ECCV 2026 论文列表

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.

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.

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.

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.

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

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.

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.

Masked modeling has emerged as a robust self-supervisedlearning framework. However, most methods rely on random masking,which disregards the structural properties of different data modalities. Toalign with the spatiotemporal and spectral characteristics of video andaudio data, we introduce a structured noise-based masking approach.By filtering white noise into different color noise distributions, we gen-erate structured masks that capture modality-specific patterns withoutrequiring handcrafted heuristics or access to the data. Our approach en-hances masked video and audio modeling frameworks without any addi-tional computational cost. Experiments show that structured noise mask-ing consistently outperforms random masking, underscoring the value ofmodality-aware masking strategies for representation learning.

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.

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.

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.

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.

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.

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.

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/.

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

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.

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.

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.