CVPR 2026 论文列表

Gait recognition has emerged as a powerful biometric technique for identifying individuals at a distance without requiring user cooperation. Most existing methods focus primarily on RGB-derived modalities, which fall short in real-world scenarios requiring multi-modal collaboration and cross-modal retrieval. To overcome these challenges, we present MMGait, a comprehensive multi-modal gait benchmark integrating data from five heterogeneous sensors, including an RGB camera, a depth camera, an infrared camera, a LiDAR scanner, and a 4D Radar system. MMGait contains twelve modalities and 334,060 sequences from 725 subjects, enabling systematic exploration across geometric, photometric, and motion domains. Based on MMGait, we conduct extensive evaluations on single-modal, cross-modal, and multi-modal paradigms to analyze modality robustness and complementarity. Furthermore, we introduce a new task, Omni Multi-Modal Gait Recognition, which aims to unify the above three gait recognition paradigms within a single model. We also propose a simple yet powerful baseline, OmniGait, which learns a shared embedding space across diverse modalities and achieves promising recognition performance. The MMGait benchmark, codebase, and pretrained checkpoints are publicly available at https://github.com/BNU-IVC/MMGait.

As an important research task of human cultural heritage, the restoration of artworks and calligraphy is of great significance. Seldom existing works have taken the multi-source (i.e., fragments are not ensured to be from the same piece of artworks) fragment-oriented restoration task into account. We propose ShreddingNet, a coarse-to-fine two-stage pipeline for multi-source manuscript restoration that operates without restrictive conditions. The proposed coarse stage compares the features of each fragment, selecting top-K candidates and clustering fragments by source. This design leverages the key insight that erroneous matches rarely cross source boundaries, enabling high-precision clustering. The proposed fine-grained stage evaluates candidates, yielding matching scores and filters out erroneous matching pairs from the candidate set; producing more precise final matching pairs for global assembly. Experiments conducted on more than 4,000 images from two datasets demonstrate the average reconstruction F1-score achieves 98.37%, which is 5.72% higher than the current state-of-the-art method, confirming the method's effectiveness and robustness. Source code is available at github.com/tqychy/shreddingnet.

The boundary representation (B-Rep) models a 3D solid as its explicit boundaries: trimmed corners, edges, and faces. Recovering B-Rep representation from unstructured data is a challenging and valuable task of computer vision and graphics. Recent advances in deep learning have greatly improved the recovery of 3D shape geometry, but still depend on dense and clean point clouds and struggle to generalize to novel shapes. We propose B-Rep Gaussian Splatting (BrepGaussian), a novel framework that learns 3D parametric representations from 2D images. We employ a Gaussian Splatting renderer with learnable features, followed by a specific fitting strategy. To disentangle geometry reconstruction and feature learning, we introduce a two-stage learning framework that first captures geometry and edges and then refines patch features to achieve clean geometry and coherent instance representations. Extensive experiments demonstrate the superior performance of our approach to state-of-the-art methods.

Transparent objects are common in daily life, and understanding their multi-layer depth information, including both the transparent surface and the objects behind it, is crucial for real-world applications that interact with transparent materials.However, existing depth methods produce only a single depth map, which is inherently ambiguous for transparent surfaces.In this work, We propose a multi-layer depth estimation method, SeeGroup, consisting of novel recurrent decomposition module design and an intensity-based formulation for multi-layer depth. Experiments demonstrate that our method significantly improves the state of the art of multi-layer depth estimation, improving quadruplet relative depth accuracy on LayeredDepth benchmark from 61.34% to 70.09%.

The creation of high-fidelity, customizable 3D indoor scene textures remains a significant challenge. While text-driven methods offer flexibility, they lack the precision for fine-grained, instance-level control, and often produce textures with insufficient quality, artifacts, and baked-in shading. To overcome these limitations, we introduce CustomTex, a novel framework for instance-level, high-fidelity scene texturing driven by reference images. CustomTex takes an untextured 3D scene and a set of reference images specifying the desired appearance for each object instance, and generates a unified, high-resolution texture map. The core of our method is a dual-distillation approach that separates semantic control from pixel-level enhancement. We employ semantic-level distillation, equipped with an instances cross attention, to ensure semantic plausibility and "reference-instance" alignment, and pixel-level distillation to enforce high visual fidelity. Both are unified within a Variational Score Distillation optimization framework. Experiments demonstrate that CustomTex achieves precise instance-level consistency with reference images and produces textures with superior sharpness, reduced artifacts, and minimal baked-in shading compared to state-of-the-art methods. Our work establishes a more direct and user-friendly path to high-quality, customizable 3D scene appearance editing.

Infrared-visible image fusion aims to integrate complementary information for robust visual understanding, but existing fusion methods struggle with simultaneously adapting to multiple downstream tasks. To address this issue, we propose a Closed-Loop Dynamic Network (CLDyN) that can adaptively respond to the semantic requirements of diverse downstream tasks for task-customized image fusion. Specifically, CLDyN introduces a closed-loop optimization mechanism that establishes a semantic transmission chain to achieve explicit feedback from downstream tasks to the fusion network through a Requirement-driven Semantic Compensation (RSC) module. The RSC module leverages a Basis Vector Bank (BVB) and an Architecture-Adaptive Semantic Injection (A2SI) block to customize the network architecture according to task requirements, thereby enabling task-specific semantic compensation and allowing the fusion network to actively adapt to diverse tasks without retraining. To promote semantic compensation, a reward-penalty strategy is introduced to reward or penalize the RSC module based on task performance variations. Experiments on the M3FD, FMB, and VT5000 datasets demonstrate that CLDyN not only maintains high fusion quality but also exhibits strong multi-task adaptability. The code is available at https://github.com/YR0211/CLDyN.

Recent unified models have made unprecedented progress in both understanding and generation. However, while most of them accept multi-modal inputs, they typically produce only single-modality outputs. This challenge of producing interleaved content is mainly due to training data scarcity and the difficulty of modeling long-range cross-modal context. To address this issue, we decompose interleaved generation into textual planning and visual consistency modeling, and introduce a framework consisting of a planner and a visualizer. The planner produces dense textual descriptions for visual content, while the visualizer synthesizes images accordingly. Under this guidance, we construct large-scale textual-proxy interleaved data (where visual content is represented in text) to train the planner, and curate reference-guided image data to train the visualizer. These designs give rise to Wan-Weaver, which exhibits emergent interleaved generation ability with long-range textual coherence and visual consistency. Meanwhile, the integration of diverse understanding and generation data into planner training enables Wan-Weaver to achieve robust task reasoning and generation proficiency. To assess the model's capability in interleaved generation, we further construct a benchmark that spans a wide range of use cases across multiple dimensions. Extensive experiments demonstrate that, even without access to any real interleaved data, Wan-Weaver achieves superior performance over existing methods.

As autonomous driving technology transitions from small-scale validation to large-scale deployment, its development in unstructured road environments has become a critical and inevitable trend. Autonomous vehicles increasingly rely on high-quality and diverse datasets for perception systems. However, existing public datasets predominantly focus on clear-weather and urban-road scenarios, leaving a significant gap in the coverage of unstructured road environments. To bridge this gap, we construct URScenes, the first multi-scenario, open-source perception dataset for unstructured road environments. The dataset consists of 472 scenes, each lasting 30 seconds, and provides over 28K annotated samples and 119K sweeps. URScenes, for the first time, covers eight typical scenarios, including rainy, snowy, foggy, dusty, high-glare, night, cloudy, and sunny conditions. Additionally, URScenes supports multi-task perception for 3D object detection, multi-object tracking, and 3D occupancy in unstructured road environments. URScenes also provides a unified annotation system and format conversion tools, enabling easy conversion to popular formats such as nuScenes, KITTI, and Waymo datasets. Finally, this study presents comparative experimental results to assess the performance of state-of-the-art algorithms on the URScenes dataset. The data and development toolkit are available at http://www.sav-lab.com.

This paper focuses on embodied task planning, where an agent acquires visual observations from the environment and executes atomic actions to accomplish a given task. Although recent Vision-Language Models (VLMs) have achieved impressive results in multimodal understanding and reasoning, their performance remains limited when applied to embodied planning that involves multi-turn interaction, long-horizon reasoning, and extended context analysis. To bridge this gap, we propose RoboAgent, a capability-driven planning pipeline in which the model actively invokes different sub-capabilities. Each capability maintains its own context, and produces intermediate reasoning results or interacts with the environment according to the query given by a scheduler. This framework decomposes complex planning into a sequence of basic vision-language problems that VLMs can better address, enabling a more transparent and controllable reasoning process. The scheduler and all capabilities are implemented with a single VLM, without relying on external tools. To train this VLM, we adopt a multi-stage paradigm that consists of: (1) behavior cloning with expert plans, (2) DAgger training using trajectories collected by the model, and (3) reinforcement learning guided by an expert policy. Across these stages, we exploit the internal information of the environment simulator to construct high-quality supervision for each capability, and we further introduce augmented and synthetic data to enhance the model's performance in more diverse scenarios. Extensive experiments on widely used embodied task planning benchmarks validate the effectiveness of the proposed approach.

Virtual try-on (VTON) has advanced single-garment visualization, yet real-world fashion centers on full outfits with multiple garments, accessories, fine-grained categories, layering, and diverse styling, remaining beyond current VTON systems. Existing datasets are category-limited and lack outfit diversity. We introduce Garments2Look, the first large-scale multimodal dataset for outfit-level VTON, comprising 80K many-garments-to-one-look pairs across 40 major categories and 300+ fine-grained subcategories. Each pair includes an outfit with 3-12 reference garment images (Average 4.48), a model image wearing the outfit, and detailed item and try-on textual annotations. To balance authenticity and diversity, we propose a synthesis pipeline. It involves heuristically constructing outfit lists before generating try-on results, with the entire process subjected to strict automated filtering and human validation to ensure data quality. To probe task difficulty, we adapt SOTA VTON methods and general-purpose image editing models to establish baselines. Results show current methods struggle to try on complete outfits seamlessly and to infer correct layering and styling, leading to misalignment and artifacts.

Large-scale multi-view clustering aims to explore the complementary and consistent information among different views in efficient manner. Despite the impressive performance gained by the existing methods, they just perform anchor learning in a single space with the orthogonal or some other constraints from the multi-view data, leading to undesired anchors. The anchors can simultaneously occur in more spaces and the complementary information among these spaces is able to be adopted for learning anchors. Meanwhile, the space with basis being the anchored cluster center is neglected to learn anchors by most existing works. In this work, we propose learning anchor in Dual Orthogonal Space for Fast Multi-view Clustering (DOSFMVC). DOSFMVC conducts anchor learning in dual orthogonal space, aiming at utilizing the complementary information among two spaces in producing anchors with high quality. DOSMFVC introduces the consensus anchored cluster center as basis of the extra space and clustering indicator of anchors based on this basis in anchor learning. The anchor learning and partition are integrated into a unified model, where the final cluster assignment can be adopted for clustering results. Extensive experiments confirm the superiority of our method compared with some state-of-the-art methods on several benchmark datasets.

Cross-view object correspondence, exemplified by the representative task of ego-exo object correspondence, aims to establish consistent associations of the same object across different viewpoints (e.g., ego-centric and exo-centric). This task poses significant challenges due to drastic viewpoint and appearance variations, making existing segmentation models, such as SAM2, non-trivial to apply directly. To address this, we present V^ 2 -SAM, a unified cross-view object correspondence framework that adapts SAM2 from single-view segmentation to cross-view correspondence through two complementary prompt generators. Specifically, the Cross-View Anchor Prompt Generator (V^ 2 -Anchor), built upon DINOv3 features, establishes geometry-aware correspondences and, for the first time, unlocks coordinate-based prompting for SAM2 in cross-view scenarios, while the Cross-View Visual Prompt Generator (V^ 2 -Visual) enhances appearance-guided cues via a novel visual prompt matcher that aligns ego-exo representations from both feature and structural perspectives. To effectively exploit the strengths of both prompts, we further adopt a multi-expert design and introduce a Post-hoc Cyclic Consistency Selector (PCCS) that adaptively selects the most reliable expert based on cyclic consistency. Extensive experiments validate the effectiveness of V^ 2 -SAM, achieving new state-of-the-art performance on Ego-Exo4D (Ego-Exo object correspondence), DAVIS-2017 (video object tracking), and HANDAL-X (robotic-ready cross-view correspondence).

Establishing consistent correspondences across images is essential for 3D vision tasks such as structure-from-motion (SfM), yet most existing matchers operate in a pairwise manner, often producing fragmented and geometrically inconsistent tracks when their predictions are chained across views. We propose MV-RoMa, a multi-view dense matching model that jointly estimates dense correspondences from a source image to multiple co-visible targets. Specifically, we design an efficient model architecture which avoids high computational cost of full cross-attention for multi-view feature interaction: (i) multi-view encoder that leverages pair-wise matching results as a geometric prior, and (ii) multi-view matching refiner that refines correspondences using pixel-wise attention. Additionally, we propose a post-processing strategy that integrates our model's consistent multi-view correspondences as high-quality tracks for SfM. Across diverse and challenging benchmarks, MV-RoMa produces more reliable correspondences and substantially denser, more accurate 3D reconstructions than existing sparse and dense matching methods.

Multi-focus image fusion (MFIF) is a crucial technique in image processing, with a key challenge being the generation of decision maps with precise boundaries. However, traditional methods based on heuristic rules and deep learning methods with black-box networks are difficult to generate high-quality decision maps. To overcome this challenge, we introduce neurodynamics-driven coupled neural P (CNP) systems, which are biological neural computation models inspired by spiking mechanisms, to enhance the accuracy of decision maps. Specifically, we first conduct an in-depth analysis of the model's neurodynamics to identify the constraints between the network parameters and the input signals. This solid analysis avoids abnormal continuous firing of neurons and ensures the model accurately distinguishes between focused and unfocused regions, generating high-quality decision maps for MFIF. Based on this analysis, we propose a Neurodynamics-Driven CNP Fusion model (ND-CNPFuse) tailored for the challenging MFIF task. Unlike current ideas of decision map generation, ND-CNPFuse distinguishes between focused and unfocused regions by mapping the source image into interpretable spike matrices. By comparing the number of spikes, an accurate decision map can be generated directly without any post-processing. Extensive experimental results show that ND-CNPFuse achieves new state-of-the-art performance on four classical MFIF datasets, including Lytro, MFFW, MFI-WHU, and Real-MFF. The code is available at https://github.com/MorvanLi/ND-CNPFuse.

On-device AI systems increasingly adopt a single foundation model equipped with task-specific Low-Rank Adaptation (LoRA) modules, forming a multi-LoRA LLM that supports multiple tasks.We study how to adapt such a model to a new task on memory-constrainted devices.Although LoRA reduces trainable parameters, fine-tuning a full set of modules remains memory-intensive.To improve efficiency, we apply sparse updating, training a subset of LoRA modules within the memory budget.However, existing sparse updating methods assume all candidate parameters are instantiated and cannot estimate the importance of modules that do not yet exist, while prior memory models designed for sequential networks fail to capture the blockwise parallel structure of Transformers.We propose TaskIT, a framework for memory-efficient fine-tuning via cross-task importance transfer. TaskIT predicts pre-insertion module importance by transferring from previously tuned tasks and employs a block-based memory predictor that captures parallel and sequential dependencies of Transformer blocks. A dynamic programming scheduler then selects module locations, numbers, and ranks to maximize accuracy within the memory budget.Experiments on uni-modal and cross-modal benchmarks show that TaskIT achieves superior accuracy-memory tradeoffs compared with Zero-FT, non-LoRA, and LoRA-based fine-tuning methods.

Unsupervised Anomaly Detection (UAD) and anomaly classification are frequently used in industrial and medical scenarios. Specifically, UAD identifies anomalous regions at the fine-grained pixel-level, while anomaly classification distinguishes anomaly types at the anomaly region level. However, existing approaches typically treat these tasks independently and sequentially, overlooking the benefits of jointly training them to suppress Local Visual Ambiguity (LVA) caused by the similarities of different types of anomalies in local visual patterns. Moreover, a multi-task learning framework cannot be directly applied to jointly train the two tasks, since UAD and anomaly classification exhibit feature preference incompatibility. To address these limitations, we propose the Prototype-Guided Semi-Supervised Feature Disentanglement (PG-SFD) framework, which makes a paradigm shift from implicit feature sharing to explicit feature disentanglement and explicitly constructs normal and category prototypes to eliminate implicit normal-abnormal semantic coupling via a Dual-Prototype Disentanglement Module (DPRM). Moreover, for cross-task feature differential injection and gradient conflict mitigation, the Differential Gated Interaction (DGI) and Geometry-Regularized Optimization (GRO) are proposed to form a cohesive framework with DPRM. PG-SFD demonstrates high effectiveness in both UAD tasks and weakly supervised classification tasks. Meanwhile, it exhibits stable performance across multiple types of datasets, including industrial and medical datasets, indicating its strong generalizability.

Multi-object tracking (MOT) based on unmanned aerial vehicle (UAV) aims to identify and continuously track the positions of multiple ground targets during UAV flight. Current mainstream methods utilize appearance matching and motion matching to match targets in consecutive frames. However, these methods often fail in the following scenarios: First, scenarios with multi-scale targets, where small targets have weak appearance features and small bounding boxes; second, scenarios with complex backgrounds or occlusions, where the background or occlusions interfere with the appearance features and change the bounding box size of targets; third, scenarios where the UAV lens shakes, rotates, or zooms, leading to misalignment between consecutive frames; and fourth, scenarios with high targets similarity, where the appearance features between targets are difficult to distinguish, such as vehicles on a road. To address these issues, we propose a multi-object tracking algorithm, ProgTrack, based on a multi-stage progressive matching mechanism. This algorithm simulates human eye tracking strategies, employing a progressive process of "first matching easily matched large targets, then matching difficult-to-match small targets, and finally matching the remaining mixed-scale targets." Similarly, ProgTrack employs three strategies for target matching at different scales and appearances: a simple Local Motion Information (LMI) matching strategy for large targets, a complex Context Enhancement Feature (CE-Feature) matching strategy for small targets, and a Global Motion Information (GMI) matching strategy for multi-scale targets matching, thereby achieving target matching. On the VisDrone2019 UAV tracking dataset, ProgTrack achieves MOTA, MOTP, and IDF1 scores of 40.2, 77.5, and 52.8, respectively, demonstrating state-of-the-art performance among ten methods.

Vision-language models such as CLIP have achieved remarkable zero-shot recognition capabilities, yet their robustness against adversarial perturbations remains limited. Test-time counterattack (TTC) was recently proposed to improve CLIP's robustness by perturbing an input image to steer it away from a corrupted state during inference. However, TTC remains fragile under strong attacks because its counterattack relies on a directly corrupted original view and employs a noise-driven hard-gating scheme that cannot adapt to varying corruption severity. To address these limitations, we introduce Multi-view guided Adaptive Counterattack (MAC), which performs counterattacks for multi-view with corruption-aware soft weighting. Specifically, MAC first constructs augmented views of an input image to obtain diverse embeddings. It then performs counterattacks to refine corrupted embeddings of views. Next, MAC adaptively scales the counterattack intensity for each view based on its estimated corruption degree. Finally, the adaptively counterattacked views are aggregated to yield a robust final prediction. Extensive experiments across 20 datasets and diverse attack scenarios demonstrate that MAC substantially improves robustness while preserving high inference speed and memory efficiency with its tuning-free design. Our code is available at https://github.com/sunoh-kim/MAC.

Existing Multi-Camera Multi-Target (MCMT) tracking models typically adopt a two-stage framework, involving single-camera tracking followed by inter-camera tracking. However, in this paradigm, the use of multiple views is confined to recovering missed matches in the first stage, providing a limited contribution to overall tracking. To address this issue, we propose a novel global MCMT tracking framework termed GMT, which effectively leverages the advantage of multi-view by performing global-level trajectory-target matching. Specifically, instead of assigning trajectories independently for each view, we propose a Cross-View Feature Consistency Enhancement(CFCE) module to reduce the feature discrepancies across different views, and encode the same historical targets across different views as global trajectories. The Global Trajectory Associate (GTA) module is then introduced to associate new targets to global trajectories, allowing the model to jointly exploit both intra-view and inter-view cues during tracking. Compared with the two-stage framework, the GMT achieves significant improvements on existing datasets, with gains of up to 13.1% in CVMA in and 19.2% in CVIDF1. Moreover, we present VisionTrack, a high-quality, large-scale MCMT dataset encompassing diverse scenes with varying illumination and target distributions, providing significantly greater diversity than existing datasets. Our code and dataset will be released at https://github.com/FoxCanned/GMT.