ECCV 2026 Papers

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.

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.

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.

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.

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 %.

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.

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.

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.

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/).

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.

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/

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.

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.

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.

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:

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.

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.

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.

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