggeredstate transitions across steps. This design enables the planner to tracecausal chains and preserve object permanence under occlusion withoutadditional training or fine-tuning. RoboStream achieves a 90.5% successrate on long-horizon RLBench tasks and a 44.4% success rate on challeng-ing real-world block-building tasks, where both SoFar and VoxPoser score11.1%, demonstrating that spatio-temporal reasoning and causal memoryare critical missing components for reliable long-horizon manipulation.

Abstract:
Learning robust navigation policies directly from visual ob-servations remains a fundamental challenge in vision-based robotic nav-igation. In end-to-end imitation learning approaches, the visual encoderand action decoder are jointly optimized using a single action loss, whichprovides only an indirect supervisory signal to the encoder. This indi-rect supervision frequently results in the encoder learning ambiguous,action-agnostic representations. The problem is further complicated bysubstantial variations in scene structure and appearance across diverseenvironments, as well as the prevalence of visual distractors inherent toreal-world navigation settings. Such action-agnostic features cause thenavigation policy to produce inconsistent actions at ambiguous deci-sion points, leading to navigation failure. To overcome these limitations,we propose ORION (Ordinal Neural Collapse for Visual Navigation), amethod that explicitly organizes the encoder’s representation space ac-cording to the ordinal structure of navigation actions. In the context ofgoal-directed navigation, ego-centric control categories from Far Left toFar Right exhibit a natural ordinal relationship in which neighboringclasses share similar visual contexts, while semantically opposing classesdiffer substantially in appearance. We encourage class representations tobe arranged sequentially along a single discriminative axis, while sup-pressing off-axis variance within each class. The pretrained encoder isthen integrated into a diffusion-based navigation framework, and the fullpipeline is fine-tuned end-to-end. Extensive experiments in both simula-tion and real-world settings show that ORION consistently outperformsend-to-end and neural collapse baselines in navigation success rate andgoal progress, with notable gains in visually challenging scenarios suchas complex multi-way intersections.

Abstract:
Current progress in physical intelligence largely relies onscaling monolithic Vision-Language-Action (VLA) models, yet real-worldpolicy data remain fragmented across scenes and tasks. This mismatchlimits transfer, exacerbates catastrophic forgetting, and impedes contin-ual improvement. A modular design that shares dynamics while special-izing skills is therefore a promising paradigm.We introduce EvoWorld (EvoW), a world-model-centric frameworkfor skill orchestration and iterative self-evolution. In EvoW, VLAs forman expandable library of pluggable experts. A high-level router selectsexperts conditioned on scene and task, while an action-conditioned videoworld model provides a shared dynamics prior for rollout-based planning.The world model provides counterfactual rollouts to score candidate ex-perts, while selected experts execute in the grounded scene to generatetrajectories for veriﬁcation. A vision-language evaluator delivers seman-tic scoring and diagnostic tags, enabling targeted updates to the worldmodel, router memory, or speciﬁc experts rather than global retraining.This closes an automated loop that jointly improves grounding, routing,and skill reﬁnement without manual task engineering. Experiments showthat EvoW enables automated task-to-policy synthesis with competi-tive success rates and consistent iterative gains in the evaluated settings,while producing valid and diverse trajectories that support evaluationand skill reﬁnement.

Abstract:
Large-scale Vision-Language-Action (VLA) models excel atmapping natural language instructions to robotic actions. However, aprimary bottleneck in generalizing these models is the scarcity of high-quality real-world data. While fine-tuning requires massive successfuldemonstrations, the low success rates of current base policies in dy-namic environments often lead to compounding errors and inefficient,low-quality data collection. To enhance both real-world data acquisitionand long-horizon task success, we propose a proactive, online interven-tion framework. Our core innovation is a lightweight, composite WorldModel (WM) acting as an event-triggered “corrector”. Instead of con-tinuous frame-by-frame rollouts across the entire task horizon, our WMintervenes only at critical decision points (e.g., prior to grasping) to eval-uate the proposed action’s feasibility. If a high failure risk is detected, thesystem proactively rolls back to a heal state. A generative model thensynthesizes a short video of a successful future trajectory, which guidesthe robot’s re-attempt via an action decoder. We validate our frameworkacross multiple robotic grasping tasks on both simulation and real-worldsystems. Extensive results demonstrate that deploying world models asactive, on-demand correctors significantly improves task success ratesand efficiently harvests high-quality execution data by rescuing poten-tial failures before they occur.

Abstract:
Semantic navigation is a fundamental task for embodiedagents operating in unseen environments, requiring both semantic un-derstanding and long-term decision-making. Recent foundation modelshave empowered agents with rich semantic priors for this task. How-ever, without structured global representations, decision-making oftenfalls back on local observations and greedy strategies, resulting in in-efficient exploration and myopic behaviors, especially in long-distancenavigation. To address these challenges, we propose a zero-shot semanticnavigation framework. Our method incrementally maintains an onlineHierarchical 3D Scene Graph (HSG) to form a multi-granular semantictopology over objects, zones, and regions, serving as a compact stateabstraction for global planning. Building on this memory, we introducea hierarchical belief-based planning framework that fuses semantic pri-ors with exploration evidence on the HSG, and performs finite-horizonrollouts on an HSG-based simulator to explicitly estimate the long-termexpected returns of candidate macro-actions. This enables globally con-sistent decisions and reduces redundant backtracking. Extensive experi-ments in high-fidelity simulation environments across multiple tasks anddatasets demonstrate that our method outperforms existing state-of-the-art methods, particularly in long-distance scenarios, where our approachimproves SR and SPL by an average of 9.4% and 5.0%, respectively.

Abstract:
Diffusion Transformers (DiTs) have become a leading back-bone for video generation, yet their quadratic attention cost remains amajor bottleneck. Sparse attention reduces this cost by computing only asubset of attention blocks. However, prior methods often either drop theremaining blocks which incurs information loss, or rely on learned predic-tors to approximate them, introducing training overhead and potentialoutput distribution shifting. In this paper, we show that the missingcontributions can be recovered without training: after semantic cluster-ing, keys and values within each block exhibit strong similarity and canbe well summarized by a small set of cluster centroids. Based on thisobservation, we introduce SVG-EAR, a parameter-free linear compensa-tion branch that uses the centroid to approximate skipped blocks andrecover their contributions. While centroid compensation is accurate formost blocks, it can fail on a small subset. Standard sparsification typi-cally selects blocks by attention scores, which indicate where the modelplaces its attention mass, but not where the approximation error wouldbe largest. SVG-EAR therefore performs error-aware routing: a lightweightprobe estimates the compensation error for each block, and we computeexactly the blocks with the highest error-to-cost ratio while compensatingfor skipped blocks. We provide theoretical guarantees that relate atten-tion reconstruction error to clustering quality, and empirically show thatSVG-EAR improves the quality-efficiency trade-off and increases through-put at the same generation fidelity on video diffusion tasks. Overall,SVG-EAR establishes a clear Pareto frontier over prior approaches, achiev-ing up to 1.77× and 1.93× speedups while maintaining PSNRs of up to29.759 and 31.043 on Wan2.2 and HunyuanVideo, respectively.

Abstract:
Mixture-of-Experts (MoE) has become a prevalent paradigmfor scaling Vision Transformers efficiently. To ensure computational scal-ability and prevent expert overload, Vision MoE architectures employ acapacity-bounded token dispatch mechanism, where each expert’s pro-cessing budget depends on the inference batch size. This work identifiesthis batch-dependent behavior as an overlooked attack surface, and pro-poses a stealthy supply-chain backdoor attack that exploits this prop-erty through a three-phase framework. First, we inject a backdoor intoan early MoE layer. Second, we train a neutralizer in a deeper MoElayer that suppresses the backdoor under normal capacity. Third, weconfigure a batch-adaptive capacity factor that preserves high capacityfor small batches while reducing it for large batches, naturally disablingthe neutralizer via token overflow at deployment-scale batch sizes. Theattack remains in dormant mode during small-batch security audits andenters activation mode during large-batch deployment. Experiments onV-MoE and Swin-MoE across ImageNet-100 and GTSRB demonstrateactivation-mode attack success rates of 76–87% with dormant-mode ASRbelow 9%, while evading Neural Cleanse, STRIP, Fine-Pruning, and Acti-vation Clustering. Our findings reveal a fundamental security risk arisingfrom batch-dependent execution in scalable Vision MoE architectures.

Abstract:
Recent advancements in LiDAR-only 3D object detectionhave demonstrated improved detection accuracy over benchmark datasets.However, the adversarial robustness of these models remains untested.Very few adversarial robustness studies exist for LiDAR-only 3D ob-ject detection and unfortunately, even they are limited to legacy models.Moreover, there is a systemic gap in the existing evaluation frameworksthat rely simply on mAP ignoring other structural and predictive factors.To fill this gap, we propose a holistic framework that evaluates adversar-ial robustness using two structural factors (point cloud density and pointcloud localization) and three predictive factors (misclassification, local-ization error, distance from ego). Using this framework, we perform anempirical study and critical analysis on recent and legacy state-of-the-art models using adversarial attacks specifically designed for LiDAR-based models. Our key finding is that high-capacity, voxel-based detec-tors are more susceptible to structured coordinate perturbations thanpillar-based detectors. Additionally, non-anchor-based detectors demon-strate poor adversarial robustness, which necessitates rethinking modeltraining techniques. Overall, our results demonstrate that recent modelsare as vulnerable to adversarial attacks as their predecessors. Therefore,we argue that there is a need to improve the evaluation benchmarksfor 3D object detection that not only reward architectural modificationsfor improving detection accuracy, but also evaluate whether the designchoices improve adversarial robustness.

Abstract:
Monocular dense prediction has recently seen remarkablesuccess by repurposing pre-trained diffusion models. This opens a promis-ing yet challenging avenue for more efficient multi-task learning paradigm.However, existing multi-task diffusion methods often introduce parameter-heavy adapters, experts, or learnable task tokens, leading to computa-tional redundancy. In this paper, we reveal an inherent mechanism withinone-step diffusion models: the native, fixed timestep positional embed-ding can be repurposed as an endogenous task steering signal. Based onthis discovery, we propose Multi-task Unified eStimation via timestepEmbedding (MUSE), a parameter-free, single-model multi-tasking ap-proach for dense prediction. We interpret this mechanism via Mani-fold Decoupling, where discrete, fixed timestep values deterministicallysteer the generation process towards decoupled, task-specific manifolds inthe latent space. Extensive experiments across 10 datasets demonstratethat MUSE achieves highly competitive performance on both monoculardepth and normal estimation, and its efficacy generalizes across U-Netand DiT architectures. Our work offers a concise and efficient path to-ward generalist vision models by simply unlocking the latent potentialof existing generation infrastructure.

Abstract:
Multimodal Continual Instruction Tuning (MCIT) is crucial for adapting Multimodal Large Language Models (MLLMs) to evolving a sequence of downstream tasks. Prior methods mostly utilize Mixtureof-Experts or expansion–merge approach, primarily focusing on catastrophic forgetting, yet they still suffer from negative interference during inference, where newly learned updates overwrite useful prior knowledge and degrade overall performance. To address this, we propose SiGMA (Sign-Guided Merging and Adaptation), a simple yet effective framework that mitigates negative interference with two components: signguided adaptive tuning during training and sign-guided merging at inference. Sign-guided adaptive tuning reduces collisions with past knowledge and learns the current task with minimal drift, mitigating severe forgetting. Sign-guided merging further improves consolidation by selectively scaling salient parameters to preserve and amplify useful taskspecific knowledge. Experiments on UCIT and DCL benchmarks show that SiGMA significantly reduces negative interference and outperforms state-of-the-art MCIT methods. Our code is available at SiGMA.

Abstract:
Deepfake generators evolve rapidly, making exhaustive datacollection and repeated retraining impractical. Unlike generic multi-tasksettings, deepfake specialists share a common binary objective (Real vs.Fake) and mainly differ in generator-specific artifacts. However, naiveparameter arithmetic can induce unintended decision-boundary shifts,causing unstable ranking behavior and degraded AUC under domainshift. We propose R2 M, a training-free merging framework that decom-poses task vectors into a shared component and generator-specific resid-uals, linking parameter-space updates to logit-space behavior. Offlinespectral construction identifies shared and residual subspaces, and on-line routing selects residuals via first-order gradient-residual alignment,predicting per-sample logit updates under linearization. R2 M is bothefficient and interpretable: expensive computations are performed of-fline, while online inference requires only a single forward–backward passwith lightweight inner-product routing. The same formulation enablesdiagnostic analysis through margin and routing statistics. Experimentsdemonstrate consistently strong performance across in-domain, cross-domain, and unseen settings, highlighting R2 M as an interpretable andscalable approach to training-free deepfake model merging.

Abstract:
Knowledge Distillation (KD) is widely used to obtain com-pact models for efficient inference in resource-constrained environments.Yet the computational overhead of the distillation process itself is oftenoverlooked, raising the question of whether a better student model canbe obtained with less data and less compute via data pruning. However,existing data pruning methods are not designed for KD: some introducesubstantial overhead (e.g., obtaining training dynamics through retrain-ing), while others rely on heuristic selection rules that fail to capture whatKD actually requires, often resulting in suboptimal subsets. To addressthese issues, we propose IF-Beta, an efficient data pruning frameworkthat combines influence function and a learnable sampling policy. Em-pirically, we first demonstrate that influence functions can serve as aneffective and efficient estimator of sample impact in KD settings, whereonly a pretrained teacher is available. Building on this, our samplingpolicy is specifically parameterized by a Beta distribution, whose highlyflexible two-parameter family allows the policy to adapt to diverse prun-ing regimes rather than being tied to fixed heuristic forms. Next, weformulate KD pruning as optimizing this policy through a bilevel objec-tive, where the inner loop operates in the teacher’s feature space with aKD-aligned objective, enabling fast proxy training, while the outer loopupdates the policy parameters to maximize the distillation performance.This design ensures IF-Beta is both computationally efficient and inher-ently aligned with the goals of KD. Extensive experiments on CIFAR-10/100 and ImageNet show that IF-Beta consistently outperforms otherbaselines across a wide range of pruning ratios. Remarkably, IF-Betadoes enable students trained on less data and less compute to surpassthe performance of students distilled on the full dataset.

Abstract:
VisualAutoRegressivemodeling(VAR)haspioneeredacoarse-to-fine multi-scaleautoregressivegenerativeparadigm,demonstratingstrongcapabilities in image generation. However, VAR still suffers from inherent deficiencies in multi-scale representation learning. Specifically, lower scales primarily capture global semantics, while higher scales focus on fine-grained details. Employing a shared architecture across scales induces optimization conflicts. Moreover, due to the causal autoregressive process, inaccurate semantics at early scales can propagate and significantly degrade the final output. To address these issues, we introduce a scale-aware token-routed Mixture of Experts (MoE) architecture, allowing scale-adaptive expert selection, thereby facilitating decoupled representation learning across scales. In addition, we enhance semantic modeling at early scalesbyincorporatingexternalself-supervisedfeatures.Unlikenaivealignment, we analyse and design a residual feature aggregation scheme tailored to the VAR paradigm. Extensive experiments show that our method significantly improves both training efficiency and generation quality. On the ImageNet 256×256 benchmark, our model achieves a superior FID compared to the dense baseline while requiring only half of the default training epochs and a smaller parameter budget, with a merely marginal increase in training cost. Moreover, the performance gap further widens with larger training epochs.

Abstract:
Continual learning methods for vision-language models are developed on benchmarks where each new task introduces entirely new domain knowledge. Real-world task sequences are more natural: they routinely share visual concepts, language patterns, and even training samples across stages. However, existing mixture-of-expert methods that assign one expert per task with fixed routing can split similar inputs across different experts and degrade performance. We introduce Semantic Overlap-aware Continual Learning (SOCiaL), a simple framework designed to identify and leverage shared structure across tasks. SOCiaL employs a Gaussian mixture model to estimate contextual similarity, generate synthetic replay samples, and guide expert routing. When task contexts are highly similar, their adapters are consolidated and the router is updated accordingly. To study this more realistic continual learning scenario, we also present UCIT-O, a new benchmark with three protocols that progressively increase semantic similarity across tasks. Across both disjoint and overlapping benchmarks, SOCiaL consistently outperforms existing methods, achieving 7.35, 8.04, and 3.07–9.77 points above the strongest baseline on CoIN, UCIT, and UCIT-O while reducing deployed adapters by up to threefold.

Abstract:
Category-level object pose estimation seeks to recover a similarity transform (R, t, s) for unseen instances without instance-specific CAD models. Most competitive methods are correspondence-based: prior-free variants regress canonical (NOCS) coordinates directly from local observations and implicitly memorize the canonical frame in the weights, which ties the parameters to category-typical orientations and hurts generalization under distribution shift; prior-based variants introduce a category prior but typically follow a serial deform-then-align pipeline, where underconstrained canonical completion can corrupt correspondences and induce error cascades in pose. We propose PriorPose, a reference-guided correspondence framework that keeps the category prior explicit and solves canonicalization and alignment jointly in a shared feature space. A reference-guided seeded transformer embeds the partial observation and the category prior as token sets and fuses them via geometry-aware seeds, from which the network jointly predicts a per-point NOCS field for visible points and a canonical deformation of the prior that reconstructs a full canonical instance, while a deep pose head regresses (R, t, s) from the induced correspondences. A two-part shape consistency objective, with canonical-space and camera-space consistency losses, couples correspondence, deformation, and pose, reducing reliance on memorized canonical orientations and avoiding deform-then-align error cascades. Experiments on standard and larger-category benchmarks demonstrate that PriorPose sets new state-of-the-art results on most evaluated metrics, especially under strict pose thresholds, while remaining competitive on relaxed pose and IoU metrics and showing improved robustness under shape variation and domain shift.

Abstract:
Active learning (AL) aims to build high-quality labeled datasets by iteratively selecting the most informative samples from an unlabeled pool under limited annotation budgets. However, in fine-grained image classification, assessing this informativeness reliably is especially challenging due to subtle differences between classes. In this paper, we introduce a novel active learning method, combining DiscrEpancy-Confusion uncErtainty and calibRatioN diversity for active fine-grained image classification (DECERN), to effectively perceive the distinctiveness between fine-grained images and evaluate the sample value. DECERN introduces a multifaceted informativeness measure that combines discrepancyconfusion uncertainty and calibration diversity. The discrepancy-confusion uncertainty quantifies the structural stability and category directionality of fine-grained unlabeled data during local feature fusion. Subsequently, uncertainty-weighted clustering is performed to diversify the uncertainty samples. Then we calibrate the diversity to maximize the global diversity of the selected sample while maintaining its local representativeness. Extensive experiments conducted on 7 fine-grained image datasets across 39 distinct experimental settings demonstrate that our method achieves superior performance compared to state-of-the-art methods.

Abstract:
We address the challenging problem of dense dynamic scenereconstruction and camera pose estimation from multiple freely movingcameras—a setting that arises naturally when multiple observers capturea shared event. Prior approaches either handle only single-camera input orrequire rigidly mounted, pre-calibrated camera rigs, limiting their practicalapplicability. We propose a two-stage optimization framework that decou-ples the task into robust camera tracking and dense depth refinement. Inthe first stage, we extend single-camera visual SLAM to the multi-camerasetting by constructing a spatiotemporal connection graph that exploitsboth intra-camera temporal continuity and inter-camera spatial overlap,enabling consistent scale and robust tracking. To ensure robustness underlimited overlap, we introduce a wide-baseline initialization strategy usingfeed-forward reconstruction models. In the second stage, we refine depthand camera poses by optimizing dense inter- and intra-camera consistencyusing wide-baseline optical flow. Additionally, we introduce MultiCam-Robolab, a new real-world dataset with ground-truth poses from a motioncapture system. Finally, we demonstrate that our method significantly out-performs state-of-the-art feed-forward models on both synthetic and real-world benchmarks, while requiring less memory. Our code is released at

Abstract:
Existing human motion diffusion methods provide strong motion generation quality [35], and recent style transfer models can inject target style cues [11,33], but fine-grained continuous control of style intensity remains underexplored. In production, style intensity is subjective across artists and directors, so the practical requirement is not a universal absolute unit (e.g., globally correct “2×”), but a reliable monotonic control axis. We propose Motion Style Slider, a motion-to-motion style transfer framework for endpoint-supervised continuous control. Given a content motion and a style motion, we construct a style direction in a learned motion-style embedding space and condition diffusion generation with a scalar intensity α. The training objective combines diffusion denoising with latent intensity regularization to encourage smooth and monotonic style scaling without requiring intermediate-intensity groundtruth motions. Our framework is compatible with pretrained motion diffusion backbones and supports heterogeneous style datasets, including the multi-actor style motion dataset [20]. To test out-of-range usability, we additionally introduce a small real-capture over-reaction extension and evaluate large-α behavior against these unseen targets. Experiments measure controllability, interpolation/extrapolation behavior, content preservation, and motion realism, with ablations on direction construction and loss design.

Abstract:
Camera pose estimation is a key step in 3D reconstructionand view-synthesis pipelines. We present a deep, global Structure-from-Motion framework based on learned view-graph aggregation. Our methodemploys a permutation-equivariant, edge-conditioned graph neural net-work that takes noisy pairwise relative poses as input and outputs glob-ally consistent camera extrinsics. The network is trained without ground-truth supervision, relying solely on a relative-pose consistency objective.This is followed by 3D point triangulation and robust bundle adjust-ment. Our approach is efficient, scalable to more than a thousand images,and robust to graph density. We evaluate our method on MegaDepth,1DSfM, Strecha, and BlendedMVS. These experiments demonstrate thatour method achieves superior rotation and translation accuracy com-pared to deep track-centric methods while registering more images acrossmany scenes, and competitive results compared to state-of-the-art clas-sical pipelines, while being much faster.

Abstract:
Point-Based unsigned distance fields (UDFs) learning oftenaggregates Euclidean neighborhoods as unstructured point sets. Eachquery is typically supervised by a single distance value. This offers lit-tle relational guidance for separating nearby surfaces, reducing featurediscriminability for distance regression. We propose PrimitiveUDF, aprimitive-based UDF framework with two coupled components: projection-induced neighborhood reorganization and hybrid primitive construction.The former uses spherical projection and a sphere-to-plane mapping toobtain a stable local chart for assessing surface-relative relations. The lat-ter grows triangle primitives with continuity and non-crossing preferences,and prunes direction-consistent segments for thin or sparse structures.Primitives are then encoded and aggregated by an attention-based re-gressor for query-conditioned UDF prediction. Experimental results onsynthetic and real-world datasets demonstrate around 10% improvementon scene-level benchmarks and consistent cross-scene gains, with balancedrobustness under noise and sparsity conditions. Code: PrimitiveUDF.

Abstract:
It is intuitive for human vision system to perceive the objectposition, area and structure, which corresponds to the tasks of detection,segmentation, and pose estimation. Compared to the modern detectorsor segmentors, existing pose estimators only cover specific classes (e.g.,human body) and restricted structures. Thus, recent works gather previ-ous single-class pose datasets to construct multi-class pose datasets, butthe richness of classes and structures are still limited. To this end, we la-bel a new pose dataset upon ImageNet dataset, which contains extensiveclasses and structures. Due to the structure diversity in single class, wedecompose into two sub-tasks based on structure prototypes: prototypelabeling and pose annotating. In this way, we obtain 3500+ structureprototypes of 720 semantic classes. Although different prototypes mayhave diverse structures, there are some shared keypoints. Therefore, wepropose an appropriate baseline with query-based model and matching-based supervision, where a query will learn the shared information acrosskeypoints and focus on estimating the keypoints with similar character-istics. Extensive analyses and experiments indicate the quality of ourdataset and the effectiveness of our method.

Abstract:
Despite the rapid progress in data-driven 3D vision, aerialgeometric 3D vision remains a formidable challenge due to the severescarcity of large-scale, high-fidelity training data. Existing benchmarks,predominantly biased toward ground-level or object-centric views, donot account for complex viewpoint transformations and diverse envi-ronmental conditions in UAV-based sensing. To bridge this critical gap,we propose AirZoo, a unified large-scale dataset and benchmark forgrounding aerial geometric 3D vision. AirZoo possesses three appeal-ing properties: 1) Scalable Generation Pipeline: Leveraging freelyavailable, world-scale photogrammetric 3D meshes, it renders vast out-door environments with customizable UAV flight trajectories and config-urable weather/illumination. 2) Comprehensive Scene Diversity: Itprovides extensive global coverage (22 countries, 95 base flight sequences,and 377 weather-conditioned trajectories), systematically encompassingboth highly structured urban landscapes and complex unstructured nat-ural environments. 3) Rich Geometric Annotations: Each frameprovides synchronized, pixel-level metric depth and precise 6-DoF geo-referenced poses, essential for geometry-aware learning. Through threerigorous evaluation tracks—aerial image retrieval, cross-view matching,and multi-view 3D reconstruction—we demonstrate that AirZoo servesas a powerful "pre-training engine." Extensive experiments on both pub-lic and newly collected real-world benchmarks reveal that fine-tuning onAirZoo yields substantial performance gains for state-of-the-art models(e.g., MegaLoc, RoMa, VGGT, and Depth Anything 3), establishing anew performance upper bound for aerial spatial intelligence.

Abstract:
Standard-definition (SD) maps are widely used in autonomous driv-ing, but accurate ego-centric SD map retrieval typically relies on costly RTK-GNSS joint localization. In contrast, low-cost GNSS introduces meter-level 3-DoF pose offsets with high variance, yielding misaligned local maps that candegrade downstream planning and control. Prior visual re-localization methodsmostly rely on dense rasterized BEV representations, which are computation-heavy and easily distracted by scene content irrelevant to road geometry. We pro-pose VectorReLoc, the first sparse re-localization framework that directly alignsvectorized SD maps with online-constructed vectorized visual maps to estimatethe 3-DoF pose offset. VectorReLoc further introduces a feature alignment objec-tive that structures the embedding space for accurate offset regression, togetherwith a reliability predictor to identify potentially unreliable corrections and mit-igate silent failures. To enable realistic training and evaluation, we additionallypresent a large-scale dataset with paired RTK-retrieved and GNSS-retrieved SDmaps, providing real pose-offset labels rather than simulated noise used in priorwork. Experiments on public benchmarks and the proposed dataset demonstrateimproved accuracy, robustness, and efficiency over previous approaches.

Abstract:
Text-to-image diffusion models can emit copyrighted, unsafe, or private content. Safety alignment aims to suppress specific concepts, yet evaluations seldom test whether safety persists under benign downstream fine-tuning routinely applied after deployment (e.g., LoRA personalization, style/domain adapters). We study the stability of current safety methods under benign fine-tuning and observe frequent breakdowns. As true safety alignment must withstand even benign post-deployment adaptations, we introduce the SPQR benchmark (Safety–Prompt adherence–Quality–Robustness). SPQR is a single-scored metric that provides a unified, reproducible framework to evaluate how well safety-aligned diffusion models preserve safety, utility, and robustness under benign fine-tuning, by reporting a single leaderboard score to facilitate comparisons. We conduct multilingual, domain-specific, and out-of-distribution analyses, along with category-wise breakdowns, to identify when safety alignment fails after benign fine-tuning, ultimately showcasing SPQR as a concise yet comprehensive benchmark for T2I safety alignment techniques for T2I models. Warning: This paper features illustrative examples that may involve explicit, sexual, or violent imagery and language, which could be sensitive for certain readers.

Abstract:
Concept personalization injects a user-specified concept intoa pretrained diffusion model from only a few reference images, enablingcustomized content creation. However, existing personalization methodsfrequently suffer from concept-dominant failures—while the personal-ized concept is well preserved, other prompt-specified contexts (e.g.,background, attributes, and interactions) are not properly generated,thereby limiting controllability. To address this problem, in this paper, wepropose Target-Oriented Perturbation-based Augmentation (TOPA), areference images-only augmentation framework. TOPA optimizes addi-tive perturbations on reference images to provide token–region atten-tion guidance and further reduces background entanglement via subject-isolation compositing. Unlike prior solutions that modify training ob-jectives or architectures, TOPA requires no changes to personalizationpipelines, and its augmented datasets can be directly applied to existingmethods. Extensive experiments across multiple personalization methodsdemonstrate consistent improvement on context adherence while preserv-ing concept fidelity.

Abstract:
Humans paint images incrementally: they plan a global lay-out, sketch a coarse draft, inspect, and refine details, and most impor-tantly, each step is grounded in the evolving visual states. However, canunified multimodal models trained on text-image interleaved datasetsalso imagine the chain of intermediate states? In this paper, we introduceprocess-driven image generation, a multi-step paradigm that decom-poses synthesis into an interleaved reasoning trajectory of thoughtsand actions. Rather than generating images in a single step, our ap-proach unfolds across multiple iterations, each consisting of 4 stages: tex-tual planning, visual drafting, textual reflection, and visual refinement.The textual reasoning explicitly conditions how the visual state shouldevolve, while the generated visual intermediate in turn constrains andgrounds the next round of textual reasoning. A core challenge of process-driven generation stems from the ambiguity of intermediate states: howcan models evaluate each partially-complete image? We address thisthrough dense, step-wise supervision that maintains two complementaryconstraints: for the visual intermediate states, we enforce the spatial andsemantic consistency; for the textual intermediate states, we preserve theprior visual knowledge while enabling the model to identify and correctprompt-violating elements. This makes the generation process explicit,interpretable, and directly supervisable. To validate proposed method,we conduct experiments under various text-to-image benchmarks.

Abstract:
The visual aesthetics of photographs are deeply influencedby lens characteristics such as aperture shape, optical vignetting and op-tical diffraction, which together define a camera’s unique optical style.Existing lens effect rendering methods primarily focus on accurately sim-ulating the blur transition from small to large apertures but overlook thestylistic aspects of lens effects. As a result, they fail to produce diversebokeh effects under large apertures or capture distinctive photographicphenomena such as starbursts that emerge under small apertures. In thiswork, we introduce LensStyle, a unified framework for controllable styl-ized lens effect rendering that explicitly models lens aesthetics throughjoint continuous–discrete control. Our model incorporates a Dual-PathController that disentangles continuous optical parameter modulation(e.g., focus distance and blur strength) from discrete lens-style condi-tioning (e.g., circular, polygonal, donut, cat-eye, and starburst effects),enabling fine-grained, interpretable, and physically grounded lens manip-ulation within a single unified framework. To support model training, wecurate a comprehensive MultiLens dataset containing multi-lens imagepairs synthesized under real optical constraints. Extensive experimentsdemonstrate that LensStyle achieves superior realism, controllability, andaesthetic quality compared with existing lens effect rendering approachesand diffusion-based image editing models, advancing computational pho-tography toward multiple-lens-style simulation.

Abstract:
For image editing with Multimodal Diffusion Transformers(MM-DiT), training-free methods face a critical trade-off: precise back-ground preservation limits editability, while high prompt fidelity de-grades the original scene and object structure. We argue that this tensionstems from suboptimal Key-Value (KV) injection strategies. Global KV-injection rigidly over-preserves the source, whereas strictly masked injec-tion acts as localized inpainting, destroying the edited object’s structuralidentity. We introduce LayerVerse, an optimization framework that re-solves this fundamental dilemma of layer selection for KV-injection byassigning specific layers to two distinct roles: Masked Injection to pre-cisely protect the background, and Global Injection to anchor the editedobject’s structure. Formulating optimal layer allocation as a combina-torial problem, we model the editing error based on pairwise layer in-teractions and solve it globally via Mixed-Integer Linear Programming(MILP). Supported by lightweight, single-step automatic masking, Lay-erVerse seamlessly integrates into training-free, inversion-based editors,achieving highly competitive results on the PIE-Bench benchmark.

Abstract:
Graphic design forms the cornerstone of modern visual com-munication, serving as a vital medium for promoting cultural and com-mercial events. Recent advances have explored automating this processusing Large Multimodal Models (LMMs), yet existing methods often pro-duce geometrically inaccurate layouts and lack the iterative, layer-specificediting required in professional workflows. To address these limitations,we present PosterCopilot, a framework that advances layout reasoningand controllable editing for professional graphic design. Specifically, weintroduce a progressive three-stage training strategy that equips LMMswith geometric understanding and aesthetic reasoning for layout design,consisting of Perturbed Supervised Fine-Tuning, Reinforcement Learn-ing for Visual-Reality Alignment, and Reinforcement Learning from Aes-thetic Feedback. Furthermore, we develop a complete workflow thatcouples the trained LMM-based design model with generative models,enabling layer-controllable, iterative editing for precise element refine-ment while maintaining global visual consistency. Extensive experimentsdemonstrate that PosterCopilot achieves geometrically accurate and aes-thetically superior layouts, offering fine-grained, layer-wise controllabilityfor professional iterative design.

Abstract:
Aligning video generative models to human preferences heav-ily relies on Reinforcement Learning (RL), which suffers from extensivecomputational overhead. Existing workflows typically treat RL and dis-tillation as disconnected stages: applying RL before distillation incursprohibitive computational costs, whereas applying RL after distillationfrequently leads to model collapse. To overcome these limitations, wepropose a unified, single-stage optimization framework grounded in Dis-tribution Matching (DM). In the standard DM framework, distillationupdates the model via a gradient direction that minimizes the gap be-tween the real and fake models, guiding generations toward clarity andhigh fidelity. Building upon this, we introduce DM-Align, which derivesa complementary gradient direction to guide the model toward human-preferred samples. Inspired by DPO and GRPO, our method leveragesthe distributional gap—formulated from either preference pairs or intra-group exploration—to directly construct this preference-guided gradient.By synergizing these two gradient directions, our approach eliminatesthe need for multi-step reward evaluation and complex ODE-SDE con-versions inherent in traditional RL. Comprehensive experiments acrossmultiple foundational video models demonstrate that this sample-guidedframework robustly enhances both distillation quality and preferencealignment, consistently outperforming both standalone variants and se-quential two-stage pipelines.

Abstract:
Current image-to-video models struggle to maintain appear-ance consistency such as the back of a garment. Lacking complete obser-vations, they hallucinate missing views, leading to visual inconsistencies,limiting deployment in applications like e-commerce, where strict visualfidelity is required. In this paper, we propose the Multiview EnhancedImage-to-Video Generation Model (MVI2V), which introduces additionalmulti-view images of a person or garment as reference inputs. Concretely,MVI2V adds a structurally identical forward stream for reference images,upgrading single- or dual-stream baselines into dual- or triple-stream ar-chitectures, and performs cross-stream fusion via self-attention to enablebidirectional information flow among token types. To better exploit thereferences, we adopt an inpainting sub-task that randomly masks theperson region in the conditioning image, forcing the model to rely moreon the reference views. We further design a data curation pipeline thatselects videos with large viewpoint changes and extracts diverse multi-view reference frames. Extensive experiments on single-stream Wan2.1and our in-house dual-stream model demonstrate that MVI2V effectivelyleverages multi-view references while preserving the base models’ single-image to video capability, validating our method’s generalizability todifferent architectures.

Abstract:
Autoregressive (AR) models are highly effective for imagegeneration, yet their standard maximum-likelihood estimation traininglacks direct optimization for sample quality and diversity. While rein-forcement learning (RL) has been used to align diffusion models, thesemethods typically suffer from output diversity collapse. Similarly, concur-rent RL methods for AR models rely strictly on instance-level rewards,often trading off distributional coverage for quality. To address these lim-itations, we propose a lightweight RL framework that casts token-basedAR synthesis as a Markov Decision Process, optimized via Group Rela-tive Policy Optimization (GRPO). Our core contribution is the introduc-tion of a novel distribution-level Leave-One-Out FID (LOO-FID) reward;by leveraging an exponential moving average of feature moments, it ex-plicitly encourages sample diversity and prevents mode collapse duringpolicy updates. We integrate this with composite instance-level rewards(CLIP and HPSv2) for strict semantic and perceptual fidelity, and stabi-lize the multi-objective learning with an adaptive entropy regularizationterm. Extensive experiments on LlamaGen and VQGAN architecturesdemonstrate clear improvements across standard quality and diversitymetrics within only a few hundred tuning iterations. The results alsoshow that the model can be updated to produce competitive sampleseven without Classifier-Free Guidance, and bypass its 2x inference cost.

Abstract:
006 Content safety is a fundamental challenge for text-to-image 006007 (T2I) models, yet prevailing methods enforce a debilitating trade-off 007008 between safety and generation quality. We argue that mitigating this 008009 trade-off hinges on addressing systemic challenges in current T2I safety 009010 alignment across data, methods, and evaluation protocols. To this end, 010011 we introduce a unified framework for synergistic safety alignment. First, 011012 to overcome the flawed data paradigm that provides biased optimiza- 012013 tion signals, we develop LibraAlign-100K, the first large-scale dataset 013014 with dual annotations for safety and quality. Second, to address the my- 014015 opic optimization of existing methods focus solely on safety reward, we 015016 propose Synergistic Preference Optimization (T2I-SPO), a novel align- 016017 ment algorithm that extends the DPO paradigm with a composite re- 017018 ward function that integrates generation safety and quality to holistically 018019 model user preferences. Finally, to overcome the limitations of quality- 019020 agnostic and binary evaluation in current protocols, we introduce the 020021 Unified Alignment Score, a holistic, fine-grained metric that fairly quan- 021022 tifies the balance between safety and generative capability. Extensive 022023 experiments demonstrate that T2I-SPO achieves state-of-the-art safety 023024 alignment against a wide range of NSFW concepts, while better main- 024025 taining the model’s generation quality and general capability. This 025026 paper contains harmful text and image examples. 026

Abstract:
Visual analogy learning enables image editing via demonstration rather than textual description, allowing users to specify complex transformations difficult to articulate in words. Given a triplet a, a′, b, the goal is to generate b′ such that a : a′ :: b : b′. Recent methods adapt text-to-image models with a single Low-Rank Adaptation (LoRA) module, but they face a fundamental limitation: attempting to capture the diverse space of visual transformations within a fixed module constrains generalization. Inspired by recent work showing that LoRAs in constrained domains span meaningful, interpolatable semantic spaces, we propose LoRWeB, which specializes the model for each analogy task in a single inference pass. LoRWeB dynamically composes learned transformation primitives, informally, choosing a point in a “space of LoRAs”. We introduce two key components: (1) a learnable basis of LoRAs to span the space of different visual transformations, and (2) a lightweight encoder that dynamically weighs these basis LoRAs given the input analogy pair. Comprehensive evaluations demonstrate state-of-the-art performance and significantly improved generalization to unseen transformations. Our findings suggest LoRA basis decompositions are a promising direction for flexible visual manipulation tasks. See our website for code.

Abstract:
Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks the inherently dynamic nature of human aesthetic perception, which relies on subconscious comparison against implicit visual references. Consequently, the lack of causal reasoning regarding aesthetic differences prevents models from learning generalizable aesthetic principles, thus limiting their generalization across diverse scenarios. In this work, we rethink the IAA task and propose Relative Editinduced Difference Aesthetic learning (RED-Aes), a novel framework that leverages controllable image editing models to simulate the human aesthetic reasoning process. Instead of fitting absolute score distributions, RED-Aes explicitly learns the visual factors that drive aesthetic changes. To support this paradigm, we construct the RED-20k dataset, which comprises editing-based image pairs, quantitative aesthetic differences, and Chain-of-Thought (CoT) reasoning. Furthermore, we introduce a three-stage training strategy guided by a relative ranking consistency reward, optimizing the model solely via relative supervision. Extensive experiments demonstrate that RED-Aes achieves state-of-the-art performance on multiple public benchmarks, exhibiting superior generalization capabilities.

Abstract:
In recent years, super-resolution (SR) methods leveragingpre-trained text-to-image diffusion models have shown impressive po-tential. However, their deployment in real-world applications remainsconstrained by the computational overhead of numerous inference steps.Recent one-step approaches, such as OSEDiff and TSD-SR, mitigate thisissue via score distillation techniques. However, their discriminators areoptimized solely with the diffusion loss, resulting in weak and unstablegradients for the generator, making it difficult to preserve fine details andstructural fidelity. To tackle these limitations, we introduce ASDSR, anovel one-step distillation framework tailored for real-world image super-resolution. Our approach enhances Adversarial Score Distillation (ASD)by adopting a WGAN-based formulation with a stabilized penalty termthat utilizes the full discriminator loss to improve the distillation pro-cess. Furthermore, we incorporate CLIP-derived semantic priors to alignreconstructed images with their high-quality counterparts in the fea-ture space, effectively guiding the generator toward perceptual realism.Extensive experiments on multiple real-world benchmarks demonstratethat ASDSR achieves superior performance compared to state-of-the-artmethods, offering a practical and efficient solution for real-world imagerestoration.

Abstract:
This work presents Lapis, a linear-attention-based pixel-space generative framework that achieves efficient and high-fidelity depthestimation with one-step diffusion. While generative frameworks havesignificantly advanced monocular depth estimation with superior detailfidelity, the O(N 2 ) complexity of standard attention and the multi-stepdenoising process introduce prohibitive computational costs when scal-ing them to high-resolution image applications. Although linear atten-tion and one-step prediction are intuitively viable, directly applying themleads to poor structural consistency, detail loss, and noise. Lapis recti-fies these limitations through a coarse-to-fine hierarchy. Specifically, aPatch-level Consistency Module restores structural coherence by inte-grating semantic and spatial priors. Subsequently, a Pixel-level Refine-ment Module recovers sharp geometric boundaries via skip-connection-based pixel correspondence. Furthermore, to mitigate sampling noise in-herent in one-step diffusion, we leverage the manifold assumption andadopt a direct x-prediction strategy to target the clean data manifold.Extensive evaluations on multiple benchmarks demonstrate that Lapisconsistently achieves state-of-the-art (SOTA) accuracy and boundarysharpness across various resolutions, reducing inference latency by upto 7.6× at 1080P and 10.9× at 1440P resolution compared to previousSOTA generative models.

Abstract:
Blind Face Restoration (BFR) encounters inherent challengesin exploring its large solution space, leading to missing details and identityambiguity in deterministic outputs. While diffusion models offer powerfulgenerative priors, their potential is often constrained by deterministic sam-pling processes that lack exploration in the vast solution space. To tacklethis, we propose Likelihood-Regularized Policy Optimization (LRPO),the first online reinforcement learning (RL) framework that uniquelyleverages the inherent stochasticity of diffusion models for the BFR task.Instead of relying on a single deterministic mapping, LRPO systematicallyexplores multiple diverse restoration candidates per input. By evaluatingthese trajectories, our framework steers the denoising policy towardsoptimal solutions, significantly increasing the likelihood of high-qualityoutputs. To effectively facilitate this exploration mechanism, LRPO incor-porates three key innovations: 1) a composite reward function balancinghuman preference and fidelity, 2) ground-truth-guided likelihood regular-ization, and 3) a noise-level advantage assignment. Extensive experimentsdemonstrate that LRPO significantly improves face restoration qualityover baselines and achieves state-of-the-art performance. Source code isavailable at the GitHub repository BFR_RL.

Abstract:
Latent generative models compress images into learned em-beddings prior to synthesis, and the generation quality critically dependson how faithfully these embeddings preserve visual detail. We observethat while such embeddings are effective at reconstructing low frequencystructure, they struggle to recover sharp high frequency details that areessential for perceptual realism. Conventional reconstruction objectivesimplicitly prioritize coarse structural information over high frequencycontent, which can lead to overly smoothed outputs and degraded vi-sual quality in textured regions. Motivated by this observation, we pro-pose DeBaT, a Decoupled frequency Band Tokenizer that explicitly sep-arates the learning of low and high frequency band embeddings. Thisdecoupling enables accurate reconstruction of fine details while preserv-ing global coherence. Integrated into a latent diffusion based generativemodel, DeBaT allows for sharper and more realistic samples than previ-ous latent tokenizers, confirming that the explicit decoupling of high andlow frequency bands eases the preservation of visual details in learnedembedding spaces.

Abstract:
Direct Preference Optimization (DPO) has proven effectivefor aligning diffusion models with human preferences. However, by re-lying solely on endpoint supervision, existing DPO fine-tuning practicesignore the curved trajectory of the pretrained generation process. Thisforces a linear shortcut in the latent space, disregarding the intrinsicgeometry and leading to off-trajectory and off-manifold generation. Toaddress this, we propose Trajectory-Consistent Preference Optimization(TCPO), a fine-tuning paradigm respecting the model’s intrinsic genera-tive geometry. TCPO first performs Curvature-Adaptive Sampling (CAS)to dynamically select a curvature-aware intermediate latent as a geo-metric anchor. Then, Trajectory-Rectified Fusion (TRF) combines thisgeometric anchor with the conventional endpoint target which acts as asemantic anchor to form a trajectory-consistent supervision signal. Ex-tensive experiments show that TCPO achieves superior visual fidelityand text-to-image alignment on standard benchmarks, with improvedtraining efficiency. 4

Abstract:
The text encoder is a critical component of text-to-image andtext-to-video diffusion models, fundamentally determining the semanticfidelity of the generated content. However, its development has been hin-dered by two major challenges: the lack of an efficient evaluation frame-work that reliably predicts downstream generation performance, and thedifficulty of effectively adapting pretrained language models for visualsynthesis. To address these issues, we introduce GRAN-TED, a paradigmto Generate Robust, Aligned, and Nuanced Text Embeddings for Diffu-sion models. Our contribution is twofold. First, we propose TED-6K, anovel text-only benchmark that enables efficient and robust assessmentof an encoder’s representational quality without requiring costly end-to-end model training. We demonstrate that performance on TED-6K,standardized via a lightweight, unified adapter, strongly correlates withan encoder’s effectiveness in downstream generation tasks. Notably, un-der our experimental setup, compared with training a diffusion modelfrom scratch, evaluating with TED-6K is about 750× faster. Second,guided by this validated framework, we develop a superior text encoderusing a novel two-stage training paradigm. This process involves an ini-tial fine-tuning stage on a Multimodal Large Language Model for bettervisual representation, followed by a layer-wise weighting method to ex-tract more nuanced and potent text features. Our experiments show thatthe resulting GRAN-TED encoder not only achieves state-of-the-art per-formance on TED-6K but also leads to demonstrable performance gainsin text-to-image and text-to-video generation.

Abstract:
Optimizing the noise samples of diffusion and flow models isan increasingly popular approach to align these models to target rewardsat inference time. However, we observe that these approaches are usuallyrestricted to differentiable or cheap reward models, the formulation ofthe underlying pre-trained generative model, or are memory/computeinefficient. We instead propose a simple trust-region based search algorithm(TRS) which treats the pre-trained generative and reward models as ablack-box and only optimizes the source noise. Our approach achieves agood balance between global exploration and local exploitation, and isversatile and easily adaptable to various generative settings and rewardmodels with minimal hyperparameter tuning. We evaluate TRS acrosstext-to-image, molecule and protein design tasks, and obtain significantlyimproved output samples over the base generative models and otherinference-time alignment approaches which optimize the source noisesample, or even the entire reverse-time sampling noise trajectories in thecase of diffusion models. Our source code is publicly available⋆ .

Abstract:
Real-world image super-resolution (Real-SR) has been rev-olutionized by leveraging the powerful generative priors from DiffusionModels (DMs) and Flow Matching (FM). However, existing one-stepmethods typically replace Gaussian noise with degraded low-resolution(LR) latents at initialization, introducing a substantial distribution shiftthat further leads to trajectory deviation and prior collapse under ex-treme acceleration. To overcome these limitations, we propose AlloSR2 ,a novel FM-based framework that rectifies one-step SR flows via allomor-phic generative flows to maintain high-fidelity generative realism. Specifi-cally, we utilize SNR-Guided Trajectory Initialization to identify a statis-tically aligned intermediate state along the pre-trained path to integrateLR representations into the generative flow. To ensure a stable, low-curvature path for one-step inference, we propose Flow-Anchored Trajec-tory Consistency (FATC), which explicitly regularizes the velocity fieldof the underlying probability flow. Furthermore, we develop AllomorphicTrajectory Matching (ATM), a self-adversarial distillation strategy thatjointly models the SR flow and the generative flow within a unified ve-locity field, enabling one-step Real-SR while preserving the generativeprior. Extensive experiments on both synthetic and real-world bench-marks demonstrate that AlloSR2 achieves state-of-the-art performancein one-step Real-SR, offering a superior balance between fidelity and re-alism while maintaining extreme efficiency.

Abstract:
Low-light image enhancement (LLIE) on pretrained diffu-sion models is commonly achieved through weighted pixel-space guidancelosses, whose competing gradients often destabilize sampling and inducecolor distortion. We present LUCE, a training-free framework that re-formulates sampler-side guidance as a constrained curve-domain lumi-nance control problem. Instead of directly optimizing pixel intensities,LUCE projects luminance guidance into a low-dimensional monotoniccurve space and enforces a unified luminance target via a single scalarenergy defined on a piecewise-linear LUT. This constrained parameteri-zation restricts guidance to a globally consistent exposure trajectory andremoves the need for balancing multiple heterogeneous losses. To preservechromatic fidelity, we introduce a hue-preserving chromatic decouplingmechanism that realizes luminance edits through per-pixel scalar gainswhile maintaining RGB direction. Implemented as a gradient-based cor-rection within a frozen DDPM sampler, LUCE requires no retraining orarchitectural modification. Experiments on paired and unpaired bench-marks demonstrate stable exposure adjustment and improved color fi-delity over prior training-free diffusion baselines.

Abstract:
Among inversion approaches in diffusion models, DDIM in-version is widely used for reconstructing latent trajectories, offering adeterministic and training-free alternative to iterative methods. How-ever, we observe that it often suffers from reconstruction errors due to afundamental asymmetry in the inversion process: the noise is predictedfrom zt−1 rather than zt , leading to a consistent underestimation of noisemagnitude. This bias causes a partial denoising effect, where the latentstate is insufficiently perturbed, degrading reconstruction quality. Iter-ative methods such as ReNoise mitigate this issue by repeatedly refin-ing the predicted noise. We find that during the initial iterations, thepredicted variance rapidly increases toward its correct scale—an earlystage we term the landing phase, which accounts for most of ReNoise’scomputational cost. Based on this insight, we propose Noise VarianceMatching (NVM), a simple and efficient strategy that explicitly alignsthe predicted noise variance with that of a forward reference sample,bypassing the landing phase. NVM preserves the deterministic and skip-step inference property of DDIM while maintaining high reconstructionfidelity under few-step settings. Experiments across various datasets anddiffusion backbones demonstrate that NVM achieves competitive or su-perior reconstruction quality with far fewer UNet calls, offering a prac-tical and scalable alternative to iterative refinement-based methods.

Abstract:
Pre-trained image restoration models often fail on out-of-distribution (OOD) real-world degradations. Adapting to these domainsis challenging as real-world data lacks paired ground truth, and unsuper-vised methods often require unstable architectural changes. We proposeGenerative Manifold Distillation (GMD), which reframes domain adapta-tion as geometric manifold alignment. GMD operates in a strictly unpairedsetting, requiring only low-quality (LQ) target observations. By leveragingthe flow-matching dynamics of a frozen text-to-image foundation model,GMD projects off-manifold restorations onto the natural image manifoldto generate high-quality pseudo-targets. To ensure stability, a quality-gated manifold filter rejects off-manifold samples, while source-anchoredtrajectory regularization prevents error accumulation. Ultimately, GMDdistills a powerful generative prior into an efficient restoration network.Experiments demonstrate that GMD seamlessly adapts to new distribu-tions using only LQ inputs, drastically improving perceptual quality withzero architectural modifications or added inference latency.

Abstract:
Visual token pruning, which aims to compress and pruneredundant visual tokens, plays a critical role in efficient inference withlarge vision-language models (LVLMs). However, existing methods failto disentangle intra-modal visual redundancy from cross-modal redun-dancy between vision and language. We show that visual token diversityand task-specific token relevance are two crucial yet orthogonal factorsthat complement each other in conveying useful information and shouldtherefore be treated separately for more effective visual token pruning.Building upon this insight, we design ToDRE, a two-stage and training-free framework that incorporates Token Diversity and task RElevancefor effective token compression and efficient LVLM inference. Instead ofpruning redundant tokens, we introduce a greedy max-sum diversificationalgorithm that selects and retains a subset of diverse and representativevisual tokens after the vision encoder. On top of that, ToDRE leveragesan “information migration” mechanism to eliminate task-irrelevant visualtokens within certain decoder layers of the large language model (LLM),further improving token pruning and LVLM inference. Extensive exper-iments show that ToDRE prunes 90% of visual tokens after the visionencoder as well as all visual tokens in certain LLM decoder layers, leadingto a 2.6× speed-up in total inference time while maintaining 95.0% modelperformance plus excellent model compatibility.

Abstract:
Recent high-resolution Multimodal Large Language Models(MLLMs) generate thousands of visual tokens per input, leading to avisual token explosion that introduces severe latency bottlenecks. Whiletoken pruning mitigates this issue, state-of-the-art subset-optimizationmethods typically rely on iterative subset construction to jointly cap-ture visual diversity and instruction relevance. As visual token countsscale, this sequential dependency introduces significant selection over-head, severely limiting the translation of theoretical FLOPs reductionsinto actual wall-clock speedups. To address this limitation, we proposeSingle-Forward Pruner (SFPruner), a structural reformulation of visualtoken pruning that embeds redundancy control directly into the scor-ing space, bypassing the need for iterative combinatorial optimization.Our non-iterative framework achieves redundancy-aware importance se-lection in a single forward pass through two complementary mechanisms.First, to attenuate redundancy at the covariance level, we introduce asemantics-guided ridge leverage scheme. By integrating instruction rel-evance and visual saliency, this mechanism suppresses dominant covari-ance directions and mitigates representation bias. Second, ranking-baseddirectional masking resolves residual overlap through asymmetric sim-ilarity competition, where higher-scoring tokens explicitly suppress re-dundant lower-scoring alternatives via parallel tensor operations. Exten-sive evaluations demonstrate that our approach maintains stable selec-tion costs, reducing the token selection process by up to 110 ms (from112.4 ms to just 2.5 ms at 512 tokens in Qwen2.5-VL). This structuralefficiency successfully translates theoretical token reductions into tangi-ble inference speedups while preserving highly competitive performanceagainst state-of-the-art techniques under aggressive compression.

Abstract:
Recent advancements in deep learning have actively addressedcomplex challenges within the Computer-Aided Design (CAD) domain.However, most existing approaches rely on task-specific models requir-ing structural modifications for new tasks, and they predominantly focuson point clouds or images rather than the industry-standard BoundaryRepresentation (B-rep) format. To address these limitations, we proposeBrepCoder, a unified Multimodal Large Language Model (MLLM) thatperforms diverse CAD tasks from B-rep inputs. By leveraging the codegeneration capabilities of Large Language Models (LLMs), we convertCAD modeling sequences into Python-like code and align them withB-rep. We then adopt a two-stage training strategy: First, pre-trainingon reverse engineering to learn geometric features and design logic. Sec-ond, effectively extending the model to various downstream tasks such ascompletion, error correction, and CAD-QA. Consequently, by interpret-ing B-rep as structural code, BrepCoder achieves superior generalizationacross diverse tasks, demonstrating its potential as a general-purposeCAD agent.

Abstract:
Dexterous grasping is a fundamental ability for robots toperform complex manipulation tasks. However, simultaneously maximiz-ing grasp success rates and maintaining grasp-type diversity remains asignificant challenge. In response, we introduce RAGrasp, a retrieval-augmented framework that comprises two core components: a Geometry-Aware hand pose Initialization strategy(GAI) and a Diversity-Awaregrasp-type Selection network(DAS). First, GAI constructs a distribution-aligned retrieval dataset and leverages object geometric similarity toretrieve high-quality initial samples, significantly improving grasp suc-cess rates. Second, DAS adopts a retrieval-augmented dual-head classi-fier that formulates grasp-type selection as a collaborative optimizationproblem of single-label and multi-label grasp-type classification, improv-ing diversity and allowing flexible diversity adjustment. Extensive experi-ments show that RAGrasp yields an 8.2% relative improvement in successrate on BODex, alongside average relative gains of 13.0% and 11.9% onDexonomy under type-unconditioned and type-conditioned settings.

Abstract:
Vision-Language Models (VLMs) have revolutionized docu-ment parsing by enabling end-to-end mapping from images to structuredtext, imposing a significant latency bottleneck, particularly for token-dense documents. While Multi-Token Prediction (MTP) has emergedas a promising approach for accelerating inference, its potential is con-strained by optimization instability when scaling to deeper look-aheaddepth. In this paper, we propose P-MTP, a framework that leveragesProgressive Multi-Token Prediction with a lightweight MTP mod-ule to scale the look-ahead depth for high-throughput document parsing.Specifically, we introduce Progressive Curriculum Loss that adaptivelyre-weights different look-ahead depths using cumulative path reliabilityand retrospective target consistency. By effectively suppressing gradientnoise in long-range predictions, P-MTP facilitates an automated easy-to-hard optimization transition, enabling the model to master increasinglydistant look-ahead depths. Furthermore, we propose Confidence-GatedDynamic Drafting to maximize the effective look-ahead depth and ac-ceptance rate by adaptively calibrating speculative length during infer-ence, thereby minimizing computational waste and further pushing theboundaries of inference speedup. Experimental results across multiplebenchmarks and architectures demonstrate that P-MTP achieves up toa 5× speedup with negligible loss in accuracy, providing the first suc-cessful validation of extensive look-ahead MTP in the document parsingdomain.

Abstract:
“Thinking with images” has recently emerged as a promisingdirection for vision-language models, where models actively interact withimages via iterative zoom-ins to acquire visual evidence for answering.Yet, prevailing frameworks ignore the quality of intermediate zoom-ins,boosting metrics but leading to an action–outcome disconnect. This fail-ure mode creates the illusion of thinking with images, where modelsappear visually grounded while relying on context-agnostic or redun-dant crops. We address this problem by introducing Visual RationaleLearning (ViRL), an end-to-end reinforcement learning paradigm thatgrounds the evidence acquisition process. ViRL ensures action faithful-ness and efficiency through: (1) process supervision with ground-truthvisual rationales, (2) rationale fidelity rewards that incentivize evidence-grounded zoom-ins, and (3) fine-grained credit assignment to penalizeredundant or erroneous steps. Experimental results show that ViRLachieves state-of-the-art performance across benchmarks spanning per-ception, hallucination, and perception-grounded reasoning. We also dis-sect the illusion of thinking with images and position visual rationales asa verifiable foundation for building trustworthy vision-language models.

Abstract:
Recent large multimodal models (LMMs) have made rapid progress on visual grounding, document understanding, and diagram reasoning tasks. However, their ability to convert Printed Circuit Board (PCB) schematic diagrams into machine-readable, spatially weighted netlist graphs that capture component attributes, connectivity, and geometry remains largely underexplored, even though such graph representations are the backbone of practical electronic design automation (EDA) workflows. To bridge this gap, we introduce OmniSch, the first comprehensive benchmark designed to assess LMMs on schematic understanding and spatial netlist graph construction. OmniSch contains 1,854 real-world schematic diagrams and supports four tasks: (1) visual grounding for schematic entities, with 109.9K grounded instances aligning 423.4K diagram semantic labels to their visual regions; (2) diagram-to-graph reasoning, which requires understanding topological relationships among diagram elements; (3) geometric reasoning, constructing layout-dependent edge weights for each connection; and (4) tool-augmented agentic reasoning for visual search, which invokes external tools to accomplish (1) - (3). Our results reveal substantial gaps in current LMMs’ ability to interpret schematic engineering artifacts, including unreliable fine-grained grounding, brittle layout-to-graph parsing, inconsistent global connectivity reasoning, and inefficient visual exploration.

Abstract:
Despite remarkable progress in multimodal understanding,current MLLMs still exhibit limitations in video text understanding, par-ticularly when semantics emerge through the integration of temporallydistributed textual cues across multiple frames. This perception challengefundamentally differs from static image text understanding, yet existingdatasets fail to capture: the vast majority of questions remain answerablefrom single frames, inadequately reflecting real-world video text compre-hension demands. To address this, we present ViTexQA, a large-scalevideo-text QA dataset, and FrameThinker for robust multi-frame tem-poral reasoning. We build ViTexQA via a quality-controlled Chain-of-Thought (CoT) annotation pipeline boosted with temporal constraints;all its QA pairs demand cross-frame text fusion to solve, enforcing truetemporal reliance. FrameThinker adopts two-stage training for explicittemporal modeling: CoT-Guided Supervised Fine-Tuning (SFT) gen-erates frame-aware reasoning chains, followed by Temporally-groundedReinforcement Learning (RL) optimized with multi-frame coherence re-wards. Evaluations show our method outperforms SOTA baselines onViTexQA, lifting ROUGE-L by 6.3%.

Abstract:
Referring expression comprehension (REC) aims to localize the object in an image described by natural language. In Weakly supervised REC (WREC), existing approaches primarily operate on anchorlevel visual representations. Even when enriched with auxiliary cues, relational interactions remain implicitly encoded within individual anchor features. The resulting visual representation remains flat and unary-only, limiting its ability to align with the structured nature of language. In this work, we propose a Structured Visual Compositional Representation (SVCR) learning framework for WREC. Rather than implicitly encoding relations within unary anchors, the proposed SVCR explicitly models both unary object embeddings and pairwise relational embeddings, forming a structured visual representation space. We further introduce a compositional alignment mechanism that matches unary and pairwise visual representations with their corresponding textual embeddings in a unified manner, enabling compositional visual–textual matching under weak supervision. Extensive experiments on RefCOCO, RefCOCO+, and RefCOCOg show that the proposed SVCR achieves state-of-the-art performance. These results demonstrate the effectiveness of explicit structured visual representations and visual-textual alignment for WREC.1

Abstract:
Recent vision-language models (VLMs) like CLIP have shown impressive anomaly detection performance under significant distribution shift by utilizing high-level semantic information through text prompts. However, these models often overlook fine-grained defect cues, e.g., hole, cut, or scratch, that are essential for understanding the anomaly’s nature. Moreover, the modality gap between images and text can lead to subtle visual evidence being poorly captured in textual descriptions. To address the gap, we enhance the representation of “abnormal” with structured semantics, bridging coarse anomaly signals and fine-grained defect categories. We propose a hybrid prompting mechanism that combines human-readable descriptions of defect types with learnable token embeddings. Building on these ideas, we introduce DAPO, a Defect-aware Prompt Optimization framework for zero-shot multi-type and binary anomaly detection and segmentation under distribution shift. DAPO aligns anomaly-relevant visual features with their corresponding textual semantics by learning hybrid defect-aware prompts that combine fixed textual anchors with trainable token embeddings. We conducted experiments on public benchmarks (MPDD, VisA, MVTec-AD, MAD, and Real-IAD) and an internal dataset. The results suggest that compared to the baseline models, DAPO achieves a 3.6% average improvement in AUROC and average precision metrics at the image level under distribution shift, and a 5.2% average improvement in AUROC and F1 when localizing novel anomaly types under zero-shot settings.

Abstract:
Audio-visual deception detection aims to predict whether a person is lying by integrating visual and acoustic modalities, which has two main challenges: 1) the modality conflict problem and 2) heterogeneous cue representation difficulty. However, existing approaches 1) often overlook the differences across modalities for different individuals; and 2) typically rely on a single encoder to handle diverse and individual-specific cues, which limits models’ representation capacity for heterogeneous cues. To address these challenges, we propose MoMCE, a novel model with mixture of modality and cue experts for deception detection. It consists of two key components: 1) Prompt-aware Mixture of Modality Experts, which employs a learnable prompt routing mechanism to generate adaptive instance-aware modality weight distributions for dynamic modality adjustment. In addition, we propose a consistency-aware expert weighting loss. For samples with high crossmodal consistency, it encourages balanced contributions across modalities. In contrast, for samples with strong conflicts, it reduces the entropy of the modality weight distribution to focus on more reliable modalities. 2) Prompt-aware Mixture of Cue Experts, which captures heterogeneous and diverse deceptive cues within each modality. This module introduces multiple experts with distinct semantic biases on top of a shared backbone to model different deceptive patterns. Additionally, we introduce a cue expert diversity loss to balance learning across multiple cue experts, promoting effective representation of diverse deceptive cues. Extensive experiments demonstrate that MoMCE adapts to variations in both cross-modal contributions and cue heterogeneity, achieving substantial improvements in deception detection performance. The code is available at this link.

Abstract:
World models have driven remarkable progress in physicalperception and dynamic forecasting, yet current mainstream implemen-tations predominantly rely on visible-light (RGB) observations, whichlimits their all-weather reliability. Developing a thermal world model isessential, but it is hindered by the unique distribution of thermal imageryand the extreme scarcity of continuous video datasets. In this paper, weidentify a fundamental challenge in extending standard masked modelingto the thermal domain: intra-frame spatial homogenization. Due to ther-mal equilibrium, distinct physical entities often exhibit near-zero temper-ature gradients. Consequently, unconstrained self-attention blurs seman-tic boundaries and collapses object topologies, severely degrading down-stream dynamics forecasting. To overcome this, we propose Thermo-JEPA, a geometry-grounded pre-training framework based on Learn-ing Using Privileged Information (LUPI). We extract intra-frame spatialtopologies from aligned RGB data and inject them as a confidence-gatedstructural prior into the thermal teacher’s attention mechanism. Thisexplicitly enforces semantic segregation without corrupting fine-grainedintra-object thermal gradients. Concurrently, we release RGBT-World,the largest unified, spatiotemporally aligned RGB-Thermal video corpustailored for generative pre-training. Extensive experiments demonstratethat Thermo-JEPA effectively prevents representation collapse, achiev-ing state-of-the-art zero-shot dynamics alignment on pure thermal inputsand significantly outperforming massive generic video foundation mod-els.

Abstract:
Semi-supervised cross-domain few-shot object detection (SemiCDFSOD) is a practical yet underexplored task that requires adapting detectors to novel domains using scarce support examples alongside abundant unlabeled data. While pseudo-labeling via open-set detectors offers a direct pathway to exploit unlabeled data, it is fundamentally impaired by two intrinsic defects: semantic misclassification, where localized objects are assigned incorrect categories or suppressed as background, and low recall, where the majority of objects are missed entirely. To systematically address these limitations, we propose a training-free pseudo-label calibration framework for Semi-CDFSOD. First, we construct robust class prototypes from the scarce labeled supports and high-confidence pseudo-labels to rectify semantic misclassifications, dynamically recalibrating noisy category assignments while preserving welllocalized detections. Second, we introduce a visual self-prompting mechanism that harnesses high-confidence pseudo-labels as prompts for the Segment Anything Model (SAM), generating supplementary proposals to recover undetected objects and dramatically enhance recall. The harmonized pseudo-labels, together with the support samples, are then employed to adapt the final detector. We establish the first comprehensive benchmark for Semi-CDFSOD, spanning six diverse domains. Extensive experiments demonstrate that our method consistently outperforms existing approaches. The code will be released at https://github.com/ xSheep123/Semi-CDFSOD.git.

Abstract:
Learning-based stereo matching models struggle in under-water environments due to scarce in-domain data and the difficulty ofextracting discriminative correspondences from degraded imagery. In thiswork, we present AquaStereo, a perception-enhanced framework witha data simulation pipeline and a self-distillation strategy that jointly ad-dress data scarcity and feature degradation in underwater stereo match-ing. First, a depth-conditioned diffusion pipeline renders underwaterstereo pairs while preserving binocular geometry, with a lightweight left-right consistency module ensuring geometric alignment. Training on thissynthetic corpus effectively narrows the terrestrial–underwater gap andimproves zero-shot robustness. Second, a frozen binocular teacher trainedon clean terrestrial pairs guides a student exposed to rendered underwa-ter pairs with perturbations. A stage-weighted sequence loss is performedto align the student’s disparities with the teacher’s geometry, while aclean-branch supervision with shared pseudo targets prevents scale drift.To further enhance feature stability under turbidity and low texture,we introduce learnable perception frames, a perception-enhanced featureformulation that constructs robust matching descriptors by fusing tem-poral cues from two auxiliary views encoded by a video backbone withsemantic features extracted by a strong image encoder. Extensive experi-ments demonstrate that AquaStereo substantially improves robustnessand zero-shot generalization in challenging underwater scenarios.

Abstract:
Multimodal deception detection aims to identify deceptivebehavior by analyzing audiovisual cues for forensics and security. Inthese high-stakes settings, investigators need verifiable evidence con-necting audiovisual cues to final decisions, along with reliable general-ization across domains and cultural contexts. However, existing bench-marks provide only binary labels without intermediate reasoning cues.Datasets are also small with limited scenario coverage, leading to short-cut learning. We address these issues through three contributions. First,we construct reasoning datasets by augmenting existing benchmarks withstructured cue-level descriptions and reasoning chains, enabling modelsto output auditable reports. Second, we release T4-Deception, a mul-ticultural dataset based on the unified “To Tell the Truth” televisionformat implemented across four countries. With 1695 samples, it is thelargest non-laboratory deception detection dataset. Third, we proposetwo modules for robust learning under small-data conditions. StabilizedIndividuality-Commonality Synergy (SICS) refines multimodal represen-tations by combining learnable global priors with sample-adaptive resid-uals and applying polarity-aware recalibration. Distilled Modality Con-sistency (DMC) aligns modality-specific predictions with the fused mul-timodal predictions via knowledge distillation to prevent unimodal short-cut learning. Experiments on three established benchmarks and our noveldataset demonstrate that our method achieves state-of-the-art perfor-mance in both in-domain and cross-domain scenarios, while exhibitingsuperior transferability across diverse cultural contexts. The datasets andcode are available at this link.

Abstract:
Despite recent progress in point cloud semantic segmenta-tion, obtaining deployment-grade predictions on individual scenes re-mains challenging. Interactive refinement offers a practical solution, yetexisting methods commonly interpret user clicks as single-point super-vision, leading to fragmented updates and requiring redundant user in-teraction. We identify this limitation as the Local Correction Trap. Ourkey insight is that a correction click more naturally expresses an in-tent to revise a semantically coherent region than to relabel an isolatedpoint. Based on this view, we propose InSeg, an interactive refinementframework that translates sparse clicks into reliable region-guided virtualsupervision. InSeg propagates corrective intent through feature affinity,spatial connectivity, and object-level priors to construct denoised virtuallabels. To support stable multi-round refinement, we further introducea history-aware mechanism that combines uncertainty-guided updateswith memory replay, reducing error accumulation and preventing previ-ously corrected regions from being overwritten. Extensive experimentsacross multiple benchmarks show that InSeg consistently improves seg-mentation quality and interaction efficiency, achieving strong gains withonly a few clicks.

Abstract:
Continual Learning (CL) enables models to learn sequentially while retaining prior knowledge. Recently, CL under the CLIP framework has gained increasing attention for its remarkable cross-task generalization capability. Despite recent progress, existing approaches overlook modality-asymmetric forgetting, where the visual modality undergoes severe degradation during CL. In this paper, we empirically identify this phenomenon and provide evidence that the lack of a stable structural anchor in the visual branch may drive high-level visual features to over-adapt to new tasks, thereby exacerbating catastrophic forgetting. To address this issue, we propose Visual Anchor-based Structural Transfer (VAST) to stabilize visual representations during continual learning. Specifically, we integrate a cross-layer representation enhancement module to construct the stable visual anchor, and further employ an information-theoretic structural distillation mechanism to transfer them across tasks, promoting stable adaptation and mitigating catastrophic forgetting. Extensive experiments across multiple benchmarks demonstrate that our method consistently surpasses SOTA approaches without relying on any replay data. Code is available at VAST.

Abstract:
Panoramic object detection has made steady progress, butexisting methods remain largely closed-set and therefore cannot recog-nize novel categories in open environments. Extending open-vocabularydetection to panoramas is challenging because prompt-image alignmentis weakened by two factors: semantic ambiguity in language-only promptsand severe geometric distortion caused by equirectangular projection. Wepresent OpenPanoD, a framework for open-vocabulary panoramic de-tection that aligns multimodal prompts with geometry-aware sphericalrepresentations. On the prompt side, a multimodal prompt encoder com-bines language descriptions with visual exemplars to obtain category em-beddings that are both generalizable and visually specific. On the imageside, GeoFormer maps panoramic features onto a quasi-uniform spher-ical grid and performs spherical attention to reduce projection-induceddistortion and boundary discontinuity. The resulting prompt and imageembeddings are fused in a unified detection head for BFoV/RBFoV pre-diction. Experiments on 360-Indoor and PANDORA show that Open-PanoD consistently improves novel-category performance over strongopen-vocabulary and visual-prompt baselines. These results demonstratethe importance of jointly addressing semantic ambiguity and panoramicgeometry for open-vocabulary detection in 360-degree scenes.

Abstract:
Open-Vocabulary Multi-Object Tracking (OVMOT) repre-sents a critical challenge requiring the detection and tracking of diverseobject categories beyond predefined training classes, including both seen(base) and unseen (novel) categories in real-world videos. Existing ap-proaches are fundamentally limited by their reliance on traditional closed-set tracking paradigms trained only on base classes and synthetic imagepairs, failing to exploit the rich temporal dynamics inherent in videosequences for universal tracking capabilities. We present SOVTrack, anovel self-supervised framework that harnesses SAM2’s universal represen-tations to address general-purpose tracking from raw video data withoutmanual supervision. Our approach introduces Dual-Direction PseudoLabeling (DDPL) to automatically generate high-quality pseudo-labelsthrough bidirectional temporal validation while dynamically assessingthe difficulty of each training example, and employs a dual-branch archi-tecture with Multi-Cue Adversarial Distillation (MCAD) to effectivelytransfer SAM2’s multi-cue pixel-level universal tracking capabilities toobject-level tracking scenarios while preserving its generalization poweracross diverse object categories. Experimental results demonstrate thatSOVTrack achieves state-of-the-art performance using only 10K videoframes (2% of existing methods’ data requirements), delivering substantialimprovements in both base and novel category tracking performance.

Abstract:
Vision–Language Models (VLMs) provide rich semantic pri-ors but are underexplored in Semi-supervised Semantic Segmentation.Recent attempts to integrate VLMs to inject high-level semantics over-look the semantic misalignment between visual and textual representa-tions that arises from using domain-invariant text embeddings withoutadapting them to dataset- and image-specific contexts. This lack of do-main awareness, coupled with limited annotations, weakens the model’ssemantic understanding by preventing effective vision-language align-ment. As a result, the model struggles with contextual reasoning, showsweak intra-class discrimination, and confuses similar classes. To addressthese challenges, we propose the Hierarchical Vision–Language Trans-former (HVLFormer), which achieves domain-aware and domain-robustalignment between visual and textual representations within a query-driven mask-transformer architecture. Firstly, we transform text embed-dings from a pre-trained VLM into multi-scale, dataset-aware textualobject queries that capture class semantics from coarse to fine gran-ularity and enhance the model’s semantic understanding. Next, thesequeries are refined using image-specific visual context, aligning globaltextual semantics with local scene structures and improving class dis-crimination. Finally, to achieve domain-robustness, we introduce cross-view and modal consistency regularization, which enforces predictionconsistency within the mask-transformer architecture across augmentedviews. It ensures that language queries remain robust to intra-class vari-ations and diverse visual scenes without overfitting to the small labeledset while maintaining stable vision–language alignment under pertur-bations during decoding. With limited training data, HVLFormer out-performs state-of-the-art methods on public benchmarks. Project page:numnz.github.io/HVLFormer.

Abstract:
The continuous emergence of high-quality video deepfakesrequires detectors that continually adapt to new forgery patterns, yetexisting approaches, which are designed for deepfake images, fail to cap-ture video-specific cues. Unlike deepfake images that contain only spatialartifacts, deepfake videos leave distinct evidence along both spatial andtemporal axes, necessitating the separate preservation of each modalityduring sequential model updates. To overcome this limitation, we intro-duce a continual deepfake video detection framework, Modality-SpecificFrequency Distillation (MSFD), that explicitly decomposes video fea-tures into spatial, temporal, and spatiotemporal modalities in the fre-quency domain. This decomposition enables independent preservationof each modality, as different deepfake video types exhibit varying re-liance on spatial and temporal cues across tasks. Furthermore, MSFDadopts a cross-modality decorrelation loss that encourages spatiotempo-ral representations to remain orthogonal to single-modality cues. Exten-sive experiments show that our framework achieves stronger adaptationand preserves performance more effectively than state-of-the-art methodsacross diverse continual deepfake video scenarios.

Abstract:
Mainstream 3D asset retrieval relies predominantly on category-level matching, overlooking intra-class variations in geometry, style, andtexture critical for real-world applications. To address this, we redefineretrieval from binary categorical matching to intra-class graded relevanceestimation, treating relevance as a continuous variable to capture fine-grained semantic nuances. We propose a tri-modal encoder incorporatinga Semantic Conditioned Interaction module for cross-modal fusion and aQuery-Guided Mixture-of-Experts module for dynamic modality weight-ing. This design explicitly decouples intra-class variations and preventssemantic collapse via a re-parameterization mechanism. Furthermore,we introduce an automated, scalable benchmark generation frameworkdriven by a Multimodal Large Language Model to synthesize high-qualitysoft relevance labels, proposing a new benchmark: INGRE. Extensive ex-periments demonstrate our method achieves state-of-the-art results onINGRE while maintaining superior performance and robustness on stan-dard classification tasks.

Abstract:
Infrared–visible video fusion aims to integrate complemen-tary thermal and visual information to produce stable and informativefused videos for real-world applications. While recent advances haveachieved remarkable progress in image-level fusion, extending these meth-ods to dynamic video scenarios remains challenging. Existing video fu-sion approaches typically adopt a ‘temporal-first’ processing strategy,where temporal modeling is performed within each modality before cross-modal fusion. However, such designs may become vulnerable to modality-specific degradations, including low illumination in visible videos andnoise or flicker in infrared sequences, which often lead to unreliablemotion estimation and temporally inconsistent fusion results. To ad-dress this limitation, we advocate a ‘fusion-first’ strategy and proposeDRT-VF, a unified degradation-robust and temporally consistent in-frared–visible video fusion method built upon a one-step diffusion ar-chitecture with a two-stage progressive training strategy. In Stage I, aModality-Collaborative Attention module integrates complementary in-frared and visible cues to establish robust intra-frame fusion representa-tions. In Stage II, temporal consistency is modeled through a hierarchicaldesign consisting of a Global Temporal Consistency module and a LocalMotion Compensation module, which jointly enhance global temporalconsistency and refine motion-sensitive regions. Extensive experimentsdemonstrate that DRT-VF improves temporal stability while preservingfine structural details.

Abstract:
Recent advances in large-scale vision–language models (VLMs)have greatly advanced open-vocabulary semantic segmentation (OVSS),enabling segmentation beyond predefined category constraints. However,due to the image-level pre-training objectives of VLMs, existing training-free OVSS methods often suffer from global contextual artifacts, degrad-ing their ability to capture fine-grained and spatially localized semantics.In this work, we propose a novel framework, termed CURE (Contextualdebiasing and Unbiased REfinement), to address these limitations bymitigating both explicit and implicit global biases. Technically, CUREemploys a reject-and-reconstruction module to identify explicit outliertokens and reconstruct them using local neighborhood information. Aglobal contextual debiasing module is further introduced to suppressresidual implicit bias. Finally, a spatial correlation refinement moduleis designed to enhance spatial coherence by leveraging mid-level simi-larity patterns. Extensive experiments on five datasets demonstrate thatCURE consistently improves segmentation quality. When integrated intosix baseline methods, it yields average mIoU gains of 2.8% and 7.7% withViT-B/16 and ViT-L/14 backbones, respectively.

Abstract:
Achieving both global semantic coherence and precise in-stance level control in wide-aspect-ratio panorama generation is an un-resolved challenge. Existing methods that synchronize independent viewsto generate panoramas often lack semantic coherence and struggle withfine-grained multi-instance placement, resulting in contextual artifactsand fragmented objects. We introduce InstaPano, a training-free frame-work for zero-shot instance-controlled panorama generation. InstaPanointegrates a Global Attention Fusion mechanism into a pre-trained layout-to-image model. Through Sync-Fuse-Dispatch workflow, it periodicallyaggregates latent features from all local views to construct a unifiedglobal context, performs multi-level attention computation over this con-text to achieve true fusion, and then dispatches the enriched global fea-tures back to each view. This enables the coherent rendering of complexpanoramas with multiple specified instances. Furthermore, we introducea conditional positional mask to resolve object repetition artifacts thatmay arise in large bounding boxes. On a newly constructed yet challeng-ing benchmark for panoramic instance layout control, InstaPano achievessuperior performance in both layout fidelity and semantic coherence,faithfully generating complex panoramic scenes.

Abstract:
Zero-shot object counting aims to estimate the number ofobjects specified by a text query without category-specific training. Re-cent approaches primarily rely on density regression or detection-styleinstance prediction. While effective, density-based models often sufferfrom spatial ambiguity and background leakage due to weakly regulatedmass allocation, leading to fragmented or part-biased representationsthat increase counting error in complex scenes. In this work, we proposean instance-aware dual-decoder framework that structurally couples den-sity and point representations for zero-shot object counting. Instead oftreating density estimation as independent pixel-wise regression, we in-terpret it as a structured mass allocation problem over a latent set ofobject instances. Predicted instance centers induce a soft instance-wisedecomposition of the density map, upon which we enforce two geometricconstraints: (1) per-instance mass conservation, ensuring each object con-tributes approximately one unit of density mass, and (2) center-of-massalignment, encouraging each density component to concentrate aroundits corresponding predicted center. These constraints introduce instance-level geometric consistency and lead to more accurate mass allocation,thereby reducing counting error. Extensive experiments on FSC-147,PUCPR+, and CARPK show that our approach consistently reducescounting error and establishes new state-of-the-art performance in zero-shot object counting.

Abstract:
Multi-View Multi-Object Tracking (MV-MOT) aims to localize and maintain consistent identities of objects observed by multiple sensors. This task is challenging, as viewpoint changes and occlusion disrupt identity consistency across views and time. Recent end-to-end approaches address this by jointly learning 2D Bird’s Eye View (BEV) representations and identity associations, achieving high tracking accuracy. However, these methods offer no principled uncertainty accounting and remain tightly coupled to their training configuration, limiting generalization across sensor layouts, modalities, or datasets without retraining. We propose ModTrack, a modular MV-MOT system that matches end-to-end performance while providing cross-modal, sensoragnostic generalization and traceable uncertainty. ModTrack confines learning methods to just the Detection and Feature Extraction stage of the MV-MOT pipeline, performing all fusion, association, and tracking with closed-form analytical methods. Our design reduces each sensor’s output to calibrated position-covariance pairs (z,R); cross-view clustering and precision-weighted fusion then yield unified estimates (zˆ,Rˆ) for identity assignment and temporal tracking. A feedback-coupled, identityinformed Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with HMM motion modes uses these fused estimates to maintain identities under missed detections and heavy occlusion. ModTrack achieves 95.5 IDF1 and 91.4 MOTA on WildTrack, surpassing all prior modular methods by over 21 points and rivaling the state-of-the-art end-to-end methods while providing deployment flexibility they cannot. Specifically, the same tracker core transfers unchanged to MultiviewX and RadarScenes, with only perception-module replacement required to extend to new domains and sensor modalities.

Abstract:
We present a real-time, task-oriented 3D instance segmenta-tion framework for unposed monocular video, enabling embodied agentsto task-adaptively perceive and interact with objects in open-world scenes.Unlike most previous bottom-up segmentation paradigms that segmentbefore recognition, we adopt a task-oriented segmentation approach.Specifically, objects are decoupled within each frame using an open-vocabulary detector combined with a prompt-based 2D segmentationmodel, while the 3D underlying geometry of the scene is simultane-ously being reconstructed using a modern dense SLAM system, whosecontinuously re-optimized camera poses and depth are unsuitable forincremental mask association. Guided by the SLAM-derived pose graph,we selectively associate multi-view masks and reuse the dense correspon-dences provided by the SLAM system, incrementally converting theminto geometric association scores with minimal additional computation.By incorporating semantic similarity and mutual exclusivity metrics, wedesign a priority-ordered mask clustering algorithm for efficient onlinemulti-view mask matching and merging. Evaluations on open-vocabulary3D instance segmentation benchmarks show that our method effectivelymitigates the performance degradation of existing approaches when usingdense SLAM reconstructions instead of depth-sensor point clouds. Onthe Replica dataset, using only unposed images, it even achieves resultscomparable to methods leveraging ground-truth depth and poses. Moreimportantly, qualitative results show that our method achieves more reli-able task-oriented 3D object disentanglement than previous bottom-upsegmentation paradigms.

Abstract:
Tiny object detection is critical for applications ranging from drone-based scene analysis to remote sensing. However, existing DETR frameworks suffer from inefficient encoder architectures and a severe query-object misalignment for tiny targets, hindering their deployment in real-time scenarios. To overcome these limitations, we propose SlimDETR, a novel framework tailored for real-time tiny object detection. Central to our approach is an Efficient Interaction (EI) encoder, which employs a gather-and-inject mechanism to aggregate global semantics. Uniquely, the EI encoder restricts semantic attention interactions exclusively to extracted foreground tokens, thereby significantly alleviating the computational overhead of conventional Transformer-based encoders. Furthermore, we introduce a Gaussian Target Guided (GTG) query selection strategy that leverages the Gaussian spatial distribution of targets to dynamically concentrate high-quality queries on regions containing tiny objects. Extensive experiments demonstrate that Slim-DETR achieves state-of-the-art performance, yielding 33.9% AP on VisDrone and 33.6% AP on AI-TOD-V2. Notably, it maintains real-time inference speeds across platforms, achieving a latency of 24.4 ms on an NVIDIA RTX 3090 GPU and 25.1 ms (40 FPS) on the embedded Jetson AGX Orin.

Abstract:
Building damage assessment is crucial for post-disaster re-lief and reconstruction. However, current assessment methods utilizinghigh-resolution remote sensing imagery struggle to represent x001C ne-graineddetails, as subtle damage signals are easily overwhelmed by complexbackgrounds. To address this, we propose a Frequency-Adaptive FeatureInjection and Change Decoupling framework. For feature encoding, we x001C ne-tune a DINOv3 encoder with a frequency-adaptive guidance strat-egy to acutely capture subtle damage. For decoding, we introduce theChange Representation Decoupling and Discriminative Decoder (CRD2 ).CRD2 leverages the Mamba model to e x001E ciently fuse multi-scale fea-tures for spatiotemporal dynamics. To prevent minor damage from be-ing submerged, it utilizes orthogonal subspace projection to isolate purechange features and applies class-prototype contrastive learning for pre-cise damage-level identi x001C cation. Extensive experiments show our CRD-Net achieves state-of-the-art Joint F1 scores on the xBD (81.26%) andEBD (81.88%) datasets.

Abstract:
Infrared small target detection (IRSTD) in high-resolutionimages is crucial for many practical applications, such as surveillanceof unmanned aerial vehicles (UAVs) and UAV-based ground monitoring.However, IRSTD remains challenging due to the small size and weakfeatures of targets, as well as significant interference from complex dy-namic backgrounds. Existing detection methods often suffer from redun-dant computations on non-target background regions and insufficient ex-ploitation of target context information, which limits their performancein complex backgrounds. To address these issues, we propose an efficientcoarse-to-fine infrared small target detection framework with attentionprior-guided knowledge distillation, termed ECFNet. In the coarse stage,we design a region binary classification network (RBCN) on grid-basedmulti-scale feature maps to efficiently recognize target-containing con-text region proposals. Moreover, we introduce a novel denoising-assistedtraining strategy that incorporates noisy ground-truth (GT) masks intoRBCN feature maps. By training the network to reconstruct the GTmasks via a denoising task, this strategy encourages the model to ex-plicitly capture target-background context, thereby enhancing its abil-ity to distinguish target proposals from background regions. In the finestage, we customize a lightweight target detector to the coarse stage’sregion proposals for balancing accuracy and efficiency. Furthermore, wepropose a knowledge distillation strategy guided by the teacher-studentcross-attention prior. This mechanism directs the student to focus oncritical target regions, thereby enhancing the discriminative feature rep-resentation for infrared small targets. Extensive experiments on threereal infrared datasets demonstrate that our method outperforms bothexisting single-stage and two-stage approaches while maintaining highreal-time processing efficiency.

Abstract:
Oriented object detection for aerial images has attractedgrowing attention due to the increasing demand in extensive applica-tions. However, the oriented box annotation is relatively expensive, andthus point-supervised oriented object detection has emerged as a practi-cal paradigm. Owing to the lack of size and angle annotations, existingmethods leverage various complementary information, but usually sufferfrom extra training stages or multiplied computational overhead. In thispaper, we propose three simple yet effective loss terms, and set a simplebaseline with the placement prior. In particular, the placement prior in-dicates that objects in aerial images are placed in a context, rather thanexhibiting arbitrary distribution. Therefore, we propose the angle priorloss, considering that the angles of objects are usually aligned in a neigh-borhood. Furthermore, we propose the size prior loss, considering thatthe sizes of objects are usually proportional for each class in an image.Besides, we propose to derive oriented boxes from the Voronoi Water-shed regions without training, which can jointly regularize the estimatedangles and sizes. By employing only loss terms, our baseline is not onlysimple and easy to extend but also readily transferable to other methods.We conduct extensive experiments on representative UAV datasets andremote sensing datasets, which indicates the effectiveness of our method.

Abstract:
Detecting anomalies in images and video is an essential task for multi-ple real-world problems, including industrial inspection, computer-assisted diag-nosis, and environmental monitoring. Anomaly detection is typically formulatedas a one-class classification problem, where the training data consists solely ofnominal values, leaving methods built on this assumption susceptible to train-ing label noise. We present Bootstrap Aggregation Anomaly Filtering (BAAF),a method that transforms an arbitrary one-class classifier-based anomaly detec-tor into a fully unsupervised method. This is achieved by leveraging the uniqueintrinsic properties of anomaly detection: anomalies are uncommon in the sam-pled data and generally heterogeneous. These properties enable us to design amodified Bootstrap Aggregation method that uses multiple independently trainedinstances of supervised one-class classifiers to filter the training dataset for anoma-lies. This transformation requires no modifications to the underlying anomaly de-tector; only the algorithmically selected data bags used for training change. Wedemonstrate empirically that our method can transform a wide variety of one-classclassifier-based image anomaly detectors into unsupervised ones. Consequently,we present the first fully unsupervised logical anomaly detection method for im-ages. We also demonstrate that our method achieves state-of-the-art performancein fully unsupervised anomaly detection on the MVTec AD, ViSA, Real-IAD andMvTec LOCO AD datasets. As improvements to one-class classifiers are made,our method directly transfers those improvements to the unsupervised domain,linking the domains.

Abstract:
Video shadow detection (VSD) aims to robustly identify shadow regions in videos and serves as a key task for improving scene understanding. Although substantial progress has been made in recent years, existing methods still suffer notable performance degradation in challenging scenarios involving fast motion and drastic illumination changes. To address these limitations, we introduce event cameras into the VSD task and propose an Event-guided Diffusion Model (EDM) tailored for complex dynamic environments. Based on the high temporal resolution and asynchronous sensing characteristics of event cameras, we design a Motion Trajectory Attention (MTA) module and a Bidirectional Mask Generation (BMG) module to capture event-driven temporal cues. These cues are further integrated through a Dual-Modal Temporal Guidance (DMTG) module and injected into the diffusion model as conditional guidance to steer the denoising process. By incorporating event streams as a complementary modality, EDM effectively captures fast scene dynamics and abrupt lighting variations, leading to more robust and accurate video shadow detection. Furthermore, to address the lack of event modalities in existing VSD benchmarks, we construct a multimodal extension of the ViSha dataset, termed Event-ViSha, and collect a new real-world multimodal dataset, the Event-based Video Shadow Dataset (EVSD). Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches across multiple benchmarks.

Abstract:
Sign Languages (SLs) are the primary means of communication for millions of deaf1 individuals, yet existing evaluation metrics for generated SL remain simplistic and poorly aligned with human judgements. We introduce BackTranslation2.0, a linguistically grounded evaluation metric for text-to-sign translation that moves beyond naïve backtranslation. Our approach adopts an agentic framework in which a deterministic pipeline orchestrates a suite of specialised tools to assess four scoring dimensions—grammatical correctness, phonological accuracy, motion fluency, and generation fidelity—aligned with human rater assessments. Tool outputs are not treated independently: a set of large language model (LLM)-based cross-referential comparison modules evaluates consistency across tools and checks outputs against linguistic expectations, enabling structured reasoning over grammatical, phonological, and motion-level evidence. Final dimension scores are computed through deterministic weighted formulas over validated tool outputs. To validate BackTranslation2.0, we introduce and evaluate on a British Sign Language (BSL) dataset rated in a human rater study across the same quality dimensions, following a protocol developed in collaboration between linguists and deaf experts, benchmarking against six baseline metrics. Our method demonstrates strong correlation with human judgements across all dimensions, providing a more comprehensive, interpretable, and linguistically principled evaluation framework for sign language production systems.

Abstract:
Multimodal learning of image and tabular data is a challenging problem, especially given the unstructured feature representations involved. We address this by framing feature sequencing as a Column Permutation Problem and showing how reordering columns can reduce dispersion and improve training stability for multimodal learning of image and tabular data. We propose iSyncTab, an approach that enforces these learned sequential constraints through a joint classification and order-consistency objective. We introduce Neural Synchrony-guided Paired Feature Sequencing (NS-PFS), a neuroscience-inspired NeuroAI approach that aligns image and tabular feature clusters through a Hungarian assignment of their synchrony matrix, derived from cluster energy and centroid similarity. The resulting cross-modal feature sequence provides a coherent feature alignment that preserves structural synchrony between modalities. We then introduce an Order-aware Memoryaugmented Transformer (OMT) with a custom loss function that enforces these learned feature sequence constraints. Experiments on multimodal benchmarks demonstrate that systematically derived feature sequences consistently boost predictive performance and model robustness. Our findings underscore feature sequencing as an important inductive bias for structured multimodal learning of image and tabular data. Neural synchrony-driven sequencing enhances cross-modal coherence and representation quality for image and tabular data.

Abstract:
Vision-Language Large Models (VLLMs) are promising forAI-generated image (AIGI) detection because they can produce both aprediction and a natural-language output. However, most existing VLLM-based detectors primarily fine-tune the language side while giving lim-ited attention to low-level visual forensic cues. They also often dependon manually crafted prompts or human-annotated rationales, which lim-its scalability. We present UC-VLM, a unified multi-stage frameworkfor AIGI detection that relies solely on binary supervision. UC-VLMfirst identifies effective instruction variants automatically. It then reusesthe same binary label within a multi-stage training framework: (i) a vi-sual discrimination objective that strengthens sensitivity to non-semanticforensic cues, and (ii) a label-conditioned generation objective that usesthe binary label to supervise textual outputs. This design turns weakbinary supervision into a shared supervision signal for both the visualpathway and the language output. Our key novelty is a unified multi-stage binary-supervised framework that consistently reuses the same au-thenticity labels for visual adaptation and label-conditioned text gener-ation, while leveraging automatically optimized instructions to reduceprompt sensitivity without requiring human-written rationales or hand-crafted prompts. Experiments show that UC-VLM achieves 96.1% aver-age accuracy on GenImage, exceeding the strongest prior result by 4.6%,and obtains 69.6% / 77.9% accuracy on Chameleon under ProGAN /SDV1.4 training, surpassing the best baseline by 11.2% / 15.3%, re-spectively.

Abstract:
Deep learning-based gaze estimation methods suﬀer severeperformance degradation in cross-domain scenarios. Our frequency anal-ysis reveals that overﬁtting to source-domain low-frequency componentsassociated with domain-speciﬁc factors, such as illumination, background,and skin tone, is a critical cause of cross-domain degradation. Basedon this ﬁnding, we propose the Adversarial Low-Frequency Perturba-tion (ALFP) framework, which employs a Perturbation Generation Net-work (PGNet) to adversarially generate instance-speciﬁc perturbationimages and mix their low-frequency amplitude statistics with those ofsource images through an AdaIN-based mechanism. The resulting inputs,with perturbed low-frequency distributions, are used to train the GazeEstimation Network (GazeNet). Adversarial training between PGNet andGazeNet progressively mitigates GazeNet’s overﬁtting to source-domainlow-frequency components, thereby improving cross-domain generaliza-tion. Experiments show that ALFP outperforms state-of-the-art meth-ods across various cross-domain settings, improving over the baseline byup to 34.9% and the best existing method by 15.0%, without requiringtarget-domain data.

Abstract:
This paper presents an enhanced LiDAR-inertial odometrysystem that addresses the challenge of spatiotemporal drift through anadaptive neural stochastic differential equation (SDE) framework. Tra-ditional IMU bias models based on random walk assumptions often failto capture the complex, high-order non-linearities inherent in low-costinertial sensors during high-dynamic UAV maneuvers. To bridge the gapbetween classical geometric constraints and deep learning, our approachintroduces three key innovations: an adaptive hybrid bias model that dy-namically integrates learned neural dynamics with linear motion priors;a neural SDE framework that explicitly models stochastic bias evolu-tion and heteroscedastic uncertainties; and a differentiable covariancepropagation mechanism that ensures consistency between learned repre-sentations and geometric state estimation within an invariant extendedKalman filter. Extensive evaluations on self-collected UAV datasets, val-idated by a Vicon motion capture system, demonstrate that our methodconsistently outperforms the baseline FAST-LIO2, providing superior ac-curacy in both position and orientation estimation. Furthermore, ourapproach offers reliable uncertainty estimates crucial for complex spatialperception and robust autonomous flight in challenging environments.

Abstract:
Recent Multimodal Large Language Models (MLLMs) strug-gle to bridge the representational gap between 2D semantic understand-ing and 3D spatial geometry. Existing 3D-aware models either rely oncostly 3D-specific data or utilize RGB-only inputs with heuristic sam-pling and monolithic, shallow fusion, which respectively disrupt essentialspatiotemporal connectivity and induce modality contention across di-verse spatial tasks. To overcome these bottlenecks, we introduce SpaR3D-MoE, an end-to-end framework that enables adaptive spatial reasoningby equipping MLLMs with geometry-aware capabilities from only sparseRGB inputs. First, we propose an adaptive spatiotemporal manifoldsampling mechanism that constructs a geometry-aware spatiotemporalgraph to extract informative keyframes, effectively mitigating sequenceredundancy while preserving the scene’s topological connectivity. Second,we introduce the heterogeneous geometry-inductive Mixture-of-Expertsdriven by an instruction-pose aware router, which adaptively routes mul-timodal tokens to specialized experts, resolving the cross-modal con-tention inherent in monolithic fusion. Extensive experiments on VSI-Bench, ScanQA, and SQA3D demonstrate that our method achievesstate-of-the-art performance. Notably, SpaR3D-MoE achieves the highestaverage score of 63.5 on VSI-Bench, outperforming the strongest base-line by 7.8 absolute points, alongside relative improvements of 35.4% and51.4% in Route Plan and Relative Direction tasks, respectively.

Abstract:
Visual generation models can produce photorealistic videosyet violate multiview geometry, exhibiting non-rigid deformations andocclusion inconsistencies (e.g., hallucinated content in disoccluded re-gions). These failures hinder downstream applications such as video worldmodel development and 3D asset creation, and are poorly captured byexisting metrics. We introduce GeCo, a geometry-grounded metric forjointly detecting geometric deformation and occlusion-inconsistency ar-tifacts in static scenes. By fusing residual motion and depth priors, GeCoproduces interpretable, dense consistency maps that localize these arti-facts. Using GeCo, we systematically benchmark recent video generationmodels, revealing common geometric failure modes. We further showthat GeCo provides an actionable signal by applying it as a training-free guidance loss that substantially reduces geometric artifacts duringgeneration.

Abstract:
Video generation has recently made striking visual progress,but maintaining coherent object motion and interactions remains dif-ficult. We trace two practical bottlenecks: (i) human-provided motionhints (e.g., small 2D maps) often collapse to too few effective tokens afterencoding, weakening guidance; and (ii) Current architectures inevitablybias toward pixel-level texture reconstruction over complex structuralmotion, yielding videos that are photorealistic but physically incoherent.We present STANCE, an image-to-video framework that addresses bothissues with two simple components. First, we introduce Instance Cues,a pixel-aligned control signal that turns sparse, user-editable hints into adense 2.5D (camera-relative) motion field by averaging per-instance flowand augmenting with monocular depth over the instance mask. Thisreduces depth ambiguity compared to 2D drag/arrow inputs while re-maining easy to user. Second, we preserve the salience of these cuesin the latent token space with Dense RoPE, which tags a small setof initial-frame motion tokens with spatially-addressable rotary embed-dings. Finally, rather than treating appearance and structure as looselycoupled tasks, STANCE integrates an auxiliary stream directly withinthe same Diffusion Transformer (DiT). By sharing identical latent tokensand Dense RoPE, this auxiliary stream acts as a "geometry witness" thatheavily penalizes geometric drift.

Abstract:
Understanding camera motion is a fundamental problem inembodied perception and 3D scene understanding. While visual methodshave advanced rapidly, they often struggle under visually degraded condi-tions such as motion blur or occlusions. In this work, we show that passivescene sounds provide cues complementary to vision for relative camerapose estimation for in-the-wild videos. We introduce a simple but effec-tive audio-visual framework that integrates direction-of-arrival (DOA)spectra and binauralized embeddings into a state-of-the-art vision-onlypose estimation model. Our results on two large datasets show consistentgains over strong visual baselines, plus robustness when the visual infor-mation is corrupted. To our knowledge, this represents the first work tosuccessfully leverage audio for relative camera pose estimation in real-world videos, and it establishes incidental, everyday audio as an unex-pected but promising signal for a classic spatial challenge.

Abstract:
Recent advances in video diffusion models have greatly im-proved visual fidelity, yet their generated motions often violate physicalplausibility. We observe a common kinematic failure, “motion entangle-ment”, the unintended coupling of independent motion sources, such ascamera movement and object motion. We identify that this issue stemsfrom data bias and the reconstruction-based training design of diffusionmodels. Training on noisy videos that still retain coarse motion cues in-advertently encourages the model to replicate existing motion without anincentive to learn how to model kinematically-grounded motions. To ad-dress this, we propose a Self-Imagination Fine-Tuning (SIFT) paradigm,which enables the model to learn from its own generated videos ratherthan directly reconstructing real ones, breaking the reconstruction short-cut. We further employ motion-aware discriminative supervision and aprogressive hard-case replay strategy to stabilize and accelerate learning.By leveraging freely-generated text prompts, our method can denselycover a broad motion space, including rare or finely-disentangled scenar-ios that would be costly to collect as video data. Extensive experimentsdemonstrate that our approach substantially improves the physical real-ism, motion disentanglement, and controllability of generated videos.

Abstract:
Generating talking avatars is a fundamental task in videogeneration. Although existing methods can generate full-body talkingavatars with simple human motion, extending this task to groundedhuman-object interaction (GHOI) remains an open challenge, requiringthe avatar to perform text-aligned interactions with surrounding objects.This challenge stems from the need for environmental perception and thecontrol-quality dilemma in GHOI generation. To address this, we proposea novel dual-stream framework, InteractAvatar, which decouples per-ception and planning from video synthesis for grounded human-object in-teraction. Leveraging detection to enhance environmental perception, weintroduce a Perception and Interaction Module (PIM) to generate text-aligned interaction motions. Additionally, an Audio-Interaction Aware† ⋆Equal contribution. Corresponding author.Generation Module (AIM) is proposed to synthesize vivid talking avatarsperforming object interactions. With a specially designed motion-to-video aligner, PIM and AIM share a similar network structure and enableparallel co-generation of motions and plausible videos, effectively miti-gating the control-quality dilemma. Finally, we establish a benchmark,GroundInter, for evaluating GHOI video generation. Extensive exper-iments and comparisons demonstrate the effectiveness of our method ingenerating grounded human-object interactions for talking avatars.

Abstract:
We present KineticGS, a novel approach for reconstructingdynamic scenes from monocular videos through momentum-driven 4DGaussian Splatting. While recent advances in motion modeling have im-proved dynamic reconstruction, they often produce temporal inconsis-tencies and non-physical deformations due to the lack of physical pri-ors constraining motion trajectories. KineticGS addresses this with aphysically grounded momentum hierarchy that models Gaussian parti-cles as momentum-carrying entities, thereby establishing a hierarchicalmotion representation from pixel observations to object-level coherence.Specifically, Gaussian particles serve as momentum carriers, with dy-namics driven by local motion energy. We perform momentum-guidedparticle sampling, concentrating representation in high-energy regionsto capture fine-grained non-rigid motions. A learnable energy-flow mod-ule then predicts per-particle temporal activation, ensuring continuousand physically plausible motion propagation. Further, a holistic kineticsynchronization mechanism enforces consistent motion evolution amongcorrelated particles, preserving structural integrity without explicit disin-tegration. Experiments on dynamic scene datasets show that KineticGSimproves reconstruction fidelity, temporal coherence, and physical real-ism, achieving +0.91 dB average PSNR gain in dynamic-object regionsover the second-best.

Abstract:
Interactive video generation systems for camera-controlled world exploration roll out growing sequences of latent video frames, entangling state transition with high-frequency observation synthesis. We propose Walking in the Implicit, a scene-centric paradigm that changes the rollout variable from frame latents to a fixed-length, renderable implicit state, termed Neural Implicit Scene (NIS). This factorizes interactive generation into stochastic transition of a compact scene state and deterministic pose-conditioned rendering given the sampled state. We instantiate this paradigm as NeuWorld: a transformer VAE learns locally anchored NIS from sparse posed frames, and a diffusion transformer evolves NIS conditioned on future camera trajectories and geometryaware retrieved history. By reusing the VAE encoder as a unified conditioner, NeuWorld maps camera, reference-image, and history cues into the same NIS modality, avoiding external heterogeneous encoders. Trained from scratch on public posed-view data without pretrained video backbones or auxiliary 3D reconstructors, NeuWorld achieves strong longhorizon consistency with favorable inference efficiency.

Abstract:
We address the challenge of novel view synthesis from only two input images under large viewpoint changes. Existing regression-based methods lack the capacity to reconstruct unseen regions, while camera-guided diffusion models often deviate from intended trajectories due to noisy point cloud projections or insufficient conditioning from camera poses. To address these issues, we propose ConfCtrl, a confidence-aware video interpolation framework that enables diffusion models to follow prescribed camera poses while completing unseen regions. ConfCtrl initializes the diffusion process by combining a confidenceweighted projected point cloud latent with noise as the conditioning input. It then applies a Kalman-inspired predict–update mechanism, treating the projected point cloud as a noisy measurement and using learned residual corrections to balance pose-driven predictions with noisy geometric observations. This allows the model to rely on reliable projections while down-weighting uncertain regions, yielding stable, geometry-aware generation. Experiments on multiple datasets show that ConfCtrl produces geometrically consistent and visually plausible novel views, effectively reconstructing occluded regions under large viewpoint changes.

Abstract:
Human-pet interaction estimation and generation remain underexplored due to the absence of high-quality large-scale dataset. We present InterPet4D, the first multimodal dataset capturing natural interactions between humans and dogs. Using a synchronized multi-view capture system, we record human–dog obedience tasks and provide annotations for both humans and dogs, including multiview and egocentric videos, segmentations, 2D/3D keypoints, meshes, and audio tracks. Interpet4D consists of 6.8 million frames collected from 13 dogs of 11 breeds interacting with 23 human participants. We further introduce the InterPetMoGen framework for human-pet interaction motion generation. Our proposed model achieves an FID score of 11.21, substantially outperforms the Seq2Seq or DiT baselines, demonstrating the effectiveness of Interpet4D for modeling realistic human–pet interactions.

Abstract:
We introduce OVOW, the first training-free system that reconstructs instance-level, simulation-ready 4D mesh scenes from a single monocular video. Recent 4D reconstruction achieves impressive rendering quality, but its outputs (e.g., implicit fields, Gaussian primitives, or point clouds) lack the watertight topology, instance separation, and standardized physical interfaces required by physics simulators and embodied AI. OVOW closes this gap with a four-stage pipeline: a vision-language model discovers, labels, and motion-classifies all instances; category-aware reconstruction yields per-instance meshes for rigid objects and topology-consistent mesh sequences for deformable ones; an iterative render-match-optimize procedure recovers metric scale and 6-DoF pose trajectories; and physics-grounded assembly enforces ground contact and inter-object support. Crucially, we model all motion, rigid and non-rigid, through direct vertex deformation without category-specific priors or skeleton rigging, producing watertight mesh scenes ready for downstream physics simulation and editing. We further establish the first benchmark for structured Video-to-4D evaluation, with metrics for geometric correctness, instance separation, and physical plausibility beyond visual fidelity; the same pipeline doubles as a scalable engine for synthesizing paired video-to-4D simulation data for future 4D world models and embodied AI. Across two synthetic benchmarks (static and 4D), OVOW attains the best overall layout and geometry accuracy and the lowest photometric and semantic error among all baselines, and on monocular video runs one to two orders of magnitude faster than the baselines, while downstream physics simulation confirms its physical stability.

Abstract:
Multimodal Large Language Models have demonstrated impressive video understanding, yet their ability to reason over long-form narratives is often masked by visual-centric evaluations and inefficient context processing. Existing benchmarks over-rely on visual heuristics while marginalizing auditory cues, effectively reducing models to "silent observers" that bypass genuine cross-modal reasoning. Moreover, standard dense sampling creates an evidence–context trade-off: increasing frames to capture evidence inevitably leads to attention distraction and token explosion. To bridge these gaps, we present VideoHolmesV2, a novel benchmark designed for Deep Audio-Visual Coupling. Unlike previous works, it enforces an Evidence-Based Evaluation, requiring models to justify answers with precise spatio-temporal audio-visual evidence, thereby reducing confounding effects of guessing and hallucinated evidence. To support this, we introduce: (1) a MultiModel Cross-Verification pipeline to ensure task rigor; (2) a Spatiotemporal Evidence-Aware Metric for fine-grained calibration. Furthermore, we propose an Audio-Text Guided Token Compression framework. By fusing task intent with auditory anchors, our method distills highvalue reasoning cues to mitigate long-context noise. In our evaluation, even strong proprietary models achieve below 60% accuracy, while our approach outperforms over comparable open-source omni-models.

Abstract:
Autoregressive (AR) visual generation has emerged as a powerful paradigm for image and multimodal synthesis, owing to its scalability and generality. However, existing AR image generation suffers from severe memory bottlenecks due to the need to cache all previously generated visual tokens during decoding, leading to both high storage requirements and low throughput. In this paper, we introduce LineAR, a novel, training-free progressive key-value (KV) cache compression pipeline for AR image generation. By exploiting the intrinsic characteristics of visual attention, LineAR manages the cache from a 2D line-level perspective, preserving the visual dependency regions while progressively evicting less-informative tokens under inter-line attention guidance, leveraging the spatial continuity prior of images to ensure that each eviction step is harmless to subsequent line generation. Experiments across seven AR image generation models validate that LineAR achieves lossless or even improved generation quality with only a few cached lines. It improves ImageNet FID from 2.77 to 2.68 on LlamaGen-XL and COCO FID from 23.85 to 22.86 on Janus-Pro-1B at 1/6 budget ratio, and also improves DPG and HPSv2.1 scores on Lumina-mGPT-768 with just 1/8 KV cache. Additionally, LineAR achieves significant memory and throughput gains across devices, e.g., up to 67.06% memory reduction and 4.17× speedup on LlamaGen-XL, and 73.65% memory reduction and 4.34× speedup on Janus-Pro-1B, evaluated on NVIDIA RTX PRO 6000 GPUs.

Abstract:
Personalized Federated Learning (PFL) has emerged as aprevalent paradigm to deliver personalized models to individual clientsthrough collaborative training. Existing PFL methods often suffer fromthe issue of Non-IID data, due to their reliance on gradient-based up-dates. Recently, Analytic Learning (AL) has exhibited great potential toaddress this issue via analytical (i.e., closed-form) solutions in a gradient-free manner. However, there remains a significant gap in introducingAL into PFL, owing to the encountered generalization-personalizationdilemma. In this paper, to bridge this gap and address the associatedchallenges, we propose an Analytic Personalized Federated Learningapproach, named AnaPFL, for addressing the Non-IID issue in PFL byintroducing and advancing AL. In AnaPFL, we develop dual-stream an-alytic models with closed-form solutions, including (1) a shared primarystream for global generalization across all clients, and (2) a dedicated re-finement stream for local personalization of each client. Experimentally,we give comprehensive results to show AnaPFL’s superior performancewith over 99% efficiency advantages against gradient-based methods.

Abstract:
Skill determination in video understanding remains challeng-ing due to the intricate temporal dynamics of skill performance and thecomplex cognitive processes inherently underlying human evaluation. Ex-isting approaches often overlook the progressive evolution of evaluators’cognitive states and the influence of key action stages on overall per-formance. To address this limitation, we propose Sequential Primacyand Attribution Ranking Modeling (SPAR). SPAR captures the tem-poral evolution of cognitive states throughout the evaluation process,enabling iterative refinement of evaluative understanding and producingprogressively enhanced representations of overall performance quality.The framework integrates adaptive weighting based on the global tempo-ral context, emphasizing critical segments through localized interactionmodeling and multi-scale temporal aggregation. To correct inconsisten-cies in intermediate recognition signals, SPAR employs a bias-mitigationloss to ensure a faithful and consistent reflection of intrinsic action qual-ity. Extensive experiments on five public datasets validate the effective-ness and generalization capability of the proposed framework.

Abstract:
This paper focuses on a critical challenge in robotics: translating text-driven generated human motions into executable actions for real humanoid robots. While existing text-to-motion (T2M) generation methods achieve semantic alignment between language and motion, they often produce physically infeasible motions unsuitable for real-world deployment. To bridge the gap between T2M and humanoid execution, we propose Reinforcement Learning from Physical Feedback (RLPF), a novel framework that integrates text-conditioned motion generation with motion-conditioned humanoid whole-body control. RLPF employs a low-level motion tracking policy to assess feasibility in physical simulators, providing rewards for (cid:28)ne-tuning the high-level motion generator. Moreover, RLPF introduces an alignment veri(cid:28)cation module to preserve semantic alignment with text instructions. This joint optimization ensures both physical feasibility and text alignment of T2M generators. Furthermore, to overcome the limitation of frozen tracking policy, we propose Hierarchical RLPF, a co-training framework that iteratively optimizes the high-level T2M generator and the low-level tracking policy. Extensive experiments show that RLPF and Hierarchical RLPF greatly outperform baseline methods in generating physically feasible motions while maintaining semantic alignment with text instructions. •

Abstract:
We introduce Synthetic Visual Genome 2 (SVG2), alarge-scale panoptic video scene graph dataset. SVG2 contains over 636Kvideos with 6.6M objects, 52.0M attributes, and 6.7M relations, pro-viding an order-of-magnitude increase in scale and diversity over priorspatio-temporal scene graph datasets. To create SVG2, we design a fullyautomated pipeline that combines multi-scale panoptic segmentation,online–offline trajectory tracking with automatic new-object discovery,per-trajectory semantic parsing, and GPT-5-based spatio-temporal re-lation inference. Human verification of SVG2 annotation accuracy con-firms its reliability (objects: 93.8%, attributes: 88.3%, relations: 85.4%).Building on this resource, we train TraSeR, a trajectory-groundedvideo scene graph generation model. TraSeR augments VLMs with atrajectory-aligned token arrangement mechanism and new modules: anobject-trajectory resampler and a temporal-window resampler to convertraw videos and panoptic trajectories into compact spatio-temporal scenegraphs in a single forward pass. The temporal-window resampler bindsvisual tokens to short trajectory segments to preserve local motion andtemporal semantics, while the object-trajectory resampler aggregates en-tire trajectories to maintain global context for objects. On PVSG, VIPSeg,VidOR, and SVG2test , TraSeR outperforms the strongest open-sourcebaselines by 15∼20 points in relation detection, 20∼40 points in objectprediction, and 13 points in attribute prediction. It also surpasses GPT-5by 13 points in object prediction and 3 points in attribute prediction.When TraSeR’s generated scene graphs are sent to a VLM for videoquestion answering, it delivers a +1.5∼4.6 absolute accuracy gain overusing video alone or video augmented with Qwen2.5-VL’s generated scenegraphs, demonstrating the utility of explicit spatio-temporal scene graphsas an intermediate representation5 .

Abstract:
Reasoning segmentation requires localizing targets based oncomplex, implicit queries. Current end-to-end models typically entangleperception and deduction into an opaque black box, severely limitinginterpretability and scalability. To address this, we propose GEAR-Seg(Grounded Explainable Agent for Reasoning Segmentation), an explic-itly decoupled agent that shifts the paradigm by translating visual pixelsinto dense, attribute-rich text. By decoupling class-agnostic segmenta-tion, semantic description, and Large Language Model (LLM) deduc-tion, GEAR-Seg transforms implicit reasoning into an explicit, track-able logic chain. As a zero-shot inference framework, it achieves highlycompetitive performance across diverse reasoning and fine-grained re-ferring segmentation benchmarks. Furthermore, GEAR-Seg inherentlyfunctions as a highly scalable data engine. Utilizing this engine, we con-struct GEAR-131K, a massive benchmark (over 38k images, 656k QA-mask pairs) introducing a multifaceted taxonomy tailored for complexreal-world manipulation-oriented reasoning. Finally, distillation exper-iments demonstrate that lightweight models supervised exclusively byour automated pipeline closely match the upper-bound performance ofcostly human-annotated baselines.

Abstract:
Recent advances in video generation models have signifi-cantly accelerated video generation and related downstream tasks. Amongthese, video stylization holds important research value in areas suchas immersive applications and artistic creation, attracting widespreadattention. However, existing diffusion-based video stylization methodsstruggle to maintain stability and consistency when processing long videos,and their high computational cost and multi-step denoising make themdifficult to apply in practical scenarios. In this work, we propose RTR-DiT (DiT as Real-Time Rerenderer), a streaming video stylization frame-work built upon a Diffusion Transformer. We first fine-tune a bidirec-tional teacher model on a curated video stylization dataset, supportingboth text-guided and reference-guided video stylization tasks, and sub-sequently distill it into a few-step autoregressive model via post-trainingwith Self Forcing and Distribution Matching Distillation. Furthermore,we propose a reference-preserving KV cache update strategy that notonly enables stable and consistent processing of long videos, but alsosupports real-time switching between text prompts and reference images.Experimental results show that RTR-DiT outperforms existing meth-ods in both text-guided and reference-guided video stylization tasks, interms of quantitative metrics and visual quality, and demonstrates ex-cellent performance in real-time long video stylization and interactivestyle-switching applications.

Abstract:
Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual streams naturally co-occur in video data, extending this success to jointly learn from both modalities is a natural next step, yet it remains challenging. Existing audio-visual self-supervised methods rely on modality-specific encoders and complex combinations of contrastive or reconstruction objectives, limiting cross-modal synergy and scalability. Joint Embedding Predictive Architectures (JEPAs) offer a simple, modality-agnostic alternative, but have to date been applied primarily to individual modalities. We introduce MJEPA, a jointembedding predictive architecture for audio-visual learning that uses a single, unified encoder for both modalities. Our approach uses only a single predictive objective, applied both within and across modalities. We show that cross-modal prediction is critical: without it, a shared encoder degrades below unimodal baselines; with it, each modality’s representation benefits from the other. Our frozen ViT-g model outperforms the best prior frozen baseline by over 6.8 mAP on AudioSet-20K, surpasses fully finetuned models on ESC-50 and FSD50K, and is competitive on video benchmarks despite using 10x less video data.

Abstract:
Metric reasoning is a critical and challenging task for VisionLanguage Models (VLMs), playing a pivotal role in embodied AI taskssuch as robotic manipulation and autonomous navigation. However, cur-rent spatial reasoning remains bottlenecked by rigid pixel-level supervi-sion; such localized optimization often compromises general multimodalintelligence, triggering performance degradation or catastrophic forget-ting of broad reasoning capabilities. To address these limitations, weintroduce Metric-Bench, a focused benchmark designed to guide metric-spatial reasoning using contextual information. By incorporating in-imagereference objects with known physical dimensions, Metric-Bench guidesmodels to implicitly learn the 2D-to-3D mapping without camera in-trinsics. We further present MetricReasoner, a task-adapted reinforce-ment fine-tuning recipe for reference-grounded metric reasoning, usingstructured prompts and verifiable numerical rewards. Extensive experi-ments on Metric-Bench demonstrate that our approach significantly en-hances spatial metric understanding, outperforming existing and evenlarger proprietary models by 43.1%, while improving downstream em-bodied performance over a spatial-specialized counterpart by 30.4% onRoboSpatial overall accuracy and 9.3% on ERQA, and additionally deliv-ering consistent gains on general benchmarks (15.9% on V⋆Bench, 88.9%on BLINK), indicating that the proposed adaptation does not necessarilycompromise general VLM capabilities.

Abstract:
Vision–Language Models (VLMs) have recently emerged asa powerful paradigm for autonomously perceiving and interacting withcomplex and dynamic Graphical User Interface (GUI) environments.However, their interaction capability remains fundamentally limited byinsufficient precision in spatial grounding. Intriguingly, simply overlay-ing visual markers on screenshots has been extensively shown to sub-stantially enhance the grounding performance of models. Despite its em-pirical effectiveness, the underlying mechanism—specifically, why theseexplicit visual cues can influence the model’s internal spatial understand-ing—remains largely unexplained. We find that the causal influence ofvisual markers on grounding performance is mediated by low-rank la-tent subspaces associated with specific neurons in the late decoder lay-ers. Based on this key insight, we propose DeCoPatch, a novel decode-time causal intervention method that precisely modulates the activa-tions of key neurons relevant to GUI grounding. DeCoPatch follows atwo-stage design: it first identifies top-k critical neurons during the pre-fill stage, effectively isolating a low-rank latent subspace, and then re-inforces these neurons during the decoding stage to causally steer themodel’s spatial grounding behavior. Extensive experiments on four chal-lenging benchmarks demonstrate that DeCoPatch consistently enhancesthe GUI grounding performance of diverse VLMs with almost no addi-tional computational overhead.

Abstract:
Large multimodal models have achieved strong reasoning oncomplex visual tasks, but their inference efficiency is often restricted bylong chains of thought. A promising solution is to pair a small draftmodel with a large target model, enabling cooperative inference employ-ing a routing signal that adaptively routes queries to either the draft ortarget model based on their difficulties for optimal efficiency and accu-racy. Yet, the remaining bottleneck is to establish a reliable query dif-ficulty signal under multimodal settings. Existing approaches designedfor language models either rely on post-hoc token probabilities, whichfall short in multimodal scenarios, or depend on supervised fine-tuning,which is a data-sensitive strategy. Both paradigms perform routing onlyafter a complete output, and ignore whether the target model can ac-tually solve the routed instances. To address this, we propose PRP,a Proactive Routing Paradigm that enables early decision-making byjointly evaluating the competence of both the draft and target models.Our Draft Rating Learning (DRL) equips the draft model with an inter-nal confidence estimator, while Joint Rating Learning (JRL) predictshow well the target model can handle a given query, thereby prioritiz-ing the allocation of samples it excels at rather than the hardest ones.These ratings enable fine-grained, instance-level Proactive Routingand substantially accelerate inference without compromising overall per-formance. Extensive experiments across multiple multimodal reasoningbenchmarks validate our effectiveness and efficiency.

Abstract:
Multimodal Large Language Models (MLLMs) are increasingly applied to pixel-level vision tasks, yet their intrinsic capacity for spatial understanding remains poorly understood. We investigate segmentation capacity through a layerwise linear probing evaluation across the entire MLLM pipeline: vision encoder, adapter, and LLM. We further conduct an intervention based attention knockout analysis to test whether cross-token attention progressively refines visual representations, and an evaluation of bidirectional attention among image tokens on spatial consistency. Our analysis reveals that the adapter introduces a segmentation representation drop-o!, but LLM layers progressively recover through attention-mediated refinement, where correctly classified tokens steer misclassified neighbors toward the correct label. At early image token positions, this recovery is bounded by causal attention, which bidirectional attention among image tokens alleviates. These findings provide a mechanistic account of how MLLMs process visual information for segmentation, informing the design of future segmentation-capable models.

Abstract:
A fundamental challenge in 3D reconstruction and robotic localization is co-visibility: determining which image pairs share overlapping visible surfaces, particularly in scenarios with minimal overlap. We demonstrate that VGGT implicitly encodes co-visibility as an emergent behavior: without any supervision for this task, its internal representations exhibit a clear hierarchical structure mirroring that of large language models — early layers build a 3D-aware scene representation, while late layers act as dedicated co-visibility reasoners. In particular, we identify layer L17 as a negative anchor that consistently routes nonco-visible pairs for this backbone, regardless of the evaluation setting, providing task-grounded evidence of layer specialization in a geometrygrounded foundation model. Building on this, we introduce Co-VGGT, which freezes VGGT and trains only a lightweight layer-wise mixtureof-experts head (∼7.5M parameters) to classify co-visibility from RGB alone, treating each layer as a specialized expert whose geometric abstraction is adaptively weighted per input pair. On the Co-VisiON benchmark, Co-VGGT surpasses the human annotation baseline and improves over prior work by more than 25% pairwise and 10% multiview. Pairwise predictions are well-calibrated (ECE=0.030), enabling direct use as edge weights in visibility graphs for downstream SfM and SLAM pipelines without post-hoc correction. Code and data available1.

Abstract:
CLIP, known for its strong zero-shot generalization, has received significant attention in prompt tuning for its ability to adapt effectively to new tasks in few-shot settings. Recent studies have employed descriptions generated by Large Language Models (LLMs) as predefined prompts to obtain class-level semantics. However, because these predefined descriptions are not directly grounded in visual information, they often misalign with an image’s visual semantics. They tend to generate generic class-level descriptions rather than instance-specific ones. As a result, LLM-based prompts are structurally biased toward class-level semantics, making it difficult to capture and reflect instancelevel cues, which in turn limits generalization in few-shot settings where both levels of knowledge are crucial. To address this limitation, we propose Disentangled Instance-Class knowlEdge (DICE) prompt tuning, a framework that restructures LLM-derived class-level priors to capture instance-level semantics. Specifically, DICE decomposes LLM-derived priors into instance-level components using a Sparse Autoencoder (SAE), which selects instance-specific concept vectors. These concept vectors are then fused with class embeddings to form enriched representations. This synergy preserves class-level semantic coherence while capturing instance-level details, improving generalization to unseen classes. Our approach achieves competitive performance across 11 few-shot recognition benchmarks, while additionally offering a plug-and-play solution that enhances interpretability through SAE.

Abstract:
While Vision-Language Models (VLMs) have advanced highlevel reasoning in autonomous driving, their ability to ground this reasoning in the underlying physics of ego-motion remains poorly understood. We introduce EgoDyn-Bench4, a diagnostic benchmark for evaluating the semantic ego-motion understanding of vision-centric foundation models. By mapping continuous vehicle kinematics to discrete motion concepts via a deterministic oracle, we decouple a model’s internal physical logic from its visual perception. Our large-scale empirical audit spanning 20+ models, including closed-source MLLMs, open-source VLMs across multiple scales, and specialized VLAs, identifies a significant Perception Bottleneck: while models exhibit logical physical concepts, they consistently fail to accurately align them with visual observations, frequently underperforming classical non-learned geometric baselines. This failure persists across model scales and domain-specific training, indicating a structural deficit in how current architectures couple visual perception with physical reasoning. We demonstrate that providing explicit trajectory encodings substantially restores physical consistency across all evaluated models, revealing a functional disentanglement between vision and language: ego-motion logic is derived almost exclusively from the language modality, while visual observations contribute negligible temporal signal. This structural finding provides a standardized diagnostic framework and a practical pathway toward physically aligned embodied AI.

Abstract:
Automated fine-grained perception of calligraphy styles—a task vital to cultural heritage preservation—remains a critical challenge for Large Vision-Language Models (LVLMs), largely constrained by existing datasets that suffer from modal mixture and flattened labels. To bridge this gap, we introduce HCSU, the first comprehensive dataset tailored for fine-grained Historical Calligraphy Style Understanding. HCSU comprises 39,307 meticulously curated character images from 49 historically prominent calligraphers across 10 dynasties, systematically decoupling authentic ink manuscripts (Tie) from stone rubbings (Bei) to resolve the long-standing modal mixture problem. Moving beyond conventional flattened labels, HCSU provides hierarchical expert-written aesthetic descriptions, enabling two rigorous evaluation protocols: finegrained style discrimination and interpretable aesthetic reasoning. Extensive evaluations reveal a persistent gap between calligraphy-related knowledge and visually grounded style perception: state-of-the-art LVLMs show non-trivial performance but remain sensitive to script-level, textual, and source-specific cues, and often struggle to ground aesthetic judgments in fine-grained brushwork evidence. Ultimately, the HCSU benchmark exposes fundamental limitations in current multimodal architectures, aiming to inspire the evolution of expert-level visual reasoning for cultural heritage preservation. The dataset is available at https://huggingface.co/datasets/Tongji209/HCSU.

Abstract:
Image Quality Assessment (IQA) requires aligning modeloutputs with human perception. While Vision-Language Models (VLMs)show promise, current methods struggle with generalization due to alack of fine-grained reasoning alignment. Existing RL-based approachesprimarily rely on outcome rewards; however, their inherent sparsity oftenresults in accurate predictions based on erroneous logical attributions.This phenomenon creates a critical bottleneck that stifles the develop-ment of robust reasoning capabilities and limits performance gains inout-of-distribution scenarios. To address this problem, we propose RGPR,which utilizes grounding process rewards to orchestrate VLM trajectoriesalong a human-emulated reasoning chain. To maintain optimization sta-bility, we introduce step-wise reward normalization to preserve trainingsignal precision. RGPR achieves state-of-the-art results across three tasks,demonstrating superior reasoning fidelity and robust generalization.

Abstract:
Existing vision-language models (VLMs) have demonstrated impressive performance in reasoning-based segmentation. However, current benchmarks are primarily constructed from high-quality images captured under idealized conditions. This raises a critical question: when visual cues are severely degraded by adverse weather conditions such as rain, snow, or fog, can VLMs sustain reliable reasoning segmentation capabilities? In response to this challenge, we introduce WeatherReasonSeg, a benchmark designed to evaluate VLM performance in reasoningbased segmentation under adverse weather conditions. It consists of two complementary components. First, we construct a controllable reasoning dataset by applying synthetic weather with varying severity levels to existing segmentation datasets, enabling fine-grained robustness analysis. Second, to capture real-world complexity, we curate a real-world adverse-weather reasoning segmentation dataset with semantically consistent queries generated via mask-guided LLM prompting. We further broaden the evaluation scope across five reasoning dimensions, including functionality, application scenarios, structural attributes, interactions, and requirement matching. Extensive experiments across diverse VLMs reveal two key findings: (1) VLM performance degrades monotonically with increasing weather severity, and (2) different weather types induce distinct vulnerability patterns. We hope WeatherReasonSeg will serve as a foundation for advancing robust, weather-aware reasoning.

Abstract:
Current Large Vision Language Models (LVLMs) excel atmany zero-shot tasks like image captioning, visual question answeringand OCR. However, these same models suffer from poor performance atimage classification tasks, underperforming against CLIP-based meth-ods. Notably, this gap is surprising because many LVLMs use CLIP-pretrained vision encoders. Yet LVLMs are not inherently limited byCLIP’s architecture with independent vision and text encoders. In CLIP,this separation biases classification toward class-name matching ratherthan joint visual–text reasoning. In this paper we show that, despite theirpoor raw performance, LVLMs can improve visual feature class separa-bility at inference using prompt conditioning, and LVLMs’ internal rep-resentations, especially attention heads, can outperform the model itselfat zero-shot and few-shot classification. We introduce Head EnsembleClassifiers (HEC) to bridge the performance gap between CLIP-basedand LVLM-based classification methods. Inspired by Gaussian Discrimi-nant Analysis, HEC ranks the most discriminative vision and text headsand combines them into a training-free classifier. We show that HECachieves state-of-the-art performance in few-shot and zero-shot classifi-cation across 12 datasets. Code: github.com/AdhemarDeSenneville/HEC

Abstract:
Large Vision Language Models (VLMs) excel at general vi-sual reasoning tasks, but their performance degrades sharply when de-ployed in novel domains with substantial distribution shifts compared towhat was seen during pretraining. Existing approaches to adapt VLMsto novel target domains rely on finetuning standard VLM components.Depending on which components are finetuned, these approaches eitherlimit the VLMs ability to learn domain-specific features, or lead to catas-trophic forgetting of pre-existing capabilities. To address this, we intro-duce Vision Contextualized Probing (VisCoP), which augments theVLM’s vision encoder with a compact set of learnable visual probes,enabling domain-specific features to be learned with only minimal up-dates to the pretrained VLM components. We evaluate VisCoP acrossthree challenging domain adaptation scenarios: cross-view (exocentric →egocentric), cross-modal (RGB → depth), and cross-task (human under-standing → robot control). Our experiments demonstrate that VisCoPconsistently outperforms existing domain adaptation strategies, achiev-ing superior performance on the target domain, while better retainingcapabilities from the source domain. We will release all code, models,and evaluation protocols to facilitate future research in this direction.

Abstract:
Large Vision Language Models (LVLMs) have achieved re-markable success on vision–language tasks, yet fine-grained perceptionover high-resolution images and long-context videos remains challenging.As the number of visual tokens increases, the visual attention sink phe-nomenon becomes increasingly severe, causing irrelevant tokens to absorba disproportionate amount of attention mass. Recent approaches attemptto mitigate this issue by explicitly predicting bounding boxes or temporalspans and re-encoding the cropped visual regions. Such methods dependon unreliable numeric localization in the discrete token space and in-cur significant computational overhead due to additional forward passes.In this work, we propose VisReflect, a simple yet effective frameworkthat improves fine-grained perception in long visual contexts through la-tent visual reflection. Instead of decoding intermediate predictions intodiscrete tokens, the model generates continuous visual reflection thatrepresents question-relevant visual features in the latent space. Thesereflections selectively emphasize salient regions or frames, guiding at-tention towards relevant visual tokens within a single forward pass. Weconduct comprehensive evaluations on challenging high-resolution im-age benchmarks, including BLINK, V∗ , and HRBench-4K/8K, as wellas video understanding benchmarks such as MVBench, VideoMME, andMLVU. Our method consistently improves over strong baselines, achiev-ing gains of 4.1% on image benchmarks and 1.8% on video benchmarks.Compared with zooming-based methods, our model achieves compara-ble performance while reducing inference time by roughly 44% on videounderstanding.

Abstract:
As Vision Language Models (VLM) are deployed across safety-critical applications, understanding and controlling their behavioral pat-terns has become increasingly important. Existing behavioral controlmethods face significant limitations: system prompting is a popular ap-proach but could easily be overridden by user instructions, while ap-plying activation-based steering vectors requires invasive runtime accessto model internals, precluding deployment with API-based services andclosed-source models. Finding steering methods that transfer across mul-tiple VLMs is still an open area of research. To this end, we introduce vi-sual input based steering for output redirection (VISOR++), a novel ap-proach that achieves behavioral control through optimized visual inputsalone. We demonstrate that a single VISOR++ image can be generatedfor two architecturally diverse VLMs that by itself can emulate each oftheir steering vectors. By crafting universal visual inputs that induce tar-get activation patterns for an ensemble of models, VISOR++ eliminatesthe need for runtime model access while remaining deployment-agnostic.This means that when an underlying model supports multimodal capa-bility, model behaviors can be steered by inserting an image input com-pletely replacing runtime steering vector based interventions. We firstdemonstrate the effectiveness of the VISOR++ images on open-accessmodels such as LLaVA-1.5-7B and IDEFICS2-8B along three alignmentdirections: refusal, sycophancy and survival instinct. Both the model-specific steering images and the jointly optimized images achieve perfor-mance parity closely following that of steering vectors for both positiveand negative steering tasks. We also show early promise of VISOR++images in achieving directional behavioral shifts for unseen models thatinclude both open-access and closed-access models. At the same time,VISOR++ images are able to preserve 99.9% performance on 14,000unrelated MMLU evaluation samples highlighting their specificity to in-ducing only behavioral shifts.

Abstract:
Foundation models (FMs) have driven significant progressin computational pathology. These models can easily exceed a billionparameters and produce high-dimensional embeddings, thus limiting theirapplicability for research or clinical use when computing resources aretight. Here we introduce Pathryoshka, a novel multi-teacher distillationframework inspired by agglomerative models and Matryoshka representa-tion learning to reduce pathology FM sizes while allowing for adaptableembedding dimensions. We evaluate our framework with a distilled modelon ten public pathology benchmarks with varying downstream tasks.Compared to its much larger teachers, Pathryoshka reduces the modelsize by 86-92% at on-par performance. It outperforms state-of-the-artsingle-teacher distillation models of comparable size by a median marginof 7.0 and other pathology multi-teacher distillation by 5.3 percentagepoints in accuracy. By enabling efficient deployment without sacrificingaccuracy or representational richness, Pathryoshka democratizes accessto state-of-the-art pathology FMs for the broader research and clinicalcommunity.

Abstract:
Chinese font production is a complex task that balances vi-sual consistency, vector editability, and topological integrity at a massivecharacter scale. Existing fragmented toolchains, relying on manual rules,often lead to geometric redundancy and defects. This paper introducesFontCopilot, a multimodal large language model that performs raster-to-vector conversion, Bézier curve sparsi x001C cation, defect marking, and geo-metric correction within a single model. We elevate font engineering fromexplicit geometric rule constraints to structured glyph code understand-ing and rewriting, serializing Bézier curves into SVG XML. This enablesthe model to perform topological reorganization in the code space andinternalize implicit designer priors as structured reasoning constraints.FontCopilot integrates few-shot reference guidance and zero-shot gen-eralization mechanisms, balancing speci x001C c style transfer and universalstandard reconstruction. To advance the development of a uni x001C ed fontengineering model, we constructed the CFCopilot-8M dataset, covering300 font types and 8 million glyph samples. Under progressive multi-taskhybrid training, the glyphs processed by FontCopilot show consistentimprovements in editability, contour compactness, and defect correction,validating its industrial-grade font production value.

Abstract:
Blind image deblurring demands the recovery of high-fidelitydetails and coherent structures from complex, unknown degradations.Current blind image deblurring methods struggle with real-world, spa-tially varying degradations, and lack the semantic awareness necessary toreliably differentiate valid textures from artifacts. To bridge this gap, wepropose CogSENet, a dynamic, semantic-aligned reconstruction frame-work inspired by the eagle’s visual system. By mimicking the eagle’sactive saccadic scanning, we devise a Semantic-Driven State Space Mod-ule (SDSSM) with semantic-aware token regrouping via differentiablerouting, enabling prompt-conditioned long-range dependency modeling.To ensure physically interpretable recovery of textures and structures,a BiFreqFusionBlock (BFFB) mirrors functional differentiation of theeagle’s retina by decomposing features into high and low frequenciesusing wavelet transforms. Finally, we estimate a continuous Blur Field(CBF) from blur image and fuse it with CLIP semantic priors to modu-late the deepest latent features, emulating focal adaptation and enablingadaptive restoration under spatially non-uniform blur. Extensive exper-iments demonstrate that CogSENet outperforms state-of-the-art deblur-ring methods in both visual quality and structural fidelity with fewerparameters, while also performing favorably on dehazing, deraining, anddenoising tasks.

Abstract:
Generating complete 3D objects under partial occlusions (i.e.,amodal scenarios) is a practically important yet challenging problem,as large portions of object geometry are unobserved in real-world sce-narios. Existing approaches either operate directly in 3D, which en-sures geometric consistency but often lacks generative expressiveness,or rely on 2D amodal completion, which provides strong appearancepriors but does not guarantee reliable 3D structure. This raises a keyquestion: how can we achieve both generative plausibility and geometriccoherence in amodal 3D modeling? To answer this question, we intro-duce GENA3D (GENerative Amodal 3D), a framework that integrateslearned 2D generative priors with explicit 3D geometric reasoning withina conditional 3D generation paradigm. The 2D priors enable the modelto plausibly infer diverse occluded content, while the 3D representationenforces multi-view consistency and spatial validity. Our design incorpo-rates a novel View-Wise Cross-Attention for multi-view alignment and aStereo-Conditioned Cross-Attention to anchor generative predictions in3D relationships. By combining generative imagination with structuralconstraints, GENA3D generates complete and coherent 3D objects fromlimited observations without sacrificing geometric fidelity. Experimentsdemonstrate that our method outperforms existing approaches in bothsynthetic and real-world amodal scenarios, highlighting the effectivenessof bridging 2D priors and 3D coherence in generating plausible and geo-metrically consistent 3D structures in complex environments.

Abstract:
Thermal infrared (TIR) imaging facilitates perception, se-curity surveillance, and industrial inspection. Compared with visiblespectrum, TIR observations are typically resolution-limited and texture-scarce. More critically, thermal diffusion smooths true geometric bound-aries into low-frequency blur, whereas non-uniform sensor response andfixed-pattern noise introduce structured high-frequency artifacts, yield-ing a pronounced signal-to-noise inversion. Consequently, reconstructinga high-resolution (HR) 3D TIR scene and synthesizing high-quality novelviews from only low-resolution (LR) infrared inputs remains challeng-ing. To address this, we propose the first imaging-calibrated TIR super-resolution 3D Gaussian Splatting (3DGS) framework that reconstructshigh-resolution 3D TIR scenes using only LR inputs. Specifically, we ex-plicitly model infrared imaging degradation on the clean radiation signalsrendered by 3DGS using physics-informed priors, enabling decoupling ofthe underlying thermal radiation from sensor-induced artifacts. To allevi-ate texture scarcity, an intensity-conditioned tone adapter is designed toapply local affine residual modulation, thereby surfacing latent thermalgradients. Finally, we implement a frequency-aware curriculum learn-ing strategy that leverages edge priors from a super-resolution teachernetwork, and employs a cosine ramp-up schedule to smoothly shift theoptimization focus from coarse low-frequency structures to fine high-frequency details. Extensive evaluations across TIR benchmarks showthat our method significantly outperforms existing works in both high-resolution synthesis quality and quantitative temperature fidelity.

Abstract:
3D Gaussian Splatting has revolutionized the field of novelview synthesis by enabling high-fidelity and real-time rendering. How-ever, its explicit representation requires millions of primitives, result-ing in massive storage overheads that hinder scalable deployment. Toaddress this challenge, we propose Explicitly Granular Gaussian Splat-ting (EGGS), a novel representation and compression framework thatachieves an ultra-compact storage footprint without compromising vi-sual quality or rendering speed. Our method is built upon two key in-novations designed to minimize attribute dimensionality. First, we intro-duce Luma-based DC Representation, which leverages the dominance ofluminance information to compress the 3-dimensional DC component ofSpherical Harmonics into a single explicit dimension. Second, we proposeVolume-Preserving Scale Factorization, which reduces the 3-dimensionalscale attribute to a single explicit dimension while ensuring training sta-bility by maintaining consistent volume. By combining these techniqueswith a lightweight neural field for implicit attribute estimation, EGGSachieves a remarkably compact representation with the smallest storagefootprint among existing methods. Extensive experiments on the Mip-NeRF360, Tanks&Temples, and Deep Blending datasets demonstratethat EGGS significantly outperforms state-of-the-art methods in rate-distortion efficiency, reducing gigabyte-scale scenes to a mere handful ofmegabytes while maintaining high-quality real-time rendering. Code isavailable here.

Abstract:
Recent advancements in 3D Gaussian Splatting (3DGS) haveachieved significant progress in novel view synthesis (NVS), offering com-putationally efficient and photorealistic rendering, even in the few-shotsetting where input training views are sparse. However, several repre-sentative 3DGS-based methods for few-shot NVS face a critical issue ofsparsity discrepancy, i.e., Gaussian initialization leverages dense fusedstereo points from extended views, creating an inconsistency with sparsetraining views. To resolve this issue, we propose ReInGS, a novel methodfeaturing a two-stage coarse-to-fine 3D Gaussian re-initialization pro-cess designed explicitly for few-shot NVS. In the coarse-level initializa-tion stage, an expansion-based hybrid point sampling (EHPS) strategyis deployed to generate dense points and effectively capture the scene’sglobal geometric structure. In the fine-level re-initialization stage, a view-dominant details-aware point sampling (VDPS) strategy, which com-prises two sequential sub-strategies on intra-view point sampling andcross-view point sampling, is adopted to reconstruct the scene’s localgeometric details with high fidelity. Comprehensive experiments on twobenchmarking datasets demonstrate that ReInGS achieves state-of-the-art performance across 2-view, 3-view, and 4-view settings, especiallydelivering PSNR gains of up to 2.92 dB, 1.92 dB, and 1.38 dB, respec-tively, while synthesizing high-quality novel views without data leakagefrom extended views.

Abstract:
Recent advances in Neural Radiance Fields and 3D Gaus-sian Splatting have demonstrated immense potential for large-scale UAV-based 3D reconstruction. However, real-world data collection often spansmultiple times of the day, violating the fundamental photometric consis-tency assumption. Such multi-temporal illumination inconsistencies in-evitably lead to severe color artifacts, coupled shadows, and degradedgeometric accuracy. Due to the lack of UAV datasets that systematicallycapture the same areas under varying lighting conditions, this crucialchallenge remains largely underexplored. To bridge this gap, we introduceSkyLume, a large-scale, real-world UAV dataset specifically designed forstudying illumination-robust 3D reconstruction and urban scene model-ing. Our dataset features three primary contributions: (1) We collect over100k high-resolution UAV images (nadir and four oblique views) span-ning 10 diverse urban regions, with each region meticulously capturedacross three distinct time periods to systematically isolate illuminationvariations. (2) To enable rigorous evaluation, we provide comprehensiveper-scene LiDAR scans, offering highly accurate ground truth for as-sessing depth, surface normals, and geometric fidelity. (3) For inverserendering and appearance decoupling tasks, we introduce the Tempo-ral Consistency Coefficient (TCC), a novel metric designed to quantifycross-time rendering stability. We envision SkyLume as a foundationalbenchmark that will advance research and real-world evaluation in large-scale inverse rendering, robust geometry reconstruction, and novel viewsynthesis. Project Page: skylume.

Abstract:
We present DASAM3D, a framework that fuses the complementary strengths of SAM3D and Depth Anything 3 (DA3) for objectaware 3D scene reconstruction. A fundamental appearance–geometry trade-off exists: SAM3D yields geometrically coherent per-object Gaussian primitives but with unrealistic appearance, while DA3 delivers photorealistic 3DGS reconstructions yet lacks object-level geometric reasoning. Our key insight is to use SAM3D exclusively as a 3D geometric layout provider, guiding DA3 geometry refinement while preserving its photorealistic textures. DASAM3D proceeds through three differentiable stages—(1) Global Alignment Optimization, (2) Per-Object Alignment Refinement, and (3) Layout-Based Geometry Optimization—and inherits DA3’s flexibility for posed or pose-free inputs at any scale. Experiments on DL3DV-10K and MipNeRF-360 show consistent gains in reconstruction accuracy and novel view synthesis; a user-preference study confirms DASAM3D’s object renderings are preferred over SAM3D’s in visual fidelity.

Abstract:
Generating high-quality 3D characters from single images re-mains a significant challenge in digital content creation, particularly dueto complex body poses and self-occlusion. In this paper, we present RCM(Rotate your Character Model ), an advanced image-to-video diffusionframework tailored for high-quality novel view synthesis (NVS). Com-pared to existing diffusion-based approaches, RCM offers several keyadvantages: (1) transferring characters with any complex poses into acanonical pose, enabling consistent novel view synthesis across the entireviewing orbit, (2) high-resolution orbital video generation at 1024×1024resolution, (3) controllable observation positions given different initialcamera poses, and (4) multi-view conditioning supporting up to 4 inputimages, accommodating diverse user scenarios. Extensive experimentsdemonstrate that RCM outperforms state-of-the-art methods in novelview synthesis on character generations. The deliverables will be updatedhere.

Abstract:
Recently, Large Language Models (LLMs) have emerged aspromising layout agents for 3D scene generation. Existing layout agentsstill suffer from implausible layout generation because most of them con-vert 3D assets and 3D layouts into textual descriptions as inputs andoutputs, which involves severe information loss due to the modality gapbetween texts and 3D assets and 3D layouts. We propose NaLA, a native3D LLM layout Agent for high-quality 3D scene generation by placing 3Dassets in the scene. For the inputs, NaLA encodes 3D scene boundariesand 3D assets directly into the LLM, preserving fine-grained geometryand enabling explicit reasoning over relationships like collisions, surfacesupporting, and containment. To accurately output the positions and ori-entations of assets, NaLA adopts a coarse-to-fine prediction mechanismthat first predicts discrete poses in an autoregressive manner and thenrefines the discrete poses with a continuous regression. Trained on diverselayout datasets, NaLA attains strong geometric perception and layout co-herence. Experiments demonstrate that NaLA outperforms prior layoutagents in both generation quality and inference efficiency, with compre-hensive ablation studies to verify each component’s effectiveness.

Abstract:
Recent diffusion-based generative models have shown strongcapabilities in 3D scene understanding and affordance reasoning. How-ever, existing methods predominantly rely on deterministic global en-coders or fully stochastic diffusion backbones, which fail to disentanglestructural certainty from contextual uncertainty, a key requirement forreliable affordance-grasp prediction in cluttered, partially observed en-vironments. We propose a split-latent hierarchical diffusion frameworkthat explicitly decomposes the global scene representation into two com-plementary subspaces: a fixed latent encoding deterministic geometricand semantic priors, and a diffused latent capturing stochastic scene-level variations through a Global Conditional Diffusion guided by RGBcontext. To propagate global uncertainty to fine-grained reasoning, weintroduce a Local Conditional Diffusion that predicts dense, point-wiseaffordance and grasp fields conditioned jointly on both latents, producinggeometrically precise yet contextually diverse predictions. Furthermore,we incorporate Harmonic Grasp Field, a differential geometric regulariza-tion that enforces smooth, obstacle-aware grasp manifolds via harmonicpotential constraints. This unified formulation bridges deterministic ge-ometry with probabilistic reasoning, yielding physically consistent anduncertainty-aware grasp generation. Extensive experiments on cluttered3D affordance and grasping benchmarks demonstrate superior stability,generalization, and diversity compared to deterministic and single-latentdiffusion approaches. Project Website.

Abstract:
3D visual grounding aims to localize language-referred ob-jects in 3D scenes. However, existing methods rely on limited annotateddatasets under controlled environments and thus struggle to generalizein the wild. Therefore, the scarcity of scalable 3D grounding supervisionremains a major challenge. In this paper, we propose AnyGround3D,a scalable framework that learns to ground arbitrary 3D objects in thewild via 2D-to-3D lifting. Instead of requiring manual 3D annotations,our approach lifts diverse Internet images into structured 3D supervisionthrough multi-level 2D-to-3D lifting. First, we perform scene-level 2D-to-3D lifting to reconstruct metric point clouds from monocular images,providing geometric context. Second, we conduct object-level 2D-to-3Dlifting to recover complete 3D object representations with fine-gained 3Dbounding boxes and neural language descriptions. To further enhancethe representation robustness and open-vocabulary generalization of thepoint encoder, we introduce feature-level lifting that distills semantic rep-resentations from 2D foundation models into the 3D encoder. Together,these lifting processes enable large-scale synthetic 3D grounding supervi-sion across diverse real-world scenes. Extensive experiments demonstrateimproved in-the-wild generalization across multiple 3D grounding bench-marks, without relying on human-annotated 3D grounding data.

Abstract:
Despite impressive performance on standard benchmarks, multimodal large language models (MLLMs) face critical challenges in real-world clinical environments where medical images inevitably suffer various quality degradations. Existing benchmarks exhibit two key limitations: (1) absence of large-scale, multidimensional assessment across medical image quality gradients and (2) no systematic confidence calibration analysis. To address these gaps, we present MedQ-Deg, a comprehensive benchmark for evaluating medical MLLMs under image quality degradations. MedQ-Deg provides multi-dimensional evaluation spanning 18 distinct degradation types, 30 fine-grained capability dimensions, and 7 imaging modalities, with 24,894 question-answer pairs. Each degradation is implemented at 3 severity degrees, calibrated by expert radiologists. We further introduce Calibration Shift metric, which quantifies the gap between a model’s prediction-consistency-based perceived confidence and actual performance to assess metacognitive reliability under degradation. Our comprehensive evaluation of 40 mainstream MLLMs reveals several critical findings: (1) overall model performance degrades systematically as degradation severity increases, (2) models exhibit a Dunning-Kruger-like overconfidence pattern, maintaining inappropriately high confidence despite severe accuracy collapse, and (3) models display markedly differentiated behavioral patterns across capability dimensions, imaging modalities, and degradation types. We hope MedQ-Deg drives progress toward medical MLLMs that are robust and trustworthy in real clinical practice.

Abstract:
While multimodal integration significantly improves computer vision models, deploying them incurs prohibitive inference costs and requires scarce, perfectly paired datasets. Recent methods address this data bottleneck by synthesizing missing modalities via generative AI, yet they introduce a severe inefficiency: the Decode-Encode Loop. Specifically, information-rich generative latents are decoded into noisy raw signals, forcing the downstream classifier to waste capacity re-encoding them. To bypass this bottleneck, we propose Direct Latent Augmentation (DLA), utilizing undecoded generative latents directly as privileged information. Furthermore, to transfer this dense knowledge to a purely visual student, we introduce Multilayer Explicit Simulated Synesthesia (MESSy). Instead of enforcing rigid representation matching, which forces the student to distort its native visual features to accommodate complex multimodal topologies, MESSy uses a predictive objective to safely internalize these physical priors. Empirical results demonstrate that our framework significantly outperforms raw data augmentation and traditional distillation. Ultimately, our approach yields highly accurate unimodal students with “synesthetic” latent structures that are inherently aligned with modalities they have never directly observed.

Abstract:
Removing reflections from mixed light using polarization of-fers the benefits of robust physical constraints. However, extending thisto high-speed scenes presents challenges due to the discrete frames ofconventional cameras. In this paper, we propose 340 FPS reflection-freevideo reconstruction by leveraging the high-speed spike camera modu-lated by a rapidly rotating polarizer. The core motivation of our approachrelies on exploiting the continuous and asynchronous firing of the spikesensor to densely capture instantaneous polarization states. However, theinherently low signal-to-noise ratio of spikes under extremely short inte-gration windows and light attenuation causes severe numerical instabilityin physical layer separation. To address this, we introduce a physically-driven framework featuring a differentiable Reliability-Weighted PhysicsSolver (RWPS). RWPS robustly separates reflection and transmissionlayers by dynamically adjusting learned confidence weights to handlesevere shot noise. Furthermore, we develop a refinement module thatleverages back-projection residuals and physical-discrepancy cues to ac-curately restore high-frequency textures. Extensive experiments demon-strate that our method enables robust reconstruction of high-fidelity andreflection-free videos.

Abstract:
Many challenges in scientific imaging involve solving ill-posedinverse problems, where the goal is to recover spatio-temporal fields fromindirect, noisy, and highly sparse measurements — often without accessto ground truth data or reliable simulators. To address this challengingscenario, we present NeuralDMD, an interpretable, untrained (per-instance) reconstruction framework that combines neural implicit rep-resentations with Dynamic Mode Decomposition (DMD) to reconstructcontinuous spatio-temporal dynamics directly from measurements. Neu-ralDMD parameterizes DMD modes as continuous neural fields, andimposes a low-rank linear dynamics prior with spectral time evolutionto enforce temporal continuity. This formulation enables both forecast-ing under sparsity, and yields interpretable modes and spectra. We findthat NeuralDMD outperforms baselines on a wide variety of tasks: fromweather data assimilation from sparse station observations to interfero-metric (Fourier domain) observations of Sagittarius A, the black hole atthe center of our galaxy. Moreover, NeuralDMD remains stable whenextrapolating into the future. While this framework is most naturallysuited to linear dynamics, we show that it can be applied to nonlinearregimes, though with extrapolation performance that degrades with in-creasing nonlinearity. Together, these results show that NeuralDMDenables interpretable reconstruction and forecasting of spatio-temporaldynamics directly from sparse and indirect measurements without rely-ing on numerical simulators or training data.

Abstract:
We introduce a novel upper bound on the local Lipschitz con-stant of the dot-product self-attention block showing its dependence onthe attention map distributions. The proposed bound is not only tighterthan the prior art, but for the first time, reveals how the distributionof attention probabilities shapes the local Lipschitz constant of the self-attention block. The theoretical basis of the proposed upper bound lies inthe refined closed-form upper bounds on singular values of the Jacobianof softmax function. Leveraging these theoretical insights, we introduceJaSMin (Jacobian Softmax norm Minimization), a lightweight regular-izer that directly controls the local Lipschitz constant of each block and,consequently, the entire model. Additionally, we discuss how the natureof the attention map distribution contributes to the gradient dynamicsand, consequently, transformer training stability.

Abstract:
High-resolution 3D asset generation is vital in various 3D ap-plications. Existing state-of-the-art diffusion-based models remain con-strained by fixed resolutions, limiting their ability to produce details. Inthis paper, we tackle the challenge of generating more detailed, higher-resolution 3D objects by introducing a 3D super-resolution (SR) frame-work built on existing 3D generative foundation models. To this end,we design PLSR, a progressive and localized super-resolution solutionto achieve this goal effectively and memory efficiently. Technically, givena coarse geometry from a pretrained 3D generator, we decompose theglobal SR task into localized sub-tasks via an associative input de-composition scheme, adapt a flow-based 3D generator into a local-ized super-resolution model through low-cost finetuning, and unifythem in an iterative patch-wise denoising pipeline for seamless high-resolution output. Experiments on challenging objects show that our ap-proach is able to generate 3D details with new strong fine-detail fidelitywhile significantly reducing the computational cost, offering a new andpractical solution for high-resolution 3D asset generation.

Abstract:
We present BWAFDA, a dual-branch no-reference image quality assessment model that replaces uniform averaging with detail-aware, block-wise fusion. The context encoder branch produces multi-scale block tokens, and a lightweight detail branch extracts high-frequency cues. These streams are combined late. First, block tokens are aggregated via block-wise attention across scales, guided by a soft multi-scale prior that stabilizes cross-scale interactions and preserves localized artifacts. The resulting descriptors are then fused per scale using learned weights. Subsequently, the detail cues are injected to refine distortion-sensitive evidence. Finally, a class-token-guided head performs “where-to-trust-what” selection over blocks to produce the quality score, without the need for region labels. Comprehensive evaluations on seven public NR-IQA benchmarks show state-of-the-art (SOTA) performance. Across multiple controlled ablations, we observe consistent drops in correlation compared to the full model, indicating that region-aware masking, contextual features, and multi-scale aggregation contribute jointly rather than redundantly. Overall, detail-aware, block-wise fusion delivers SOTA accuracy while preserving data efficiency and transferability by aligning localized artifact cues with global context.

Abstract:
The recent large video foundation model, SAM2, enablessegment anything in both images and videos, serving as a powerful basemodel for various applications. However, many of such use cases requireto operate on resource-constrained devices like mobile phones and laptops.In this work, we aim to make SAM2 more mobile-friendly by distillingthe heavyweight SAM2 into a lightweight model, facilitating segmentanything in both images and videos on mobile devices. To this end, wepropose Hypergraphical Knowledge Distill (HyperKD), which introducesthe idea of hypergraph into knowledge distillation, aiming to effectivelymodel and transfer SAM2’s generalizable and comprehensive knowledge.HyperKD consists of Temporal HyperKD and Granularity HyperKD thatconstruct hypergraphs to explicitly model and extract the generalizabletemporal knowledge and the comprehensive multi-granularity knowledgefrom SAM2 respectively, which are then distilled into the lightweightstudent model by aligning it with the constructed hypergraphs. Besides,we present MobileSAM2, a new family of lightweight SAM2 that balancesefficiency and effectiveness via searching the best model architectures withHyperKD during model size reduction. Extensive experiments validateMobileSAM2 across multiple benchmarks and show promising generaliza-tion performance on embodied AI tasks.

Abstract:
Generative models for volumetric medical images have foundmany applications in medical imaging, ranging from data augmentationto serving as priors for inverse problems. For these applications, generat-ing high-resolution 3D images with strong controllability is essential butremains highly challenging. Existing approaches typically control gener-ation either through radiology reports used as text prompts or throughfull image segmentation. While text-based prompting is flexible, it pro-vides limited spatial control over the location, shape, and boundary ofabnormalities. In contrast, segmentation-based methods receive precisespatial guidance but are restrictive in requiring full-organ annotations.In this work, we propose MedSynV2, a flexible multimodal framework forcontrollable volumetric image generation that supports input from radi-ology reports and segmentation prompts (both optional). Our approachallows users to provide segmentation of a specific anatomy or abnormalitywithout requiring full-organ annotations. The semantic meaning of thesegmentation mask is specified through an accompanying text descrip-tion, resulting in a highly flexible and scalable conditioning mechanism.We develop a memory-e!cient architecture based on a modified di"usiontransformer that jointly processes image and segmentation tokens. Themodel further incorporates gated attention to e"ectively attend to longradiology reports. Experiments demonstrate that our method achievesstate-of-the-art perceptual and semantic scores (e.g., → 24% relative im-provement in mean FID), generates high-resolution anatomically consis-tent CT volumes, and improves data e!ciency when used for data aug-mentation. Radiologists’ evaluation further confirms strong alignmentbetween generated and real medical images. We also explore the abilityof the model to generalize towards concepts beyond training data. Codewill be released at https: // github. com/ batmanlab/ MedSynV2 .

Abstract:
Trajectory forecasting for autonomous driving has advancedrapidly, yet representative models often produce uninformative posteri-ors over forecast modes, causing problems for mode pruning. We tracethis to a modeling-training mismatch: forecasters are typically modeledas conditional Gaussian mixture models (GMMs) but trained with awinner-take-all (WTA) loss that assigns each sample to its nearest mode.We argue that this K-means-like hard assignment (one-hot), while pre-venting mode collapse, is the source of uninformative mode probabil-ities: it over-segments the trajectory space, ignores relatedness amongnearby modes, and yields assignment instability under small perturba-tions. Guided by this lens, we introduce two post-hoc treatments: (1)test-time posterior-weighted merging that aggregates nearby candidatetrajectories; and (2) a one-step expectation-maximization (EM) updatethat replaces hard labels with soft responsibilities, sharing probabilitymass across neighboring modes. Across several WTA-trained architec-tures, these lightweight steps produce more informative, faithfully rankedmode posteriors and strengthen final forecasts on popular displacementmetrics—without retraining. Our analysis unifies recent design choicesthrough a GMM-vs-K-means perspective and offers principled, practicalcorrections that better align training objectives with inference.

Abstract:
Video-based embodied world models provide an appealingsubstrate for robotic manipulation by predicting future states, yet cur-rent approaches remain limited by a fundamental entanglement: accu-rately modeling dynamics typically requires low-level temporal reason-ing, while producing high-resolution frames demands expansive visualsynthesis according to high-level semantics. This entanglement results inslow inference speed for iterative planning or too coarse predictions toretain contact-rich details. To solve this dilemma, we present Disentan-gled Video Generation World Model (DVG-WM), an efficient frameworkthat explicitly decomposes world modeling into dynamics learn-ing and visual synthesis. Conditioned on an initial observation anda language instruction, our model first generates a plausible sequence ofintermediate visual states to preview the physical interaction and refinesthem to obtain high-fidelity videos. Furthermore, an efficient cascadingmechanism is proposed, where DVG-WM leverages flow matching to di-rectly map the dynamics to video latents, and introduces a latent degra-dation mechanism to enable the capability of regenerating contact-richdetails. Experiments on LIBERO and real-world platforms demonstrateimproved video quality with up to 3.97 × acceleration, validating thatdisentangled video generation can be an efficient embodied world modelfor robotic manipulation.

Abstract:
Autonomous driving relies on LiDAR simulation for gener-ating realistic 3D point clouds, typically achieved using computation-ally expensive raycasting. We introduce GnoSphere360, a scalable Li-DAR data generation method that eliminates raycasting by projectingtriangular meshes onto a unit sphere and interpolating point sampleswith gnomonic projection. This computational approach bypasses tra-ditional ray-mesh intersection, significantly reducing computation timewhile maintaining compatibility for further downstream physics-basedmodeling. Benchmarking against NVIDIA OptiX and native CARLAengines, GnoSphere360 achieves up to 35× speedup over CARLA forphysical LiDAR models and maintains robust performance against Op-tiX, while preserving similar LiDAR point cloud output. However, forextremely dense LiDAR configurations, GnoSphere360 achieves over 3.6xspeedup against OptiX, showcasing the scalability of our proposed method.Ablation studies demonstrate robustness of GnoSphere360 across vary-ing scenes and sensor models, while preserving data fidelity. Our resultsshow GnoSphere360 is an efficient, accurate, and parallelizable alterna-tive to raycasting and rasterization for autonomous driving simulation.Project website: gnosphere360.github.io

Abstract:
Embodied agents must explore partially observed environ-ments while maintaining reliable long-horizon memory. Existing graph-based navigation systems improve scalability, but they often treat un-explored regions as semantically unknown, leading to inefficient frontiersearch. Although vision-language models (VLMs) can predict frontiersemantics, erroneous predictions may be embedded into memory andpropagate through downstream inferences, causing structural error ac-cumulation that confidence attenuation alone cannot resolve. These ob-servations call for a framework that can leverage semantic predictionsfor directed exploration while systematically retracting errors once newevidence contradicts them. We propose Hypothesis Graph Refinement(HGR), a framework that represents frontier predictions as revisable hy-pothesis nodes in a dependency-aware graph memory. HGR introduces(1) semantic hypothesis module, which estimates context-conditioned se-mantic distributions over frontiers and ranks exploration targets by goalrelevance, travel cost, and uncertainty, and (2) verification-driven cas-cade correction, which compares on-site observations against predictedsemantics and, upon mismatch, retracts the refuted node together withall its downstream dependents. Unlike additive map-building, this allowsthe graph to contract by pruning erroneous subgraphs, keeping mem-ory reliable throughout long episodes. We evaluate HGR on multimodallifelong navigation (GOAT-Bench) and embodied question answering (A-EQA, EM-EQA). HGR achieves 72.41% success rate and 56.22% SPL onGOAT-Bench, and shows consistent improvements on both QA bench-marks. Diagnostic analysis reveals that cascade correction eliminates ap-proximately 20% of structurally redundant hypothesis nodes and reducesrevisits to erroneous regions by 4.5×, with specular and transparent sur-faces accounting for 67% of corrected prediction errors.

Abstract:
Executing long-term tasks in dynamic environments requiresembodied agents to maintain robust and adaptive 3D scene represen-tations. However, most existing 3D memory frameworks rely on staticworld assumptions. When objects are displaced by human activities orunobserved events, agents encounter memory-observation conflicts andoften require costly geometric recomputations or inefficient global re-exploration. To address this, we propose HitMem, a hierarchical tempo-ral 3D memory framework with a multi-modal context-aware retrievalmechanism. Through continuous perception, HitMem unifies semanticand spatial information into a lightweight topological graph that cap-tures support relationships, while a temporal decay mechanism dynami-cally regulates memory activeness to mitigate the impact of stale repre-sentations. In addition, the multi-modal context-aware retrieval mecha-nism defaults to filtering candidates using integrated semantic, spatial,and temporal memory features, and activates a specialized two-stage re-trieval process when object displacement is detected. This process com-bines spatial constraints inferred from external agent trajectories withsemantic common sense grounded in class affinities, efficiently identi-fying high-probability candidate regions. Extensive evaluations on ourconstructed Dyna-THOR benchmark demonstrate that HitMem signif-icantly improves object relocation accuracy, reduces exploration costs,and enhances task execution performance in dynamic environments.

Abstract:
Learning a generalist control policy for robotic manipula-tion typically relies on large-scale datasets. Given the high cost of real-world data collection, a practical alternative is to generate synthetic datathrough simulation. However, the resulting synthetic data often exhibitsa significant gap from real-world distributions. While many prior studieshave proposed algorithms to bridge the Sim-to-Real discrepancy, thereremains a lack of principled research that grounds these methods in real-world manipulation tasks, particularly their performance on generalistpolicies such as Vision-Language-Action (VLA) models. In this study,we empirically examine the primary determinants of Sim-to-Real gen-eralization across four dimensions: multi-level domain randomization,photorealistic rendering, physics-realistic modeling, and reinforcementlearning updates. To support this study, we design a comprehensive eval-uation protocol to quantify the real-world performance of manipulationtasks. The protocol accounts for key variations in background, light-ing, distractors, object types, and spatial features. Through experimentsinvolving over 10k real-world trials, we derive critical insights into Sim-to-Real transfer. To inform and advance future studies, we release boththe robotic platforms and the evaluation protocol for public access tofacilitate independent verification, thereby establishing a realistic andstandardized benchmark for robotic manipulation policies.

Abstract:
Dynamical systems theory and reinforcement learning viewworld evolution as the dynamics of latent states driven by actions, withvisual observations providing partial information about those states. Re-cent video world models attempt to learn these action-conditioned dy-namics from data. However, existing datasets rarely meet these require-ments: they typically lack diverse and semantically meaningful actionspaces, and actions are directly tied to visual observations rather thanmediated by underlying states. As a result, actions are often entangledwith pixel-level changes, making it difficult for models to learn structuredworld dynamics and maintain consistent evolution over long horizons. Inthis paper, we propose WildWorld, a large-scale action-conditioned worldmodeling dataset with explicit state annotations, automatically collectedfrom a photorealistic AAA action role-playing game (Monster Hunter:Wilds). WildWorld contains over 108 million frames and features morethan 450 actions, including movement, attacks, and skill casting, togetherwith synchronized per-frame annotations of character skeletons, worldstates, camera poses, and depth maps. We further derive WildBench toevaluate models through Action Following and State Alignment. Exten-sive experiments reveal persistent challenges in modeling semanticallyrich actions and maintaining long-horizon state consistency, highlightingthe need for state-aware video generation.

Abstract:
Web agents—autonomous systems that navigate and executetasks on the web on behalf of users—have the potential to transformhow people interact with the digital world. However, the most capableweb agents today rely on proprietary models with undisclosed trainingdata and recipes, limiting scientific understanding, reproducibility, andcommunity-driven progress. We believe agents for the open web shouldbe built in the open. To this end, we introduce (1) MolmoWebMix, alarge and diverse mixture of browser task demonstrations and web-GUIperception data and (2) MolmoWeb a family of fully open multimodalweb agents. Specifically, MolmoWebMix combines over 100K synthetictask trajectories from multiple complementary generation pipelines with30K+ human demonstrations, atomic web-skill trajectories, and GUIperception data, including referring expression grounding and screen-shot question answering. MolmoWeb agents operate as instruction-conditioned visual-language action policies: given a task instruction anda webpage screenshot, they predict the next browser action, requiring noaccess to HTML, accessibility trees, or specialized APIs. Available in 4Band 8B size, on browser-use benchmarks like WebVoyager, Online-Mind2Web, and DeepShop, MolmoWeb agents achieve state-of-the-art results outperforming similar scale open-weight-only models suchas Fara-7B, and Holo1-7B. MolmoWeb-8B also surpasses set-of-marks(SoM) agents built on much larger closed frontier models like GPT-4o.We further demonstrate consistent gains through test-time scaling viaparallel rollouts with best-of-N selection, achieving 94.7% and 60.5%pass@4 (compared to 78.2% and 35.3% pass@1) on WebVoyager andOnline-Mind2Web respectively. We release model checkpoints, train-ing data, code, and a unified evaluation harness to enable reproducibilityand accelerate open research on web agents (GitHub).

Abstract:
Diffusion models have shown strong potential for multi-modalplanning in end-to-end autonomous driving. However, most existing meth-ods confine diffusion to the planning module, conditioning on fixed out-puts from separate discriminative perception networks. This decoupleddesign propagates perception errors to the planner, increasing optimiza-tion difficulty and reducing robustness. To overcome these limitations, wepropose UniTeD, a Unified Temporal Diffusion framework that jointlymodels perception and planning through iterative denoising in a sharedgenerative space. By enabling bidirectional information exchange, theframework facilitates mutual refinement between tasks and improves ro-bustness via noise-conditioned multi-task training. We further extendthis unified diffusion paradigm to a streaming setting by incorporat-ing temporal context. A Temporal Transition Module (TTM) is intro-duced to resolve the noise-level mismatch between historical and currentframes. In addition, we propose an Anchor Refresh Strategy (ARS) toalleviate the training–inference distribution shift commonly observed insparse diffusion-based end-to-end driving frameworks. Without bells andwhistles, UniTeD achieves state-of-the-art performance across multiplebenchmarks, surpassing both recent discriminative end-to-end methodsand diffusion-based planning approaches.

Abstract:
Coordinating navigation and manipulation with robust per-formance is essential for embodied AI in complex indoor environments.However, as tasks extend over long horizons, existing methods oftenstruggle due to catastrophic forgetting, spatial inconsistency, and rigidexecution. To address these issues, we propose ESCAPE (EpisodicSpatial memory Coupled with an Adaptive Policy for Execution), op-erating through a tightly coupled perception-grounding-execution work-flow. For robust perception, ESCAPE features a Spatio-Temporal FusionMapping module to autoregressively construct a depth-estimation-free,persistent 3D spatial memory, and a Memory-Driven Target Groundingmodule for precise interaction mask generation. To achieve flexible ac-tion, our Adaptive Execution Policy dynamically orchestrates proactiveglobal navigation and reactive local manipulation to seize opportunis-tic targets. ESCAPE achieves state-of-the-art performance on the AL-FRED benchmark, reaching 65.09% and 60.79% success rates in test seenand unseen environments with step-by-step instructions. By reducing re-dundant exploration, our ESCAPE substantially improves path-length-weighted metrics and remains robust (61.24%/56.04%) even without de-tailed guidance for long-horizon tasks.

Abstract:
Autonomous driving world models are expected to work effectively across three core dimensions: state, action, and reward. However, existing methods are typically restricted to fragmented modality modeling, short-horizon drift, and imprecise action control, while lacking intrinsic mechanisms for policy evaluation. In this paper, we introduce OmniNWM, an Omniscient panoramic Navigation World Model that addresses all three dimensions within a consistent probabilistic framework. For State, OmniNWM generates panoramic videos of RGB, semantics, metric depth, and 3D occupancy, ensuring pixel-level alignment across modalities with joint distribution modeling. To mitigate autoregressive exposure bias, we propose a structured panoramic forcing strategy to stabilize long-horizon generation via stochastic manifold thickening. For Action, we introduce canonical geometric action encoding with normalized panoramic Plücker ray-maps. This representation decouples motion dynamics from sensor intrinsics, enabling precise, zero-shot trajectory control across heterogeneous datasets and camera configurations. For Reward, we derive intrinsic occupancy-grounded dense rewards directly from generated 3D volumes, establishing a reliable closed-loop simulation cycle for evaluating diverse planning agents. Extensive experiments demonstrate that OmniNWM achieves SOTA performance in generation fidelity and control precision, with remarkable zero-shot robustness to novel scenes on nuPlan and in-house datasets with distinct camera rigs.

Abstract:
Collaborative perception mitigates occlusions and extendsthe perception range, yet its accuracy remains susceptible to both poseand feature noise. Given these limitations, we propose NoiseGT, anoise-driven framework built upon the principle that “Noise is a GoodTeacher”. Instead of treating noise as a disturbance, NoiseGT leveragesit as a self-supervised signal to enhance robustness at both the poseand feature levels. Specifically, a pose calibration module first appliesmulti-level noise injection, followed by graph-based alignment refinement.This process enables the module to learn pose-invariant alignment de-spite localization uncertainties. Meanwhile, a feature denoising moduleadopts a “high-noise-over-low-noise” strategy to inject high-magnitudenoise into bird’s-eye-view (BEV) features, guiding the denoiser towardnoise-robust representations without relying on clean intermediate super-vision. Together, these components form a noise-driven learning processthat promotes consistent fusion across agents. At the most challengingnoise setting ((σt , σr ) = (1.2 m, 1.2◦ )), NoiseGT achieves state-of-the-artperformance over previous methods, improving AP@0.5/0.7 by a rela-tive 12.03%/14.56% on V2X-Sim, 2.67%/0.89% on DAIR-V2X, and6.12%/9.69% on OPV2V. These results highlight the effectiveness andgenerality of noise-driven learning for robust collaborative perception.

Abstract:
With the emergence of Vision-Language Models (VLMs),cloud-based VLM inference services have raised critical privacy concernsregarding the potential leakage of sensitive visual prompts. However, ex-isting defenses compromise either efficiency or model utility to achieveprivacy. Recently, the covariant obfuscation paradigm has shown greatpotential for private large language model inference, but it is incom-patible with cross-modal architectures and tasks. In this paper, we pro-pose COVERT, the first practical privacy-preserving VLM inferenceframework, which explicitly adapts covariant obfuscation for multimodalarchitectures. Specifically, it integrates exact architectural reparameter-ization to robustly obfuscate visual features against naive inversion at-tacks, alongside utility-aware parameter tuning tailored to neutralizehidden-state inversion attacks, thereby achieving a favorable privacy-utility-efficiency trade-off. Extensive evaluations show that COVERT ef-fectively shields sensitive visual attributes against hidden-state inversionattacks, while keeping the average accuracy degradation below 3% acrossmajor benchmarks and sustaining high inference throughput with a neg-ligible overhead of less than 6%.

Abstract:
Fine-tuning foundation models for Earth Observation is com-putationally expensive, with high training time and memory demands forboth training and deployment. Parameter-efficient methods reduce train-ing cost but retain full inference complexity, while post-hoc compressionoptimizes inference only after costly full fine-tuning. We introduce SIM-PLER, a pre–fine-tuning architecture selection method that reduces in-ference and deployment costs by identifying an effective model depthbefore adaptation. SIMPLER exploits stabilization of representations indeeper layers of pre-trained vision transformers: it computes layer-wiserepresentation similarity on unlabeled task data and applies an auto-mated scoring function to select redundant layers, with no gradients,magnitude heuristics, or hyperparameter tuning required. On Prithvi-EO-2, SIMPLER prunes up to 79% of parameters while retaining 94% ofbaseline performance, yielding a 2.1× training speedup and 2.6× infer-ence speedup. The method generalizes to TerraMind (a multimodal EOfoundation model) and ImageNet-pretrained ViT-MAE, demonstratingapplicability across tasks, architectures, and spectral modalities. Code isavailable at https://gitlab.citius.gal/hpc4rs/simpler.

Abstract:
High-performance Self-Supervised Learning (SSL) encodersdeployed as Encoder-as-a-Service (EaaS) are highly vulnerable to ad-vanced contrastive model stealing attacks, such as RDA and ContSteal.Traditional defenses often stack complex modules or rely on heuristicnoise, struggling to balance privacy and utility. To address this, we pi-oneer the use of active perturbations for EaaS protection by proposingUniTrap, a simple yet effective single-module defense specifically tai-lored against contrastive-based stealing paradigms. Instead of mere dis-tance maximization, UniTrap explicitly optimizes global feature entropyvia a Gaussian potential function. This generates a stealthy perturba-tion that strictly enforces an isotropic, uniform distribution on the unithypersphere—acting as “one trap” to fundamentally block contrastivealignment. We theoretically demonstrate that this perfect uniformitycauses the attacker’s informative gradients to cancel out in the tangentspace, inducing gradient vanishing and permanently stalling surrogateoptimization. Extensive experiments confirm that our method achievesa state-of-the-art privacy-utility Pareto frontier across various bench-marks.

Abstract:
Cross-view object geo-localization (CVOGL) aims to locatea target object from a query view (e.g., ground or drone) within ageo-tagged reference image (e.g., satellite). Existing approaches heav-ily rely on 2D appearance matching and are constrained by limiteddatasets lacking geometric metadata, diverse prompts, and standardfield-of-view imagery. To address these intertwined challenges, we firstintroduce CMA-Loc, a large-scale, high-fidelity building dataset compris-ing over 220,000 ground-satellite and drone-satellite pairs. It providesmulti-modal prompts (points, boxes, masks) and camera poses to en-able flexible target referring and explicit spatial modeling. Furthermore,we propose a novel single-stage Geometry-Aware Geo-localization frame-work (GAGeo), built upon the permutation-equivariant 3D foundationmodel π 3 . By seamlessly integrating visual features, referring prompts,and learnable task tokens, our model adapts the inherited 3D prior tojointly predict bounding boxes, segmentation masks, and camera posesin a single forward pass. Additionally, we introduce a contrastive lossthat utilizes the satellite view as a universal anchor, implicitly aligningground and drone representations to enable zero-shot ground-to-drone lo-calization without requiring triplet training data. Extensive experimentsdemonstrate that our approach significantly outperforms state-of-the-artmethods, exhibiting exceptional generalization ability in unseen scenesand novel cross-view setups.

Abstract:
Event cameras capture per-pixel brightness changes with mi-crosecond resolution, offering continuous motion information lost be-tween RGB frames. However, existing event-based motion estimatorsdepend on large-scale synthetic data that often suffers from a signifi-cant sim-to-real gap. We propose TETO (Tracking Events with TeacherObservation), a teacher-student framework that learns event motionestimation from only ∼25 minutes of unannotated real-world record-ings through knowledge distillation from a pretrained RGB tracker. Ourmotion-aware data curation and query sampling strategy maximizes learn-ing from limited data by disentangling object motion from dominantego-motion. The resulting estimator jointly predicts point trajectoriesand dense optical flow, which we leverage as explicit motion priors tocondition a pretrained video diffusion transformer for frame interpola-tion. We achieve state-of-the-art point tracking on EVIMO2 and opticalflow on DSEC using orders of magnitude less training data, and demon-strate that accurate motion estimation translates directly to superiorframe interpolation quality on BS-ERGB and HQ-EVFI.

Abstract:
Neural map matchers estimate an image’s 3-DoF pose rela-tive to a 2D map. These models are trained on large-scale datasets ofgeo-referenced images, whose position and heading labels often containnoise that affects the trained models. To address this, we present Auto-Compass, a supervision approach for training neural map matchers frominaccurate absolute pose labels. First, we show that heading labels areunnecessary: trained from raw GPS labels, models learn to predict accu-rate headings, automatically. Second, defining a tolerance region aroundraw GPS improves positional accuracy. Third, if available, our supervi-sion uses relative poses between training images, obtained via SLAM orSfM, which provide a more accurate training signal. Across driving andegocentric benchmarks, AutoCompass consistently outperforms counter-parts trained with the usual strong reliance on absolute pose labels.

Abstract:
In recent years, significant progress has been made in estimat-ing 3D human joint positions from monocular 2D images or videos. How-ever, existing approaches that lift 2D to 3D often rely on mean per-jointposition error (MPJPE) as explicit supervision, while ignoring implicitsemantic consistency across 2D–3D sequences and disregarding humankinematic constraints. As a result, these methods may produce physio-logically implausible poses. To address this issue, we propose SemGAN,an adversarial learning framework that integrates explicit supervisionwith implicit semantic constraints. Specifically, we employ a two-stageadversarial learning approach that complements explicit supervision withmotion semantic consistency as an implicit constraint, which is furtherenforced through an Entropy-based Cross-Frequency Discriminator(ECF-Discriminator). This design jointly optimizes both joint coordinate ac-curacy and motion semantic consistency. Moreover, guided by humankinematic principles, we construct a Semantic Prototype-guided Hierarchi-cal Generator(SPH-Generator), which enhances local part-level uniformityand global inter-part coordination, thereby alleviating abnormal 3D posegeneration. Extensive experiments on the Human3.6M and MPI-INF-3DHP benchmark datasets demonstrate that our method outperformsprevious state-of-the-art approaches.

Abstract:
Unified multimodal models have recently shown remarkablegains in both capability and versatility, yet most leading systems are stilltrained from scratch and require substantial computational resources. Inthis paper, we show that competitive performance can be obtained farmore efficiently by strategically fusing publicly available models special-ized for either generation or understanding. Our key design is to re-tain the original blocks while additionally interleaving multimodal self-attention blocks throughout the networks. This double fusion mechanism(1) effectively enables rich multi-modal fusion while largely preservingthe original strengths of the base models, and (2) catalyzes synergisticfusion of high-level semantic representations from the understanding en-coder with low-level spatial signals from the generation encoder. By train-ing with only ∼35B tokens, this approach achieves strong results acrossmultiple benchmarks: 0.91 on GenEval for compositional text-to-imagegeneration, 82.16 on DPG-Bench for complex text-to-image generation,6.06 on GEditBench, and 3.77 on ImgEdit-Bench for image editing. Wewill fully release the entire suite of code, model weights, and datasets tosupport future research on unified multimodal modeling.

Abstract:
While Text-to-Image (T2I) models have shown remarkablesuccess in generating photorealistic visual content, they still strugglewith the rigorous semantic alignment and logical reasoning requiredfor scientific imagery. Inspired by Peirce’s Semiotic Triad, we introduceScientific Image Reasoning (SciIR), a comprehensive resource for trainingand evaluation of scientific image generation. We formalize scientificreasoning into three core dimensions: Entity Structure (Icon), ScientificProcess (Index ), and Scientific Law (Symbol ). Specifically, to overcomethe scarcity of training data in scientific image generation, we elaboratelycreate SciIR-82k, a large-scale dataset containing over 80,000 high-qualityscientific image-text pairs from cutting-edge publications. The datasetis hierarchically organized according to the semiotic dimensions andincorporates a Scientific Reasoning Chain-of-Thought (Sci-RCoT) toexplicitly model underlying visual logic. For evaluation, we propose SciIR-Bench, which aligns with these three semiotic levels and employs anAtomic Checklist to convert the outcome-oriented scientific accuracyinto process-oriented, verifiable, fine-grained questions. Our extensiveexperiments reveal significant deficiencies in current models’ scientificreasoning capabilities. Furthermore, by fine-tuning on the SciIR-82kdataset, we developed the Qwen-Image-SciIR model, which achieves asubstantial improvement on the SciIR-Bench, increasing the final scorefrom 35% to 43%, laying a solid foundation for future advances in scientificimage generation.

Abstract:
Recent advances in Large Language Model (LLM)-based agentshave shown remarkable progress in code generation. However, currentagent methods mainly rely on text-output-based feedback (e.g. command-line outputs) for multi-round debugging and struggle in graphical userinterface (GUI) that involve visual information. This is mainly due totwo limitations: 1) GUI programs are event-driven, yet existing methodscannot simulate user interactions to trigger GUI element logic. 2) GUIprograms possess visual attributes, making it difficult for text-based ap-proaches to assess whether the rendered interface meets user needs. Tosystematically address these challenges, we first introduce InteractGUIBench, a novel benchmark comprising 984 commonly used real-worlddesktop GUI application tasks designed for fine-grained evaluation ofboth interaction logic and visual structure. Furthermore, we propose VF-Coder, a vision-feedback-based multi-agent system for debugging GUIcode. By perceiving visual information and directly interacting with pro-gram interfaces, VF-Coder can identify potential logic and layout issuesin a human-like manner. On InteractGUI Bench, our VF-Coder approachincreases the success rate of Gemini-3-Flash from 21.68% to 28.29% andraises the visual score from 0.4284 to 0.5584, indicating the effectivenessof visual feedback in GUI debugging.

Abstract:
Vision-Language Navigation in Continuous Environments(VLN-CE) requires agents to learn complex reasoning from long-horizonhuman interactions. While multi-modal large language models (MLLMs)have driven recent progress, current training paradigms struggle to bal-ance generalization capability, error recovery, and training stability. Specif-ically, (i) policies derived from supervised fine-tuning (SFT) suffer fromcompounding errors, struggling to recover from out-of-distribution states,and (ii) Reinforcement fine-tuning (RFT) methods such as GRPO arebottlenecked by sparse outcome rewards. Their binary feedback fails toassign credit to individual steps, leading to gradient signal collapse infailure-dominant batches. To address these challenges, we introduce Step-Aware Contrastive Alignment (SACA), a framework designed to extractdense supervision from imperfect trajectories. At its core, the Perception-Grounded Step-Aware auditor evaluates progress step-by-step, disentan-gling failed trajectories into valid prefixes and exact divergence points.Leveraging these signals, the Scenario-Conditioned Group Constructionmechanism dynamically routes batches to specialized resampling andoptimization strategies. Extensive experiments on VLN-CE benchmarksdemonstrate that SACA achieves state-of-the-art performance.

Abstract:
As virtual try-on (VTON) advances, a growing number ofreal-world scenarios have emerged, pushing beyond the ability of the ex-isting specialized VTON models. Meanwhile, universal multi-referenceimage editing models have progressed rapidly and exhibit strong gen-eralization in visual editing, suggesting a promising route toward moreflexible VTON systems. However, despite their strong capabilities, thestrengths and limitations of universal editors for VTON remain insuffi-ciently explored due to the lack of systematic evaluation benchmarks. Toaddress this gap, we introduce VTEdit-Bench, a comprehensive bench-mark designed to evaluate universal multi-reference image editing modelsacross various realistic VTON scenarios. VTEdit-Bench contains 24,220test image pairs spanning five representative VTON tasks with progres-sively increasing complexity, enabling systematic analysis of robustnessand generalization. We further propose VTEdit-QA, a reference-awareVLM-based evaluator that assesses VTON performance from three keyaspects: model consistency, cloth consistency, and overall image quality.Through this framework, we systematically evaluate 8 universal editingmodels and compare them with 7 specialized VTON models. Resultsshow that top universal editors are competitive on conventional tasksand generalize more stably to harder scenarios, but remain challengedby complex reference configurations, especially multi-cloth conditioning.

Abstract:
Recent text-to-video (T2V) diffusion models rely heavily on auxiliary rewardsignals (e.g., via reward models or DPO) to align generated content with human aesthetics andimprove realism. These signals, however, incur substantial computational overhead, requirecostly human annotations, and often yield limited improvement in fine-grained local details. Inthis paper, we argue that your data manifold is secretly a reward model. By explicitlymodeling the manifold structure of high-quality Supervised Fine-Tuning (SFT) data andencouraging video latents to lie on this manifold, we derive dense, differentiable, and nearlycost-free reward signals that significantly improve video quality, particularly in mitigating low-level distortions. Our modeling builds upon Local Coordinate Coding (LCC), which capturesthe ‘skeleton’ of the manifold. However, directly applying LCC suffers from mean regression,pulling latents toward the geometric mean and losing high-frequency details. We thereforeextend it to Shell Local Coordinate Coding (Shell-LCC), which models the manifold ‘surface’as an isotropic shell to align with the true high-density region. Experiments demonstratethat our approach improves realism, enhances high-frequency details, reduces over-smoothingartifacts, and alleviates motion blur.

Abstract:
While prior research on text-to-image generation has predominantly focused on biases in human depictions, demographic bias in generated objects remains relatively underexplored. We introduce SODA (Stereotyped Object Diagnostic Audit)1, a novel framework for systematically measuring these biases through automated attribute discovery and three standardized metrics: Base vs. Demographic Divergence (BDS), Cross-Demographic Disparity (CDS), and Visual Attribute Concentration (VAC). Applying SODA to 8,000 images across five state-of-the-art models and eight object categories (e.g., cars), we find that “neutral” prompts produce outputs most visually similar to middle-aged and White people, suggesting these groups are implicitly over-represented in model defaults. Furthermore, demographic cues trigger highly skewed stereotypical outputs: 26.6% of object-model-demographic combinations produce results where all 20 generated images share the exact same attribute value (e.g., rose gold laptops for women). Finally, prompt-level debiasing reduces inter-group disparity but paradoxically collapses within-group diversity, replacing one stereotype with another. SODA offers a practical pipeline for making these implicit associations measurable, serving as a step toward more responsible AI development.

Abstract:
Speech-driven 3D facial animation aims to synthesize natural and synchronized facial motions from arbitrary speech audio. Deterministic models struggle with the complex many-to-many mapping, thus yielding over-smoothed motions and lacking expression nuances. In comparison, diffusion-based approaches excel in capturing expressive distributions but suffer from significant latency due to iterative sampling. To this end, we propose MeanTalker, a novel one-step speech-to-motion generative framework built upon Mean Flow. It utilizes a Dual-Time Denoising Transformer (DTDT) for training stability and Geometric-Aware Trajectory Learning (GATL) for precise manifold alignment. Specifically, DTDT introduces an interleaved dual-time encoding to support a progressive curriculum from modeling instantaneous to average velocities, effectively circumventing the instability of direct mean flow training. Furthermore, GATL rectifies the generation trajectory through synergistic dual-space supervision. By applying explicit vertex constraints and projecting latent velocity errors onto the surface manifold, it strictly regularizes the flow direction to guarantee precisely synchronized one-step synthesis. Extensive experiments demonstrate that MeanTalker establishes state-of-the-art lip-sync accuracy while accelerating inference by over 200× compared to iterative diffusion models. Achieving an exceptional Real-Time Factor (RTF) of 0.004, our method effectively bridges the gap between precise motion generation and real-time deployment.

Abstract:
All-in-one image restoration aims to handle diverse degra-dations within a unified framework. While Mixture-of-Experts (MoE)architectures offer a scalable path, existing designs predominantly relyon vanilla end-to-end fidelity-oriented experts that lack task-adaptivegenerative priors, limiting their ability to recover realistic structuresand textures in complex scenarios. In this paper, we first investigatecomplementary task-adaptive generative priors: Masked Auto-Regressive(MAR) experts are tailored for local degradations (e.g., shadow) by re-constructing semantic structures via clean local backgrounds; conversely,Diffusion experts excel at global degradations (e.g., noise) by recoveringtextural details through iterative denoising. Motivated by these insights,we propose Mixture-of-Synergy-Experts (MoSE), a novel framework thatdeeply integrates these complementary generative priors into the MoEarchitecture and synergizes MAR and Diffusion experts with vanillabackbones at both image and feature levels. MoSE is orchestrated by aDINOv3-guided semantic-aware router that adaptively regulates not onlyexpert coordination but also generation control: mask sizes are adjustedto handle varying scales of local semantic corruption, while iterationsteps are modulated according to degradation intensity. By synergistically"weaving" complementary experts, our approach achieves a controllablefidelity-perception trade-off. Comprehensive experiments demonstrate theeffectiveness of MoSE, achieving competitive performance across variousall-in-one image restoration benchmarks.

Abstract:
Video generation models based on Diffusion Transformers (DiTs) have achieved remarkable performance in video synthesis, yet they suffer from high inference latency and computational costs due to the quadratic complexity of 3D attention. Existing acceleration methods primarily reduce computational complexity within each individual denoising steps through techniques such as sparse attention and KVcaching. However, they rigidly adhere to the inherent constraint of the standard diffusion pipeline: every frame in the target video sequence must be subjected to a complete, dense denoising process across all diffusion timesteps. We observe that due to the corresponding contents and motions among adjacent frames, when keyframes with critical semantic transitions are anchored, the intermediate states of others often follow more predictable trajectories, which indicates that such uniform, dense denoising process is inherently redundant for natural video data. To this end, we introduce RhymeFlow, a training-free framework that decouples the denoising trajectories of different frames. Specifically, we first identify a sparse set of pivotal key frames that dominate the latent semantic evolution. Then, only these keyframes undergo dense, step-by-step denoising to ensure structural integrity, while non-keyframes progressively skip denoising steps to minimize computational cost. Since skipped intermediate states of non-keyframes break the temporal coherence in keyframe denoising steps, leading to visual degradation, we further introduce a latent trajectory projection module, which enables keyframes to interact with a complete and temporally consistent sequence representation. Extensive experiments on current DiT-based video generation models demonstrate our method outperforms existing acceleration baselines with higher inference speed and better visual quality.

Abstract:
Latent diffusion models (LDMs) have emerged as a lead-ing paradigm for image generation by performing denoising in a com-pressed latent space, which provides high fidelity and efficiency but alsointensifies forensic and security concerns. While most existing detectionapproaches rely on supervised learning, training-free detectors are ap-pealing for their simplicity and ease of deployment. However, their per-formance remains limited and degrades markedly on novel generators.Our analysis reveals a more pronounced context dependence in LDM-generated images compared to real ones. Motivated by this, we intro-duce GLARE, a novel training-free method designed to exploit this de-pendence as a detection signal. Specifically, GLARE measures the rela-tive difference between full-image and patch-wise reconstruction errorswithin a shared autoencoder. Dividing the image into patches implicitlyremoves global context, thereby inducing a characteristic shift in recon-struction error that serves as a discriminative feature. Furthermore, alightweight semantic complexity calibration is incorporated to compen-sate for content-induced variation. Extensive experiments across a widerange of generators demonstrate the effectiveness and remarkable gener-alization capability of GLARE. Our method outperforms state-of-the-artsupervised and training-free baselines significantly and shows strong ro-bustness against common post-processing operations.

Abstract:
Scaling video diffusion transformers is fundamentally bottle-necked by two compounding costs: the expensive quadratic complexityof attention per step, and the iterative sampling steps. In this work, wepropose EFlow, a efficient few-step training framework, that tacklesthese bottlenecks simultaneously. To reduce sampling steps, we build ona solution-flow objective that learns a function mapping a noised state attime t to time s. Making this formulation computationally feasible andhigh-quality at video scale, however, demands two complementary innova-tions. First, we propose Gated Local–Global Attention, a token-droppablehybrid block which is efficient, expressive, and remains highly stable underaggressive random token-dropping, substantially reducing per-step com-pute. Second, we develop an efficient few-step training recipe. We proposePath-Drop Guided training to replace the expensive guidance target witha computationally cheap, weak path. Furthermore, we augment this witha Mean-Velocity Additivity regularizer to ensure high fidelity at extremelylow step counts. Together, our EFlow enables a practical from-scratchtraining pipeline, achieving up to 2.5× higher training throughput overstandard solution-flow, and 45.3× lower inference latency than standarditerative models with competitive performance on Kinetics and large-scaletext-to-video datasets.

Abstract:
Style transfer must match a target style while preservingcontent semantics. DiT-based diffusion models often suffer from con-tent–style entanglement, leading to reference-content leakage and unsta-ble generation. We present UniCSG, a unified framework for content-constrained, style-driven generation in both text-guided and reference-guided settings. UniCSG employs staged training: (i) a latent-space se-mantic disentanglement stage that combines low-frequency preprocess-ing with conditioning corruption to encourage content–style separation,and (ii) a latent-space frequency-aware detail reconstruction stage thatrefines details via multi-scale frequency supervision. We further incorpo-rate pixel-space reward learning to align latent objectives with percep-tual quality after decoding. Experiments demonstrate improved contentfaithfulness, style alignment, and robustness in both settings.

Abstract:
Real-world image dehazing remains a challenging problem due to the significant domain gap between synthetic training data and natural haze, often leading to poor generalization. While unsupervised Generative Adversarial Networks (GANs) attempt to bridge this gap, they frequently suffer from training instability and hallucinatory artifacts. Recently, Reinforcement Learning (RL) has emerged as a promising alternative; however, current RL-based restoration paradigms predominantly rely on diffusion models, leading to prohibitive computational costs and ineffective exploration. To address these bottlenecks, we propose Dehaze-RL, an efficient framework tailored for unpaired real-world dehazing. Bypassing the expensive iterative sampling of diffusion models, we design an efficient policy network to predict hybrid actions in a Physics Hybrid Action Space. By explicitly estimating physical parameters, enhancement factors, and a gating policy map, we anchor the agent’s exploration in reliable physical priors, effectively resolving the exploration dilemma. These components are dynamically integrated via a gating fusion mechanism. Furthermore, we introduce a Multi-Granularity Reward Mechanism to provide comprehensive feedback, seamlessly aligning the outputs with human perception without structural distortions. Extensive experiments demonstrate that Dehaze-RL outperforms stateof-the-art methods in both visual fidelity and quantitative metrics.

Abstract:
Pre-trained Latent Diffusion Models (LDMs) have recentlyshown strong perceptual priors for low-level vision tasks, making thema promising direction for multi-exposure High Dynamic Range (HDR)reconstruction. However, directly applying LDMs to HDR remains chal-lenging due to: (1) limited dynamic-range representation caused by 8-bitlatent compression, (2) high inference cost from multi-step denoising,and (3) content hallucination inherent to generative nature. To addressthese challenges, we introduce GMODiff, a gain map-driven one-stepdiff usion framework for multi-exposure HDR reconstruction. Instead ofreconstructing full HDR content, we reformulate HDR reconstructionas a degradation-aware Gain Map (GM) refinement problem, where theGM encodes the extended dynamic range while retaining the same bitdepth as LDR images. We initialize the denoising process from an in-formative regression-based estimate rather than pure noise, allowing themodel to generate high-quality GMs in a single denoising step. Further-more, recognizing that regression-based models excel in content fidelitywhile LDMs favor perceptual quality, we leverage regression priors toguide both the denoising process and latent decoding of the LDM, sup-pressing hallucinations while preserving structural accuracy. Extensiveexperiments demonstrate that our GMODiff performs favorably againstseveral state-of-the-art methods and is 100× faster than previous LDM-based methods.

Abstract:
Multimodal large language models (MLLMs) achieve strongperformance on benchmarks that evaluate text, image, or video un-derstanding separately. However, these settings do not assess a criticalreal-world requirement, which involves retrieving relevant evidence fromlarge, heterogeneous multimodal corpora prior to reasoning. Most ex-isting benchmarks restrict retrieval to small, single-modality candidatesets, substantially simplifying the search space and overstating end-to-end reliability. To address this gap, we introduce MultiHaystack, thefirst benchmark designed to evaluate both retrieval and reasoning underlarge-scale, cross-modal conditions. MultiHaystack comprises over 46,000multimodal retrieval candidates across documents, images, and videos,along with 747 open yet verifiable questions. Each question is groundedin a unique validated evidence item within the retrieval pool, requiringevidence localization across modalities and fine-grained reasoning. In ourstudy, we find that models perform competitively with evidence, but per-formance drops sharply when retrieving evidence from the full corpus.Additionally, even the strongest retriever, E5-V, achieves only 40.8%Recall@1, while state-of-the-art MLLMs such as GPT-5 experience asignificant drop in reasoning accuracy from 80.86% when provided withthe corresponding evidence to 51.4% under top-5 retrieval. These resultsindicate that multimodal retrieval over heterogeneous pools remains aprimary bottleneck for MLLMs, positioning MultiHaystack as a valuabletestbed that highlights underlying limitations obscured by small-scaleevaluations and promotes retrieval-centric advances in multimodal sys-tems. Our code and benchmark are available at link.

Abstract:
Document layout understanding has long relied on bound-ing boxes with non-hierarchical and fixed label spaces. This abstractionfails on irregular regions, discards structural hierarchy, and falls shortof what real-world document tasks demand. We present DocLayout-VL, a foundation model that reframes document layout understandingas promptable open-set hierarchical segmentation. DocLayout-VL pro-duces a hierarchical layout tree, a pixel-accurate mask for each region,and a natural language explanation. Our novel Hierarchical GroundingContextualizer introduces Hierarchical Contextual Attention which mod-els inter-region relationships across hierarchical levels. DocLayout-VLis instruction-tuned on diverse layout tasks and domain-specific ontolo-gies. This enables highly customizable layout understanding via natu-ral language prompts, without any task-specific fine-tuning. To supportfoundation-scale training, we introduce HOPLay-Docs-80M, a large-scale dataset of 2.5M pages, 80M pixel-level masks, 400k open-set labelsand 5M promptable annotations across 12 domains and 58 documenttypes. We also release HOPLay-Docs-Bench, a human-verified evalu-ation suite covering open-set, hierarchical, and promptable layout seg-mentation. DocLayout-VL achieves state-of-the-art results across allbenchmarks, outperforming both specialized layout models and strongmultimodal baselines. Our work establishes a new paradigm for control-lable, semantically grounded layout understanding.

Abstract:
Although Multimodal Large Language Models (MLLMs) havedemonstrated impressive performance in Medical Visual Question An-swering (Med-VQA), their reliance on global image features often lacksprecise pixel-level grounding, thereby limiting clinical trustworthiness.To bridge the semantic gap between high-level clinical reasoning andspatial localization, we propose MedREAL (Medical REasoning-drivenAnswering and Localization), a unified framework that seamlessly alignslinguistic reasoning with spatial grounding. Specifically, MedREAL in-troduces Seg Anchored Reasoning Pooling (SARP) to distill task-relevantsemantic evidence directly from [SEG] tokens within the MLLM’s hid-den states. Furthermore, a Reasoning-to-Visual (R2V) fusion mecha-nism is proposed to effectively inject these reasoning-aware features intoa segmentation pipeline for accurate mask decoding. To facilitate thisparadigm, we construct MedRAVS-13K, a comprehensive dataset com-prising 13,824 expertly validated samples across four diverse imagingmodalities. Extensive experiments demonstrate that MedREAL signifi-cantly outperforms state-of-the-arts, achieving 68.49% gIoU and 70.47%cIoU on benchmark evaluations. By generating evidence masks that arestrictly consistent with textual diagnoses, MedREAL provides a robust,interpretable framework for reasoning-driven medical image analysis.

Abstract:
Although Contrastive Language-Image Pretraining (CLIP)has shown promising transfer ability for ophthalmic image analysis, itsopaque decision-making process hinders interpretability, especially inophthalmic settings where probability-only outputs may be difficult forclinicians and patients to trust. Providing visual evidence for predictionscan therefore make models more inspectable and interpretable. How-ever, existing explainability methods often struggle to produce spatiallyconsistent and semantically aligned heatmaps that highlight prediction-relevant anatomical or pathological evidence. To bridge this gap, wepropose OTSMDL, a post-hoc framework for interpretable region selec-tion that couples submodular-inspired scoring with unbalanced optimaltransport. We formulate the identification of salient evidence regions as asubset selection problem. Specifically, submodular-inspired utilities firstconstruct an initial candidate subset and a score-guided source distri-bution, which are then used to initialize the Optimal Transport (OT)process. Subsequently, OT performs global transport-based re-rankingover all candidates, constructing a unified cost matrix to encourage cross-region competition and align candidate superpixels with text prompts.We evaluate OTSMDL on seven ophthalmic datasets spanning color fun-dus photography, OCT, and glaucoma assessment, with additional Ima-geNet experiments for out-of-domain analysis. Under standard post-hocDeletion/Insertion protocols and ground-truth mask localization met-rics, OTSMDL generally outperforms baselines in region-level evidencegrounding, achieving top-1/2 performance in most evaluation settings.

Abstract:
Localizing document tampering is extremely challenging, asmanipulations are crafted to appear visually consistent and often leaveonly subtle traces that are nearly invisible to the human eye. In priorwork, evaluation has been largely dominated by synthetic benchmarksthat closely match the training distribution, and methods have shownsteady progress under this setting. However, these gains often translatepoorly to human-made forgeries and to cross-domain evaluation, whereboth the source documents and the tampering pipeline can change, lead-ing to a distribution shift. In addition, since the introduction of the Fre-quency Perception Head for the discrete cosine transform (DCT) modal-ity, it has become a standard choice, and subsequent work has largelyfocused on downstream modules and fusion strategies rather than revis-iting the backbone itself. To help close this gap in cross-domain perfor-mance and improve the DCT backbone design, we propose DiffNet, arelatively simple yet effective RGB–DCT early-fusion architecture drivenby two key design choices. First, to ensure that the decoder aggregatesmulti-scale inconsistency evidence rather than operating on raw, content-heavy activations, we apply a lightweight multi-level discrepancy trans-formation at the output of each backbone stage, replacing features withmagnitude-only responses to learned zero-sum filters. Second, we designan efficient DCT-domain backbone that relies on a lightweight frequency-index-aware DCT–quantization joint embedding. Our approach achievesstate-of-the-art performance on cross-domain and human-made docu-ment tampering localization, outperforming prior methods by around30%, with up to 7× higher throughput than the previous best model.

Abstract:
Recently, Multimodal Large Language Models (MLLMs) have demonstrated significant potential in complex visual tasks through the integration of Chain-of-Thought (CoT) reasoning. However, in Video Question Answering, extended thinking processes do not consistently yield performance gains and may even lead to degradation due to “visual anchor drifting”, where models increasingly rely on self-generated text, sidelining visual inputs and causing hallucinations. While existing mitigations typically introduce specific mechanisms for the model to reattend to visual inputs during inference, these approaches often incur prohibitive training costs and suffer from poor generalizability across different architectures. To address this, we propose FrameRepeat, an automated enhancement framework which features a lightweight repeat scoring module that enables Video-LLMs to autonomously identify which frames should be reinforced. We introduce a novel training strategy, Add-One-In (AOI), that uses MLLM output probabilities to generate supervision signals representing repeat gain. This can be used to train a frame scoring network, which guides the frame repetition behavior. Experimental results across multiple models and datasets demonstrate that FrameRepeat is both effective and generalizable in strengthening important visual cues during the reasoning process.

Abstract:
Multimodal large models are increasingly used to generate scalable vector graphics (SVG), but reliable evaluation remains underexplored. Existing protocols are often code-centric or borrow raster-image metrics after rendering SVGs, which fail to reflect human perception and overlook SVG-specific qualities such as geometry and spatial composition. We introduce SVGEval, a vision-grounded multimodal benchmark for human-aligned SVG quality assessment. SVGEval explicitly incorporates visual renderings to evaluate whether models can judge the rendered outcome rather than only inspect SVG code, and provides highquality annotations obtained via multi-round human labeling with expert refinement. Systematic evaluations across representative multimodal models reveal a clear gap: models perform relatively well on semantic alignment and aesthetics, yet struggle on geometryand layout-related judgments. Building on SVGEval, we train an explainable SVG quality scorer that outputs multi-aspect scores with textual rationales. Ablations show that explicit visual grounding and reasoning supervision are crucial, especially for spatial and geometric assessment. SVGEval offers a reliable testbed and practical scorer for evaluating and improving SVG generation in the era of multimodal models.

Abstract:
Few-shot semantic segmentation (FSS) often degrades when support and query images exhibit large pose or scale differences since conventional prototype matching operates in geometry-entangled feature spaces. To mitigate this brittleness, we propose a lightweight encoderdecoder framework that disentangles object representations into a geometryinvariant semantic code and a low-dimensional modulation code capturing instance-specific geometric variation. The reconstruction decoder is trained with equivariance constraints so that geometric information gets encoded by the modulation pathway while preserving semantic consistency in a separate branch. As a result, robustness to pose-scale discrepancies is improved before segmentation without explicit cross-image alignment. To further strengthen supervision from limited supports, we introduce a refinement module that synthesizes lightweight augmented views of each support and performs cyclic ensemble refinement to generate more stable predictions. Predictions from multiple supports are then fused using spatially adaptive reliability weighting, producing cleaner and better-aligned query predictions. Across standard FSS benchmarks, our method consistently improves performance - particularly under large viewpoint changes. Ablations confirm that the disentanglement combined with cyclic ensemble and spatial refinement are critical to the gains. Project Website.

Abstract:
Human-object interaction detection (HOID) has traditionally been formulated as a supervised detection problem over predefined interaction categories. While such paradigms achieve strong performance on closed-set benchmarks, they fundamentally entangle interaction understanding with dataset-specific supervision, limiting their ability to generalize to open-world and compositional scenarios. Recent HOI detectors attempt to leverage MLLMs through prompting strategies to transfer interaction-specific knowledge. However, such prompt-based approaches primarily focus on extracting discriminative representations from pretrained models, while underexploring their inherent multimodal reasoning capabilities. As a result, they struggle to provide informative contextual reasoning for ambiguous and open-world interaction scenarios. In this work, we present AgentHOI, a training-free, agentic framework that transfers the generalist multimodal reasoning capabilities of foundation models to HOI detection in the wild. Instead of learning interaction classifiers, AgentHOI modularly orchestrates complementary vision foundation modules to perform open-ended semantic reasoning and spatial grounding in a coordinated manner. To address the challenges of incomplete interaction discovery and ambiguous localization in complex scenes, we introduce two key mechanisms: (1) Context-aware Multi-round Reasoning, which progressively refines interaction hypotheses to ensure exhaustive and compositional HOI discovery, and (2) Multifaceted Interaction Localization, which enhances grounding precision by generating instance-specific descriptions that integrate semantic, spatial, and appearance cues. Extensive experiments demonstrate that AgentHOI achieves superior performance over state-of-the-art supervised and weakly supervised methods in real-world settings, despite requiring no HOID data for training. Code is available at https://github.com/ oceanflowlab/AgentHOI.

Abstract:
Composed Image Retrieval (CIR) retrieves a target imagefrom a reference image and a textual modification. While supervisedCIR relies on costly triplets, Zero-Shot CIR (ZS-CIR) alleviates thisreliance through proxy tasks trained on image–text pairs. However, ex-isting proxy tasks primarily enhance visual and textual representationsto accommodate a predefined composition mechanism such as pseudo-word injection into a frozen text encoder or linear feature arithmetic.As a result, the composition function itself remains unlearned, limitingthe model’s ability to express diverse and fine-grained semantic mod-ifications. To address this, we propose FoCo, which models composi-tion as two coordinated stages: focusing on modification-relevant visualcontent, and then completing the target semantics. We realize thesethrough two proxy tasks: text-anchored visual aggregation to selectivelygather visual content guided by localized textual semantics, and context-conditioned semantic completion to transform these aggregated visualswith the remaining scene context into a coherent composed represen-tation. The tasks are trained jointly with a cross-instance contrastiveobjective, encouraging semantic diversity and discouraging shortcut com-position strategies. Extensive experiments on four ZS-CIR benchmarksshow FoCo’s state-of-the-art performance and improved generalization.

Abstract:
Open-Vocabulary Camouflaged Object Segmentation aimsto segment camouflaged objects from unseen categories. Existing two-stage methods typically treat category semantics as a one-time priorfor segmentation, thereby lacking deep and iterative interaction betweentextual semantics and visual spatial structures. To address this limita-tion, we propose an iterative two-stage refinement framework based onsemantic context and spatial structure. Specifically, we first design aspatial-structure-aware category re-ranker that leverages segmentationmasks generated by SAM2 to reorder CLIP’s candidate categories ac-cording to region-level visual consistency. Furthermore, we introduce asemantic-context-based segmentation modulator that injects the refinedcategory information into SAM2, guiding SAM2 to produce more preciseand discriminative segmentation results. Notably, the proposed re-rankerand modulator are jointly optimized in an iterative manner, forming aclosed-loop refinement process that enables mutual guidance between se-mantic representations and spatial features. Through iterative updates,semantic context and spatial structure are progressively enhanced. Ex-tensive experiments demonstrate that the proposed method significantlyoutperforms existing open-vocabulary methods and camouflaged objectsegmentation approaches. In particular, the proposed method achieves a14.4% improvement in the cSm metric compared with OVCoser.

Abstract:
Zero-Shot Skeleton Action Recognition (ZSAR) aims to rec-ognize unseen action categories by establishing a generalizable mappingbetween skeletal kinematics and semantic representations. However, ex-isting methods frequently struggle with two fundamental issues: (i) theloss of high-frequency dynamics caused by recursive feature aggrega-tion, and (ii) rigid semantic anchoring, which leaves cross-modal align-ments highly vulnerable to semantically irrelevant background noise.To overcome these limitations, we propose the Scale-Aware BipartiteEnergy-guided Registration (Saber) framework, a unified kinematics-driven paradigm for robust cross-modal alignment. Specifically: (i) Scale-Aware Bipartite Encoder actively reshapes the spatio-temporal topol-ogy and applies instance-adaptive filtering to effectively isolate high-frequency motion details, thereby preserving critical structural dynam-ics against “spectral smoothing”. (ii) A Kinetically Driven Probabilis-tic Alignment strategy, comprising energy-guided dynamic focusing anduncertainty-aware probabilistic anchoring, maps part-level representa-tions into variance-adaptive distributions. This mechanism dynamicallyresolves structural uncertainties and robustly anchors physical move-ments to explicit semantics. Extensive evaluations on various bench-mark datasets validate that Saber achieves state-of-the-art performance,demonstrating exceptional robustness and generalization.

Abstract:
Partial label learning in the instance-dependent setting (ID-PLL) poses significant challenges, as candidate labels are highly corre-lated with sample features, and strong semantic overlap among candi-dates often leads to error-prone disambiguation. Meanwhile, the infor-mative dark knowledge inherent in non-candidate labels remains largelyunderexplored. To address these issues, we propose DKDP, a dual dis-tillation method that extracts and transfers more stable and reliableknowledge from the teacher model. Specifically, we first use constrainedprojection to structurally rectify the teacher’s prediction distribution,enforcing the prior that the minimum value of candidate labels exceedsthe maximum value of non-candidate labels. On this basis, we guide thestudent model to fit the corrected teacher outputs. We further derive theminimum modification required to satisfy the prior, which theoreticallyguarantees the interpretability of the rectification process. Moreover, weintroduce class prototypes to model the distribution of fine-grained se-mantic relations, extending reliable knowledge distillation from the out-put space to the embedding space. This yields a stable, separable geomet-ric structure in the feature space. Extensive experiments on benchmarkdatasets demonstrate the effectiveness of our proposed method.

Abstract:
Continual anomaly detection (CAD) addresses the need forindustrial inspection systems to adapt to evolving production conditions,yet existing methods share three critical gaps: unrealistic evaluation, nosystematic comparison, and no consideration of edge deployment con-straints. We introduce a unified benchmark combining discrete-task eval-uation on structural and logical anomalies, a novel continuous drift proto-col, the first head-to-head comparison of all published CAD methods, andcomputational efficiency profiling on edge hardware. Our results revealthat existing CAD methods do not consistently outperform traditionalapproaches with simple experience replay. Thus motivated, we proposeDINOSaur, a training-free method combining a frozen DINOv3 back-bone with spatially-indexed coreset memory and neighborhood-restrictedanomaly scoring. DINOSaur achieves zero forgetting by construction,outperforms all evaluated methods on every protocol except geometricdrift (where all methods collapse to chance), and runs at sub-100 ms in-ference on an NVIDIA Jetson Orin Nano, with on-device adaptation tonew tasks in under 30 seconds.

Abstract:
Recent advances in foundation models (FMs) have led to aproliferation of Whole Slide Image (WSI) feature extractors, yet WSIanalysis still faces a practical challenge: no single FM consistently per-forms best across all tasks, and the performance gap among differentMultiple Instance Learning (MIL) aggregators built on a single strongFM can become limited in some settings. Recent research further showsthat simple ensembles of FMs can outperform the best individual FM,indicating that different FMs capture partially overlapping yet comple-mentary information. This motivates a shift from selecting a single FMtoward coordinating and exploiting multiple FMs jointly. However, exist-ing multi-FM approaches mostly rely on naive feature concatenation orpatch-level selection, operating on raw correlated FM features withoutexplicitly disentangling shared and view-specific information. To addressthis challenge, we reformulate FM ensembling as a Multi-View Learn-ing (MVL) problem. We propose Multi-View Multiple Instance Learning(MV-MIL), a plug-and-play framework that treats features from eachFM as a distinct view of a WSI. MV-MIL introduces an information-theoretic disentanglement module to exploit cross-view complementar-ity while suppressing redundancy, together with a Multi-Branch Predic-tion Head that integrates shared and view-specific representations withinMIL pipelines. Extensive experiments on 14 public benchmarks showthat MV-MIL consistently achieves state-of-the-art performance, withits plug-and-play design yielding robust gains across diverse MIL meth-ods and FM combinations.

Abstract:
Object-centric models inspired by DETR have become thedominant paradigm for open-vocabulary video instance segmentation(OV-VIS). While recent efforts have reduced the computational cost ofpixel decoding, textual modality fusion, and object decoding to makethese architectures more suitable for mobile devices, real-time on-deviceinference at high frame rates remains an open challenge. In this paper,we introduce SegFS, a dual-stream fast-slow framework that significantlyimproves efficiency without sacrificing accuracy. On sparse keyframes, anopen-vocabulary object-based model predicts instance-level representa-tions. These representations are then projected back into the backbonefeature space to condition a lightweight fast network, which efficientlyrelocalizes and segments the instances in subsequent frames. By shiftinginstance propagation from object decoding to feature-space condition-ing, our approach decouples multimodal semantic understanding fromdense mask prediction and enables efficient temporal propagation. Theproposed fast branch achieves up to 14× lower latency than the mobile-oriented MOBIUS model, while maintaining competitive segmentationperformance on standard OV-VIS benchmarks.

Abstract:
The Segment Anything Model (SAM) relies heavily on mas-sive manual annotations, creating a fundamental bottleneck for modelscaling. While unsupervised methods attempt to learn object conceptsfrom motion, they typically overfit to moving entities, lacking both multi-granularity understanding and the ability to generalize to static objects.To overcome this, we introduce Motion-Grounded Segment Anything(MoSA), a highly scalable unsupervised framework that learns a trans-ferable objectness prior from unlabeled videos. MoSA operates in threeprogressive stages: (1) automatically generating multi-granularity mo-tion pseudo-labels from large-scale video data; (2) training a PerceptualGrouping Model (PGM) via contrastive learning to internalize a gen-eralized, appearance-driven concept of objects; and (3) transferring thislearned prior into a prompt-guided architecture for segment-anything-style inference on images. Extensive zero-shot evaluations across sevenchallenging benchmarks (e.g., COCO and ADE20K) demonstrate thatMoSA significantly outperforms existing unsupervised methods. Notably,despite using zero manual annotations, MoSA achieves segmentation per-formance comparable to the fully supervised SAM. Our findings revealthat harnessing large-scale unlabeled motion is a feasible and highly scal-able alternative to annotation-driven segment-anything pipelines.

Abstract:
Concealed object segmentation aims to segment objects thatblend into their surroundings, presenting significant challenges due tothe high similarity between objects and backgrounds. Existing methodsrely on task-specific designs and independent training, leading to limitedgeneralization and structural redundancy. Although a unified parameter-shared model for diverse concealed scenarios is highly desirable andpromising, it is hindered by two challenges: large representation gapsacross scenarios and the intrinsic difficulty of concealed targets. From afrequency-domain perspective, we observe that different concealed sce-narios exhibit more discriminative and interpretable spectral character-istics compared to those in the RGB domain. These findings motivate usto approach unified concealed scene segmentation from the perspectiveof frequency modulation. To this end, we propose Frequency Director(FreqDirect), a dynamic frequency adaptation framework that directsspectral representations across diverse concealed scenes. It features amixture of frequency experts to perform adaptive routing over frequencycomponents, capturing heterogeneous task-specific patterns, along witha spatial commonality anchor to complement the spatial and structurecues. These components collectively enable effective and efficient adap-tation to diverse concealed segmentation tasks within a single model.Extensive experiments on eight concealed scenarios demonstrate thatFreqDirect surpasses existing unified models and achieves performancecomparable to, or better than, specialist models.

Abstract:
Domain adaptation addresses the distributional shifts be-tween source and target domains. Given increasing data privacy con-straints, Source-Free Domain Adaptation (SFDA) has attracted growinginterest, as it precludes access to raw source data during adaptation.While existing SFDA methods predominantly rely on source-like datageneration or heuristic pseudo-labeling, their connection to target errorgeneralization is often unclear. In this work, we first present a theoreticalanalysis by establishing an upper bound on the target error that over-comes explicit dependence on the classical joint-error term. Guided bythis analysis, we propose an optimization principle based on AdversarialProxy Perturbation (APP), serving as a practical bridge between thederived bound and empirical training. We further extend the frameworkto multi-source scenarios and enable the integration of zero-shot priorsfrom vision-language models (e.g., CLIP) through mutual distillation.Extensive experiments on standard benchmarks demonstrate consistentimprovements over state-of-the-art methods in both unimodal and multi-modal settings, validating the proposed theoretical framework in practice.

Abstract:
Multi-agent discussions have been widely adopted, motivat-ing growing efforts to develop attacks that expose their vulnerabilities.In this work, we study a practical yet largely unexplored attack sce-nario, the discussion-monitored scenario, where anomaly detectors con-tinuously monitor inter-agent communications and block detected ad-versarial messages. Although existing attacks are effective without dis-cussion monitoring, we show that they exhibit detectable patterns andlargely fail under such monitoring constraints. But does this imply thatmonitoring alone is sufficient to secure multi-agent discussions? To an-swer this question, we develop a novel attack method explicitly tailored tothe discussion-monitored scenario. Extensive experiments demonstratethat effective attacks remain possible even under continuous monitoring,indicating that monitoring alone does not eliminate adversarial risks.

Abstract:
Bio-inspired Small Target Motion Detection (STMD) mod-els often suﬀer “false alarm collapse” due to non-stationary environ-mental interference, failing to adapt to dynamic background statistics.We propose a bio-inspired architecture integrating macro-motion decou-pling with micro-scale Adaptive Gain Control (AGC). Macroscopically, itleverages lobula plate tangential cells (LPTC)-based population codingto eliminate ego-motion-induced baseline drifts in real-time with mini-mal overhead. Microscopically, we introduce an adaptive shunting inhi-bition operator inspired by Natural Scene Statistics (NSS). By quan-tifying local temporal variance, this mechanism maps non-stationaryclutter into a gain control factor to suppress heavy-tailed noise fromhigh-frequency flickers. This versatile plug-and-play module integratesseamlessly into existing STMD frameworks. Extensive experiments onsynthetic and real-world datasets demonstrate that our model signifi-cantly reduces false alarms while maintaining high sensitivity for dimtargets, achieving state-of-the-art precision and robustness in complexdynamic environments. Our code is publicly available at AGC.

Abstract:
While recent video world models can generate highly real-istic videos, their ability to perform semantic reasoning and planningremains unclear and unquantified. We introduce Target-Bench, thefirst benchmark that enables comprehensive evaluation of video worldmodels’ semantic reasoning, spatial estimation, and planning capabil-ities. Target-Bench provides 450 robot-collected scenarios spanning 47semantic categories, with SLAM-based trajectories serving as motiontendency references. Our benchmark reconstructs motion from generatedvideos with a metric scale recovery mechanism, enabling the evaluationof planning performance with five complementary metrics that focus ontarget-approaching capability and directional consistency. Our evalua-tion result shows that the best off-the-shelf model achieves only a 0.368overall score, revealing a significant gap between realistic visual genera-tion and semantic reasoning in current video world models. Furthermore,we demonstrate that fine-tuning process on a relatively small real-worldrobot dataset can significantly improve task-level planning performance.

Abstract:
Recovering 3D human motion from sparse wearable sensorsis challenging due to the inherent noise, occlusion, and instability ofreal-world tracking signals. Existing approaches are restricted to fixedsensor configurations and, when built on diffusion models, are computa-tionally prohibitive for real-time deployment. We propose OmniPoser, auniversal framework that recovers full-body SMPL motion from a flex-ible combination of heterogeneous inputs, such as 3D keypoints, sparseIMU rotations, head-mounted 6-DoF poses, or any subset thereof, withina single model. OmniPoser introduces a cross-modal masking mechanismpaired with a Masked Conditioning Encoder, enabling one architectureto incorporate diverse sensor configurations through a masked design. Adecoupled dual-stream generative backbone generates complete motionvia Conditional Flow Matching, requiring only a single ODE integrationstep at inference. Across three settings and seven benchmarks, includingin-the-wild Nymeria and Ego-Exo4D dataset, OmniPoser performs onpar with state-of-the-art methods while running at over 700 FPS. Thecode will be open-sourced.

Abstract:
Robotic manipulation with deformable objects represents adata-intensive regime in embodied learning, where shape, contact, andtopology co-evolve in ways that far exceed the variability of rigids. Al-though simulation promises relief from the cost of real-world data acqui-sition, prevailing sim-to-real pipelines remain rooted in rigid-body ab-stractions, producing mismatched geometry, fragile soft dynamics, andmotion primitives poorly suited for cloth interaction. We posit that simu-lation fails not for being synthetic, but for being ungrounded. To addressthis, we introduce SIM1, a physics-aligned real-to-sim-to-real data enginethat grounds simulation in the physical world. Given limited demon-strations, the system digitizes scenes into metric-consistent twins, cali-brates deformable dynamics through elastic modeling, and expands be-haviors via diﬀusion-based trajectory generation with quality ﬁltering.vision with near-demonstration ﬁdelity. Experiments show that policiestrained on purely synthetic data achieve parity with real-data baselinesat a 1:15 equivalence ratio, while delivering 90% zero-shot success and50% generalization gains in real-world deployment. These results vali-date physics-aligned simulation as scalable supervision for deformablemanipulation and a practical pathway for data-eﬃcient policy learning.

Abstract:
Connector-based video unified models have demonstratedstrong capability in instruction-grounded video synthesis, but integratinga large high-fidelity generator into the unified training loop is computa-tionally prohibitive, limiting achievable visual quality. We therefore pro-pose Lumos-Nexus, a training-efficient unified video generation frame-work that facilitates the development of strong reasoning-driven gen-eration capabilities while significantly enhancing visual fidelity. Lumos-Nexus adopts a two-stage design: 1) During training, only a lightweightgenerator is aligned with the understanding block to learn to take inreasoning-driven semantic control. 2) During inference, we introduceUnified Progressive Frequency Bridging (UPFB) to progressively handoff generation to a high-capacity pretrained generator in the shared la-tent space, enabling coarse-to-fine refinement and producing high-fidelityvideos without compromising reasoning quality. To fill the gap in reasoning-driven video generation benchmarks, we introduce VR-Bench, which as-sesses a model’s capability to translate inferred intent into coherent andsemantically aligned video content. Extensive experiments demonstratethat Lumos-Nexus achieves substantial gains in visual realism and tem-poral coherence on VBench, while exhibiting strong reasoning-based gen-erative performance on VR-Bench.

Abstract:
Creating photorealistic and temporally coherent animatable human avatars from RGB videos remains challenging. Current methods struggle to capture realistic cloth dynamics, producing over-smoothed appearance or severe artifacts on out-of-distribution poses. This limitation stems from a fundamental oversight: existing approaches neglect the temporal causality inherent in cloth physics, where current states emerge from previous states through temporal evolution rather than instantaneous skeletal configurations alone. Without explicit modeling of this causal structure, networks learn pose-appearance correlations instead of motion evolution, leading to poor generalization. We introduce a dual-stream autoregressive framework that explicitly models both observable geometric information and implicit internal state. The geometric stream propagates surface displacement from the previous frame, while the state stream fuses current features with historical states retrieved from a memory bank. Motion-adaptive aggregation handles spatiallyvarying dynamics, and adaptive regularization balances smoothness with flexibility.Experiments on challenging datasets demonstrate significant improvements in rendering quality, temporal consistency, and generalization to motion patterns beyond training distributions, validating that dual-stream temporal modeling enables realistic cloth dynamics.

Abstract:
We present GCA (Gaussian Constitutive Alignment), a frame-work for learning implicit constitutive laws from monocular dynamicvideo of deformable objects represented by 3D Gaussians. Given a staticmulti-view scan for geometric initialization, our method learns intrin-sic physical dynamics solely from a single fixed-viewpoint video of themoving object. Existing implicit methods often suffer from local minimaunder noisy supervision and lack physical interpretability, while explicitapproaches rely on predefined constitutive equations, limiting generaliz-ability and becoming unstable in monocular settings. To address thesechallenges, our framework unifies LoRA-based adaptation with two keyalignment modules. First, we propose Rank-based Depth-Geometric An-chors (RDGA) to establish robust geometric constraints from monoc-ular dynamic observations via scale-invariant rank-based depth align-ment, reducing the reliance on unreliable pixel-level color supervision.Second, a Constitutive Prior Regularizer (CPR) integrates classical con-stitutive models as soft differentiable priors, regularizing the optimiza-tion while preserving the flexibility of implicit modeling—even when theactual material is absent from the hypotheses. Extensive experiments onsynthetic, real-to-sim, and real-world datasets demonstrate that GCAoutperforms existing methods, achieving 48% lower Chamfer Distancethan the strongest baseline on synthetic benchmarks while remainingrobust under monocular supervision.

Abstract:
While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding. To accommodate this constraint, models typically resort to keyframe selection. However, uniform sampling or static query-guided selection often overlooks critical temporal context, failing to adapt to the varying query temporal granularities. In this paper, we propose ReMem, a temporal granularityadaptive keyframe selection framework for training-free LongVideoQA. ReMem introduces a dual-level memory-augmented adaptation. At the query level, Memory-Driven Question Parsing leverages LLM longterm memory to decode question temporal granularity and extract semantic entities. At the video level, Synergistic Dual-Semantic Frame Alignment exploits intrinsic structural memory to align frames with query semantics, guiding Structure-Aware Dynamic Frame Routing to cluster events and optimally distribute sampling budgets. By explicitly preserving temporal information with memory mechanisms, ReMem suppresses redundancy and empowers MLLMs to perform robust multi-granular video reasoning. Evaluations across four popular LongVideoQA benchmarks using three MLLMs demonstrate highly efficient, state-of-the-art zero-shot performance; notably, LLaVA-Video with ReMem reaches 54.5% (+12.3%) on LVBench and 67.1% (+8.2%) on LongVideoBench. Code is available at ReMem.

Abstract:
Face forgery detection (FFD) is essential in preventing themisuse of diverse generation methods like generative adversarial net-works (GANs) and diffusion models. Although advanced FFD methodshave started to explore the benefits of Vision-Language Model (VLM),they either rely solely on visual modality or optimize visual-linguisticmodalities independently, leaving researches on cross-modal interactionlargely unexplored. We find that visual-linguistic asymmetry in-herent in VLM tends to cause cross-modal misalignment, underminingits ability to discriminate and generalize on visually similar real-forgedfaces. This finding shows that this asymmetry is twofold: (1) cross-modal: visual-linguistic spaces affect VLM’s generalizability oppositely.(2) intra-modal: spaces with different-level semantics have inverse effectson VLM’s discriminability and generalizability. Building upon these in-sights, we propose Asy-Det, a parameter-efficient detector that follows anasymmetry-guided visual-linguistic interaction paradigm, to fully unlockthe cross-modal potential of VLM for reliable FFD. With only 4.41Mtrainable parameters, Asy-Det achieves an image-level AUC of 92.94%on unseen Celeb-DF-v2 dataset.

Abstract:
Image Manipulation Localization (IML) aims to achieve pixel-level localization of locally manipulated images. Its core challenge is howto distinguish the inconsistencies between authentic and tampered ar-eas. To this end, existing IML methods typically exploit patch-level andedge-level forensic cues for accurate localization. However, these methodsextract forged information at both levels with fixed parameters, leadingto interference from real information that is detrimental to localizationas more realistic manipulated textures are enhanced by the upgrading oftampering techniques. To tackle the above issue, this paper proposes aDisentangling and Gathering Forensic Cues method, called DG-Force,which explicitly disentangles and adaptively aggregates region and edgecues to preserve the most informative evidence. Specifically, we proposea Patch-based Forensic Disentangling (PFD) module and an Edge-basedForensic Disentangling (EFD) module to decompose forensic cues of dif-ferent granularities to explicitly suppress the imbalance between forensictraces in both patch-level and edge-level areas. In addition, a Multi-scale Forensic Transfer (MFT) module is further designed to aggregateand balance multi-granularity information through the interaction acrossdifferent scales for robust key inconsistencies in forensic traces discovery.Extensive experiments on multiple benchmarks demonstrate the superi-ority of our method in the image manipulation localization task.

Abstract:
Autoregressive diffusion models have shown remarkable prog-ress in long video generation by producing frames causally. To boundmemory and computational cost, these models rely on a fixed-size sliding-window KV cache. While attention sinks retain a few initial frames astemporal anchors, the vast majority of historical context is irrecover-ably discarded as the window advances, severing long-range dependen-cies essential for scene consistency and narrative coherence. Meanwhile,the fixed multi-step denoising schedule applies uniform effort to everychunk, ignoring the varying demands imposed by diverse scene dynamicsacross temporal segments. In this paper, we propose Surprise Forcing,a training-free framework that addresses both bottlenecks. We introducea Surprise-Gated Memory Bank that scores each evicted frame by† Corresponding authors.its aggregate divergence from the bank and nearest-neighbor novelty,admitting only the most informative frames via a budget-normalizedgate that adapts to video dynamics. A priority-based eviction policykeeps the bank compact yet informative, while dynamic routing retrievesthe most relevant historical frames at attention time. We further pro-pose Surprise-Aware Denoising, which uses intra-chunk cosine dis-tance among adjacent frames after a single denoising step as a self-referential difficulty predictor, enabling adaptive step reduction for easychunks while preserving full refinement for complex ones. Experimentson VBench show that Surprise Forcing maintains robust narrative coher-ence in long videos without compromising real-time responsiveness.

Abstract:
Applying Multimodal Large Language Models (MLLMs) tolong-form video is challenged by the substantial computational cost ofprocessing long token sequences. Current training-free reduction meth-ods present a difficult trade-off between efficient and content diversity.Query-focused approaches are efficient but sacrifice content diversity,which is critical for general-purpose understanding and open-ended dia-logue. Conversely, methods that preserve diversity by operating on high-dimensional features often incur significant computational overhead.We introduce Perceptual Projection Pruning (P 3 ), a training-free frame-work that tries to address this trade-off. Our approach is based on the hy-pothesis that a low-dimensional yet informative representation can serveas an efficient proxy for diversity-aware visual token pruning. We firstproject high-dimensional features into a 3D perceptual space by comput-ing three complementary saliency axes: dynamic (motion), regional (con-textual contrast), and focal (local complexity). This representation thenserves as a foundation for a subsequent diversity-aware pruning strat-egy. Experiments across SOTA MLLMs show our method retains nearly95% of the vanilla model’s performance while pruning up to 75% of to-kens. Our work demonstrates that a carefully designed low-dimensionalrepresentation is highly effective for guiding diversity-aware visual tokenpruning in MLLMs.

Abstract:
Garment draping aims to fit clothing onto 3D human mod-els. Existing physics-based methods are computationally expensive andstruggle to integrate with general differentiable systems. In contrast, deeplearning-based methods often require extensive annotations and lack ex-plicit physical constraints, limiting their ability to model accurate details.We innovate by bridging physical models with deep learning and proposePhysDrape. It integrates three modules using forces as an intermediary:the force-driven GNN predicts forces at each node, the stretching solvermodels physical deformation, and the collision handler penalizes inter-penetration. Each module includes learnable parameters to fit underly-ing force propagation processes and physical properties, enabling gener-alization to unseen templates and control over properties like stiffness.PhysDrape enables end-to-end optimization, leveraging intrinsic garmentenergy for physically plausible deformation via self-supervised learning.Following the previous training and evaluation protocol on CLOTH3Ddataset, PhysDrape achieves lower energy scores, negligible interpene-tration, more realistic visualizations, and comparable time consumption.

Abstract:
Frontier Multimodal Large Language Models (MLLMs) ex-hibit remarkable capabilities in Visual-Language Comprehension (VLC)tasks. However, they are often deployed as zero-shot solution to newtasks in a black-box manner. Validating and understanding the behav-ior of these models become important for application to new task. Wepropose an Explicit Logic Channel, in parallel with the black-box modelchannel, to perform explicit logical reasoning for model validation, selec-tion and enhancement. The frontier MLLM, encapsulating latent vision-language knowledge, can be considered as an Implicit Logic Channel. Theproposed Explicit Logic Channel, mimicking human logical reasoning, in-corporates a LLM, a VFM, and logical reasoning with probabilistic infer-ence for factual, counterfactual, and relational reasoning over the explicitvisual evidence. A Consistency Rate (CR) is proposed for cross-channelvalidation and model selection, even without ground-truth annotations.Additionally, cross-channel integration further improves performance inzero-shot tasks over MLLMs, grounded with explicit visual evidence toenhance trustworthiness. Comprehensive experiments conducted for tworepresentative VLC tasks, i.e., MC-VQA and HC-REC, on three chal-lenging benchmarks, with 11 recent open-source MLLMs from 4 frontierfamilies. Our systematic evaluations demonstrate the effectiveness of pro-posed ELC and CR for model validation, selection and improvement onMLLMs with enhanced explainability and trustworthiness.

Abstract:
Online skeleton-based action recognition requires per-frameclassification using only past and current observations. We identify twokey challenges in this setting: (1) Not all frames are equal: discrimina-tive motion concentrates within specific temporal regions whose loca-tion varies across action categories, yet existing temporal modeling relieson fixed sampling patterns and causal attention that treat all positionsequally. (2) Not all futures need to be predicted: prior methods attempt toexplicitly predict future features to compensate for the unobserved por-tion, but the inherent ambiguity of human motion causes such predictionto degenerate into trivial identity mapping of the input. To address thesechallenges, we propose DART (Deformable Adaptive Reasoning withTemporal Queries). DART employs a Deformable Multi-scale TemporalNetwork (DMTN) that introduces input-dependent offsets into causalconvolutions, enabling content-adaptive sampling of the most informa-tive historical moments. The learned sampling positions naturally forma compact temporal memory, upon which a Temporal Query Decoder(TQD) operates: learnable queries interact with the memory to distillhigh-level action evolution patterns, providing complementary temporalreasoning without explicit future forecasting. Extensive experiments onNTU RGB+D 60, NTU RGB+D 120, and NW-UCLA demonstrate theeffectiveness of DART. On NTU RGB+D 60, DART achieves 73.99%AUC, surpassing the SOTA by +3.04%. Code is available.

Abstract:
Dataset distillation (DD) has proven to reduce training costwhile preserving accuracy. While promising, the factors that make onedistilled dataset more effective than another remain poorly understood.In this work, we investigate this question through the lens of discretevisual tokenizers. Whereas many prior DD efforts emphasize matchingglobal data distributions, we suggest that the effectiveness depends onwhich semantic concepts are captured and how they are composed. Dis-crete visual tokenizers provide a finite vocabulary that enables directstatistical analysis of such compositional structure. Through quantita-tive analysis of token-level statistics, we introduce the structural scoreto measure the adequacy of token compositions. We observe that dis-tilled datasets with balanced token composition yield higher validationperformance. On the other hand, divergence from the original data doesnot necessarily harm performance. We further show that samples withhigh structural scores in the discrete token space can effectively guidediffusion-based DD. Our findings highlight the importance of token com-position in dataset effectiveness, offering a principled complement to dis-tributional similarity considerations in DD.

Abstract:
Despite the remarkable progress of Large Multimodal Models(LMMs), deploying autonomous agents to navigate web Graphical UserInterfaces (GUIs) remains a significant challenge. Most existing agentsare “blind” when encountering unfamiliar websites, as they rely heav-ily on patterns memorized during in-domain training, which inevitablyfails in the open-world web. To bridge this generalization gap, we pro-pose that agents should mimic human behavior: leveraging external ex-pertise to navigate unknown environments. In this paper, we introduceWebOne, a novel benchmark designed to evaluate an agent’s ability to mas-ter unseen websites by referencing Multimodal Tutorials—heterogeneousknowledge sources derived from instructional videos, historical trajec-tories, and human demonstrations. WebOne comprises 1,342 real-worldtasks and 970 high-quality tutorials across 60+ websites. Building uponthis, we propose WebLearner, a reinforcement learning-based frameworkthat utilizes a hierarchical referencing strategy to synthesize informa-tion from these multimodal tutorials. Experimental results demonstratethat WebLearner significantly outperforms current state-of-the-art open-source models, achieving a 56.9% success rate on held-out websites andshowing competitive performance against recent proprietary models.

Abstract:
Multimodal Large Language Models (MLLMs) have demon-strated immense potential in Earth observation. However, the massivevisual tokens generated when processing Ultra-High-Resolution (UHR) im-agery introduce prohibitive computational overhead, severely bottleneck-ing their inference efficiency. Existing visual token compression methodspredominantly adopt static and uniform compression strategies, neglectingthe inherent Semantic–Geometric Duality in remote sensing interpretationtasks. Specifically, object semantic tasks focus on the abstract seman-tics of objects and benefit from aggressive background pruning, whereasscene geometric tasks critically rely on the integrity of spatial topol-ogy. To address this challenge, we propose DualComp, a task-adaptivedual-stream token compression framework. Dynamically guided by alightweight pre-trained router, DualComp decouples feature processinginto two dedicated pathways. In the object semantic stream, the Spatially-Contiguous Semantic Aggregator (SCSA) utilizes size-adaptive clusteringto aggregates redundant background while protecting small object. Inthe scene geometric stream, the Instruction-Guided Structure Recoverer(IGSR) introduces a greedy path-tracing topology completion mechanismto reconstruct spatial skeletons. Experiments on the UHR remote sens-ing benchmark XLRS-Bench demonstrate that DualComp accomplisheshigh-fidelity remote sensing interpretation at an exceptionally low com-putational cost, achieving simultaneous improvements in both efficiencyand accuracy.

Abstract:
While Audio-Visual Language Models (AVLMs) have achievedremarkable progress over recent years, their reliability is bottleneckedby cross-modal hallucination. A particularly pervasive manifestation isvideo-driven audio hallucination: models routinely exploit visual short-cuts to hallucinate expected sounds, discarding true auditory evidence.To counteract this deeply ingrained visual dominance, we propose Audio-Contrastive Preference Optimization (ACPO). This dual-axis preferencelearning framework introduces an output-contrastive objective to penal-ize visual descriptions masquerading as audio facts, alongside an input-contrastive objective that swaps audio tracks to explicitly penalize gener-ation invariant to the true auditory signal. Extensive experiments demon-strate that ACPO establishes highly faithful audio grounding and mit-igates audio hallucination. Project page: https://vision.cs.utexas.edu/projects/acpo/

Abstract:
While Vision–Language Models (VLMs) demonstrate strongcapabilities, they continue to suffer from a critical limitation: insufficientfine-grained visual perception, which fundamentally limits their mul-timodal understanding. We attribute this bottleneck to text-dominantoptimization biases during pre-training, which encourage the model tooverlook fine-grained visual details, thereby limiting the capability ofmultimodal understanding. We investigate that overcoming this bottle-neck requires two key elements: (1) a unified token space paradigm thatensures stable training dynamics, and (2) a modality-aligned dense vi-sual supervision signal enriched with both structural granularity andsemantic information to capture critical visual representations. Basedon these insights, we propose VIVAS, a framework built upon the uni-fied token space paradigm, which introduces a dense-structural–semanticvision tokenizer, which expands the textual vocabulary into a unified vi-sion–language vocabulary by incorporating a visual vocabulary. Duringpretraining, VIVAS performs vision–language unified autoregressive su-pervision over both visual details and linguistic content, thereby enhanc-ing visual perception to improve multimodal understanding. Trained end-to-end on 12.4T tokens, VIVAS achieves state-of-the-art performanceacross 7 tasks and 39 multimodal benchmarks.

Abstract:
In this paper, we present BLOB-Q, a novel Mixed-PrecisionPost Training Quantization (MPQ) approach for Vision Transformers(ViTs). MPQ aims to assign different bit-widths across layers under amodel size budget. Most existing MPQ methods either rely on layerwisesensitivity heuristics or surrogate objectives. These approaches simplifyoptimization, but they no longer optimize the same model-level objec-tive that determines accuracy. Others solve the global MPQ problemby non-heuristic and non-analytical algorithms, such as ReinforcementLearning, genetic algorithms or gradient-based learning. However, theyare inefficient for modern large vision transformers. In this work, we revisitthe global MPQ problem and show that this problem can be solved byefficient analytical algorithms while still maintaining its global-optimality.This comes from two key insights. Firstly, through systematic signalanalysis on quantization errors, we discover that under 4„6-bit range,the quantization error on converged ViTs are indeed small perturbations.This encourages that the global MPQ objective is possible to be quadrat-ically decomposed for empirical ViT models. Secondly, motivated by thesmall perturbation observation, we further discover that the global MPQobjective satisfies an additivity property. Utilizing the additivity property,the NP-hard global MPQ optimization problem can be decomposed intosub-problems and solved practically, while still retain the global optimal-ity. Specifically, we solve the decomposed analytical optimization problemusing dynamic programming algorithm, which efficiently find the globallyoptimal solution with only linear time complexity. Extensive experimentson numerous ViT models demonstrate the effectiveness of our approach.Results show that BLOB-Q significantly improves state-of-the-art andcan further reduce the size of ViT models to 4 bits to 6 bits withouthurting ImageNet accuracy. Moreover, BLOB-Q is highly efficient, onlyrequiring on average sub-2 minutes for regular ViT-S to ViT-B models.

Abstract:
Recent zero-shot 3D visual grounding methods leverage vision–language models (VLMs) to localize objects in 3D scenes from natural language queries. However, these methods typically rely on heuristic rules to select which camera views are provided to the VLM, often prioritizing object visibility rather than grounding relevance. We present IVSGround, a framework that learns Influential View Selection for VLM-based 3D visual grounding. Instead of using fixed heuristics, a lightweight view selector is trained to identify views that provide discriminative evidence for grounding. To obtain supervision signals, we generate training signals using feedback from a reasoning VLM through a two-stage rejection sampling process. During inference, the learned selector predicts query-conditioned influential views for each candidate object, which are then evaluated by a frozen reasoning VLM through comparative grounding. Experiments on ScanRefer and NR3D show that IVSGround consistently improves grounding accuracy over existing zeroshot pipelines, demonstrating that selecting where to look is crucial for effective 3D visual grounding.

Abstract:
Food cutting is a representative non-rigid manipulation taskinvolving deformation, contact forces, and material separation, yet itRobot Simulation remains largely unexplored in VLA research. Existing robot manipula-tion datasets are primarily built around rigid objects and struggle tojointly capture the spatial precision, deformation, topology changes, andCut the banana force interactions required for cutting. To systematically study theseat the center. challenges, we introduce CulinaryCut, a benchmark that integrates anniskill MPM-based deformable simulator with a robot environment to generatedataset rather than provided as policy inputs, enabling evaluation of ex-isting VLAs while exposing where physical grounding is needed. Usingthree representative VLA baselines, we identify two core limitations innon-rigid cutting. First, a geometry gap: VLAs struggle to map ratio-and direction-based instructions onto object geometry, especially whensequential cuts change the object’s topology. Second, a physics gap: be-cause policies output motion without a notion of material stiffness orcontact resistance, a geometrically accurate path may still fail to severthe object. We show that training VLAs on physics-grounded trajecto-ries, without modifying the policy architecture or adding force inputs,improves cutting success and sim-to-real transfer. Together, CulinaryCutprovides a foundation for evaluating spatial reasoning and physical plau-sibility in deformable manipulation.

Abstract:
Although recent multimodal large language models (MLLMs)have advanced rapidly in vision–language reasoning, their capability bound-aries, particularly in cross-modal integration and reasoning, remain un-derexplored. Existing benchmarks primarily focus on evaluating uni-modal or loosely coupled multimodal abilities, leaving a gap in assess-ing complex cross-modal reasoning. To address this gap, we introduceVisWordBench, a benchmark comprising 2,625 English and 2,000 Chi-nese visual word puzzles with detailed human annotations. The bench-mark is designed to evaluate not only fundamental perceptual under-standing and world knowledge but also deep cross-modal reasoning skillsthat require consistently integrating visual and linguistic information.Through evaluations on VisWordBench, we find that current MLLMs ex-hibit under-diversified hypothesis search during reasoning. We thereforepropose a data construction pipeline and a training-free inference-timesteering strategy that promotes more diverse hypothesis exploration dur-ing CoT reasoning. Experimental results show consistent improvementsin reasoning quality and hypothesis diversity, supporting the utility ofVisWordBench for studying multimodal reasoning. The benchmark isavailable at: https://zenodo.org/records/20957955.

Abstract:
On-device vision-language models (VLMs) deployed on edgedevices often encounter distribution shifts between pre-training data andtest data, which can significantly degrade their performance. To miti-gate this degradation, device-server hybrid inference allows an on-deviceVLM to delegate uncertain test inputs to a more powerful server-sideVLM. However, frequently delegating to the remote server at test timeinflates data-transfer costs and server-call latency, limiting the practi-cality of hybrid inference in real-world applications. Thus, to reduce thedelegation frequency without sacrificing the benefits of server assistance,we present CURE, a cumulative knowledge reuse framework for device-server hybrid inference, which efficiently enhances on-device VLMs byaccumulating and reusing the server-side VLM’s knowledge. As a result,CURE enables the on-device VLM to approximate server-level predic-tions with far fewer delegations, thereby amortizing both data-transfercosts and server-call latency across the test stream. Extensive experi-ments across 13 datasets show that CURE closely matches the accuracyof an always-delegate-to-server baseline, reduces delegation frequency by66.74% on average, and improves end-to-end runtime by up to 3.67×,demonstrating the practicality of CURE for real-world deployment.

Abstract:
Vision-Language-Action (VLA) models have advancedrapidly, yet most gains still come from larger backbones, larger embod-ied datasets, or expensive iterative decoders, while action representa-tion remains under-explored. We argue that action space design is aprimary bottleneck and present LEAP (Latent-Enhanced ActionPrototyping)-VLA, a two-stage framework that learns a structuredaction latent space from demonstrations via Multi-level Soft ResidualQuantization (MSRQ), then trains a lightweight VLM-external aligner topredict actions by prototype-aware soft selection in that space. This de-sign replaces discrete code assignment and multi-step denoising with fullydifferentiable single-pass latent prediction, preserving pretrained vision-language alignment without modifying the VLM. Empirically, LEAP-VLA outperforms the state-of-the-art methods with substantially fewertrainable parameters and no embodied pretraining, showing that a well-structured continuous action latent space can offset model scale and datarequirements.

Abstract:
Despite the impressive manipulation capabilities of Vision-Language-Action (VLA) models, their operational safety under strictconstraints remains largely unverified. To address this, we introducea parametric safety benchmark to procedurally generate safety-criticalscenarios with comprehensive stochasticity. To overcome the scalabilitybottlenecks of human teleoperation, we develop a novel keypose-drivendata generation pipeline. Leveraging this infrastructure, we curate alarge-scale dataset of 19,664 strictly collision-free demonstrations withextensive domain randomization. We then conduct a systematic cross-paradigm evaluation of eight VLA and two embodied foundation mod-els. Our analysis reveals a critical generalization-safety tension: althoughhigh-diversity training fosters safer trajectories, task success remains fun-damentally bottlenecked by sub-optimal trajectory synthesis and seman-tic misalignment. By providing a scalable pipeline, a robust dataset, andprofound failure-mode insights, LIBERO-Safety establishes a crucialfoundation for developing safe and reliable VLA models.

Abstract:
While Vision-Language-Action (VLA) models have emergedas powerful generalist policies, their severe vulnerability to adversarialpatches significantly hinders their deployment in safety-critical domains.Moreover, existing patch attacks primarily focus on white-box settings,heavily overfitting to the specific action output space of the target model,which results in poor cross-architecture transferability. To overcome thislimitation, we propose VLA-Hijack, a unified adversarial framework thatbreaks the transferability bottleneck by exploiting a fundamental vulner-ability identified in this work: before planning any motion, a VLA modelmust first use visual information to locate its own robotic arm withinthe environment. Targeting this shared visual self-localization process,our approach concurrently optimizes Attention-Guided ProprioceptiveSuppression to inhibit the real robotic arm’s features, and MultimodalProprioceptive Injection to establish the patch as a surrogate "phantomembodiment". By alternating between semantic concept anchoring andvisual prototype projection, VLA-Hijack effectively severs the semanticrelationship between the agent’s true embodiment and its control pol-icy. Extensive experiments across diverse architectures (OpenVLA, Uni-VLA, and CronusVLA) demonstrate that VLA-Hijack achieves superioroptimization efficiency in white-box settings and sets a new SOTA forcross-architecture and cross-domain black-box transferability.

Abstract:
Vision-Language Models (VLMs) improve generalization andinterpretability in autonomous driving but suffer from efficiency issuesdue to long visual token sequences, particularly in standard multi-viewsettings. Existing token pruning methods employ fixed pruning rate al-location and static importance metrics, ignoring dynamic inter-view im-portance differences and the evolving information importance during in-ference. Our analysis reveals that multi-view VLMs inherently encodetask-related view priors in deeper layers and exhibit dynamic informa-tion requirements. Motivated by these findings, we propose MVPruner,a two-stage adaptive token pruning method that aligns pruning behaviorwith the model’s dynamic information requirements. The first stage al-locates pruning budgets based on the information diversity of each view,and retains tokens with consistent contribution across stages, ensuringsemantic representational capacity. The second stage allocates budgetsand selects tokens guided by instruction text to guarantee task align-ment. Experimental results on four benchmarks demonstrate the supe-rior performance of our method. For example, DriveMM equipped withMVPruner achieves 87.3% reduction in FLOPs, 4.97× speedup in pre-filling phase while retaining 98.5% accuracy on DriveLM benchmark.

Abstract:
Leveraging the priors of 2D diffusion models for 3D editing has emerged as a promising paradigm. However, maintaining multiview consistency in edited results remains challenging, and the extreme scarcity of 3D-consistent editing paired data renders supervised finetuning (SFT), the most effective training strategy for editing tasks, infeasible. In this paper, we observe that, while generating multi-view consistent 3D content is highly challenging, verifying 3D consistency is tractable, naturally positioning reinforcement learning (RL) as a feasible solution. Motivated by this, we propose RL3DEdit, a single-pass framework driven by RL optimization with novel rewards derived from the 3D foundation model, VGGT. Specifically, we leverage VGGT’s robust priors learned from massive real-world data, feed the edited images, and utilize the output confidence maps and pose estimation errors as reward signals, effectively anchoring the 2D editing priors onto a 3D-consistent manifold via RL. Extensive experiments demonstrate that RL3DEdit achieves stable multi-view consistency and outperforms state-of-the-art methods in editing quality with high efficiency. To promote the development of 3D editing, we will release the code and model.

Abstract:
Medical image modality translation is hindered by the entan-glement of geometry and modality-specific textures, often causing struc-tural distortions. Existing methods operate at the pixel or feature level,failing to separate geometry from appearance and yielding anatomicallyinconsistent results. We present Proto-Gaussian, a novel MRI modalitytranslation framework that explicitly disentangles structural geometryfrom modality-specific appearance using 2D Gaussian image representa-tion, surpassing conventional pixel or patch-based approaches in preserv-ing anatomical consistency. The framework operates in two stages: first, ashared Gaussian Bank captures modality-invariant geometric prototypesthrough 2D Gaussian primitives and self-supervised learning; second, aLabel-Adaptive Token Refinement module maps texture features acrossmodalities while leveraging the learned geometric structures, achievingclear structure-texture separation. Finally, a fully differentiable 2D Gaus-sian splatting renderer synthesizes high-fidelity target-modality images,maintaining both structural accuracy and visual realism. Extensive ex-periments on brain MRI datasets demonstrate the superiority of ourmethod through quantitative and qualitative comparisons with state-of-the-art approaches.

Abstract:
Group Relative Policy Optimization (GRPO) has emergedas a powerful framework for preference alignment in text-to-image (T2I)flow models. However, we have observed that the standard paradigmthat evaluates a group of generated samples against a single conditionsuffers from insufficient exploration of inter-sample relationships, con-straining both alignment efficacy and performance ceilings. To addressthis sparse single-view evaluation scheme, we propose Multi-View GRPO(MV-GRPO), a novel algorithm that enhances relationship explorationby augmenting the condition space to create a dense multi-view rewardmapping. Specifically, for a group of samples generated from one prompt,MV-GRPO leverages a flexible Condition Enhancer to generate seman-tically adjacent yet diverse captions. These captions enable multi-viewadvantage re-estimation, capturing diverse semantic attributes and pro-viding richer optimization signals. By deriving the probability distribu-tion of the original samples conditioned on these new captions, they canbe incorporated into the training process without costly sample regen-eration. Extensive experiments demonstrate that MV-GRPO achievessuperior alignment performance over state-of-the-art methods.

Abstract:
Foundational feed-forward visual geometry models enableaccurate and efficient camera pose estimation and scene reconstruction bylearning strong scene priors from massive RGB datasets. However, theireffectiveness drops when applied to mixed sensing modalities, such asRGB-thermal (RGB-T) images. We observe that while a visual geometrygrounded transformer pretrained on RGB data generalizes well to thermal-only reconstruction, it struggles to align RGB and thermal modalitieswhen processed jointly. To address this, we propose SEAR, a simple yet ef-ficient fine-tuning strategy that adapts a pretrained geometry transformerto multimodal RGB-T inputs. Despite being trained on a relatively smallRGB-T dataset, our approach significantly outperforms state-of-the-artmethods for 3D reconstruction and camera pose estimation, achievingsignificant improvements over all metrics and delivering higher detailand consistency between modalities with negligible overhead in inferencetime compared to the original pretrained model. Notably, SEAR enablesreliable multimodal pose estimation and reconstruction even under chal-lenging conditions, such as low lighting and dense smoke. We validateour architecture through extensive ablation studies and demonstratehow the model aligns both modalities. Additionally, we introduce a newdataset featuring RGB and thermal sequences captured at different times,viewpoints, and illumination conditions, providing a robust benchmarkfor future work in multimodal 3D scene reconstruction. Code and modelsare publicly available at https://doi.org/10.5281/ZENODO.21077295.

Abstract:
Brain tumor MRI segmentation is essential for clinical diagnosis and treatment planning. Although deep learning methods have achieved significant progress, most models operate solely in the image domain and overlook the influence of k-space sampling on spatial structure formation. This physical information gap often limits model generalization when encountering varying scanning conditions across different clinical centers. To address this issue, we propose SPHERE, Sampling-Prior Harmonized rEconstruction-aware Representation lEarning, a samplingaware framework for brain tumor MRI segmentation. SPHERE-Recover estimates k-space sampling–related geometric parameters directly from reconstructed MRI and transforms discrete scanning conditions into a continuous deformation vector field (DVF) that represents samplinginduced structural perturbations. Building on this prior, we develop SPHERE-Seg and introduce a Deformation Prior Module (DPM) to inject the DVF into feature modeling, enabling sampling-driven structural consistency learning. Experiments on BraTS and MSD show that SPHERE improves segmentation accuracy, boundary stability, and crossdataset generalization. These results demonstrate that explicitly modeling MRI sampling mechanisms yields robust, generalizable, and efficient brain tumor segmentation.

Abstract:
Adapting video foundation models to volumetric medicalsegmentation by treating axial depth as a temporal dimension is attrac-tive, but the resulting slice propagation can be unstable when anatom-ical structures branch, disappear, or become poorly contrasted acrossslices. Static geometric prompts provide little guidance for newly ap-pearing or disconnected components, and SAM2-style streaming mem-ory may accumulate errors in these regions. We propose Di x001B uPrompt,a backbone-frozen, single-prompt framework that augments SAM2 withlatent trajectory priors for volumetric propagation. Given a user prompton a representative slice, an LLM-conditioned Neural ODE predicts acontinuous, patient-conditioned latent trajectory and converts it into apersistent prior memory. A dual-stream spatial gate then fuses this priormemory with the standard observation-driven working memory, allowingthe model to use image evidence when it is reliable and trajectory pri-ors when local propagation becomes ambiguous. Experiments on MSD,BTCV, and BraTS show improved promptable volumetric segmentationover SAM/SAM2-based baselines, especially on boundary-sensitive met-rics.

Abstract:
Learned image compression (LIC) operates under a rate–distortion (RD) trade-off, where representation quality is constrained bythe target bitrate. We revisit knowledge distillation in LIC through thelens of bitrate asymmetry and introduce a self-teacher distillation frame-work, where a high-rate instance of a codec supervises multiple lower-rateencoders of identical architecture. Because allocating more bits naturallyleads to richer latent representations, the high-rate model provides infor-mative supervision across rate levels. Direct latent matching, however,is problematic under tight rate budgets. We therefore propose variance-normalized latent distillation (VNLD), a rate-aware alignment strategythat scales channel-wise supervision by the teacher’s variance, selectivelytransferring stable, informative structure while suppressing componentsthat cannot be reliably reproduced at lower rates. Across different dis-tillation objectives, networks, and bitrate levels, self-teacher distillation,particularly with VNLD, improves RD performance and yields consistentBD-rate gains over RD-only training. Our method remains compatiblewith fixed-decoder deployments, such as those targeted by JPEG AIstandards.

Abstract:
We propose DOME-HDR, a dual-output multi-exposure HDRreconstruction framework that jointly produces a perceptually balancedSDR image and a consistent HDR image via gain map inverse tone map-ping. Given three bracketed LDR inputs, DOME-HDR first synthesizesa base SDR using a LoRA-adapted latent diffusion model. A dual cross-attention fusion module injects complementary structural and color cuesfrom the under- and over-exposed images while anchoring on the mid ex-posure for stability. The synthesized SDR then guides HPGM, our HDRPrior-guided Gain Map network, to predict a spatially varying gain mapfor reliable dynamic-range expansion. We evaluate on Kalantari, Tel, andChallenge123 using both full-reference and no-reference metrics, whereDOME-HDR achieves state-of-the-art HDR reconstruction quality; abla-tions further confirm the effectiveness of dual cross-attention and SDR-guided gain map estimation.

Abstract:
Vision Transformers (ViTs) and their hierarchical variants have achieved strong performance in Computational Pathology (CPath). However, most are pre-trained on single-resolution Whole Slide Images (WSIs), limiting their generalization across arbitrary resolutions. Gigapixel WSIs inherently contain diagnostic patterns at multiple scales, including cellular morphologies, tissue architectures, and global context, mirroring how expert pathologists examine WSIs. We introduce MultiResolution Pyramid Transformer (MRPT), a model that hierarchically aggregates multi-resolution information from cellular to tissue and WSI levels. MRPT employs a biologically meaningful Consecutive Cross-Resolution Attention (CCRA) mechanism to capture scale-independent interactions and enforces multi-resolution semantic consistency by aligning embeddings across resolutions, yielding robust and generalizable WSI representations. Pre-trained in a multi-resolution self-supervised manner on 624M patches, 2.4M regions, and 36K WSIs, MRPT learns rich coarse-to-fine histopathology features. Extensive experiments on 34 diverse datasets show that MRPT surpasses recent foundation models and Multimodal Large Language Models (MLLMs) in cancer subtype classification, tissue phenotyping, and Visual Question Answering (VQA) for WSI understanding. Our code and models available on link.

Abstract:
Diffractive Snapshot Spectral Imaging (DSSI) encodes spec-tral and spatial information into a single 2D measurement via a diffrac-tive optical element (DOE), offering a compact and light-efficient al-ternative to conventional Coded-Aperture Snapshot Spectral Imaging(CASSI). However, the spatially uneven coding of DSSI, which is mainlyconcentrated in texture-rich regions, has led existing reconstruction net-works to rely heavily on global attention modules to propagate spectralfeatures into texture-less regions, making them short-sighted in locallycoded areas and computationally redundant. To address these challenges,we propose Local-to-Global Spectral Reconstruction Network (LGSR-Net), a two-stage progressive framework comprising complementary lo-cal and global subnetworks. The local part extracts information fromtexture-rich regions under direct supervision from effectively coded ar-eas, while the global part models long-range dependencies to facilitateglobal information flow. We introduce a spatially guided channel atten-tion module to emphasize informative regions and an edge-aware gatedfusion mechanism to integrate local features into the global subnetwork’sdecoders. Moreover, a coding-aware sample selection strategy and a tai-lored loss function are designed to improve training efficiency and re-construction fidelity. Extensive experiments on both simulated and realDSSI datasets show that LGSRNet achieves state-of-the-art reconstruc-tion performance with significantly lower computational cost and latency.

Abstract:
Cross-view localization (CVL) estimates the pose of a groundimage by matching it to a geo-referenced satellite image. To bridge theextreme viewpoint gap, mainstream pipelines rely on Bird’s-Eye-View(BEV) transformations or 2D-to-3D lifting. However, deriving 3D struc-tures from a single ground image is fundamentally ill-posed, causing thesemethods to endure geometric distortions and computational costs dur-ing 3D lifting or BEV projection. Furthermore, relying on external depthfoundation models to resolve this introduces latency and remains suscep-tible to noisy predictions. In this work, we present a different approachinspired by a human navigation technique called resection, that can per-form direct ground-to-satellite image matching and localization withoutrelying on external depth foundation models. The key insights of ourmethod are that (i) ground keypoints can be translated into azimuthalrays on the satellite map, and (ii) these rays ideally converge at theuser location. Exploiting this geometric constraint through direct line-to-point correspondences, we introduce a minimal Azimuthal Ray Con-vergence (ARC) solver to identify the intersection alongside an ARC lossto optimize the matching network. By eliminating dependencies on com-putationally heavy BEV transformations and external depth foundationmodels, our approach achieves faster, memory-efficient inference, whileits explicit feature matching ensures straightforward compatibility withexisting frameworks. Experiments on VIGOR and KITTI demonstratethat ARC-Loc maintains competitive localization accuracy compared torecent approaches, highlighting its practicality.

Abstract:
Recent hybrid Structure-from-Motion (SfM) systems com-bine the robustness of feed-forward 3D reconstruction with the accuracyof traditional bundle adjustment (BA) with pixel matching. They areusually the best performing methods however their scalability and us-ability remains limited since estimating dense correspondences betweenviews is prohibitively costly, especially considering time constraints in-herent to online applications like Visual SLAM (VSLAM). In this paper,we introduce a regularized BA framework that leverages a fast multi-view matcher and monocular priors for initialization and regularization.In contrast to existing systems, our unified approach seamlessly supportsboth online VSLAM and offline reconstruction from unordered image col-lections within the same optimization framework and sharing commonhyperparameters for all tasks. Extensive experiments across both do-mains demonstrate improved performance and speed tradeoffs over tra-ditional, feed-forward, and hybrid baselines. Notably for VSLAM, ouruncalibrated method outperforms all previous calibrated approaches.

Abstract:
We propose a self-supervised pretraining framework for learn-ing sub-surface scattering (SSS) light transport representations fromminimal input. Our method leverages a stereo projector–camera setupthat captures only eight high-frequency phase-shift profilometry (PSP)images per view to pretrain an encoder in a multi-view, multi-object set-ting. We introduce a tailored augmentation strategy for PSP-based SSSdata, and show that it significantly outperforms standard ImageNet-styleaugmentations for SSL pretraining. The pretrained encoder learns gen-eralizable SSS representations that transfer effectively to downstreamtasks, including spatially varying relighting and representation evalua-tion using a kNN classifier. Combined with a decoder, the model recon-structs dense scattering footprint responses, trained using a dedicatedcost function that improves accuracy, particularly for anisotropic foot-prints. An overview of our method is presented in Fig. 3. Despite usingonly eight input images per view, our approach generalizes to unseenobjects with complex geometry and material properties, achieving high-fidelity reconstructions while requiring orders of magnitude fewer imagesthan prior methods. Our code is publicly available at GitHub.

Abstract:
Building robots that can perceive, reason, and act in dy-namic, unstructured environments remains a core challenge. Recent em-bodied systems often adopt a dual-system paradigm, where System 2handles high-level reasoning while System 1 executes low-level control.In this work, we refer to System 2 as the embodied brain, emphasizingits role as the cognitive core for reasoning and decision-making in ma-nipulation tasks. Given this role, systematic evaluation of the embodiedbrain is essential for advancing robotic intelligence. Yet existing bench-marks emphasize execution success, or, when targeting high-level reason-ing, su x001B er from incomplete dimensions and limited task realism, o x001B eringonly a partial picture of cognitive capability. To bridge this gap, we intro-duce RoboBench, a benchmark that systematically evaluates multimodallarge language models (MLLMs) as embodied brains. Motivated by thedistinct cognitive roles required across the full manipulation pipeline,RoboBench de x001C nes x001C ve dimensions x0016 Instruction Comprehension, Percep-tion Reasoning, Generalized Planning, A x001B ordance Prediction, and Fail-ure Analysis x0016 spanning 14 capabilities, 25 tasks, and 6092 QA pairs. Toensure realism, we curate datasets across diverse embodiments, attribute-rich objects, multi-view scenes, and memory-driven navigation, drawingfrom large-scale real robotic data and in-house collection. For planning,RoboBench introduces an evaluation framework that uses an MLLM as aworld simulator. It moves beyond symbolic matching to evaluate embod-ied feasibility by simulating whether predicted plans can achieve criticalobject-state changes under physical and visual constraints, enabling faith-ful assessment of long-horizon reasoning. Experiments on 18 state-of-the-art MLLMs reveal fundamental limitations: di x001E culties with implicit in-struction comprehension, spatiotemporal reasoning, cross-scenario plan-ning, x001C ne-grained a x001B ordance understanding, and execution failure diagno-Dimension Subdimension Performance DownstreamInstuctionComprehension Explicit Object Goal Implicit Demand GoalPerception Robotic- Object- Scene- Task-Reasoning Centric Centric Centric CentricGeneralization EmbodimentCross- Cross- Cross- Cross-Planning Object View TaskAﬀordance Static Dynamic NavigationPrediction Aﬀordance Aﬀordance AﬀordanceFailure Low-Level High-LevelAnanlysis Execution Error Planning Error. Instuction Comprehension - . Long-Horizon Planning - . Next-Step Planning - . Static AﬀordanceImplicit Demand Goal: Question: Question: Question:Please help me tidy up the apple How to complete the goal? What's the next subgoal? What's the contact point?Put the apple into the drawer open(drawer) pick up(apple) open(drawer) [x , y ]place(apple, drawer) close(drawer)decomposelong-horizen goal adapt subgoalinto substeps in closed-loopinterpret gather diagnose, monitor state reﬁne subgoal intohuman task-relevant analyze, in closed-loop Yes spatial aligned cuesintention. Perception Reasoning adjust error - . Dynamic Aﬀordanceinformation . Failure Analysis - . Task State EstimationTask-Related:robotic type? single arm Question: Question: Question:object attribute? apple: rigid Is there any error? What type of error? Has current subgoal been completed? What's the movement trajectory?operation note? drawer: closed Yes, high-level error---missing steps Yes No [x , y ], [x ,y ], [x , y ],[x , y ]spatial relation? apple lower than drawermonitor state reﬁne subgoal intoin closed-loop spatial aligned cuesNoFig. 1: Overview of RoboBench We evaluate MLLMs as embodied brains with25 tasks color-coded by 5 dimensions (top left). These dimensions follow the em-bodied execution pipeline (bottom) x0016 from intent understanding to failure diagnos-ing x0016 capturing the core cognitive roles of System 2. Performance comparison (top mid-dle) reveals signi x001C cant gaps among state-of-the-art MLLMs (top right) RoboBenchscores strongly correlate with downstream VLA performance on CALVIN.sis. We further analyze how embodied cognitive abilities relate to down-stream robotic control. RoboBench provides a comprehensive sca x001B_oldto quantify high-level cognition, clarify the role of the embodied brain,and guide the development of next-generation MLLMs for more robustrobotic intelligence. Project page: https://robo-bench.github.io.

Abstract:
Reaction diagram parsing (RxnDP) is critical for extractingchemical synthesis information from literature. Although recent Vision-Language Models (VLMs) have emerged as a promising paradigm toautomate this complex visual reasoning task, their application is funda-mentally bottlenecked by the inability to align visual chemical entitieswith pre-trained knowledge, alongside the inherent discrepancy betweentoken-level training and reaction-level evaluation. To address these dualchallenges, this work enhances VLM-based RxnDP from two comple-mentary perspectives: prompting representation and learning paradigms.First, we propose Identifier as Visual Prompting (IdtVP), whichleverages naturally occurring molecule identifiers (e.g., bold numeralslike 1a) to activate the chemical knowledge acquired during VLM pre-training. IdtVP enables powerful zero-shot and out-of-distribution ca-pabilities, outperforming existing prompting strategies. Second, to fur-ther optimize performance within fine-tuning paradigms, we introduceRe3 -DAPO, a reinforcement learning algorithm that leverages verifi-able rewards to directly optimize reaction-level metrics, thereby achiev-ing consistent gains over standard supervised fine-tuning. Additionally,we release the ScannedRxn benchmark, comprising scanned historicalreaction diagrams with real-world artifacts, to rigorously assess model ro-bustness and out-of-distribution ability. Our contributions advance theaccuracy and generalization of VLM-based reaction diagram parsing.

Abstract:
Pre-trained vision-language models excel across varied per-ception tasks, but adapting them to novel downstream settings with-out sacrificing generalization remains non-trivial. Existing parameter-efficient prompt learning method often yields inconsistent representa-tions and fails to account for semantic distribution shifts. In this work,we present DiscoVL, a disentangled cross-modal representation learningframework that couples orthogonal adversarial regularization with struc-tured cross-modal alignment for vision-language models. To address theinsufficient cross-modal interaction, our DiscoVL designs a multi-branchlow-rank residual aligner that decomposes representations into subspacesand enables bidirectional cross-modal feedback between visual and tex-tual streams at each layer. Furthermore, while conventional triplet con-straints overfit features to class centroids, we design an orthogonal reg-ularization for adversarial triplet loss, which prevents centroid collapseand substantially boosts generalization. Evaluations on 15 benchmarksdemonstrate that DiscoVL delivers consistent improvements over state-of-the-art methods for base-to-novel generalization, cross-dataset evalu-ation, and few-shot learning.

Abstract:
Pre-trained Vision-Language Models (VLMs) like CLIP haveproven highly effective as foundation models for various downstream ap-plications. However, prompt learning in VLMs encounters a performance-generalization dilemma: while prompts can be tuned to achieve highaccuracy on seen distributions, this tuning process often undermines theirgeneralizability to unseen data. The limited set of learnable prompts,which contextualize and condition the input to steer it toward the taskwithin the pretrained VLM, tends to overfit the training data, leading toa trade-off between task-specific performance and preserving generaliza-tion. To address this dilemma, we introduce SAMPLe (Sharpness-AwareMinimization Prompt Learning), a plug-in sharpness-aware optimizerthat enhances prompt generalizability by accounting for loss landscapesharpness. Unlike conventional methods, SAMPLe balances explorationand exploitation by satisfying objective function constraints at each step,dynamically adapting to the current optimization state based on the localcurvature and gradient properties. This approach reduces overfitting onseen distributions and improves adaptability to unseen data, preservingthe generalization potential of pre-trained VLM models. We integrateSAMPLe into multiple prompt learning frameworks, including CoOp,CoCoOp, MaPLe, TCP, and Co-Prompt, demonstrating its effectivenessacross diverse methods. Experiments show that SAMPLe elevates promptlearning frameworks and consistently outperforms existing optimizersacross diverse settings, establishing itself as a robust, model-agnosticsolution for prompt learning.

Abstract:
Real-world intelligent systems often require both distributedcollaboration across data-isolated clients and continual adaptation toevolving tasks. This setting naturally gives rise to Federated Class In-cremental Learning (FCIL), which combines Federated Learning (FL)and Continual Learning (CL). However, their combination introducestwo coupled sources of interference: spatial interference from heteroge-neous clients and temporal interference from sequential tasks, jointlyleading to Spatial x0015 Temporal Catastrophic Forgetting (ST-CF). Exist-ing approaches typically address spatial and temporal interference withseparate mechanisms, often incurring additional client-side computationor communication, while leaving directional interactions among updatesduring aggregation unregulated. In this paper, we reinterpret FCIL as auni x001C ed multi-task learning problem, where both client and task updatesare represented as adaptation vectors in a shared parameter space. Basedon this view, we proposeSurgery & Merge Sum ( ), a purely server-sideframework that performs geometric surgery on adaptation vectors duringaggregation. Spatial Sum mitigates client-level interference within eachSumround, while causal online temporal removes cross-task interferenceover time without additional client-side computation, communication, ormemory beyond standard federated training. Empirically, Sum achievesup to 22% improvement over prior FCIL methods across diverse visionand language benchmarks while remaining robust to unreliable clientsand maintaining computational e x001E ciency.

Abstract:
Reconstructing building wireframe from airborne LiDAR pointclouds yields a compact, topology-centric representation that enablesstructural understanding beyond dense meshes. Yet a key limitation per-sists: conventional methods have failed to achieve accurate wireframe re-construction in regions afflicted by significant noise, sparsity, or internalcorners. This failure stems from the inability to establish an adaptivesearch space to effectively leverage the rich 3D geometry of large, sparsebuilding point clouds. In this work, we address this challenge with De-launay Canopy, which utilizes the Delaunay graph as a geometric priorto define a geometrically adaptive search space. Central to our approachis Delaunay Graph Scoring, which not only reconstructs the underlyinggeometric manifold but also yields region-wise curvature signatures torobustly guide the reconstruction. Built on this foundation, our cornerand wire selection modules leverage the Delaunay-induced prior to focuson highly probable elements, thereby shaping the search space and en-abling accurate prediction even in previously intractable regions. Exten-sive experiments on the Building3D Tallinn city and entry-level datasetsdemonstrate state-of-the-art wireframe reconstruction, delivering accu-rate predictions across diverse and complex building geometries.

Abstract:
Long-term aerial localization requires matching live flightimagery against archival reference maps, demanding feature represen-tations that are invariant to severe appearance changes and perceptualaliasing. However, current benchmarks report only aggregate accuracyover predominantly man-made environments, masking severe terrain-dependent performance gaps. As a result, localization performance overunstructured natural landscapes—where self-similar textures and drasticseasonal changes dominate—remains effectively unmeasured. To addressthis, we introduce Leaving the City, the first large-scale aerial benchmarkdesigned to isolate and quantify terrain-dependent localization gaps. Cap-tured via a microlight aircraft, our dataset comprises 1,379 km of flighttrajectories flown repeatedly to capture distinct seasonal variations. Wepair high-frame-rate imagery and inertial measurements with semanticterrain masks, multi-year-old orthophotos, and precise 6-DoF groundtruth. Evaluating state-of-the-art matchers through our terrain-stratifiedprotocol reveals a systematic bias: methods that succeed on man-madesurfaces degrade sharply over natural terrain undergoing strong appear-ance change. By exposing where current methods fail, our benchmarkprovides a rigorous foundation for developing robust, all-terrain aeriallocalization. The dataset and code are publicly available.4

Abstract:
Micro-expressions are brief, involuntary facial movementsthat reveal subtle affective states. While most computational work fo-cuses on micro-expression recognition, which classifies clips into dis-crete emotion categories, Micro-Expression Action Unit (ME-AU) de-tection offers a more theory-consistent formulation grounded in the Fa-cial Action Coding System. Despite its finer granularity, ME-AU de-tection remains comparatively under-explored due to the limited avail-ability of high-quality data for efficient automatic models. Recent volu-metric micro-expression datasets provide temporally coherent 3D facialrecordings with reliable AU labels, creating new opportunities for fine-grained analysis. However, their adoption remains limited due to largedata volumes, heterogeneous formats, and the absence of standardizedbenchmarks, which together hinder reproducibility and cross-study com-parison. Moreover, because of the small spatio-temporal spanning of theAUs, designing models that effectively combine volumetric representa-tions with temporal dynamics remains a non-trivial research challenge.To address these barriers, we introduce an extended and standardizedrelease of the 4DME dataset with cleaned data and normalized format-ting, accompanied by a compressed version to reduce storage overhead.We further design VoluME, a family of dual-stream optical-flow-basedarchitecture for volumetric ME-AU detection. To determine appropriatesettings for compression, we combine state-of-the-art compression base-lines, perceptual evaluation, and downstream task-specific benchmark-ing. As the first work dealing with volumetric ME-AU detection undercompression, our results show that a compression ratio of approximately3000× can be achieved with ≈ 1% F1 degradation. The extended releaseadditionally introduces explicit two-way cultural balancing, supportingfuture cross-cultural micro-expression studies.

Abstract:
Subject-driven image generation and manipulation are fun-damental for personalized content creation, such as identity-preservingportrait synthesis, multi-character storytelling, and controllable photoediting. However, existing models still struggle with multi-reference set-tings, often showing identity drift and unstable editing quality. A keybottleneck is data: current resources are often limited in scale, diversity,and consistency of subject-level details, especially for unified support ofboth generation and manipulation. To address this gap, we introduceOpenSubject, a video-derived large-scale corpus for subject-driven gen-eration and manipulation. Our pipeline leverages cross-frame identitypriors through four key stages: (i) Video Curation. We apply resolutionand aesthetic filtering to obtain high-quality clips. (ii) Cross-FrameSubject Mining and Pairing. We utilize vision language model (VLM)-based category consensus, local grounding, and diversity-aware pairing toselect image pairs. (iii) Identity-Preserving Reference Image Syn-thesis. We introduce segmentation map-guided outpainting to synthesizeinput images for subject-driven generation and box-guided inpaintingto generate input images for subject-driven manipulation, together withgeometry-aware augmentations and irregular boundary erosion. (iv) Ver-ification and Captioning. We utilize a VLM to validate synthesizedsamples, re-synthesize failed samples based on stage (iii), and then con-struct short and long captions. Furthermore, we introduce a benchmarkcovering both subject-driven generation and subject-driven manipulation,evaluated with a VLM-based judge on identity fidelity, prompt adherence,and consistency. Extensive experiments demonstrate that training onOpenSubject significantly enhances performance in complex scenes.

Abstract:
Existing visual privacy benchmarks largely treat privacy asa binary property, labeling images as private or non-private based onvisible sensitive content. We argue that privacy is fundamentally compo-sitional. Attributes that are benign in isolation may combine to producesevere privacy violations. We introduce the Compositional Privacy RiskTaxonomy (CPRT), a regulation-aware framework that organizes visualattributes according to standalone identifiability and compositional harmpotential. CPRT defines four graded severity levels and is paired with aninterpretable scoring function that assigns continuous privacy severityscores. We further construct a taxonomy-aligned dataset of 6.7K imagesand derive compositional risk scores. By evaluating frontier and open-weight VLMs we find that frontier models align well with compositionalseverity when provided structured guidance, but systematically underes-timate composition-driven risks. Smaller models struggle to internalizegraded privacy reasoning. To bridge this gap, we introduce a deploy-able 8B SFT model that closely matches frontier-level performance oncompositional privacy assessment. Our dataset and models are publiclyavailable at: https://huggingface.co/collections/timtsapras23/cprt.

Abstract:
Synthesizing native 2K multi-garment virtual try-on is aformidable frontier in digital fashion, critically bottlenecked by two fun-damental limitations: the O(N 2 ) memory explosion induced by 2k con-ditions, and the spectral bias of diffusion models that over-smooths high-frequency fabric details. We present WearWow, an end-to-end, mask-freegenerative framework that pioneers ultra-high-resolution multi-garmentsynthesis. To mitigate the memory explosion , we propose Adaptive 2DToken Packing (ATP). ATP leverages inherent garment sparsity to al-gorithmically pack heterogeneous items onto a unified 2D canvas andprune uninformative background tokens, minimizing the effective se-quence length and subsequent memory overhead while rigorously pre-serving 2D spatial priors. To rectify texture degradation, we introducethe Multi-dimensional Try-on Reward (MTR) system. MTR synergizes aSemantic Guidance Reward to explicitly drive tactile restoration with aCloth Distribution Reward to implicitly anchor the physical distribution,a joint formulation that effectively mitigates the severe reward hacking.Furthermore, we curate WearWow-2K, an extreme-quality dataset com-prising native 2K triplets, providing physically correct spatial interac-tions that naturally empower the model’s mask-free generation. Exten-sive experiments demonstrate that WearWow establishes a new state-of-the-art, exceeding existing commercial baselines in native 2K multi-garment synthesis.

Abstract:
Recent advances in text-to-image (T2I) models have revolu-tionized the field of image generation and editing. However, identifyingsemantics that a T2I model can successfully edit in an image contin-ues to be a challenging task. Most existing approaches require usersto manually specify semantics to modify a particular image, a time-consuming process that often involves extensive trial and error. In thispaper, we present RankT2I, a novel, training-free, and model-agnosticframework that automates the discovery of editable semantics in diffu-sion and FLUX-based models. Given a visual domain, we first utilizea multimodal vision-language model to gather a broad set of candidatesemantics. We then frame semantic discovery as a set selection problemand use a submodular objective to identify semantics that are relevant,editable, and diverse. Our method helps users efficiently identify a widerange of semantics for text-to-image editing models across several do-mains while outperforming existing methods.

Abstract:
A recent cutting-edge topic in multimodal modeling is tounify visual comprehension and generation within a single model. How-ever, the two tasks demand mismatched decoding regimes and visualrepresentations, making it non-trivial to jointly optimize within a sharedfeature space. In this work, we present Cheers, a unified multimodalmodel that decouples patch-level details from semantic representations,thereby stabilizing semantics for multimodal understanding and improv-ing fidelity for image generation via gated detail residuals. Cheers in-cludes three key components: (i) a unified vision tokenizer that encodesand compresses image latent states into semantic tokens for efficient LLMconditioning, (ii) an LLM-based Transformer that unifies autoregressivedecoding for text generation and diffusion decoding for image generation,and (iii) a cascaded flow matching head that decodes visual semanticsfirst and then injects semantically gated detail residuals from the vi-sion tokenizer to refine high-frequency content. Experiments on popularbenchmarks demonstrate that Cheers matches or surpasses advancedUMMs in both visual understanding and generation. Notably, Cheersoutperforms the Tar-1.5B on the popular benchmarks GenEval and MM-Bench, while requiring only 20% of the training cost, indicating effectiveand efficient (i.e., 4× token compression) unified multimodal modeling.Code and checkpoints are available at github.com/AI9Stars/CHEERS.

Abstract:
Face super-resolution (FSR) aims to enhance low-resolution facial im-ages into high-resolution versions. Existing FSR methods face significant chal-lenges, including the lack of publicly available and racially diverse datasets,which hinders reproducibility and subgroup-aware benchmarking. Additionally,many methods struggle to balance the restoration of fine-grained local detailswith the preservation of global facial structures, often producing results that areeither geometrically inconsistent or lack realistic details. To address these is-sues, we introduce SFHQFSR, a new FSR dataset offering high-quality, raciallydiverse facial images generated from generative models, addressing challengesin reproducibility, fairness, and dataset construction. Building on this, we pro-pose GLASNet, a hybrid state-space network that integrates global-local adaptivescanning with multi-domain refinement for high-quality facial image reconstruc-tion. GLASNet combines global semantic reasoning with region-specific localmodeling through a Region-Adaptive Scan Module and enhances restoration witha Multi-domain Refiner using channel, spatial, and frequency attention. Extensiveexperiments demonstrate that GLASNet achieves state-of-the-art performance.

Abstract:
While text-to-image diffusion models achieve impressive vi-sual quality, they frequently struggle to maintain precise alignment withcomplex compositional prompts. An effective strategy is to improve theinference process of diffusion models, thereby better leveraging their pre-trained priors to address misalignment. Existing training-free methodscan be divided into two categories. The first category focuses on improv-ing the randomly sampled initial noise to obtain an initialization thatencapsulates semantics relevant to the target text. However, existing ap-proaches either perform costly search over noise pools, with no guaranteeof finding a truly prompt-compatible noise within the limited candidates,or manipulate sampled noise without simultaneously ensuring reliablesemantic injection and preservation of the Gaussian distribution. Thesecond category focuses on improving the denoising trajectory. However,these methods lack explicit mechanisms to timely diagnose and correctCorresponding author: Tao Zhang.semantic errors, and therefore fail to prevent error propagation duringgeneration. To address these limitations, we propose AnchorSteer, atraining-free framework that exerts fine-grained control over both ini-tialization and the denoising trajectory. AnchorSteer consists oftwo synergistic components: Semantic Anchoring replaces uninfor-mative Gaussian noise with text-aligned initializations via CLIP-basedprior extraction and a novel Latent-Prior Score Distillation Sampling(LP-SDS) objective. Specifically, LP-SDS distills CLIP visual priors intothe knowledge distribution of diffusion models, mitigating the domaingap between CLIP-based priors and diffusion-based priors. ReflectiveSteering transforms passive denoising with an active Think–Erase–Retouch loop that enables mid-generation self-correction. Think phaseemploys VLM-based dual diagnosis to detect semantic deviations. Erasephase performs a targeted latent rollback with negative-guided inversionto suppress erroneous content, and Retouch phase subsequently appliespositive-guided refinement to recover the missing attributes. Therefore,Reflective Steering enables timely correction of semantic deviationsalong the denoising trajectory, preventing error propagation. Extensiveexperiments on GenEval and T2I-CompBench++ demonstrate that An-chorSteer consistently outperforms existing baselines in text–image align-ment while preserving high visual quality.

Abstract:
Multimodal Large Language Models (MLLMs) achieve re-markable progress in widespread vision-language tasks, while achievingfaithful MLLM reasoning remains a critical challenge: generating con-tent grounded in comprehensive visual evidence without hallucination.This challenge often stems from the inherent perceptual limitations ofthe single visual encoder design in most MLLMs. While many workshave turned to integrating multiple vision experts, existing methods failto leverage the unique insights of diverse experts due to unspecializedmixture and alignment strategies. To resolve these challenges, we in-troduce MoSVE (Mixture of Specialized Vision Experts), a holisticframework built upon the core insight of inter-expert complementarity.To achieve fine-grained mixture, we introduce Query-guided Com-plementary Clustering, which selectively preserves text-critical andinformative visual perception without redundancy. To cultivate expertspecialization, we propose Complementary Rejection Fine-Tuning,which explicitly routes hard samples from the anchor MLLM to the mostvisually-disparate auxiliary expert, enhancing unique insights withouthomogenization. Extensive experiments demonstrate that MoSVE notonly mitigates hallucinations across the POPE, CHAIR and MMVP, butalso improves general multimodal reasoning on MMBench. Ultimately,MoSVE provides an advanced and efficient solution for faithful MLLM

Abstract:
Visuo-Tactile policies leveraging optical tactile sensors haveshown great promise in contact-rich manipulation. These sensors achievehigh spatial resolution and multi-dimensional force sensing by utilizingan internal camera to monitor the deformation of their elastic gel sur-face, thereby indirectly inferring tactile cues. Despite their advantages,extracting fine-grained contact states necessary for contact-rich manipu-lation remains an open challenge. Existing methods typically use eitherraw images or cumulative motion fields to represent tactile cues. However,both are prone to perception ambiguity. Raw tactile images mainly cap-ture appearance changes, while cumulative motion fields only reflect theaggregate gel deformation. Consequently, distinct fine-grained contactstates can exhibit highly similar patterns, making it difficult to explicitlydistinguish subtle contact variations. To address this issue, we explorethe dynamic priors of tactile motion and discover that the correlationbetween transient and cumulative motion can explicitly distinguish fine-grained contact states. Based on this insight, we propose a motion-awaretactile representation to facilitate contact-rich manipulation. Beyond tac-tile representation, effective fusion of tactile and visual modalities is alsocritical. Most existing fusion methods either directly concatenate featuresfrom each modality or train modality-specific networks separately andfuse their outputs. However, these strategies struggle to simultaneouslymodel cross-modal interactions and preserve modality-specific character-istics. In this work, we take advantage of the Mixture-of-Transformersarchitecture and propose a unified modality-aware visuo-tactile policythat captures cross-modal complementarity while maintaining modality-specific properties. Extensive experiments on four challenging contact-rich manipulation tasks indicate the superior performance of our method.

Abstract:
Multimodal Large Language Models (MLLMs) demonstrateexceptional semantic reasoning but struggle with 3D spatial perceptionwhen restricted to pure RGB inputs. Despite leveraging implicit geo-metric priors from 3D reconstruction models, image-based methods stillexhibit a notable performance gap compared to methods using explicit3D data. We argue that this gap does not arise from insufficient geo-metric priors, but from a misalignment in the training paradigm: text-dominated fine-tuning fails to activate geometric representations withinMLLMs. Existing approaches typically resort to naive feature concate-nation and optimize directly for downstream tasks without geometry-specific supervision, leading to suboptimal structural utilization. To ad-dress this limitation, we propose GAP-MLLM, a Geometry-AlignedPre-training paradigm that explicitly activates structural perception be-fore downstream adaptation. Specifically, we introduce a visual-promptedjoint task that compels the MLLMs to predict sparse pointmaps along-side semantic labels, thereby enforcing geometric awareness. Further-more, we design a multi-level progressive fusion module with a token-level gating mechanism, enabling adaptive integration of geometric priorswithout suppressing semantic reasoning. Extensive experiments demon-strate that GAP-MLLM significantly enhances geometric feature fusionand consistently enhances performance across 3D visual grounding, 3Ddense captioning, and 3D video object detection tasks.

Abstract:
While Vision-Language Models (VLMs) achieve near-perfect scores on digital document benchmarks like OmniDocBench, their performance in the unpredictable physical world remains largely unknown due to the lack of controlled yet realistic evaluations. We introduce Real5- OmniDocBench, the first benchmark to provide a full-scale, one-to-one physical reconstruction of the complete OmniDocBench v1.5 test set (1,355 images) across five critical real-world scenarios: Scanning, Warping, Screen-Photography, Illumination, and Skew. Unlike prior benchmarks that either lack digital correspondence or employ partial sampling, our complete ground-truth mapping enables, for the first time, rigorous factor-wise attribution of performance degradation, allowing us to pinpoint whether failures stem from geometric distortions, optical artifacts, or model limitations. Our benchmark establishes a challenging new standard for the community, demonstrating that the “reality gap” in document parsing is far from closed, and provides a diagnostic tool to guide the development of truly resilient document intelligence. The Real5-OmniDocBench dataset is publicly available at https:// huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench.

Abstract:
Referring Expression Counting (REC) requires distinguishing visually similar objects described by fine-grained text cues. Existing methods tackle this via image-text contrastive learning of visual features which aims to distinguish visual features corresponding to positive and negative referring expressions. However, this strategy relies heavily on accurate image–text alignment and is constrained by the limited number of available negative expressions. We argue that these limitations can be avoided by performing contrastive learning entirely in the visual embedding space. To this end, we introduce C-REX, a simple but effective supervised contrastive learning framework that learns fine-grained visual representations by contrasting visual tokens within the same image. By shifting the negative samples from incorrect Referring Expressions to visual tokens from the image, C-REX vastly increases the number of potential negatives, providing richer and more stable supervision. This design leads to stronger fine-grained visual discrimination and better generalization across complex open-world counting settings. C-REX serves as a plug-in module that can be seamlessly applied to any existing REC model without architectural changes. We apply it in three different REC architectures and achieve state-of-the-art results improving by up to 28% in MAE and 24.5% in RMSE. Moreover, we show that our framework is versatile and general, and can be applied to other counting tasks like class-agnostic counting, improving the performance of existing models.

Abstract:
Open-World Object Detection (OWOD) requires detectors to identify previously unseen objects as unknown and incrementally incorporate them into the set of known categories, while preserving previously acquired knowledge. Existing frameworks rely heavily on exemplar replay to mitigate catastrophic forgetting, but in some real applications, storing raw data conflicts with data access restrictions and leads to data exposure risks, while incurring significant memory overhead. In this paper, we propose REAL-OW, a novel rehearsal-free framework that decouples incremental knowledge through a collaborative adapter architecture based on Low-Rank Adaptation (LoRA). Specifically, we deploy General Adapters (GAs) in the backbone to enable the significance-aware refinement of cross-task universal representations, while Specific Adapters (SAs) in the decoder provide orthogonal storage for task-specific expertise. To resolve representation drift in objectness modeling under rehearsal-free constraints, we introduce Dual-Stage Objectness Modeling (DSOM), which alternates between feature aggregation and boundary consolidation to stabilize objectness distributions while maintaining the separation between known and unknown categories. Furthermore, DSOM is supported by a Calibrated Gaussian Negative Log-Likelihood (CG-NLL) distance tailored for the dispersed feature distributions inherent in rehearsal-free settings. Extensive evaluations demonstrate that REAL-OW achieves state-of-the-art performance, surpassing existing exemplar replay methods in both detection precision and unknown discovery. Our approach establishes a new baseline for rehearsal-free OWOD.

Abstract:
Existing multimodal visual recognition methods addressing missing modalities have achieved great progress, mainly based on the feature reconstruction framework. However, feature reconstruction often introduces distribution inconsistency between synthesized and real features. Considering each modality as a different view of the same instance, each view is noisy and incomplete, but important representations, such as semantic details, tend to be shared between all modalities. Thus, we propose that AlignPrompt alleviates the distribution inconsistency problem by completing and aligning the semantic representation between synthesized and non-missing prompts. AlignPrompt is an instance-level prompt, implemented via a modal-shared-specific structure including a modalshared factor and a modal-specific factor. Specifically, for each modality pair, AlignPrompt aligns modality-specific factors of all modalities in a shared space by the modal-shared factor as an anchor, and completes the semantic representation by maximizing the mutual information between the synthesized factor and the non-missing factor. Besides, we also propose HyperPrompt to globally regulate the association of prompts of each layer based on different modalities missing scenarios: it transfers global prompts across layers by the inter-layer modules generated from an independent network. This collaborative prompts framework achieves state-of-the-art performance on multiple multimodal classification datasets while maintaining parameter efficiency.

Abstract:
DNNs are highly susceptible to noisy labels, often leadingto unstable training and severe performance degradation. Graph-basedmethods have emerged as a promising solution, utilizing sample rela-tionships to refine labels through neighborhood aggregation. However,most existing approaches rely on dense connectivity (e.g., KNN graphs),which tends to aggregate noisy cues and cause oversmoothing. Whilesparse graphs offer a potential remedy, current sparse topologies oftensuffer from blind error propagation between samples, particularly forhard samples near class boundaries. To address these challenges, we pro-pose Maximum Spanning Tree (MST) Guided Confidence and SparseGraph Network (MST-GCSN). We introduce the MST as a robust sparsebackbone to filter out redundant local noise while preserving a globalmanifold skeleton. To rectify the indiscriminate mutual influence betweennodes, a Confidence-Gated Propagation (CGP) mechanism is designedto adaptively regulate information flow based on node reliability, ensur-ing that only high-confidence semantic signals are propagated. Buildingon this optimized structure, a Progressive Label Prediction (PLP) mod-ule integrates local information and global anchor-guided cues to itera-tively correct labels. Extensive experiments on synthetic and real-worldnoisy datasets demonstrate that MST-GCSN significantly enhances ro-bustness.

Abstract:
Sim-to-real transfer remains a major obstacle for reinforce-ment learning (RL), especially for vision-based control where image ob-servations exacerbate the state-distribution shift between simulation andthe real world. Domain adaptation (DA) is a promising remedy for thischallenge. Prior sim-to-real DA works have demonstrated encouragingresults, yet these approaches typically assume substantially more targetdata, which is not available in practice. Indeed, their performance de-grades significantly when the target data budget is reduced. To addressthis challenge, we propose AIDA (Adaptive Imagination for DomainAdaptation), a domain adaptation framework for visual reinforcementlearning that addresses sim-to-real transfer under scarce target datawithout requiring additional interaction with the target environment.Our key idea is adaptive imagination: generating reliable and semanticimagination rollouts to augment limited target data. Specifically, AIDAemploys a distribution-shift-aware discriminator that truncates rolloutswhen imagined transitions drift into low-confidence regions, so that onlyreliable transitions contribute to the augmentation. On these reliabletransitions, AIDA introduces a self-consistency loss that cycles throughstate → image observation → state, penalizing discrepancies betweenthe original and reconstructed states. This provides additional adap-tation signals beyond the scarce target data. Our experiments demon-strate that adaptive imagination effectively truncates unreliable rollouts.By enforcing a self-consistency loss on the resulting reliable transitions,AIDA learns semantically meaningful state representations and outper-forms baselines across five MuJoCo tasks and two Gymnasium-Roboticstasks.

Abstract:
Interactive 3D segmentation aims to extract object masks inpoint clouds with minimal user clicks. Despite recent progress, most ex-isting approaches still struggle with (i) coarse voxel resolution that blursfine boundaries under limited clicks and (ii) hard false positives causedby confusing background structures. These issues are exacerbated bydensity and scale shifts across datasets (e.g., dense RGB-D reconstruc-tions vs. sparse LiDAR scans), where fixed refinement heuristics andpurely click-driven decoding generalize poorly. To address them, we pro-pose NegROI — a novel transformer-based interactive framework thatcouples click-centric multi-resolution refinement with scene-conditionednegative prompts. Given a coarse voxel prediction, it refines only a lo-cal Region Of Interest (ROI) around the current click on a finer gridand fuses refined logits back to the coarse mask. To improve robustnessand efficiency, we introduce uncertainty-driven selective refinement thatprioritizes ambiguous regions. Meanwhile, we model hard backgroundpatterns via a set of scene-conditioned negative prompts obtained bycross-attention over scene tokens. We further stabilize these prompts witha diversity regularizer. Finally, we propose boundary-aware hard nega-tive mining to supervise negative-prompt attention toward boundary-proximal, high-confidence false positives. Our experiments on commonbenchmark datasets (i.e., ScanNet, S3DIS, and KITTI) demonstrate im-proved click efficiency and reduced false positives, with stronger cross-dataset robustness than the state-of-the-art baselines.

Abstract:
Data scarcity is a major constraint for data-driven Text-to-3D Motion generation. Prior attempts exploit abundant 2D motion datato address it, but typically treat 3D generation as a post-hoc stage of 2Dprediction, resulting in camera-pose-dependent multi-stage pipelines andunderutilization of rich 2D motion semantics (e.g., temporal dynamics).To respond, we propose SWSL, a Semantic-aware Weakly SupervisedLearning framework that leverages 2D-motion semantics to directly opti-mize end-to-end Text-to-3D Motion models. SWSL comprises three mod-ules: Semantic Space Construction (SSC), which first constructs a sharedsemantic space aligning text, 2D-motion, and 3D-motion; Semantic-awarePseudo-label Enhancement (SPE), which then uses weak supervisionfrom 2D-motion embeddings to refine 3D pseudo-labels; and Semantic-level Feedback Optimization (SFO), which finally supplies embedding-level feedback for optimization. SWSL offers: (i) reliance reduction ontext-3D motion data by utilizing text-2D motion pairs; (ii) 2D data con-straint looseness by extracting semantics from single-view, easy-accessible2D motions; and (iii) scalability along mainstream Text-to-3D Motionadvancement. On HumanML3D, SWSL yields consistent FID reductionsunder both a domain-specific protocol and a more general TMR-basedprotocol: -0.012 and -0.017 on simple baselines, along with -0.010 on acomplicated one in a domain-specific setting, and -0.048, -0.038, and -0.056 under TMR setting - demonstrating its efficacy, robustness, andgeneralization beyond the domain-specific bias.

Abstract:
Underwater object detection is fundamentally ill-posed dueto severe light attenuation and scattering. These physical degradationsinduce highly non-linear geometric distortions in the visual feature space,deviating significantly from the canonical manifold of clear images andrendering standard Euclidean convolutions suboptimal. To address this,we propose PhysFlowNet, that reformulates representation rectificationas a physics-guided geometric evolution on a Riemannian manifold. Phys-FlowNet first extracts a joint spatial-frequency physical prior to encap-sulate macroscopic scattering mechanics. Conditioned on this prior, weintroduce the Parallel Physics-Residual Bottleneck (PPRB), which exe-cutes a metric-preconditioned Riemannian feature retraction to transportdistorted features back to their canonical states. Moreover, to counter-act cascaded smoothing during scale transitions, we introduce a Physics-Guided Manifold Downsampling (PMD) strategy to strictly preserve finedetail textures and structural boundaries. Ultimately, the network is op-timized via a Unified Evidential-Contrastive Objective (UECO). Com-prising a Supervised Contrastive Manifold Loss (SCML) and an Eviden-tial Uncertainty Regulation (EUR), UECO jointly ensures latent geo-metric alignment and reliable uncertainty calibration. Extensive evalua-tions across challenging underwater benchmarks demonstrate that Phys-FlowNet achieves state-of-the-art performance, establishing a principledgeometric paradigm for physically degraded vision tasks.

Abstract:
Single-Source Domain Generalization (SDG) in medical multi-organ image segmentation confronts severe challenges of distributionshifts caused by varying imaging protocols. However, we identify a crit-ical yet overlooked phenomenon in current methodologies: CatastrophicForgetting in SDG (CF-SDG). Specifically, existing generalization strate-gies often lack explicit protection for source domain features, leading toperformance degradation on the source domain while improving general-ization capabilities. To address this, we propose the Collaborative CausalLearning Network (CCL-Net), a unified framework that simultaneouslyachieves robust generalization and source knowledge preservation. Froma causal perspective, we construct a dual-path causal intervention mech-anism with source domain knowledge constraints. First, we design anOrthogonal Structure Disentanglement (OSD) module to learn struc-tural mediator representations, establishing a pure structural pathwayby blocking the intrusion of non-causal information via orthogonaliza-tion strategies. Second, we introduce an Anatomy-Guided Local CausalIntervention (AG-LCI) module to physically sever the spurious corre-lations between anatomical semantics and local appearances throughmask-guided counterfactual generation. Furthermore, to mitigate CF-SDG, a novel Collaborative Learning Constraint mechanism is designedto anchor source domain memory via dynamic local-global consistencyregularization. Extensive experiments on multi-modality and multi-organdatasets demonstrate that CCL-Net not only achieves state-of-the-artgeneralization performance on unseen domains but also maintains a sig-nificantly lower forgetting rate on the source domain.

Abstract:
Open-vocabulary 3D scene understanding aims to segment3D scenes beyond predefined categories by transferring semantic knowl-edge from vision-language models. Existing methods have advanced thistask by lifting language-aligned 2D features into 3D, yet they oftenrely on context-independent semantic representations, leaving object re-lationships underexplored for contextual refinement. We propose Rel-GraphOV, a relationship-aware framework that uses 3D scene graphs toenhance open-vocabulary 3D understanding. Our method constructs re-lational scene graphs from multi-view observations by leveraging vision-language reasoning to infer object relationships and prune geometricallyimplausible connections, without manual relationship annotations. Toaggregate relational context while avoiding feature interference, we in-troduce an Adaptive Gated Dual-Stream Contextual GAT that separatesdense geometric features and semantic CLIP embeddings, performs edge-guided message passing, and adaptively fuses complementary semantics.A hierarchical contrastive objective further promotes instance-level con-sistency and category-level discrimination. Experiments on ScanNetV2,ScanNet200, ScanNet++, and Replica demonstrate strong performanceand generalization ability. Project Page: cxavireh.github.io/relgraphov-projectpage

Abstract:
Due to the absence of temporal annotations, recent weaklysupervised temporal action localization (WTAL) methods often adoptpseudo-label learning to boost localization. A segment-level WTAL base-line first generates proposals and constructs pseudo labels, which thensupervise a fully supervised localization head. However, the constructionof pseudo labels depends on the proposals’ original confidence scores.Accumulated noise in proposal generation induces a mismatch betweenthese confidence scores and proposal quality, thereby limiting the achiev-able quality of the pseudo labels. To address this, we propose SCLR, aproposal-level score realignment framework driven by Semantic Com-pleteness Learning (SCL). SCL proceeds in three steps. First, for eachaction class, we construct a core semantic center that focuses on localhigh-response cues and a global teacher center that aggregates class-levelcontextual commonalities. Second, guided by the two centers, we self-supervise the decomposition of the core semantic center into generic andspecific components, then reconstruct the class center by a weighted sum.Third, we derive soft labels from proposal–to–center similarity to super-vise a per-proposal semantic-completeness score, which is then used forscore realignment. Extensive experiments on two public datasets demon-strate that SCLR, when integrated as a plug-and-play framework, consis-tently reduces score misalignment across diverse baselines and improveslocalization accuracy. Further, leveraging re-aligned proposals as pseudolabels to train the localization head yields state-of-the-art performance.

Abstract:
Segmentation in 3D microscopy is challenging due to hard (tosegment) patches, noisy labels resulting from the use of semi-automatedlabeling methods, image artifacts, off-target fluorescence, and lack of la-beled data because of high labeling effort. In this work, we introducea novel unified method to tackle these issues simultaneously in 3D mi-croscopy. First, we introduce µ3DINO, a 3D model pretrained on anultra-large multimodal dataset of over 2 million microscopy volumes.We then create µDivSeg, a segmentation pipeline that uses pretrainedweights to detect noisy labels and hard patches to guide and correct seg-mentation training. We evaluate our methods on a toy dataset and 4 real-world datasets from light-sheet and two-photon microscopy with a varietyof markers, in comparison to two state-of-the-art (SOTA) pipelines. Ourmethods outperform SOTA techniques on increasing levels of syntheticlabel perturbations and real-world data with diverse distributions.

Abstract:
When capturing images through glass surfaces or windshields on rainy days, raindrops and reflections frequently co-occur to significantly reduce the visibility of captured images. This practical problem lacks attention and needs to be resolved urgently. Prior de-raindrop, dereflection, and all-in-one models have failed to address this composite degradation. To this end, we first formally define the unified removal of raindrops and reflections (UR3) task for the first time and construct a real-shot dataset, namely RainDrop and ReFlection (RDRF), which provides a new benchmark with substantial, high-quality, diverse image pairs. Then, we propose an effective diffusion-based framework (i.e., DiffUR3) with several target designs to address this challenging task. By leveraging the powerful generative prior, DiffUR3 successfully removes both types of degradations. Extensive experiments demonstrate that our method achieves state-of-the-art performance on our benchmark and on challenging in-the-wild images.

Abstract:
As generative models increasingly produce samples that are indistinguishable from human-created content, it becomes difficult to determine whether a given data point was part of a model’s natural training set or was generated by the model itself, especially when models memorize and reproduce training data. We formalize this challenge as Member vs Generated Inference (MGI): given a sample and a target generative model, infer whether the sample is a true training member or a generated output of that model. Focusing on image generation, we show that existing membership inference methods systematically misclassify generated samples as training members, while attributionbased methods often misclassify true members as generated. This failure arises because both approaches rely on likelihood-related signals that are similarly elevated for training examples and for the model’s own outputs. To address MGI, we propose Data Circuit Breaker (DCB), a three-stage method that combines complementary signals from a generative model’s autoencoder and latent generator to distinguish training members from generated samples. Across multiple generative models, including image autoregressive and diffusion models, DCB consistently addresses the shortcomings of membership inference and attribution methods, remains effective even when models reproduce near-duplicates of training samples, and generalizes to challenging model derivative settings in which new models are trained on generated data. Our code is available at https: //github.com/sprintml/MemberGeneratedInference.

Abstract:
Existing data pruning methods predominantly rely on high-dimensional feature embeddings to measure sample importance. How-ever, these compressed vectors often obscure fine-grained semantic inter-actions, leading to suboptimal coverage of rare semantic concepts in thepruned subsets. In this paper, we propose Mapping the Concept Land-scape (MCL), a novel structural perception framework for transparentdata pruning. Instead of abstract embeddings, we represent each image-caption pair as an explicit sample-level graph comprising entities, events,and attributes. By integrating these individual graphs into a comprehen-sive dataset-level graph, we characterize the global distribution of seman-tic concepts and quantify their rarity across the entire corpus. Basedon this structured perception, we develop a greedy concept-coveragemaximization algorithm that iteratively selects samples to maximize themarginal gain of high-value, under-represented concepts. Experimentalresults on various benchmarks demonstrate that our method not onlyachieves superior pruning efficiency compared to state-of-the-art meth-ods but also provides a transparent and interpretable audit trail for theselection process.

Abstract:
Autoregressive diffusion models have enabled high-qualityvideo generation, yet their sequential nature inherently suffers from erroraccumulation. In long-horizon video synthesis, minor prediction devia-tions compound over time, inevitably leading to unconstrained genera-tive drift, structural collapse, and severe visual degradation. To addressthis, we propose Cycle-World, a novel framework designed for stable andtemporally consistent long-video generation. Our approach tackles errordrift by enforcing strict temporal reversibility across both the trainingand inference phases. Theoretically, we demonstrate that forward gener-ative drift can be strictly bottlenecked by a cycle-consistency objective.During training, we integrate an efficient reverse-prediction model to im-plicitly embed causal constraints into the forward generator, compellingit to produce reversible sequences that tightly adhere to the natural videomanifold. At inference time, we repurpose this frozen reverse model as aruntime corrector. Through gradient-based cycle guidance, it iterativelyrefines the generated latent representations, actively suppressing accu-mulated errors before they are committed to the historical context. Ex-tensive experiments on the VBench benchmark demonstrate that Cycle-World’s dual-phase synergy significantly mitigates error drift, achievingstate-of-the-art overall generation quality and long-horizon temporal con-sistency in 60-second synthesis.

Abstract:
Photorealistic 3D face avatars are increasingly deployed asreusable digital assets across applications such as telepresence, anima-tion, and personalized media. At the same time, vision–language models(VLMs) can infer sensitive attributes from rendered images with open-ended semantic reasoning without any fine-tuning. This creates a newprivacy challenge: once a 3D face avatar is shared, any of its renderingscan be analyzed to extract high-level facial attributes. Existing defenseslargely operate in 2D image space and do not address identity-preservingsemantic manipulation of 3D facial representations.We propose 3D FaceShell, a framework for steering VLM interpreta-tions of faces rendered from 3D models while preserving geometric fidelityand facial identity. 3D FaceShell augments the original 3D representa-tion with a learnable Gaussian shell that produces subtle, spatially dis-tributed perturbations optimized through multi-view embedding align-ment. The perturbations are designed to be visually inconspicuous yetsufficient to redirect VLM-based attribute inference in a view-consistentExtensive experiments on reconstructed celebrity face avatars and mul-tiple black-box VLMs demonstrate that 3D FaceShell significantly in-creases attribute injection and mismatch rates while maintaining highperceptual similarity and identity consistency. Our results show that itis possible to manipulate VLM-level semantic interpretation of 3D faceswithout compromising their human-recognizable appearance.

Abstract:
Generative video compositing, which involves inserting ex-ternal assets seamlessly into existing video sequences, is essential forcontent creation and visual effects. However, existing approaches suf-fer from a control-fidelity trade-off: they either hallucinate motion fromstatic images, failing to preserve the dynamics of pre-animated assets, orlack fine-grained spatial control for precise asset placement along user-defined trajectories. We propose FlexComposer, a unified framework thatstandardizes video compositing as a trajectory-guided conditional gen-eration task, enabling the seamless integration of both static images anddynamic footage. Our approach introduces three key designs: (1) a Uni-fied Canonical Foreground Representation that decouples an object’s in-trinsic motion from its global displacement, standardizing heterogeneousinputs into a stabilized, centered latent space; (2) a Spatial-Aware La-tent Injection strategy that exploits the translation equivariance of VAElatent spaces to transport canonical features onto target trajectories viaa parameter-free mechanism; and (3) a Hybrid Dataset and Synthetic-to-Real Curriculum that synergizes procedural simulation, real-world cine-matic footage, and generative data to implicitly learn physically plausibleillumination and shadow harmonization. This unified design handles di-verse inputs—from product photos to dynamic subjects—achieving high-fidelity motion control and environmental integration without the needfor explicit 3D reconstruction or auxiliary learnable adapters. Extensiveexperiments demonstrate that FlexComposer outperforms state-of-the-art methods in visual quality, temporal consistency, and trajectory ad-herence.

Abstract:
We introduce EgoPolice, a carefully curated dataset of real,egocentric police–civilian interactions, sourced from publicly availablebody-worn camera videos. We select police-civilian action labels that arecritical for police behavioral research and annotate them at a second-by-second granularity. The videos feature rapid and irregular cameramotion, dense human interactions, and rare high-stakes events, mak-ing the dataset a challenging benchmark for motion-robust and context-aware egocentric perception. We provide two different tasks, classifica-tion and multiple-choice question-answering, and benchmark both open-source and closed-source models. We find that even the best video modelslike Gemini 2.5 Pro still struggle to accurately predict high-risk actionssuch as “Weapon Out”. Beyond serving as a benchmark, EgoPolice pro-vides a foundation for developing models capable of identifying eventsof interest in large-scale body-worn camera video repositories, enablingmore efficient downstream human review.Content Warning: This paper includes real police body-worn camerafootage, including potentially distressing scenes.

Abstract:
As audio-visual generative models evolve into world simula-tors, cross-modal synchronization stands as a critical proxy for assessingthe consistency of world dynamics and causality in generated content.However, existing evaluation metrics presume structural correctness, re-ducing synchronization to mere temporal alignment. Consequently, theyfail on generative outputs, especially when exhibiting structural hallu-cinations and asymmetric cross-modal relations, which currently man-date expert human annotation to assess synchronization. Thisdependency introduces a critical paradox: human evaluators rely on rel-ative, reference-dependent comparisons, whereas automated metrics re-quire reference-free, absolute scalars. We resolve this paradox by propos-ing a framework that distills relative human perception into a continu-ous, globally consistent metric. First, we introduce SynthSync, a datasetof generative failures ranked via pairwise human annotations. Second,we adapt the Omni-LLM equipped with a continuous latent projectionto translate relative human rankings into continuous absolute values.Third, we propose Real-Valued Group Relative Policy Optimization (R-GRPO) to internalize the global causal structure of synchronization vialistwise score distributions. Empirically, our metric achieves state-of-the-art human preference alignment. We leverage this estimator to establisha standardized benchmark, advancing AV-Gen assessment from low-levelsignal correlation to visually grounded causality.

Abstract:
Video generation has advanced to produce visually compelling and temporally coherent results. Yet, whether these models can genuinely think with video—executing symbolic rules, respecting physical laws, and pursuing intentional goals—remains an open question. Existing benchmarks only partially address this, often conflating visual quality with cognitive correctness. We introduce VWG-Bench (Video World Generalist Benchmark), a comprehensive benchmark spanning 9 reasoning dimensions and 38 fine-grained tasks. To enable precise diagnosis, we design a three-level VLM-as-Judge protocol that independently assesses video-level fluency, task-level rule adherence, and samplelevel goal realization. Evaluations of leading models reveal a striking gap: while models achieve strong rendering scores, they consistently fail on logic-heavy and rule-constrained tasks. To address this, we propose Vid-PRE (Video Prompt Reasoner and Enhancer), a model-agnostic prompt rewriter that offloads the cognitive burden of reasoning to a dedicated VLM. Trained via reinforcement learning with purely textbased rewards, Vid-PRE produces concise, constraint-aware prompts without the instability of video-level reward signals. Experiments show that Vid-PRE yields substantial reasoning improvements across multiple generators without architectural modifications. Together, VWG-Bench and Vid-PRE offer a rigorous diagnostic lens and a scalable path toward true think-with-video capabilities. All data and code are publicly available at https://huggingface.co/datasets/KlingTeam/VWG-Bench.

Abstract:
Efficiently merging several models fine-tuned for differenttasks, but stemming from the same pretrained base model, is of greatpractical interest. Despite extensive prior work, most evaluations of modelmerging in computer vision are restricted to image classification usingCLIP, where different classification datasets define different tasks. In thiswork, our goal is to make model merging more practical and show itsrelevance on challenging scenarios beyond this specific setting. In mostvision scenarios, different tasks rely on trainable and usually heteroge-neous decoders. Differently from previous studies with frozen decoders,where merged models can be evaluated right away, the non-trivial cost ofdecoder training renders hyperparameter selection based on downstreamperformance impractical. To address this, we introduce the task align-ment proxy, and show how it can be used to speed up hyperparameterselection by orders of magnitude while retaining performance. Equippedwith the task alignment proxy, we extend the applicability of modelmerging to multi-task vision models beyond CLIP-based classification.

Abstract:
Humans develop object orientation understanding progressively, from recognizing which way an object faces, to mentally rotating it, to reasoning about how multiple objects are oriented relative to each other. Yet current vision-language benchmarks treat orientation as an afterthought, conflating it with positional relationships and general scene understanding. We introduce Discriminative Orientation Reasoning Intelligence (DORI), a cognition-informed hierarchical benchmark that establishes object orientation as the primary evaluation target. Specifically, DORI decomposes orientation into four dimensions, each assessed at both coarse (categorical) and granular (metric) levels. This results in 33,656 multiple-choice questions over diverse open-vocabulary objects within 13,652 real-world and synthetic images taken from 14 sources. DORI’s coarse-to-granular design isolates orientation from confounds such as object recognition difficulty, scene clutter, and linguistic ambiguity through bounding-box isolation, standardized spatial reference frames, and structured prompts. Our evaluation of 26 state-of-the-art vision-language models reveals a consistent pattern: models competent on general spatial benchmarks remain near-random on object-centric orientation tasks. Even the best models achieve only 64.2% on coarse and 42.9% on granular judgments, with the largest drops on compound rotations and inter-object reference frame shifts. Large coarse-to-granular gaps further expose that models rely on categorical heuristics rather than geometric reasoning, a limitation invisible to existing benchmarks. These findings establish orientation understanding as an unsolved challenge in multimodal systems, with direct implications for robotic manipulation, 3D scene reconstruction, and human-AI interaction. Find the dataset here: https://huggingface.co/datasets/appledora/DORI-Benchmark

Abstract:
Understanding human attention is fundamental for scene interpretation, yet existing approaches often rely on heavily trained models that lack interpretability. Prior methods struggle to jointly reason about gaze targets, attended objects, and visual grounding without extensive supervision. To the best of our knowledge, this work introduces the first training-free Gaze Target Agent (GTA) for gaze-guided reasoning across tasks such as gaze target prediction, attention localization, and object identification. This is achieved by leveraging pretrained vision–language models, augmenting them with visually guided prompts, and employing a memory-based retrieval strategy for high-uncertainty samples to improve performance without additional training. We evaluate our approach using both quantitative metrics and qualitative results. Quantitatively, our method achieves state of the art performance on the GazeFollow and GazeHOI benchmarks. Qualitatively, our agent provides detailed semantic predictions, predicts the correct targets even when ground truth labels are wrong, and remains flexible without vocabulary constraints.

Abstract:
This paper introduces the concept of Microscopic Spatial In-telligence (MiSI), the capability to perceive and reason about the spatialrelationships of invisible microscopic entities, which is fundamental toscientific discovery. To assess the potential of Vision-Language Models(VLMs) in this domain, we propose a systematic benchmark frameworkMiSI-Bench. This framework features over 163,000 question-answer pairsand 587,975 images derived from approximately 4,000 molecular struc-tures, covering nine complementary tasks that evaluate abilities rangingfrom elementary spatial transformations to complex relational identifi-cations. Experimental results reveal that current state-of-the-art VLMsperform significantly below human level on this benchmark. However,a fine-tuned 7B model demonstrates substantial potential, even sur-passing humans in spatial transformation tasks, while its poor perfor-mance in scientifically-grounded tasks like hydrogen bond recognitionunderscores the necessity of integrating explicit domain knowledge forprogress toward scientific AGI. The datasets are available at https://huggingface.co/datasets/zongzhao/MiSI-bench.

Abstract:
The long-standing vision of general-purpose robots hinges on theirability to understand and act upon natural language instructions. Vision-Language-Action (VLA) models have made remarkable progress toward thisgoal, yet their generated actions can still misalign with the given instructions.In this paper, we investigate test-time verification as a means to shrinkthe “intention-action gap.” We first characterize the test-time scaling lawsfor embodied instruction following and demonstrate that jointly scaling thenumber of rephrased instructions and generated actions greatly increasestest-time sample diversity, often recovering correct actions more efficientlythan scaling each dimension independently. To capitalize on these scalinglaws, we present CoVer, a contrastive verifier for vision–language–actionalignment, and show that our architecture scales gracefully with additionalcomputational resources and data. We then introduce CoVer-VLA, a hierar-chical test-time verification pipeline using the trained verifier. At deployment,our framework precomputes a diverse set of rephrased instructions from aVision-Language-Model (VLM), repeatedly generates action candidates foreach instruction, and then uses the verifier to select the optimal high-levelprompt and low-level action chunks. Compared to scaling policy pre-training⋆ †Equal contribution. § Equal advising.on the same data, our verification approach yields 22% gains in-distributionand 13% out-of-distribution on the SIMPLER benchmark, with a further45% improvement in real-world experiments. On the PolaRiS benchmark,CoVer-VLA achieves 14% gains in task progress and 9% in success rate.

Abstract:
Estimating category-level 9-DoF object poses (rotation, trans-lation, scale) is fundamental for robotic manipulation and autonomoussystems. Generative approaches, particularly diffusion-based models, haveachieved strong accuracy by effectively bridging the sim-to-real domaingap through large-scale synthetic training. However, it is difficult fordiffusion-based approaches to achieve both high accuracy and real-timeperformance due to their reliance on a multi-step iterative inference pro-cess. To address this limitation, we propose Geometric Prior GeodesicFlow Matching (G2FM), an efficient, simulation-free training objectivethat learns a deterministic vector field to directly map noise to pose dis-tributions. G2FM learns a vector field mapping noise to pose distribu-tions with only three ODE steps, achieving high accuracy and real-timeperformance (> 30 FPS). To avoid the structural distortion and singular-ities caused by Euclidean approximations, we construct a geometrically-consistent trajectory that operates directly on the SO(3) manifold bytransporting rotations along geodesic paths. Furthermore, to dynami-cally refine the generative flow during inference and enhance accuracywithout retraining, we design a training-free geometric guidance mech-anism by leveraging Chamfer distance gradients to project pose correc-tions directly onto the SO(3) tangent space. Extensive experiments showthat G2FM delivers a favorable accuracy-efficiency-generalization trade-off for category-level RGB-D 9-DoF pose estimation. Code will be re-leased upon publication.

Abstract:
Deep generative models, while revolutionizing image gener-ation, largely operate as opaque “black boxes”, hindering human un-derstanding, control, and alignment. While methods like sparse autoen-coders (SAEs) show remarkable empirical success, they often lack theo-retical guarantees, risking subjective insights. Our primary objective is toestablish a principled foundation for interpretable generative models. Wedemonstrate that the principle of causal minimality – favoring the sim-plest causal explanation – can endow the latent representations of gener-ative models with clear causal interpretation and robust, component-wiseidentifiable control. We introduce a novel theoretical framework for hi-erarchical selection models, where higher-level concepts emerge from theconstrained composition of lower-level variables, better capturing thecomplex dependencies in data generation. Under theoretically derivedminimality conditions that manifest as sparsity constraints, we show thatlearned representations can be equivalent to the true latent variables ofthe data-generating process. Empirically, applying these constraints totext-to-image diffusion models allows us to extract their innate hierarchi-cal concept graphs, offering fresh insights into their internal knowledgeorganization. Furthermore, these causally grounded concepts serve aslevers for fine-grained model steering, paving the way for transparent,reliable systems.

Abstract:
The development of unified multimodal large language mod-els (MLLMs) is fundamentally challenged by the granularity gap be-tween visual understanding and generation: understanding requires high-level semantic abstractions, while image generation demands fine-grainedpixel-level representations. Existing approaches usually enforce the twosupervision on the same set of representation or decouple these two su-pervision on separate feature spaces, leading to interference and incon-sistency, respectively. In this work, we propose EvoTok, a unified imagetokenizer that reconciles these requirements through a residual evolutionprocess within a shared latent space. Instead of maintaining separatetoken spaces for pixels and semantics, EvoTok encodes an image intoa cascaded sequence of residual tokens via residual vector quantization.This residual sequence forms an evolution trajectory where earlier stagescapture low-level details and deeper stages progressively transition to-ward high-level semantic representations. Despite being trained on a rel-atively modest dataset of 13M images, far smaller than the billion-scaledatasets used by many previous unified tokenizers, EvoTok achieves astrong reconstruction quality of 0.43 / 0.25 rFID at 256×256 / 384×384resolutions on ImageNet-1K, respectively. When integrated with a largelanguage model, EvoTok shows promising performance across 8 out of 9visual understanding benchmarks, and remarkable results on image gen-eration benchmarks such as GenEval, GenAI-Bench, and DPG. Theseresults demonstrate that modeling visual representations as an evolvingtrajectory provides an effective and principled solution for unifying visualunderstanding and generation.

Abstract:
We introduce the challenging Sterkfontein Caves dataset comprising ten underground scenes from a UNESCO World Heritage Site, and use it to find a new simple baseline method that beats existing low-light reconstruction methods upon it. Each scene is of complex surface geometry and high-frequency texture from cave rock structures, including humanmade markings on the rock face. The captured images exhibit varied or uncontrolled lighting over a large dynamic range, with glare artefacts, and low signal-to-noise ratios from the challenging dark real-world capture scenario.Weproposeaviewsynthesisbenchmarkforlow-lightRAWandsRGB reconstruction. All tested NeRF and Gaussian splatting baseline methods struggleonthisdata,withthebestperformingmethodintermsofreliability andaveragePSNRbeingourRaw-Nerfactomethod.Wediscusstheseerrors in detail to find directions of future work for the community in overcoming the significant challenges that remain in low-light high-detail scenes. Our dataset is publicly available at https://visual.cs.brown.edu/sterkfontein.

Abstract:
While 3D Gaussian Splatting (3DGS) has revolutionized novelview synthesis with its photorealistic and real-time rendering, its lengthytraining process remains a critical bottleneck. Existing acceleration meth-ods largely treat algorithm design and system optimization in isolation,failing to fully unleash hardware potential. To systematically addressthis, we propose LiteGS, a high-performance framework driven by afundamental system-algorithm codesign. At the GPU and data layers,LiteGS introduces a warp-based rasterization paradigm and a Cluster-Cull-Compact pipeline, fundamentally resolving data conflicts and spa-tial locality issues. Empowered by this robust backend, we introduce avariance-guided densification metric at the algorithm layer to accuratelyidentify under-reconstructed regions. This powerful synergy establishes anew state-of-the-art: LiteGS consistently achieves the highest renderingquality and the fastest training speed across all parameter scales. Exten-sive evaluations demonstrate our milestone performance. Compared tothe high-quality SOTA (3DGS-MCMC), LiteGS achieves at least a 10×speedup while matching or exceeding its fidelity. Furthermore, by unlock-ing a “space-for-time” aggressive densification strategy, LiteGS success-fully compresses the training time to the sub-minute level on RTX3090(further to ∼30 seconds on RTX 4090) while securing rendering qualitycomparable to the original 3DGS.

Abstract:
4D Gaussian Splatting (4DGS) can render dynamic scenesphotorealistically. However, with limited viewpoint coverage, some spa-tiotemporal regions remain sparsely observed, leading to artifacts, partic-ularly in scenes with large motion. Existing approaches leveraging gener-ative models rely on heuristic virtual-viewpoint selection before refiningrendered views. As a result, they cannot actively explore such sparselyobserved regions. To address this issue, we propose a pipeline that ac-tively selects spatiotemporal virtual viewpoints to improve 4DGS recon-struction. Our method selects virtual viewpoints for generative enhance-ment based on the rendering sensitivity and motion-aware observationdensity of 4D Gaussians, prioritizing views that alleviate observationsparsity. In the refined images, we filter out regions that conflict withcaptured observations or are likely to contain generative artifacts andthen fine-tune 4DGS using only the reliable regions. We evaluate ourmethod on multi-view video benchmarks using new train/test splits de-signed to induce observation gaps. Results show consistent improvementsover prior viewpoint selection strategies and fine-tuning methods in bothqualitative and quantitative evaluations, while reducing artifacts. 1

Abstract:
Despite rapid advances in chest X-ray (CXR) foundationmodels, most radiology report generation (RRG) systems still rely onheavily downsampled inputs (e.g., 256×256) due to the fixed visual tokenbudgets of pretrained vision encoders, suppressing subtle yet clinicallyimportant cues present in native-resolution images. However, enablinghigh-resolution (high-res) perception remains challenging: naïve tilingcauses prohibitive token inflation, while global compression suppressessubtle lesions and degrades diagnostic fidelity. Inspired by radiologists’workflow, localizing suspicious regions before detailed high-res assess-ment. We propose Lesion-Aware High-Resolution Patch Discovery andFusion for Chest X-ray Reporting (LePaX), the first RRG frameworkthat enables efficient high-res CXR perception (up to 1920×1920) with-out increasing the vision-token count. LePaX formulates high-res percep-tion as a constrained spatial resolution allocation problem under a fixedtoken budget and introduces two key components: Learnable Spatial Res-olution Allocation (LSRA), which learns a spatial utility map that adap-tively allocates limited high-res capacity to diagnostically relevant re-gions, enabling targeted extraction of high-res patches from native CXRs;and Global–Regional Fusion (GRF), which performs token-preservingregion-to-global refinement by projecting high-resolution regional evi-dence back onto the global feature grid through spatially aligned resolu-tion write-back, avoiding token inflation. Experiments on multiple CXRbenchmarks demonstrate that LePaX consistently improves both clinicaland linguistic metrics while enabling native-resolution CXR perceptionwith over 10× fewer visual tokens than naïve high-res tiling.

Abstract:
Single Image Super-Resolution (SISR) is a fundamental com-puter vision task that aims to reconstruct a high-resolution (HR) im-age from a low-resolution (LR) input. Transformer-based methods haveachieved remarkable performance by modeling long-range dependenciesin degraded images. However, their feature-intensive attention compu-tation incurs high computational cost. To improve efficiency, most ex-isting approaches partition images into fixed groups and restrict atten-tion within each group. Such group-wise attention overlooks the inher-ent asymmetry in token similarities, thereby failing to enable flexibleand token-adaptive attention computation. To address this limitation,we propose the Individualized Exploratory Transformer (IET), which in-troduces a novel Individualized Exploratory Attention (IEA) mechanismthat allows each token to adaptively select its own content-aware andindependent attention candidates. This token-adaptive and asymmetricdesign enables more precise information aggregation while maintainingcomputational efficiency. Extensive experiments on standard SR bench-marks demonstrate that IET achieves state-of-the-art performance undercomparable computational complexity. The code is available at here.

Abstract:
Generalized face anti-spoofing (FAS) has attracted increas-ing attention due to the need for robustness in unseen scenarios. Do-main adaptation methods can improve performance by leveraging target-domain information. However, in FAS, collecting spoof samples is signif-icantly more expensive than acquiring a few bonafide samples, makingit impractical to gather diverse attack types and finetune models foreach deployment scenario. To address this challenge, we propose Text-guided RelAtionship Modeling (TRAM), a finetuning-free test-time do-main adaptation approach for generalized FAS using only a few bonafidesamples. Since samples within a target domain share common domainfactors, we treat genuine sample as the anchor and model its relation-ship with incoming inputs to mitigate domain bias. To learn discrimina-tive spoof cues from these relationships, we introduce a Relative Image-Pair-Text Contrastive Learning strategy that leverages fine-grained FASrelation prompts. Furthermore, to compensate for the lack of detailedspoof annotations, we design a Fine-grained Relation Prompt Learn-ing strategy that mines low-level photometric discrepancies through pre-computed measurements to generate robust prompts. Extensive exper-iments on eight FAS datasets with typical variations demonstrate thatTRAM achieves consistent improvements over state-of-the-art methodswhile using only a few bonafide samples from the target domain (as fewas five). Comprehensive ablation studies further validate the effectivenessof each component.

Abstract:
While there are many techniques (e.g., QR codes) that con-vey information via visual patterns, some applications would benefit fromusing imperceptible patterns. We present a method for designing subtlecode-conveying patterns that can be printed on transparent sticker paperand applied to real-world surfaces. An image of a scene with an encodedsticker can be captured and the sticker localized and decoded. Thus,our stickers, while mostly unobtrusive to the human eye, are detectableand decodable to our model. We jointly optimize the encoder, localizer,and decoder end-to-end, balancing imperceptibility and accuracy. Ourmethod encodes 100-bit secrets and experimental results show that itis robust to human placement errors, imaging conditions, multiple stick-ers, occlusions, and wrinkles in stickers. Importantly, this method is easyto deploy and applicable in situations such as object tracking for aug-mented reality, conveyance of information about tourist sites, and subtleidentification of manufactured goods or their components.

Abstract:
Dense 3D sensors in various real-world fields produce point clouds that are geometrically redundant for real-time processing. In this paper, we propose an efficient and scalable learning-based anisotropic surface approximation framework, HD-PEA, that operates directly on unstructured point clouds, integrating anisotropic optimization into reconstruction to produce compact, geometry-aligned surface representations with higher fidelity, fewer elements, and improved numerical stability compared to isotropic and adaptive meshes. Firstly, we develop a novel learning-based high-dimensional (high-d) Euclidean point embedding method to map the input point clouds into a high-d manifold embedding space. For handling large-scale point clouds without retraining and fine-tuning, a patch-based meta-embedding scheme is designed during the inference stage. Then, we develop a new tangent subspace estimation for the high-d embedding manifold approximation and anisotropic manifold reconstruction in high-d space. The main contribution of this work is to propose a scalable deep learning framework and a variety of datasets for constructing a high-d Euclidean point embedding space aimed to 3D anisotropic surface mesh approximation and Riemannian curvature tensor estimation from point clouds. We extensively evaluate our method against state-of-the-art surface reconstruction approaches using several datasets, such as Thingi10K dataset, AIM@SHAPE and Stanford 3D Scanning Repository, ScanNet dataset, and further demonstrate its generalization and usability on diverse unseen shapes and applications from these datasets.

Abstract:
Due to the scarcity of large-scale in-the-wild triplet data and the improper use of masks, the performance of video virtual try-on models remains limited. In this paper, we first introduce TripVVT-10K, the largest and most diverse in-the-wild triplet dataset to date, providing explicit video-level cross-garment supervision that existing video datasets lack. Built upon this resource, we develop TripVVT, a Diffusion Transformer–based framework that replaces fragile garment masks with a simple, stable human-mask prior, enabling reliable background preservation while remaining robust to real-world motion, occlusion, and cluttered scenes. To support comprehensive evaluation, we further establish TripVVT-Bench, a 100-case benchmark covering diverse garments, complex environments, and multi-person scenarios, with metrics spanning video quality, try-on fidelity, background consistency, and temporal coherence. Compared to state-of-the-art academic and commercial systems, TripVVT achieves superior video quality and garment fidelity while markedly improving generalization to challenging in-thewild videos. We publicly release the dataset and benchmark at https://huggingface.co/datasets/TripVVT/TripVVT-10K, which we believe provide a solid foundation for advancing controllable, realistic, and temporally stable video virtual try-on.

Abstract:
Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depictingtarget subjects according to user instructions. However, evaluating thesemodels remains a significant challenge. Existing benchmarks exhibit criti-cal limitations: 1) insufficient diversity and comprehensiveness in subjectimages, 2) inadequate granularity in assessing model performance acrossdifferent subject difficulty levels and prompt scenarios, and 3) a pro-found lack of actionable insights and diagnostic guidance for subsequentmodel refinement. To address these limitations, we propose DSH-Bench,a comprehensive benchmark that enables systematic multi-perspectiveanalysis of subject-driven T2I models through four principal innovations:1) a hierarchical taxonomy sampling mechanism ensuring comprehensivesubject representation across 58 fine-grained categories, 2) an innovativeclassification scheme categorizing both subject difficulty level and promptscenario for granular capability assessment, 3) a novel Subject IdentityConsistency Score (SICS) metric demonstrating a 9.4% higher correlationwith human evaluation compared to existing measures in quantifyingsubject preservation, and 4) a comprehensive set of diagnostic insightsderived from the benchmark, offering critical guidance for optimizing fu-ture model training paradigms and data construction strategies. Throughan extensive empirical evaluation of 19 leading models, DSH-Bench un-covers previously obscured limitations in current approaches, establishingconcrete directions for future research and development.

Abstract:
Video object removal is a fundamental yet challenging task in video editing. Despite recent progress, existing methods typically fall into two categories. Traditional approaches based on optical flow or attention mechanisms often introduce noticeable artifacts and yield unnatural results. In contrast, diffusion-based methods improve visual realism but demand multiple denoising steps, limiting their practicality. To address these issues, we propose From-Draft-to-Draft-Free (D2DF), a framework that distills the ability of transforming coarse drafts into refined videos into a one-step video generation model. Within D2DF, a teacher model is trained to refine low-quality removal results (“drafts”) into high-fidelity videos by multiple steps. Then, through Prior-Privileged Consistency Distillation (PPCD), we distill this capability into a student model that performs one-step removal conditioned on the draft. To eliminate draft dependency, we introduce a Self-Guided Fast Planting (SGFP) module based on our Temporal Masked Transformer that autonomously generates scene-consistent pseudo-drafts in latent space, enabling a fully draft-free one-step model. Extensive experiments show that both draft-conditioned and draft-free versions achieve state-of-the-art performance on multiple metrics, surpassing traditional and multi-step generative methods in both quality and efficiency. The denoising process for a single video takes only about 1 second.

Abstract:
Volume microscopy, including electron and light microscopy,suffers from severe anisotropic resolution due to physical axial section-ing. Existing self-supervised axial super-resolution (ASR) methods facea trilemma bounded by overly smoothed regression textures, structuralhallucinations of pure diffusion models, and prohibitive inference latency.In this paper, we propose Skeleton-refinE Microscopy (SkelEM), a self-supervised framework that decouples ASR at the training-signal level:a frozen topological network and a diffusion refiner are optimized bydisjoint objectives, separating low-frequency topology formulation fromhigh-frequency detail enhancement. Building on this deterministic skele-ton, we exploit a unified cycle-consistent mechanism on input sparseslices to simultaneously extract a real-domain residual prior and bidi-rectionally align the diffusion refiner, washing away cross-plane arti-facts without synthetic bias. By truncating the reverse diffusion processwith this physical prior, SkelEM achieves high-fidelity detail restorationin merely ≤ 5 steps. To rigorously assess cross-instrument generaliza-tion, we further introduce BRAVE-ASR, a new benchmark of co-alignedanisotropic and isotropic volumes acquired on a Plasma-FIB instrument.Across public benchmarks, SkelEM achieves the most favorable balanceacross the fidelity-perception trade-off among self-supervised methods,with state-of-the-art downstream membrane segmentation performanceand robust zero-shot generalization across distinct modalities.

Abstract:
Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and training-free loss guidance that compresses rich conditions into scalar objectives and applies stepwise guidance, leading to information bottlenecks and error accumulation along the sampling trajectory. Given the urgent need for an effective unified framework across diverse conditional image generation tasks, we propose Data Injection and Contrastive Trajectory Refinement (DICT), a training-free inference method that enhances conditional image generation without introducing task-dependent architectures. DICT introduces Data Injection, where noise-perturbed conditional signals are integrated into early denoising stages; by performing guided denoising on these injected signals, DICT adaptively selects and distills task-salient information from the raw condition, effectively preserving spatial richness and ensuring precise condition-to-generation alignment. Furthermore, DICT applies Contrastive Trajectory Refinement across adjacent denoising states, enabling pairwise comparisons that progressively improve sample quality. These designs keep inference simple while improving cross-task transfer under a unified diffusion formulation. Extensive experiments on conditional image generation tasks (e.g., style transfer, image super-resolution, and image deblurring) show consistent gains in fidelity and perceptual quality over representative task-specific and loss-guided baselines.

Abstract:
Large Multimodal Models (LMMs) show strong few-shotgeneralization, but industrial anomaly detection remains difficult becausedefects are small, input resolution is limited, and textual standardsare not always grounded in visual evidence. Recent optimization-basedmethods improve alignment through fine-tuning, but they often requiremany defective samples, which are unavailable in early deployment. Wepresent Global Logic and Local Search (GLLS), a training-freeframework for reference-guided multimodal in-context verification. GLLSuses a Part-Aware Visual-Logical Atlas to organize normal references andstructured specifications in the inference context. It combines a Global& Logic Stream, where SAM 3 extracts partially checkable visual facts,with a Fine-Grained & Actions Stream, where MCTS selects localevidence crops under a fixed budget. Experiments on MMAD-QA andadditional anomaly detection datasets show consistent gains over matchedand general-purpose baselines, while keeping the final diagnostic decisiontraceable to explicit visual evidence throughout the inspection trace.

Abstract:
Humans seamlessly integrate egocentric perceptions with exocentric perspectives to comprehend dynamic spatial positioning. For Embodied AI, mastering this ego-exo spatial reasoning is a fundamental prerequisite for real-world interaction. Although Multimodal Large Language Models (MLLMs) have demonstrated significant progress in general visual reasoning, their capacity for ego-exo spatial reasoning in real-world environments remains largely unexplored. To investigate whether current MLLMs exhibit such human-like spatial cognition, we introduce SEERBench, a rigorous benchmark comprising 1,198 highquality, human-annotated question-answer pairs across 35 diverse realworld Ego-Exo scenarios. SEERBench evaluates models through 10 distinct tasks structured into three progressive cognitive levels: Spatial Perception, Spatial Imagination, and Ego-Exo Collaboration. Extensive evaluations of 19 representative MLLMs reveal a substantial performance gap: even the best-performing frontier models trail human experts by a margin of 46.8%, highlighting severe limitations in current multimodal foundations. To address this, we propose SEER-Map, a training-free, tool-augmented baseline. By explicitly constructing top-down Bird’s-Eye View (BEV) spatial topologies to assist in ego-exo geometric alignment, SEER-Map functions as an Oracle-informed probing baseline lower the threshold of spatial understanding. Codes and data are available here.

Abstract:
Zero-shot skeleton-based action recognition aims to identifyunseen actions via semantic transfer. The existing methods often strug-gle with the topological mismatch between skeleton and text manifolds,leading to loose decision boundaries due to weak constraints and fail-ing to adapt to unseen distributions during static inference. To miti-gate this challenge, we propose ProtoFM, a novel generative frameworkbased on conditional flow matching and prototype guidance. First, in-stead of heterogeneous skeleton-text matching, ProtoFM introduces agenerative matching paradigm that performs homogeneous skeleton andpseudo-skeleton matching by synthesizing features via ODE-based flowintegration. Second, we propose a prototype-guided center loss that an-chors flow-trajectory states to learnable class prototypes, encouragingcompact semantic clusters and improved class separation. Last, at theinference phase, we develop a training-free prototype-guided rectificationstrategy that exploits a global memory bank for progressive test-timerefinement. Extensive experimental results obtained on multiple bench-marking datasets demonstrate the superiority of the proposed ProtoFMmethod over the state-of-the-art approaches.

Abstract:
As primary consumers in the marine food chain, zooplanktonplay a crucial role in maintaining marine ecological balance. However, theSegment Anything Model (SAM) exhibits limited performance in micro-scopic image instance segmentation due to its lack of zooplankton-specificdomain knowledge. To address these challenges, we propose a novel in-stance segmentation model based on SAM and wavelet transform (ZMIS-SAM), effectively tackling issues such as inaccurate classification, dis-continuous segmentation of slender appendages, and incomplete bound-ary segmentation. Our framework incorporates three core innovations:ZM-ViT enhances SAM’s capability to model zooplankton morphologyand image intensity distributions through two lightweight adapters, theNeighboring Feature Aggregation Module (NFAM) improves continu-ous segmentation of semi-transparent slender appendages by integrat-ing general-purpose and domain-specific features, and the Wavelet-basedMulti-scale Multi-directional Feature Enhancement (WM2FE) moduleeffectively recovers high-frequency details to refine boundary segmenta-tion completeness. Extensive experiments demonstrate that ZMIS-SAMachieves state-of-the-art instance segmentation performance on the zoo-plankton dataset and exhibits strong generalization capability acrossmultiple public cross-domain datasets. Code: ZMIS-SAM.

Abstract:
Prototype networks provide an intrinsic case-based expla-nation mechanism, but their interpretability is often undermined byprototype collapse, where multiple prototypes degenerate to highly re-dundant evidence. We attribute this failure mode to the terminal dy-namics of Neural Collapse, where cross-entropy optimization suppressesintra-class variance and drives class-conditional features toward a low-dimensional limit. To mitigate this, we propose Adaptive Manifold Pro-totypes (AMP), a framework that leverages Riemannian optimizationon the Stiefel manifold to represent class prototypes as orthonormalbases and make rank-one prototype collapse infeasible by construction.AMP further learns class-specific effective rank via a proximal gradi-ent update on a non-negative capacity vector, and introduces spatialregularizers that reduce rotational ambiguity and encourage localized,non-overlapping part evidence. Extensive experiments on fine-grainedbenchmarks demonstrate that AMP achieves state-of-the-art classifica-tion accuracy while significantly improving causal faithfulness over priorinterpretable models.

Abstract:
Cross-modal music generation from text and visual inputsis uniquely challenging due to its subjective, one-to-many nature. Cur-rent methods face three major limitations: they are bottlenecked byscarce paired data, lack explicit one-to-many modeling, and isolate gen-eration from retrieval. To address these issues, we propose Meric, aunified framework for multimodal music generation and retrieval. Ourcore innovation is a music semantic anchor that decouples multimodalunderstanding from acoustic synthesis. We employ a two-stage train-ing pipeline. First, a Flow Matching decoder learns to synthesize audiofrom anchor embeddings using large, unpaired music corpora. Second, alightweight generative diffusion module maps multimodal inputs into thiscontinuous anchor space using limited paired data. This decoupled de-sign explicitly models cross-modal ambiguity and exploits unpaired datato bypass the paired data bottleneck. During inference, the predicted an-chor embedding simultaneously acts as a condition for acoustic synthesisand a query for music retrieval. Extensive experiments demonstrate thatMeric achieves state-of-the-art performance across text-to-music andvision-to-music generation and retrieval benchmarks.

Abstract:
Generating lifelike facial animation for dyadic conversationsrequires reconciling high-level cognitive intent with precise low-level mo-tor reflexes, yet existing methods fall short in the semantic understandingof dialogue context and in precise dynamic control. In this paper, we pro-pose MindFlow, a dual-pathway generative framework inspired by theVentral-Dorsal pathway model in neuroscience, which decouples genera-tion into two collaborative streams, thereby harmonizing deep semanticreasoning with fine-grained control. In the Ventral module, we trans-form the conventional Sentence-Action approach into a novel Chunk-State approach that models raw acoustic streams as a context-aware,evolving emotional state chain, capturing subtle paralinguistic nuancesand mid-utterance emotional shifts missed by sentence-level modeling.The Dorsal module features a conditional autoregressive flow matchingnetwork for high-fidelity facial motion, driven by high-frequency acousticcues and modulated by emotion states, plus a Selective Acoustic Injectorfor adaptive audio gating to ensure robustness in talking-and-listeningdynamics without interference. Extensive experiments demonstrate thatMindFlow achieves superior semantic appropriateness and motion natu-ralness compared to state-of-the-art baselines.

Abstract:
Camera traps are crucial for large-scale biodiversity monitor-ing, yet accurate automated analysis remains challenging due to diversedeployment environments. While the computer vision community haspredominantly framed this challenge as cross-domain (e.g., cross-site)generalization, this perspective overlooks a primary challenge faced byecological practitioners: maintaining reliable recognition at the fixed siteover time, where the dynamic nature of ecosystems introduces profoundtemporal shifts in both background and animal distributions. To bridgethis gap, we present the first unified study of camera-trap speciesrecognition over time. We introduce a realistic, large-scale bench-mark comprising 546 camera traps with a streaming protocol that evalu-ates models over chronologically ordered intervals. Our end-user-centricstudy yields four key findings. (1) Biological foundation models (e.g.,BioCLIP 2) underperform at numerous sites even in initial intervals,underscoring the necessity of site-specific adaptation. (2) Adaptation ischallenging under realistic evaluation: when models are updated usingpast data and evaluated on future intervals (mirrors real deployment life-cycles), naive adaptation can even degrade below zero-shot performance.(3) We identify two main drivers of this di!culty: severe class imbalanceand pronounced temporal shift in both species distribution and back-grounds between consecutive intervals. (4) We find that e"ective integra-tion of model-update and post-processing techniques can largely improveaccuracy, though a gap from the upper bounds remains. Finally, we high-light critical open questions, such as predicting when zero-shot modelswill succeed at a new site and determining whether/when model updatesare necessary. Together, our benchmark and analysis provide actionabledeployment guidelines for ecological practitioners while establishing newdirections for future research in vision and machine learning.

Abstract:
Synthetic data for autonomous driving is surging, poweredby diffusion models that promise scalable scene generation. Yet key ob-stacles remain, since multi-view and temporal consistency often requiresbackbone fine-tuning or added layers, which can erode pretrained knowl-edge and weaken text alignment. Models also stay close to the train-ing distribution, struggling under adverse weather and unseen configu-rations, while fidelity favors frequent over rare classes. We address thesegaps with FrozenDrive, a controllable generative framework that pre-serves pretrained diffusion knowledge while achieving strong consistency.FrozenDrive conditions on driving-stack signals and text prompts, andexpands the context of frozen self-attention across views and frames topromote cross-view alignment and temporal coherence in one pass, with-out trainable spatio-temporal modules in the diffusion backbone. Anobject-focused constraint further improves fidelity for rare categories.Without weather- or scene-specific fine-tuning, FrozenDrive synthesizesglobally coherent multi-view driving scenes from text and surpasses priorbaselines under adverse and rare conditions. On nuScenes, FrozenDrive-augmented data improves AD model performance, especially at night andin rain, demonstrating strong robustness with scenario-targeted data.

Abstract:
SAM 3D Body (3DB) achieves state-of-the-art accuracy in monocular 3D human mesh recovery, yet its inference latency of several seconds per image precludes real-time application. We present Fast SAM 3D Body, a training-free acceleration framework that reformulates the 3DB inference pathway to achieve interactive rates. By decoupling serial spatial dependencies and applying architecture-aware pruning, we enable parallelized multi-crop feature extraction and streamlined transformer decoding. Moreover, to extract the joint-level kinematics (SMPL) compatible with existing humanoid control and policy learning frameworks, we replace the iterative mesh fitting with a direct feedforward mapping, accelerating this specific conversion by over 10,000×. Overall, our framework delivers up to a 10.25× end-to-end speedup while maintaining largely on-par reconstruction fidelity, even surpassing 3DB on benchmarks such as LSPET. We demonstrate its utility by deploying Fast SAM 3D Body in a vision-only teleoperation system that—unlike methods reliant on wearable IMUs—enables real-time humanoid control and the direct collection of manipulation policies from a single RGB stream.

Abstract:
Vision-Language Models (VLMs) have advanced rapidly invisual and semantic understanding, yet they remain weak at dynamic 4Dspatial grounding in video. On spatio-temporal reasoning benchmarks,raw-video input often yields low and variable accuracy. 4D Scene Graphs(4DSGs) can supply missing structure, but two interface gaps remain:identity facets (appearance, geometry, motion) are often encoded throughlanguage-side descriptors rather than modality-separated inputs, andmost training targets static spatial relations instead of dynamic 4D at-tributes such as trajectories and velocity. We introduce Visual Spatio-Temporal Anchor (VISTA), a multimodal node encoding that representseach tracked object as a persistent, queryable entity, delegating appear-ance to anchor crops processed by the VLM’s vision encoder and geome-try and motion to explicitly structured tokens. We introduce Real-TimeOpen-World Scene Understanding (ROSE), a training-free monocularpipeline (single GPU) that runs at 10 Hz. Beyond inference-time prompt-ing, the 4DSGs produced by ROSE can serve as structured training-timecontext: fine-tuning a VLM on 4DSG-augmented data teaches it to usetrajectory and velocity tokens for dynamic reasoning, yielding a new stateof the art on DSR-Bench (67.5, +8.6 over the previous best), with gainsacross all 13 subtasks. ROSE achieves competitive accuracy across allthree benchmarks under real-time, single-GPU monocular constraints.

Abstract:
Accurate process supervision remains a critical challenge forlong-horizon robotic manipulation. A primary bottleneck is that currentvideo MLLMs, trained primarily under a Supervised Fine-Tuning (SFT)paradigm, function as passive “Observers” that recognize ongoing eventsrather than evaluating the current state relative to the final task goal.In this paper, we introduce PRIMO R1 (Process Reasoning InducedMOnitoring), a 7B framework that transforms video MLLMs into ac-tive “Critics”. We leverage outcome-based Reinforcement Learning to in-centivize explicit Chain-of-Thought generation for progress estimation.Furthermore, our architecture constructs a structured temporal inputby explicitly anchoring the video sequence between initial and currentstate images. Supported by the proposed PRIMO Dataset and Bench-mark, extensive experiments across diverse in-domain environments andout-of-domain real-world humanoid scenarios demonstrate that PRIMOR1 achieves state-of-the-art performance. Quantitatively, our 7B modelachieves a 50% reduction in the mean absolute error of specialized rea-soning baselines, demonstrating significant relative accuracy improve-ments over 72B-scale general MLLMs. Furthermore, PRIMO R1 exhibitsstrong zero-shot generalization on difficult failure detection tasks. Weestablish state-of-the-art performance on the RoboFail benchmark with67.0% accuracy, surpassing closed-source models like OpenAI o1 6.0%.The project website is: 10-oasis-01.github.io/primo-r1-website.

Abstract:
In DiT-based video generation models equipped with3D Rotary Position Embeddings (3D RoPE), the attention mech-anism remains a primary computational bottleneck due to its quadraticcomplexity with respect to sequence length. While quantized FlashAt-tention o x001B ers a promising path toward hardware acceleration, exist-ing low-bit quantization methods overlook two critical challenges in thissetting: 1) applying online rotation matrices x0016 a widely used techniquefor mitigating outliers in Queries (Q) and Keys (K ) x0016 is di x001E cult toreconcile with RoPE; and 2) the non-negative attention matrix P =exp(QK − max(QK)) makes symmetric quantization waste half of the 4-bit dynamic range. In this work, we observe that the outlier distributionsof Q and K are strongly a x001B ected by the dimensional partitioning of 3DRoPE. Based on this x001C nding, we propose RotateAttention, an e x001E -cient mixed-precision INT4 FlashAttention framework tailored forDiT-based video generation models with 3D RoPE, using selec-tive FP16 fallback for accuracy-sensitive attention blocks and denoisingsteps. RotateAttention introduces two core techniques: 1) RoPE-awareRotation, which employs either mergeable rotation matrices that canbe fused into RoPE or negligible-overhead matrices to mitigate RoPE-induced outliers in Q and K ; and 2) Range-optimized P Quantiza-tion, which uses x001C xed scales and zero-points to fully exploit the INT4numerical range with minimal computational overhead. Experimentsshow that RotateAttention preserves video generation quality nearlyidentical to full-precision baselines while achieving up to 1.68× end-to-end speedup and 2.2× kernel-level acceleration.

Abstract:
Multi-modal Large Language Models have demonstrated im-pressive capabilities in vision-language tasks, yet they remain highly sus-ceptible to hallucinations, in which generated text diverges from the ac-tual visual evidence. Current mitigation strategies, such as self-refinementand specialized decoding, often incur significant latency due to multi-round iterations or reliance on high-performance auxiliary models, limit-ing their real-time practical utility. In this paper, we systematically ana-lyze the hidden states of MLLMs across the entire inference pipeline andreveal that hallucination-inducing redundant features persist not onlyin image tokens during the prefill stage but also propagate into Imageand Answer Value Caches during decoding. Motivated by the observationthat Gaussian noise injection can effectively suppress these redundancies,we propose HalMask (Hallucination Feature Masking), a lightweightand real-time self-correction framework. HalMask employs specializedImage and Text Noise Modulation Modules, optimized via a dynamic reg-ularization term, to accurately discern and mask hallucination-triggeringfeatures. During inference, it applies a dynamic Value Cache maskingmechanism that continuously filters redundant information across bothgeneration phases without disrupting critical semantic cues. Extensiveexperiments on multiple benchmarks demonstrate that HalMask signifi-cantly reduces hallucinations while introducing only marginal computa-tional overhead.

Abstract:
Existing jailbreak defense frameworks for Large Vision-LanguageModels (LVLMs) often suffer from a safety–utility tradeoff, where strength-ening safety inadvertently degrades performance on general visual-groundedreasoning tasks. In this work, we investigate whether safety and utilityare inherently antagonistic objectives. We focus on a modality-inducedbias direction consistently observed across datasets, which arises fromsuboptimal coupling between the LLM backbone and visual encoders.We further demonstrate that this direction undermines performance onboth tasks. Leveraging this insight, we propose TBOP (Two Birds, OneProjection), an efficient inference-time jailbreak defense that projectscross-modal features onto the null space of the identified bias direction toremove the corresponding components. Requiring only a single forwardpass, our method effectively breaks the conventional tradeoff, simultane-ously improving both safety and utility across diverse benchmarks.

Abstract:
Autonomous agents navigating human society must masterboth production activities and social interactions, yet existing bench-marks rarely evaluate these skills simultaneously. To bridge this gap,we introduce StarDojo, a novel benchmark based on Stardew Valley,designed to assess AI agents in open-ended production–living simulations.In StarDojo, agents are tasked to perform essential livelihood activitiessuch as farming and crafting, while simultaneously engaging in socialinteractions to establish relationships within a vibrant community. Star-Dojo features 1,000 meticulously curated tasks across five key domains:farming, crafting, exploration, combat, and social interactions. Addition-ally, we provide a compact subset of 100 representative tasks for efficientmodel evaluation. The benchmark offers a unified, user-friendly interfacethat eliminates the need for keyboard and mouse control, supports allmajor operating systems, and enables the parallel execution of multipleenvironment instances, making it particularly well-suited for evaluatingthe most capable foundation agents, powered by multimodal large lan-guage models (MLLMs). Extensive evaluations of state-of-the-art MLLMsagents demonstrate substantial limitations, with the best-performingmodel, GPT-4.1, achieving only a 12.7% success rate, primarily due tochallenges in visual understanding, multimodal reasoning and low-levelmanipulation. As a user-friendly environment and benchmark, StarDojoaims to facilitate further research towards robust, open-ended agents incomplex production-living environments.

Abstract:
3D Visual Grounding (3DVG) is a critical bridge from vision-language perception to robotics, requiring both language understandingand 3D scene reasoning. Traditional supervised models leverage explicit3D geometry but exhibit limited generalization, owing to the scarcity of3D vision-language datasets and the limited reasoning capabilities com-pared to modern vision-language models (VLMs). We propose a gener-alizable 3DVG framework, PanoGrounder, that couples multi-modalpanoramic representation with pretrained 2D VLMs for strong vision-language reasoning. Panoramic renderings, augmented with 3D semanticand geometric features, serve as an intermediate representation between2D and 3D, and offer two major benefits: (i) they can be directly fed toVLMs with minimal adaptation and (ii) they retain long-range object-to-object relations thanks to their 360-degree field of view. We devise athree-stage pipeline that places a compact set of panoramic viewpointsconsidering the scene layout and geometry, grounds a text query on eachpanoramic rendering with a VLM, and fuses per-view predictions into asingle 3D bounding box via lifting. Our approach achieves state-of-the-artresults on ScanRefer and Nr3D, and demonstrates strong generalizationto unseen 3D datasets and text rephrasings.

Abstract:
Vision-language models increasingly support everyday communication and decision-making from images. Their outputs shape how people talk about disability and can affect how people with disabilities are treated. Disability is common, but it is almost entirely absent from vision-side, controlled evaluations of model behavior. We ask a simple question: when a prompt is not about physical ability, should a model change its judgment just because visible limb-deficiency cues are present? A reliable model should base judgments and descriptions on task-relevant visual evidence, not on the mere presence of a disability cue. We introduce InclusiveCFImageBias, a benchmark of 924 images and more than 22k image-level queries. Each example contains two image versions that show the same person in the same scene, with and without visible limb-deficiency cues. We keep the background and activity fixed, and only edit cues such as a prosthesis or a residual limb. We query both versions with the same prompt and compare the outputs directly. Our evaluation spans both open-weight families (Qwen3-VL, DeepSeek-VL2, Gemma-3, and Ministral-3) and proprietary models (GPT-5 and Gemini-2.5). Many models shift both structured judgments and open-ended wording when limb-deficiency cues are visible. In our tests, these cues often push decisions toward more affirmative answers and higher ratings, and they make free responses less neutral and more evaluative. Higher scores are not automatically fairer. The key issue is that outputs change under a minimal, controlled visual substitution. InclusiveCFImageBias makes this cue-driven instability measurable. More importantly, it aims to draw community attention to disability-related VLM bias and provide a clear target for building more consistent disability-facing multimodal systems.

Abstract:
Vision-Language Models (VLMs) frequently `hallucinate' x0015 generate plausible yet factually incorrect statements x0016 posing a criticalbarrier to their trustworthy deployment. In this work, we propose a newparadigm for diagnosing hallucinations, recasting them from static out-put errors into an auditable process-level anomaly. Our framework isgrounded in a normative principle of computational rationality, allowingus to model a VLM's generation as a dynamic cognitive trajectory. Wedesign a suite of information-theoretic probes that project this trajec-tory onto an interpretable, low-dimensional Cognitive State Space.Our central discovery is a governing principle we term the geometric-information duality: a cognitive trajectory's geometric abnormalitywithin this space is fundamentally equivalent to its high information-theoretic surprisal. Hallucination detection is thus elegantly re-framedas a geometric anomaly detection problem. Evaluated across diversesettings x0016 from rigorous binary QA (POPE) and comprehensive reason-ing (MME) to unconstrained open-ended captioning (MS-COCO) x0016 ourframework achieves state-of-the-art performance. Crucially, it operateswith high e x001E ciency under weak supervision and remains highly robusteven when calibration data is heavily contaminated. This approach en-ables a causal attribution of failures, mapping observable errors to dis-tinct pathological states: perceptual instability (measured by Percep-tual Entropy, HEvi ), logical-causal failure (measured by InferentialCon x001D ict, SConf ), and decisional ambiguity (measured by Decision En-tropy, HAns ). Ultimately, this opens a path toward building AI systemswhose reasoning is transparent, auditable, and diagnosable by design.

Abstract:
3D Gaussian Splatting (3DGS) has established itself as apremier technique for high-fidelity radiance field rendering. However, itsapplication in SLAM tasks is frequently hindered by the explosive growthof redundant Gaussian primitives, which imposes prohibitive memoryand rendering overhead. Existing compaction strategies predominantlyrely on heuristic pruning based on importance scores; such methods oftenfail to provide global fidelity guarantees and can disrupt the optimizationcontinuity required for online tracking. To address these challenges, weintroduce Manifold-Aware Spectral Compaction (MASC), a frameworkthat reformulates map reduction as a continuous signal reconstructiontask mapped onto a Riemannian manifold. We first construct a topology-aware graph representation using Symmetrized Kullback-Leibler diver-gence to precisely capture the statistical connectivity between primi-tives. To ensure real-time efficiency, we implement an incremental Nys-tröm spectral embedding strategy to project the Gaussian map onto alow-dimensional spectral subspace. We further derive a mathematicallyrigorous, closed-form solution for primitive aggregation leveraging Bures-Wasserstein barycenters, which theoretically ensures the conservation oflocal radiance field moments. Extensive evaluations on the Replica andTUM RGB-D datasets demonstrate that MASC achieves a 2.8× reduc-tion in GPU memory and a 2.4× speedup in rendering throughput.Remarkably, our method maintains or even enhances tracking accuracywhile eliminating approximately 60% of redundant primitives, achiev-ing an ATE of 0.98 cm on the TUM dataset. These results substantiatethat MASC provides an efficient and principled plug-and-play pathwayfor scalable, long-term neural rendering SLAM.

Abstract:
While surface normal estimation from point clouds is a fun-damental problem in computer vision, there has been limited explorationon LiDAR point clouds. Recent approaches adopt Transformer-basedmodels designed for large-scale point clouds to achieve single-step esti-mation, still struggling to estimate surface normals with high computa-tional efficiency and accuracy. In this paper, we propose an efficient sur-face normal estimation method for LiDAR point clouds, termed ESNE.It converts point clouds into a sequence data format and leverages astate space model to capture long-range dependencies. To capture lo-cal structure, the proposed method introduces a local attention moduleincorporating an efficient and explicit search for neighboring points onthe sequence. Furthermore, the proposed method introduces a featurerefinement module using structural variability as guidance to enhancethe shape representations of local regions. Experimental results on large-scale LiDAR point cloud datasets demonstrate that the proposed methodimproves the mean accuracy by over 33.4% and achieves more than twicethe inference speed compared to state-of-the-art methods.

Abstract:
Slide design requires personalizing both deck themes andpage layouts. Yet, current AI agent-based methods struggle with fine-grained, page-level design. Solely relying on prespecified templates oruser verbose instructions, they fail to capture latent design intents, leav-ing Page-level Slide Personalization (PSP) unresolved. To close this gap,this work formulates PSP as an inverse planning problem. We proposeto learn a design intent without assuming any knowledge of the spe-cific executing tools (e.g., PowerPoint, Beamer) being used. However,relinquishing control over these tools makes the problem intractable tooptimize end-to-end. To overcome this, we propose Spire, a principledframework to solve PSP approximately. By intentionally corrupting thevisual structures of clean slides, Spire creates a verifiable task to de-noise the corruption, whereby two agents learn to collaboratively refineexecutable designs via reinforcement learning (RL). We present a proofthat structural denoising is a consistent surrogate for PSP, and that themulti-agent formulation strictly reduces policy gradient variance in RL.Extensive experiments demonstrate the superiority of Spire.

Abstract:
Trained policies for real-world robotics rely on computer vi-sion components, typically in the form of pre-trained visual encoders.These encoders are an essential component and it has been shown thattheir power does not emerge from training on robotics downstream lossesalone. Pre-training with auxiliary losses in the form of computer-visionpre-text tasks is a defining factor and heavily conditions agent perfor-mance in robotics tasks. In this unprecedented large-scale study, we ran966 navigation episodes of static point goal navigation in a real-worldbuilding for 24km and asked which components really matter for thecomputer vision aspects of robotics: we evaluate state-of-the art visualencoders in realistic conditions. We explore the usefulness of heteroge-neous multi-teacher distillation leading to encoders with multiple dif-ferent and complementary skills. We investigate how much informationfrom these encoders is necessary for robotics by bottlenecking them in aprincipled and “spatially useful” way and we show that this leads to theemergence of interpretable features linked to affordances. We also arguethat training policies on RGB data alone does not lead to an optimalusage of visual features and show this by finetuning policies pre-trainedon privileged information. All in all, we paint a more complete picture ofwhat aspects of computer vision are relevant for real-world navigation.

Abstract:
State-of-the-art 3D reconstruction models, whether from visual, range, or both, tend to underperform on thin objects. This is partially due to the small amount of space such objects occupy in RGB images and in 3D point clouds. To test the extent of their errors, we collected the first thin object dataset comprising synchronized RGB images, depth maps, and tactile response maps, where each frame is associated with inertial measurements, camera pose and calibration, and groundtruth depth and segmentation maps obtained from laser scanning of thin objects. We hypothesize that tactile data can aid in the reconstruction of thin objects as their response maps provide local shape and deformation information. Our dataset, termed VisTa3D, comprises of 387 scenes covering 70 thin objects over 17 environments. We benchmarked current 3D reconstruction models on VisTa3D and found that, indeed, they exhibit low fidelity on thin objects. To test if tactile data can help, we introduce the first visual-range-tactile 3D reconstruction model as a baseline. Code and data: https://huggingface.co/datasets/shaniaguo/VisTa3D.

Abstract:
While feed-forward 3D reconstruction models have advancedrapidly, they still exhibit degraded performance on panoramas due tospherical distortions. Moreover, existing panoramic 3D datasets are pre-dominantly collected with 360◦ cameras fixed at discrete locations, re-sulting in discontinuous trajectories. These limitations critically hin-der the development of panoramic feed-forward 3D reconstruction, espe-cially for the multi-view setting. In this paper, we present Holo360D,a comprehensive dataset containing 109,495 panoramas paired with reg-istered point clouds, meshes, and aligned camera poses. To our knowl-edge, Holo360D is the first large-scale dataset that provides continuouspanoramic sequences with accurately aligned high-completenessdepth maps. The raw data are initially collected using a 3D laser scan-ner coupled with a 360◦ camera. Subsequently, the raw data are pro-cessed with both online and offline SLAM systems. Furthermore, to en-hance the 3D data quality, a post-processing pipeline tailored for the 360◦dataset is proposed, including geometry denoising, mesh hole filling, andregion-specific remeshing, etc. Finally, we establish a new benchmark byfine-tuning 3D reconstruction models on Holo360D, providing key in-sights into effective fine-tuning strategies. Our results demonstrate thatHolo360D delivers superior training signals and provides a comprehensivebenchmark for advancing panoramic 3D reconstruction models. Datasetsand Code will be made publicly available. Dataset Page: Holo360D

Abstract:
State-of-the-art flow models generate stunning images from text or image prompts. However, they suffer from diversity collapse when generating multiple samples under the same conditioning. Existing methods address this issue via either latent guidance, which has limited effectiveness, or sample selection, which relies on external reward models that incur significant inference-time overhead. In this work, we introduce an efficient, training-free self-guidance mechanism to mitigate diversity collapse without requiring additional reward models. Specifically, we disperse the internal features of the flow model during batch generation with feature self-guidance. Further, to keep the features close to the manifold, we introduce a manifold regularization step that projects these dispersed features back onto the data manifold, ensuring diverse generation without sacrificing alignment with the input conditions. Our method integrates seamlessly as a plug-and-play module into pretrained flow models, adding only a marginal inference cost. Experiments demonstrate significant improvements in diversity while preserving fidelity across several conditional flow models, including multi-step and few-step text-to-image, depth-to-image, and reference image generation.

Abstract:
The connoisseurship of antique Chinese porcelain demands extensive historical expertise, material understanding, and aesthetic sensitivity, making it difficult for non-specialists to engage. To democratize cultural-heritage understanding and assist expert connoisseurship, we introduce CiQi-Agent—a domain-specific Porcelain Connoisseurship Agent for intelligent analysis of antique Chinese porcelain. CiQi-Agent supports multi-image porcelain inputs and enables vision tool invocation and multimodal retrieval-augmented generation, performing finegrained connoisseurship analysis across six attributes: dynasty, reign period, kiln site, glaze color, decorative motif, and vessel shape. Beyond attribute classification, it captures subtle visual details, retrieves relevant domain knowledge, and integrates visual and textual evidence to produce coherent, explainable connoisseurship descriptions. To achieve this capability, we construct a large-scale, expert-annotated dataset CiQi-VQA, comprising 29,596 porcelain specimens, 51,553 images, and 557,940 visual question–answering pairs, and further establish a comprehensive benchmark CiQi-Bench aligned with the previously mentioned six attributes. CiQi-Agent is trained through supervised fine-tuning, reinforcement learning, and a tool-augmented reasoning framework that integrates two categories of tools: a vision tool and multimodal retrieval tools. Experimental results show that CiQi-Agent (7B) outperforms all competitive openand closed-source models across all six attributes on CiQi-Bench, achieving on average 12.2% higher accuracy than GPT-5.

Abstract:
Video Temporal Grounding (VTG) localizes the temporalboundaries of query-relevant moments in long, untrimmed videos, mak-ing video-language-model prohibitively expensive. While recent training-free token pruning has shown success in video question answering, naivelyapplying these objectives to VTG causes drastic degradation, as VTGcrucially depends on boundary-sensitive evidence and cross-frame rea-soning chains. We therefore identify two VTG-specific pruning principles:evidence retention, which keeps query-critical patches especially aroundevent boundaries, and connectivity strength, which preserves cross-frameconnectivity for long-range evidence aggregation. Building on these in-sights, we propose SemVID, a training-free pruning framework that con-structs a compact yet coherent token subset with complementary seman-tic roles. SemVID first allocates per-frame budgets by balancing queryrelevance and inter-frame variation to avoid over-pruned segments, andthen selects three types of tokens: object tokens for diverse query-criticalevidence, motion tokens to capture meaningful transitions and serve ascross-frame relays, and context tokens for scene continuity. Extensive ex-periments show that SemVID achieves a strong accuracy-efficiency trade-off, retaining up to 95.4% mIoU with only 12.5% visual tokens and de-livering up to a 5.8× prefill speedup, consistently outperforming priormethods under the same budgets. Our code is available here.

Abstract:
Open-vocabulary semantic segmentation (OVSS) makes seg-mentation driven by text descriptions, enabling generalization to novelclasses. However, fine-grained OVSS in remote sensing is challenged byvisual similarity and semantic ambiguity, with existing methods lackingexplicit fine-grained visual-linguistic alignment and relying on a singleforward pass. To address these issues, we propose SANO3, a training-free framework that leverages refined textual and spatial guidance toboost the performance of SAM 3. Leveraging the visual self-similaritypriors of vision foundation models, we extract discriminative visual prim-itives and align them with text through visual-conditioned relation-awarealignment strategy. Next, we enhance text representations with hybridprototype injection for deep visual-linguistic fusion. To refine bound-aries, we treat spatial prompts generation as an iterative optimizationprocess, gradually selecting the most informative points via a greedy en-tropy reduction strategy. Further, we introduce FG-OVSSRS Bench, thefirst fine-grained remote sensing OVSS benchmark, encompassing 8 di-verse datasets across different sensors and domains. Evaluated on thesedatasets, SANO3 outperforms existing approaches and achieves state-of-the-art performance.

Abstract:
Previous works based on Segment Anything Model (SAM)have achieved promising performance in unified scene text detection andlayout analysis. However, the typical reliance on pixel-level text segmen-tation for sampling thousands of foreground points as prompts leadsto unsatisfied inference latency and limited data utilization. To addressabove issues, we propose ET-SAM, an Efficient framework with twodecoders for unified scene Text detection and layout analysis based onSAM. Technically, we customize a lightweight point decoder that pro-duces word heatmaps for achieving a few foreground points, thereby elim-inating excessive point prompts and accelerating inference. Without thedependence on pixel-level segmentation, we further design a joint train-ing strategy to leverage existing data with heterogeneous text-level an-notations. Specifically, the datasets with multi-level, word-level only, andline-level only annotations are combined in parallel as a unified trainingset. For these datasets, we introduce three corresponding sets of learn-able task prompts in both the point decoder and hierarchical mask de-coder to mitigate discrepancies across datasets. Extensive experimentsdemonstrate that, compared to the previous SAM-based architecture,ET-SAM achieves about 3× inference acceleration while obtaining com-petitive performance on HierText, and improves an average of 11.0% F-score on Total-Text, CTW1500, and ICDAR2015. The code and modelsare available at ET-SAM.

Abstract:
Detecting and localizing defects in 3D point clouds is chal-lenging because abnormal samples are scarce and diverse, while train-ing is often limited to normal data. We propose Anomaly Factory 3D(AF3AD), a modular framework that synthesizes diverse pseudo-anomaliesfrom normal point clouds to expand the training data for unsupervised3D anomaly detection methods that rely on pseudo-anomalies. AF3ADuses a center-conditioned parametric deformation model defined in localPCA frames, with kernel-controlled spatial falloff, anisotropy, directionalgating, and normal/tangential displacement fields, enabling a broad setof geometric defect presets. We demonstrate its ease-of-use and effec-tiveness by integrating AF3AD with an offset-prediction detector and areconstruction-based anomaly detection method, showing that AF3ADtransfers across detection paradigms. Experiments on AnomalyShapeNetand Real3D-AD show consistent improvements in object- and point-leveldetection and localization, supported by ablations on preset groups androbustness under noise. AF3AD is designed as a standalone synthesis toolto facilitate adoption across different 3D anomaly detection paradigms.Code is available at github.com/vpc-ccg/AF3AD.

Abstract:
Partial-label learning (PLL), which refers to the classifica-tion task where each training instance is associated with a set of candidate-labels, among which only one is correct. While recent advances in deepPLL primarily focus on designing sophisticated disambiguation strate-gies, the role of PLL-specific data augmentation has been less explored.Most methods often treat data augmentation as a generic plug-in, over-looking its potential interaction with the label disambiguation processunique to PLL. To address this gap, we propose a local-ambiguity-awareaugmentation framework that consists of label identification and modelregularization modules and accommodates a wide range of PLL algo-rithms. Within this framework, a PLL-specific augmentation strategybased on a mixing-renormalization process is applied to partial-labeldata. Theoretically, we show that our augmentation can make the PLLdataset easier by reducing the local ambiguity degree, and it induces mul-tiple implicit regularizations during training thereby facilitating highlyeffective disambiguation. Extensive experiments on both benchmark andreal-world datasets demonstrate that our approach consistently improvesthe generalization performance of state-of-the-art PLL algorithms.

Abstract:
The performance of deep spiking neural networks (SNNs) often relies on batch normalization (BN). However, the advanced dynamic BN variants used in state-of-the-art models introduce runtime multiplications, which weaken the hardware-efficiency motivation of SNNs. To address this tension, we identify catastrophic firing-rate decay as a primary cause of severe performance degradation in normalization-free SNNs. Guided by this insight, this work proposes the Intrinsically Stable SNN (IS-SNN) architecture, which removes activation-normalization layers by enforcing signal homeostasis through topology-aware weight standardization and modified residual connections. By folding the standardization operations into static weights offline, IS-SNN removes the runtime statistics tracking and multiplications introduced by activation normalization, restoring an accumulation-oriented inference datapath. Comprehensive experiments show that IS-SNN achieves performance competitive with or superior to computationally expensive dynamic BN techniques across VGG, ResNet, and Transformer-based models. Notably, it achieves a competitive accuracy of 68.05% on ImageNet and overcomes the severe depth limitations of prior BN-free attempts. Together with a 96.4% reduction in FPGA lookup table resource consumption for neuron implementations, these results support IS-SNN as a practical framework for building accurate and hardware-friendly deep neuromorphic systems.

Abstract:
Modern smartphones capture Live Photos—short video bursts sur-rounding a still image—offering a dynamic and engaging photographic experi-ence. However, the cover photo and video components are generated by two dis-tinct imaging pipelines: the photo stream undergoes full computational photog-raphy processing, while the video stream is constrained by real-time efficiencyand heavy compression. This intrinsic separation produces a substantial qual-ity gap in resolution, color fidelity, and dynamic range between the cover photoand video frames. When users reselect an alternative frame from the video toreplace an imperfect cover, the chosen frame often suffers from severe degrada-tion, making direct replacement visually unsatisfactory. Restoring such framesrequires simultaneous enhancement of spatial detail and color appearance, a taskconsiderably more challenging than ordinary super-resolution or color enhance-ment. To address this, we define the Live Photo Cover Frame Reselection andEnhancement (LPRE) task, which leverages the intrinsic cues available withineach Live Photo: the high-quality cover image as a structural and color reference,the user-reselected low-quality frame as the reconstruction target and several ad-jacent video frames providing temporal cues. Building upon this formulation, weconstruct Live2K, a real-world dataset of 2,042 Live Photos, and develop a unifiedone-stage baseline that integrates multi-frame fusion, guided color enhancementand super-resolution—establishing the first benchmark for Live Photo enhance-ment research.

Abstract:
Editing dynamic 3D scenes reconstructed from monocularvideos is important for applications such as content creation and dataaugmentation, but remains challenging. Prior works adapt image diffu-sion models across frames while enforcing spatial–temporal consistency,yet they often produce uniform, appearance-only edits. We instead lever-age a video diffusion model to enable photorealistic 4D edits and control-lable motion changes. In monocular settings, however, fitting dynamic3D Gaussians with pure photometric loss is under-constrained, lead-ing to shallow geometric reconstructions and temporal artifacts in theedited scene. We address this with a novel 4D editing pipeline that usescompact latents distilled from DINO features as additional supervisionduring scene optimization and preserves them during editing as a se-mantic identity signal. Such semantic consistency regularization providesstrong geometric and motion cues, enabling robust scene editing evenunder large or complex edits from the video diffusion model. Further-more, to promote semantically novel content generation during editing,we apply a gradient-based feature lifting method based on accumulatedfeature gradients during Gaussian densification. Experiments show im-proved spatial-temporal realism and text alignment over image editingbaselines, and demonstrate compositional motion-appearance edits withsignificant geometry changes that prior methods cannot achieve.

Abstract:
Large vision-language models have endowed GUI agents with strong general capabilities for interface understanding and interaction. However, due to insufficient exposure to domain-specific software operation data during training, these agents exhibit significant domain bias— they lack familiarity with the specific operation workflows (planning) and UI element layouts (grounding) of particular applications, limiting their real-world task performance. In this paper, we present GUIDE (GUI Unbiasing via Instructional-Video Driven Expertise), a trainingfree, plug-and-play framework that resolves GUI agent domain bias by autonomously acquiring domain-specific expertise from web tutorial videos through a retrieval-augmented automated annotation pipeline. Guide introduces two key innovations. First, a subtitle-driven Video-RAG pipeline unlocks video semantics through subtitle analysis, performing progressive three-stage retrieval—domain classification, topic extraction, and relevance matching—to identify task-relevant tutorial videos. Second, a fully automated VLM pairwise video annotation pipeline feeds consecutive keyframes enhanced with UI element detection into VLMs, inferring the required planning and grounding knowledge that are injected into the agent’s corresponding modules to address both manifestations of domain bias. Extensive experiments on OSWorld demonstrate Guide’s generality as a plug-and-play component for both multi-agent systems and single-model agents. It yields +4.47–+7.48 percentage-point improvements and reduces execution steps—without modifying any model parameters or architecture—validating Guide as an architecture-agnostic enhancement to bridge GUI agent domain bias. Code, dataset, and additional results are available on our project page: https://sharryXR. github.io/GUIDE/.

Abstract:
Vision and language models (VLMs) hold immense promiseto transform biomedical imaging workflows, from detecting lesions inchest X-rays to profiling cellular features in microscopy. Realizing this po-tential, however, requires robust and fine-grained visual perception. Mod-els need to correctly interpret subtle features in images, and they mustdo so across diverse biomedical modalities, scales, and contexts. Never-theless, current benchmarks remain limited. To address these gaps, weintroduce the Massive Multimodal Biomedical Understanding (MMBU)benchmark. It is the largest biomedical vision and language benchmarkto date, covering 35 submodalities with rich structured metadata. Itincludes both open and closed versions of ungrounded classification,grounded classification, and object detection, enabling systematic evalu-ation of model performance across biological scales, clinical settings, andimaging modalities. Evaluating 15 open-weight and 2 frontier VLMs, wefind that while medical adaptation provides measurable gains for somemodels, the high accuracy often reported on established benchmarks canmask deficiencies in visual perception and domain generalization.

Abstract:
Accurate 3D delineation of prostate cancer lesions in biparametric MRI (bpMRI) supports targeted biopsy and therapy planning, yet clinical reports typically provide only sparse 2D measurements or a single contour. We present SAPP (Spacing-Aware Prompt Propagation), a prompt-to-volume adaptation of SAM 2 that converts one clinical prompt on a single slice (diameter, box, circle, or contour) into a coherent 3D lesion mask from multimodal bpMRI. SAPP couples adaptive multimodal fusion for accurate image-level prompted segmentation with a spacing-aware volume-level prompt propagation module. The propagation uses spacing-decayed, confidence-gated streaming-memory attention and a learned stop rule to prevent leakage in anisotropic volumes. Trained on 3,256 scans from 7 institutions, SAPP generalizes to 14 held-out cohorts (3,504 fully annotated scans plus 516 routine-care weak-label studies), achieving 0.84/0.81 DSC on the prompted slice (box/diameter) and 0.86/0.78/0.76 DSC for volumetric masks (oracle-mask/box/diameter), consistently outperforming baselines while reducing over-propagation. Beyond segmentation, SAPP can accelerate annotation workflows and enable scalable generation of high-quality volumetric lesion masks, supporting the development of robust clinical AI models.

Abstract:
Vision-Language Pretrained Models (VLPMs) offer a scal-able path to open-vocabulary chest radiology understanding, yet two as-pects remain underexplored: how structured clinical semantics extractedfrom medical reports can reduce in-batch noise during contrastive learn-ing, and how cross-modal fusion can be designed to produce more faithfulspatial grounding without added complexity. We introduce AlphaRad,addressing these opportunities through two contributions. First, we con-struct a large-scale structured medical concept space from medical re-ports parsed by a Large Language Model for training, thereby mit-igating in-batch learning noise and removing heuristic pair matchingin contrastive learning, and thus naturally positioning AlphaRad asa medical concept discriminator trained via α-Corrected Binary Cross-Entropy. Second, we propose FLaS (Factorized Latent Supervision), anextremely simple yet effective cross-modal feature fusion module thatfactorizes VLPM representations into independent subspaces, using ded-icated alignment supervision to enhance the expressiveness of spatialgrounding without introducing additional model parameters. Throughextensive empirical validation, AlphaRad shows strong zero-shot gen-eralization across diverse chest radiology tasks. Notably, it establishesstate-of-the-art average performance across 16 classification benchmarks,while achieving individual state-of-the-art results via distinct gains on 7grounding/phrase grounding and 3 segmentation datasets.

Abstract:
Clinical decision-making in oncology requires forecasting howdisease evolves under treatment, yet most AI systems remain static pre-dictors that cannot model longitudinal, treatment-conditioned progres-sion. Although generative and world models have demonstrated strongcapabilities in general domains, their adaptation to medicine remainslimited and insufficient for capturing complex, treatment-induced phys-iological dynamics across temporal scales. To address these gaps, weintroduce CLARITY, a medical world model that enables counterfac-tual simulation of treatment-conditioned disease trajectories for clini-cal decision-making. By jointly encoding imaging-derived latent states,temporal intervals that capture irregular follow-ups, and patient-specificclinical context, CLARITY learns smooth and interpretable representa-tions of disease progression, allowing the model to simulate how alter-native treatments reshape future disease dynamics. Because treatmentoptimization is inherently sequential and uncertain, requiring evaluationof long-term outcomes across multiple possible interventions, we furtherpropose an entropy-regularized, computationally efficient long-horizonprediction-to-decision framework that plans treatment strategies overimagined disease trajectories and iteratively refines therapy proposalsthrough survival-aware feedback, forming a closed-loop simulation-to-decision framework for treatment planning. CLARITY achieves state-of-the-art performance in treatment planning and survival predictionacross three cancer datasets, including two brain tumor cohorts (MU-Glioma-Post and zero-shot on UCSF-ALPTDG) and one breast can-cer dataset (ISPY-2), demonstrating strong generalization across can-cer types while consistently outperforming prior generative methods andmedical-domain large language model baselines.

Abstract:
Visual reasoning may require models to interpret images/videosand respond to implicit text queries across diverse output formats, frompixel-level segmentation masks to natural language descriptions. Exist-ing approaches rely on supervised fine-tuning with task-specific architec-tures. For example, reasoning segmentation, grounding, summarization,and visual question answering each demand distinct model designs andtraining, preventing unified solutions and limiting cross-task/-modalitygeneralization. Hence, we propose DT-R1, a reinforcement learning frame-work that trains large language models (LLMs) to construct digital twin(DT) representations of complex multi-modal visual inputs and then rea-son over these high-level representations as a unified approach to visualreasoning. Specifically, we train DT-R1 using GRPO with a novel rewardthat validates both structural integrity and output accuracy. Evaluationsin six visual reasoning benchmarks, covering two modalities and four tasktypes, demonstrate that DT-R1 consistently achieves improvements overstate-of-the-art task-specific models. DT-R1 opens a new direction wherevisual reasoning emerges from reinforcement learning on with DT repre-sentations.

Abstract:
The domain gap between abstract sketches and natural im-ages, combined with the challenge of zero-shot generalization, remainsa critical bottleneck in Sketch-Based Image Retrieval (ZS-SBIR). Whilescaling up to large-scale Vision-Language Models offers powerful baselinerepresentations, naive utilization—such as relying solely on the terminal[CLS] token—fails to fully exploit their potential, often discarding thefine-grained structural primitives essential for abstract sketch alignment.In this paper, we propose a robust framework that bridges this modalitygap by decoupling and composing visual states and semantics. First, ourMulti-Layer Feature Aggregation (MLFA) acts as an anti-compressionmechanism, explicitly reviving low-level geometric primitives alongsidehigh-level semantic objects. Second, a Phased Cross-Modal Interaction(PCMI) serves as a dynamic compositional engine: learnable promptsprogressively query and bind these structural states with global seman-tics for precise coarse-to-fine alignment. Extensive experiments demon-strate our framework achieves state-of-the-art results on several bench-marks and remains competitive on QuickDraw Ext.

Abstract:
Unsupervised physical parameter estimation from video lacksa common benchmark: existing methods evaluate on non-overlappingsynthetic data, the sole real-world dataset is restricted to single-bodysystems, and no established protocol addresses governing-equation identi-fication. This work introduces IRIS, a high-fidelity benchmark comprising240 real-world videos captured at 4K resolution and 60 fps, spanning bothsingle- and multi-body dynamics with independently measured ground-truth parameters and uncertainty estimates. Each dynamical systemis recorded under controlled laboratory conditions and paired with itsgoverning equations, enabling principled evaluation. A standardized evalu-ation protocol is defined encompassing parameter accuracy, identifiability,extrapolation, robustness, and governing-equation selection. Multiplebaselines are evaluated, including a multi-step physics loss formulationand four complementary equation-identification strategies (VLM tempo-ral reasoning, describe-then-classify prompting, CNN-based classification,and path-based labelling), establishing reference performance across allIRIS scenarios and exposing systematic failure modes that motivate futureresearch. The dataset, annotations, evaluation toolkit, and all baselineimplementations are publicly released.

Abstract:
Large Vision-Language Models (VLMs) show immense po-tential in autonomous driving but struggle with precise spatial ground-ing, often hallucinating when forced to regress continuous image coordi-nates. To overcome this, we introduce a "Less is More" object-centricprompting strategy that fundamentally decouples spatial localizationfrom logical reasoning. By overlaying detected bounding boxes and dis-crete IDs directly onto multi-view images, the VLM only predicts dis-crete IDs, which are deterministically mapped back to precise coordi-nates via an offline lookup table. This model-agnostic approach trans-forms error-prone regression into reliable discrete matching without re-quiring structural modifications. Extensive evaluations on the DriveLM-nuScenes dataset demonstrate striking improvements in comprehensivedriving reasoning and spatial grounding. Notably, our method booststhe Accuracy and Match scores of the DriveLM-agent by absolute mar-gins of 8.26% and 10.39%, respectively. Furthermore, when applied tostronger models, it achieves impressive increases of 11.85% and 20.02%in Accuracy and Match for Qwen2.5-VL, as well as 8.75% and 19.88% forInternVL-3.5. These consistent gains definitively prove the strong plug-and-play generalizability of our paradigm across varying architectures.

Abstract:
Disaster damage is spatial: buildings rarely fail in isolation.Yet using spatial context for damage classification remains surprisinglyunderexplored, and many pipelines still rely primarily on per-buildingappearance cues even when the dominant uncertainty is spatially struc-tured. Complicating matters, the right neighbourhood is not the sameacross events. Floods, hurricanes, and wildfires can exhibit very differ-ent clustering behaviour, making spatial reasoning valuable but easy tomisuse—naive context aggregation can improve visual coherence whileoversmoothing boundaries or propagating structured errors. We studythis tension on xBD (the dataset used in the xView2 challenge) in a con-trolled post-localization, classification-only setup: each building is repre-sented by a pre/post combined (PPC) patch cropped from the providedpolygons, and spatial context is modelled with GPS-derived buildinggraphs. Our approach keeps local evidence “close” by preserving strongspatial relationships in disaster damage patterns, while bringing onlythe right neighbours “closer” through a disaster-type-conditioned graphmodel that injects a learnable multi-scale spatial kernel prior into atten-tion, allowing the effective neighbourhood scale to adapt across disastertypes rather than being learned as a single global smoothing rule. Todiscourage coherence-by-smoothing, we add a residual de-correlation lossthat penalizes positive Moran’s I in prediction residuals. We evaluate themethod with (i) an xView2 holdout external-reference comparison un-der fixed building instances and (ii) zero-shot transfer evaluations thatstress-test generalization under event and dataset shift: leave-one-event-out (LOEO) on xBD and cross-dataset transfer from xBD to Ida-BD.The model improves macro-F1 and substantially reduces residual spa-tial autocorrelation under zero-shot event shift, indicating better use ofspatial context rather than naive smoothing and enabling more reliabletransfer to unseen events within known disaster types.

Abstract:
On-device AI for resource-constrained systems, such as wear-ables and IoT sensors, requires models that are both highly efficientand reliable for mission-critical applications. Out-of-Distribution (OOD)detection is vital for ensuring this reliability, yet mainstream OOD re-search has focused on achieving state-of-the-art (SOTA) accuracy withoutregard for inference-time computational cost. This has led to powerful butcomplex methods (e.g., CLIP-based or supervised learning-based models)that are orders of magnitude too slow for real-world on-device deployment.We argue that the more meaningful benchmark for this domain is maxi-mizing accuracy within a practical, constrained inference computationalbudget. However, reducing inference complexity is typically achieved atthe expense of lower performance, exhibiting a trade-off between inferencespeed and accuracy. This paper breaks that trade-off. We introduce anovel framework that establishes an accuracy-SOTA under constrainedinference complexity. This is achieved through two key innovations: (i)a latent-space de-biasing technique that learns to remove pixel-intensitybias during training, drastically reducing inference complexity, and (ii) anintelligent negative sampling strategy that generates high-quality negativesamples to significantly boost performance. On both a real edge deviceand standard GPU infrastructure, extensive experiments reveal thatour method surpasses existing accuracy-SOTA OOD detection methodswithin a TinyML-appropriate inference budget.

Abstract:
Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets (e.g., NSFW content or copyrighted styles) while preserving model utility on image generation for benign concepts. Current CETs often face a trade-off between erasure robustness and model utility: stronger edits erase target more reliably, but degrade model utility on non-target concepts and vice-versa. A key source of this trade-off is how existing methods define what to erase and what to preserve. Many CETs rely on static concept banks, where erase or retain concepts are manually specified, generated by Large Language Models (LLMs), or selected using CLIP-based image–text similarity. However, such static banks do not explicitly model how prompts steer the diffusion model during denoising, leaving edited models vulnerable to triggers that can reintroduce the target while unintentionally suppressing nearby benign concepts. We present Preservation aware Adaptive Ranked Subspace Expansion (PARSE), a training-free framework for robust concept erasure in latent diffusion models. Given a target, PARSE queries the diffusion model using classifier-free guidance to dynamically discover target-inducing erase concepts and nearby retain concepts from the model vocabulary. PARSE then edits the cross-attention value space with a preservation-aware projection that removes target directions while leaving retain directions intact. To address triggers beyond the vocabulary-indexed search space, PARSE iteratively searches for re-emergence triggers using textual inversion and adaptively expands the erased subspace only when a new trigger direction does not conflict with retain semantics. We further introduce Balanced Erasure Utility Score (BEUS), a balanced erasure utility score that combines robustness (ASR under multiple attacks) and utility preservation (FID) via bounded monotone transforms and harmonic mean aggregation. Extensive experiments across NSFW, artistic style, and object erasure, together with a large-scale robustness utility analysis over many CET baselines, show that PARSE achieves robust erasure across multiple concepts without sacrificing post-edit model utility. Project page: https: //shaswati1.github.io/parse/

Abstract:
Open World Classification (OWC) with Large MultimodalModels (LMMs) is an emerging and promising task that moves beyondthe closed-world setting by enabling direct answer generation in responseto open-ended queries. However, we observe that the OWC performanceof LMMs significantly degrades on fine-grained tasks. Empirically, wefind that their performance is sensitive to contextual information in thequery. To address this limitation, we propose Adaptive Memory ContextInjection (AMCI), a training-free and model-agnostic framework that dy-namically restores contextual information during OWC inference. AMCImaintains a non-parametric memory that aligns visual indices with theircorresponding attribute-augmented descriptions. By employing an adap-tive similarity threshold to preserve memory diversity and utilizing arefinement LLM to transform accumulated context into a structuredprompt, AMCI transforms the initial zero-shot description into a cali-brated fine-grained prediction without parameter updates. Furthermore,we construct a specialized low-resource fine-grained benchmark to sim-ulate extreme data scarcity and introduce a new metric that penalizesoverly generic predictions, tailored for fine-grained OWC evaluation. Ex-tensive experiments demonstrate that AMCI consistently improves per-formance by a substantial margin while remaining computationally effi-cient and compatible with various LMMs.

Abstract:
3D dog pose estimation is hindered by two limitations in ex-isting benchmarks: distributional bias toward canonical viewpoints, sta-ble poses, and narrow morphologies, and alignment degradation betweenimages and 3D annotations. We instead synthesize a large-scale bal-anced benchmark (4.2M images with exact 3D ground truth) by decou-pling pose, shape, and texture into independent libraries (70K retargetedposes, 500 breed shapes, 1,000 appearance maps) and rendering directlyto guarantee pixel-perfect alignment and broad view coverage. However,this balanced distribution exposes a challenge previously masked by bi-ased data: non-canonical combinations of pose, shape, and viewpointcreate multimodal 3D ambiguities that deterministic regression cannotresolve. We therefore propose a flow-based probabilistic framework thatmodels the conditional distribution of 3D poses given an image, pro-ducing multiple plausible hypotheses under ambiguity while convergingto precise estimates for clear views. Experiments on StanfordExtra, An-imal3D, and our synthetic benchmark demonstrate that our method,trained solely on synthetic data, outperforms state-of-the-art approaches(79.9 vs. 33.2 PCK on the synthetic benchmark). Dataset available here.