Visual Foundation Models (VFMs) such as the Segment Anything Model (SAM) have significantly advanced broad use of image segmentation. However, SAM and its variants necessitate substantial manual effort for prompt generation and additional training for specific applications. Recent approaches address these limitations by integrating SAM into in-context (one/few shot) segmentation, enabling auto-prompting through semantic alignment between query and support images. Despite these efforts, they still generate sub-optimal prompts that degrade segmentation quality due to visual inconsistencies between support and query images. To tackle this limitation, we introduce PR-MaGIC (Prompt Refinement via Mask Decoder Gradient Flow for In-Context Segmentation), a training-free test-time framework that refines prompts via gradient flow derived from SAM's mask decoder. PR-MaGIC seamlessly integrates into in-context segmentation frameworks, being theoretically grounded yet practically stabilized through a simple top-1 selection strategy that ensures robust performance across samples. Extensive evaluations demonstrate that PR-MaGIC consistently improves segmentation quality across various benchmarks, effectively mitigating inadequate prompts without requiring additional training or architectural modifications.
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Accurate weather forecasts are essential across various domains and are safety-critical in extreme weather conditions. Compared to simulation-based forecasting, data-driven approaches show greater efficiency, enabling short-term, high-resolution nowcasting. In particular, diffusion models proved effective in weather nowcasting due to their strong probabilistic foundation. However, existing methods rely on deterministic compression to reduce the complexity of high-dimensional weather data, limiting their ability to capture uncertainty in the decoding process. In this work, we introduce FREUD, a FRame-wise Encoder and United Decoder model based on rectified flow transformers for efficient compression of spatio-temporal weather data. Frame-wise encoding enables continuous forecast updates, while the unified video decoder ensures temporal consistency. Our uncertainty-preserving first stage allows us to capture aleatoric uncertainty through ensembling, which is particularly beneficial for extreme weather events with high decoding variability. We achieve state-of-the-art performance in precipitation nowcasting with a compact latent-space rectified flow transformer on the SEVIR benchmark and show further performance gains by model and test-time scaling.
We introduce WebChain, the largest open-source dataset of human-annotated trajectories on real-world websites, designed to accelerate reproducible research in web agents. It contains 31,725 trajectories and 318k steps, featuring a core Triple Alignment of visual, structural, and action data to provide rich, multi-modal supervision. The data is collected via a scalable pipeline that ensures coverage of complex, high-value tasks often missed by synthetic methods. Leveraging this dataset, we propose a Dual Mid-Training recipe that decouples spatial grounding from planning, achieving state-of-the-art performance on our proposed WebChainBench and other public GUI benchmarks. Our work provides the data and insights necessary to build and rigorously evaluate the next generation of scalable web agents.
Diffusion models have shown impressive performance in generative tasks, demonstrating their ability to capture detailed structural and semantic information. Recently, these capabilities have been extended to visual understanding, with studies employing diffusion models as the backbone for various perception tasks. However, existing diffusion-based perception models are generally restricted to a single task or a fixed set of predefined tasks, lacking an efficient mechanism to generalize to novel tasks. To overcome this limitation, we propose a unified DiT-based perception framework called UniPercept, which introduces a novel foundation-adapter paradigm for general visual perception. In this framework, a shared diffusion-based foundation model is trained to capture common and generalizable visual knowledge across diverse perception tasks, with task-specific adapters integrated for each individual task. Leveraging its superior generalization ability, the foundation model can be efficiently adapted to novel domains through lightweight adapters, requiring as few as **1,000** training samples and less than **1%** of trainable parameters. Furthermore, UniPercept demonstrates strong performance across various perception tasks, outperforming state-of-the-art generalist models in most cases and achieving accuracy comparable to specialist models. Project page: https://VIPL-GENUN.github.io/Project-UniPercept.
Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through discrete spike signals, making them ideal for event-based tracking. However, current approaches combining Artificial Neural Networks (ANNs) and SNNs suffer from suboptimal architectures that compromise energy efficiency and limit tracking performance. To address these limitations, we propose the first Transformer-based Spike-Driven Tracking (SDTrack) pipeline. It incorporates a novel event frame aggregation method called Global Trajectory Prompt (GTP) and a Transformer-based tracker. The GTP method effectively captures global trajectory information and aggregates it with event streams into event frames to enhance spatiotemporal representation. The Transformer-based tracker comprises a fully spike-driven SNN backbone and a simple tracking head. The SDTrack pipeline operates end-to-end without data augmentation or post-processing. Extensive experiments demonstrate that our SDTrack-Tiny pipeline achieves competitive accuracy with only 19.61M parameters and 8.16mJ energy consumption, while our Base version achieves state-of-the-art accuracy across three datasets. Our work establishes a solid foundation for future neuromorphic vision research.
Inter-object relations underpin spatial intelligence, yet existing representations--linguistic prepositions or object-level scene graphs--are too coarse to specify which regions actually support, contain, or contact one another, leading to ambiguous and physically inconsistent layouts. To address these ambiguities, a part-level formulation is needed; therefore, we introduce PARSE, a framework that explicitly models how object parts interact to determine feasible and spatially grounded scene configurations. PARSE centers on the Part-centric Assembly Graph (PAG), which encodes geometric relations between specific object parts, and a Part-Aware Spatial Configuration Solver that converts these relations into geometric constraints to assemble collision-free, physically valid scenes. Using PARSE, we build PARSE-10K, a dataset of 10,000 3D indoor scenes constructed from real-image layout priors and a curated part-annotated shape database, each with dense contact structures and a part-level contact graph. With this structured, spatially grounded supervision, fine-tuning Qwen3-VL on PARSE-10K yields stronger object-level layout reasoning and more accurate part-level relation understanding; furthermore, leveraging PAGs as structural priors in 3D generation models leads to scenes with substantially improved physical realism and structural complexity. Together, these results show that PARSE significantly advances geometry-grounded spatial reasoning and supports the generation of physically consistent 3D scenes.
Under-display cameras (UDCs) allow for full-screen designs by positioning the imaging sensor underneath the display. Nonetheless, light diffraction and scattering through the various display layers result in spatially varying and complex degradations, which significantly reduce high-frequency details. Current PSF-based physical modeling techniques and frequency-separation networks are effective at reconstructing low-frequency structures and maintaining overall color consistency. However, they still face challenges in recovering fine details when dealing with complex, spatially varying degradation. To solve this problem, we propose a lightweight Uncertainty-aware Context-Memory Network (UCMNet), for UDC image restoration. Unlike previous methods that apply uniform restoration, UCMNet performs uncertainty-aware adaptive processing to restore high-frequency details in regions with varying degradations. The estimated uncertainty maps, learned through an uncertainty-driven loss, quantify spatial uncertainty induced by diffraction and scattering, and guide the Memory Bank to retrieve region-adaptive context from the Context Bank. This process enables effective modeling of the non-uniform degradation characteristics inherent to UDC imaging. Leveraging this uncertainty as a prior, UCMNet achieves state-of-the-art performance on multiple benchmarks with 30% fewer parameters than previous models. Project page: https://kdhrick2222.github.io/projects/UCMNet.
3D visual grounding task aims to accurately identify and grounding target objects in 3D space based on natural language descriptions,where the effective exploitation of relative relations between the target and anchor is crucial.However, in existing methods, relative relations are often tightly entangled with entity semantics. This tight coupling encourages models to rely on semantic shortcuts from entity names, making it difficult to maintain good generalization under multi-view and complex multi-object scenarios.To address this, we propose an object-relation decoupling framework that treats target-anchor relations as first-class geometric and semantic primitives and models them explicitly.First, we construct a scene-level relative geometric representation that encodes the direction and distance between the target and anchor, and introduce a scene-level hyper-object token as a unified prior for scale and viewpoint.Second, we develop a predicate-decoupled cross-modal alignment strategy that preserves only predicates carrying spatial relational semantics while masking out all other tokens, thereby suppressing semantic leakage from entity names.Finally, we design an anchor-guided regression module that predicts auxiliary anchors and samples their features to guide the model in learning entity semantics from text, explicitly injecting target-anchor priors and effectively resolving ambiguities in complex multi-object scenes.Extensive experiments on multiple 3D visual grounding benchmarks demonstrate that our method consistently outperforms state-of-the-art approaches and exhibits strong robustness and generalization under challenging multi-view and relation-intensive settings.
Hear What You See: Video-to-Audio Generation with Diffusion Transformer and Semantic-Temporal Alignment-Ranked Direct Preference Optimization
PDF ↗Generating high-fidelity audio that is both semantically meaningful and temporally synchronized with silent videos remains a challenging problem in video-to-audio generation. Existing approaches often fail to capture fine-grained temporal correspondence between visual events and audio dynamics, leading to unrealistic or desynchronized outputs. To address these limitations, we propose VisioSonic, a Video-Aligned Sound generation framework that unifies flow-matching diffusion and preference-guided alignment. VisioSonic introduces a multimodal conditioning module that jointly leverages video frames and textual cues to provide semantic and frame-level temporal guidance. A co-attention diffusion transformer efficiently fuses visual and audio representations, enabling content-aware sound synthesis with minimal computation costs. To further enhance alignment beyond supervised training, we introduce Semantic-Temporal Alignment Ranked Direct Preference Optimization (STAR-DPO), a novel preference-learning paradigm that automatically generates audio candidates, ranks them based on both semantic and temporal alignment, and subsequently fine-tunes the diffusion model using the derived preference pairs. Extensive experiments on various benchmarks demonstrate that VisioSonic achieves state-of-the-art audio-video synchronization and audio fidelity while using the fewest trainable parameters among competing approaches. Project page: https://kaiw7.github.io/VisioSonic/
Unlocking Motion from Large Vision Models with a Semantic and Kinematic Duality for Gait Recognition
PDF ↗Existing set-based gait recognition methods achieve remarkable performance by capturing global semantic context.However, their order-invariant nature prevents them from modeling the fine-grained kinematic patterns that unfold over time.To unify the global and process-level representations, we propose GaitMax, a framework that captures both semantic context and kinematic motion.GaitMax leverages attention-based spatiotemporal modeling to dynamically represent detailed part-level trajectories.While this detailed representation is more powerful, it also captures more nuisance factors (e.g., clothing, viewpoint), leading to potential shortcuts.To mitigate this, we introduce CDLoss, a Conditional Decorrelation Loss that explicitly disentangles the gait embeddings from nuisance factors using vision-language supervision.This loss requires high-quality nuisance descriptions. We therefore construct GCaption, a new resource that provides natural language annotations for multiple gait datasets, moving beyond simple categorical labels. GCaption not only enables CDLoss but also serves as a foundation for future context-aware gait analysis.The superiority of GaitMax is validated through extensive experiments on multiple large-scale gait benchmarks. Models, code, and resources will be released upon publication.
Generalizing video matting models to real-world videos remains a significant challenge due to the scarcity of labeled data. To address this, we present Video Mask-to-Matte Model VideoMaMa that converts coarse segmentation masks into pixel accurate alpha mattes, by leveraging pretrained video diffusion models. VideoMaMa demonstrates strong zero-shot generalization to real-world footage, even though it is trained solely on synthetic data. Building on this capability, we develop a scalable pseudo-labeling pipeline for large-scale video matting and construct the Matting Anything in Video MA-V dataset, which offers high-quality matting annotations for more than 50K real-world videos spanning diverse scenes and motions. To validate the effectiveness of this dataset, we fine-tune the SAM2 model on MAV to obtain SAM2-Matte, which outperforms the same model trained on existing matting datasets in terms of robustness on in-the-wild videos.These findings emphasize the importance of large-scale pseudo-labeled video matting and showcase how generative priors and accessible segmentation cues can drive scalable progress in video matting research.
State-of-the-art video generative models produce promising visual content yet often violate basic physics principles, limiting their utility. While some attribute this deficiency to insufficient physics understanding from pre-training, we find that the shortfall in physics plausibility also stems from suboptimal inference strategies. We therefore introduce WMReward and treat improving physics plausibility of video generation as an inference-time alignment problem. In particular, we leverage the strong physics prior of a latent world model (here, VJEPA-2) as a reward to search and steer multiple candidate denoising trajectories, enabling scaling test-time compute for better generation performance. Empirically, our approach substantially improves physics plausibility across image-conditioned, multiframe-conditioned, and text-conditioned generation settings, with validation from human preference study. Notably, in the ICCV 2025 Perception Test PhysicsIQ Challenge, we achieve a final score of 62.64%, winning first place and outperforming the previous state of the art by 7.42%. Our work demonstrates the viability of using latent world models to improve physics plausibility of video generation, beyond this specific instantiation or parameterization.
Robust perception and reasoning require consistency across sensory modalities. Yet, current multimodal models often violate this principle, yielding contradictory predictions for visual versus textual representations of the same input. Rather than masking these failures with standard voting mechanisms--which amplify systematic biases--we demonstrate that cross-modal inconsistency provides a rich, natural signal for learning. We introduce R-C2, a reinforcement learning framework that resolves internal conflicts by enforcing cross-modal cycle consistency. By requiring a model to perform backward inference, switches modalities, and reliably reconstruct the answer via forward inference, we establish a dense, label-free reward. This cyclic constraint forces the model to autonomously align its representations. Optimizing for this structure mitigates modality-specific errors and improves reasoning accuracy by up to 7.6 points. Our results suggest that advanced reasoning emerges not just from scaling data, but from enforcing a structurally consistent understanding of the world.
Diffusion models have recently advanced video restoration, but applying them to real-world and AIGC-generated video super-resolution (VSR) remains challenging due to high latency, prohibitive computation, and poor generalization to ultra-high resolutions. Our goal in this work is to make diffusion-based VSR practical by achieving efficiency, scalability, and near real-time performance. To this end, we propose FlashVSR, the first diffusion-based one-step streaming framework for efficient video super-resolution. FlashVSR runs at 17 FPS for 768x1408 videos on a single A100 GPU by combining three complementary innovations: (i) a train-friendly three-stage distillation pipeline that enables streaming super-resolution, (ii) locality-constrained sparse attention that reduces redundant computation while bridging the train--test resolution gap, and (iii) a tiny conditional decoder that accelerates reconstruction without sacrificing quality. To support large-scale training, we also construct VSR-120K, a new dataset containing 120 K videos and 180 K images. Extensive experiments demonstrate that FlashVSR scales reliably to ultra-high resolutions and achieves state-of-the-art performance with up to 12x speed-up over prior one-step diffusion-based VSR models. We will release the code, pretrained models, and dataset to foster future research in efficient diffusion-based video super-resolution.
Text-guided 3D editing aims to modify existing 3D assets using natural-language instructions. Current methods struggle to jointly understand complex prompts, automatically localize edits in 3D, and preserve unedited content. We introduce Vinedresser3D, an agentic framework for high-quality text-guided 3D editing that operates directly in the latent space of a native 3D generative model. Given a 3D asset and an editing prompt, Vinedresser3D uses a multimodal large language model to infer rich descriptions of the original asset, identify the parts to edit and the edit type (addition, modification, deletion), and generate decomposed structural and appearance-level text guidance. The agent then selects an informative view and applies an image editing model to obtain visual guidance. Finally, an inversion-based rectified-flow inpainting pipeline with an interleaved sampling module performs editing in the 3D latent space, enforcing prompt alignment while maintaining 3D coherence and unedited regions. Experiments on diverse 3D edits demonstrate that Vinedresser3D outperforms prior baselines in both automatic metrics and human preference studies, while enabling precise, coherent, and mask-free 3D editing.
PortraitDirector: A Hierarchical Disentanglement Framework for Controllable and Real-time Facial Reenactment
PDF ↗Existing facial reenactment methods struggle with a trade-off between expressiveness and fine-grained controllability. Holistic facial reenactment models often sacrifice granular control for expressiveness, while methods designed for control may struggle with fidelity and robust disentanglement. Instead of treating facial motion as a monolithic signal, we explore an alternative compositional perspective. In this paper, we introduce PortraitDirector, a novel framework that formulates face reenactment as a hierarchical composition task, achieving high-fidelity and controllable results. We employ a Hierarchical Motion Disentanglement and Composition strategy, deconstructing facial motion into a Spatial Layer for physical movements and a Semantic Layer for emotional content. The Spatial Layer comprises: (i) global head pose, managed via a dedicated representation and injection pathway; (ii) spatially separated local facial expressions, distilled from cropped facial regions and purged of emotional cues via Emotion-Filtering Module leveraging an information bottleneck. The Semantic Layer contains a derived global emotion. The disentangled components are then recomposed into an expressive motion latent. Furthermore, we engineer the framework for real-time performance through a suite of optimizations, including diffusion distillation, causal attention and VAE acceleration. PortraitDirector achieves streaming, high-fidelity, controllable 512 x 512 face reenactment at 20 FPS with a end-to-end 800 ms latency on a single 5090 GPU.
Generalized Category Discovery (GCD) aims to transfer knowledge from known categories to automatically discover new, unseen ones while preserving recognition of the known classes. Despite recent progress, existing GCD approaches typically assume that all data are drawn from the same distribution, which is rarely valid in real-world scenarios. In practice, data often experience simultaneous domain shifts and novel category emergence, causing severe performance degradation of existing systems. To address this challenge, we propose CausalGCD, a causality-inspired framework designed to mitigate domain-shift bias in category discovery. Specifically, we first analyze the causal graph to uncover the relationships among key variables in cross-domain GCD. We then introduce the concept of causal dependency risk and propose a Causal Dependency Risk Estimator to capture causal semantics, further deriving a theoretically computable upper bound to optimize this risk under cross-domain GCD settings. Furthermore, we propose a Causal Geometric Manifold Constraint that enforces invariant manifold-level associations between known and unknown categories across domains, thereby facilitating robust discovery of novel classes. Extensive experiments on the SSB-C and DomainNet benchmarks demonstrate the effectiveness of CausalGCD and highlight the significance of causal reasoning in open-world category discovery.
Natural videos exhibit heterogeneous temporal dynamics, with certain segments undergoing high-dynamic scene transitions and others dominated by low-dynamic visual changes. However, treating all frames identically, a common practice in most MLLMs, leads to redundant visual encoding, which results in significant computational overhead. The recent state-of-the-art model, i.e., Qwen2.5-VL, adopts a fixed two-frame encoding scheme, but our pilot experiments indicate that it encounters a visual confusion problem under high-dynamic frame pairs. To address this issue, we propose FlexiVideo, an efficient MLLM that models temporal dynamics leveraging visual variation. FlexiVideo first employs an adaptive temporal segmentation module to estimate inter-frame differences, grouping consecutive frames into scene segments with subtle visual changes. Subsequently, a dynamical spatio-temporal embedding module adjusts the temporal window for scene-level encoding. By restructuring scene-level visual representations within a structured temporal organization, our approach models dynamics more effectively and reduces the encoding burden while preserving fine-grained visual variations. Extensive experiments show that FlexiVideo-3B consistently outperforms Qwen2.5-VL-3B across 6 general video benchmarks. Notably, when evaluated on MotionBench at 10 FPS, FlexiVideo-3B reduces visual tokens by 43.5% compared with Qwen2.5-VL-3B while achieving a 1.3% performance gain, striking a significantly better balance between efficiency and effectiveness. Code and checkpoints are available at github.com/AI9Stars/FlexiVideo.
Single-image-to-3D shape generation has seen remarkable progress, driven by latent diffusion models trained on the compressed latent space of 3D VAEs. However, the task remains intrinsically ill-posed: recovering complete 3D geometry--especially occluded surfaces--from a single view is inherently ambiguous. Existing VecSet-based approaches further exacerbate this challenge by treating shape tokens as an unordered set without explicit positional encoding. This design forces diffusion models to simultaneously learn visible correspondences from the input image and hallucinate invisible geometry within a large, permutation-invariant token space, where the lack of structure significantly hinders training efficiency and convergence stability.To address this, we propose Visibility Learning, a training paradigm that injects visibility structure and positional inductive bias into the image-to-3D pipeline. Our method comprises two synergistic components: (1) Visibility Grouping (VG), which explicitly partitions VecSet tokens into visible and invisible subsets by exploiting the spatial locality of VecSet VAE decoders; and (2) Visibility-Aware Positional Encoding (VAPE), which assigns shared positional embeddings to image tokens and visible shape tokens to amplify their correspondence, while using distinct encodings for invisible tokens to guide hallucination. By explicitly disentangling visible reconstruction from invisible hallucination, our approach shrinks the effective hypothesis space and provides clear structural guidance for diffusion models. Extensive experiments demonstrate that Visibility Learning accelerates training convergence by up to 4.4xwhile achieving superior generation quality compared to strong VecSet-based baselines.
Single Image Super-Resolution (SISR) aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input, a task that becomes increasingly challenging under real-world computational constraints. However, most efficient SISR methods adopt lightweight, spatially uniform strategies that allocate equal computation and attention across all regions, ignoring the uneven distribution of visual complexity. From an information-theoretic perspective, textures and edges inherently carry more critical information, resulting in reconstruction errors that are disproportionately concentrated in these regions. This motivates the allocation of greater computational resources and attention to these informative areas. In this paper, we propose IAFMNet, an Information-Aware Feature Modulation network for efficient SR. At its core lies the Information Density Map (IDM), which is estimated in an unsupervised manner by minimizing the Information Entropy Loss, thereby highlighting informative regions with high estimated encoding costs. Guided by the IDM, IAFMNet adopts a synergistic dual-branch design: (1) a sparse convolution branch that dynamically allocates computation to informative areas while bypassing low-information regions, and (2) an implicit modulation branch that adaptively emphasizes complex regions through information-aware affine transformations. Extensive experiments demonstrate that IAFMNet effectively identifies informative regions and achieves superior visual fidelity with reduced computational overhead.