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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

Benchmarking Dynamic Affective Reasoning: A Viewer-Centric Video Emotion Dataset

Zhiyan Zhang, Peipei Song, Jinpeng Hu, Jingyang Jia, Xun Yang, Xiaojun Chang

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

ECCV 2026

Art Beyond Semantics: Sheaf-Informed Contrastive Learning for Multi-Relational Representations

Ludovica Schaerf, Antonio Purificato, Piera Riccio, Fabrizio Silvestri, Noa Garcia

Understanding a painting is never a single act. Art histori-ans may analyze the same work through concepts of style, iconography,or historical context, dimensions that are not interchangeable, and eachcarries distinct semantic relationships between the visual and the tex-tual. Vision-Language Models (VLMs) like CLIP, which learn a singleshared embedding space, collapse this richness into a single homogeneousalignment, thereby losing the multi-relational structure that defines art-historical reasoning. We introduce CANVAS (Contrastive Art-awareNetwork for Vision-Language Alignment with Sheaves), a frameworkfor learning relation-aware multimodal representations inspired by sheaftheory. Each artwork is projected into multiple embeddings conditionedon the type of relation (i.e., the context), and a novel contrastive lossencodes contextual information during training, with no dependency onexternal data at inference. We evaluate on three newly introduced bench-marks of artworks for multi-relational art understanding: WikiArt+,derived from WikiArt and Wikipedia, HertzianaDP, from the Biblio-theca Hertziana collection, and SemArt+, refined from the SemArtdataset. In multimodal retrieval and art understanding, CANVAS out-performs the baselines, supporting the view that multi-relational align-ment is not just theoretically motivated but also practically essential.

ECCV 2026

Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors

Guanyu Lu, Fang Zhou, Cheqing Jin

Zero-shot anomaly detection aims to identify defects in ar-bitrary novel domains; however, existing models assume that the auxil-iary data contains a rich diversity of anomalies, neglecting the far morecomplex and unpredictable variations in real-world target domains. Thisstudy introduces DIVE, the first approach to investigate the scenario oflimited auxiliary anomaly priors and resolve the resulting substantial per-formance degradation. Through a shallow-and-deep text embedding in-jection strategy during visual encoding, DIVE learns to abstract genericanomaly concepts shared across the auxiliary training domain and di-verse target domains. Moreover, we propose a disentanglement mech-anism to tackle the suboptimal alignment between visual embeddingsentangled with object semantics and object-agnostic textual prompts.Experiments demonstrate that, under the setting of limited anomalypatterns in auxiliary data, DIVE outperforms SOTA baselines by up to16.2% and 28.5% on two classification metrics, and 23.4%, 24.1%, and47.0% on three segmentation metrics, in terms of average performanceacross twelve datasets. Furthermore, it maintains highly competitive per-formance when auxiliary data exhibits sufficient anomaly diversity.

ECCV 2026

VOID: Video Object and Interaction Deletion

Saman Motamed, William Harvey, Benjamin Klein, Luc Van Gool, Zhuoning Yuan, Ta Ying Cheng

Existing video object removal methods excel at inpaintingcontent “behind” the object and correcting appearance-level artifactssuch as shadows and reflections. However, when the removed object hasmore significant interactions, such as collisions with other objects, cur-rent models fail to correct them and produce implausible results. Wepresent VOID, a video object removal framework designed to performphysically-plausible inpainting in these complex scenarios. To train themodel, we generate a new paired dataset of counterfactual object re-movals using Kubric and HUMOTO, where removing an object requiresaltering downstream physical interactions. During inference, a vision-language model identifies regions of the scene affected by the removedobject. These regions are then used to guide a video diffusion model thatgenerates physically consistent counterfactual outcomes. Experiments onboth synthetic and real data show that our approach better preservesconsistent scene dynamics after object removal compared to prior videoobject removal methods. We hope this framework sheds light on howto make video editing models better simulators of the world throughhigh-level causal reasoning.

ECCV 2026

VIGOR: VIdeo Geometry-Oriented Reward for Temporal Generative Alignment

Tengjiao Yin, Jinglei Shi, Heng Guo, Xi Wang

Video diffusion models lack explicit geometric supervisionduring training, leading to inconsistency artifacts such as object defor-mation, spatial drift, and depth violations in generated videos. To addressthis limitation, we propose a geometry-based reward model that leveragespretrained geometric foundation models to evaluate multi-view consis-tency through cross-frame reprojection error. Unlike previous geometricmetrics that measure inconsistency in pixel space, where pixel intensitymay introduce additional noise, our approach conducts error computa-tion in a pointwise fashion, yielding a more physically grounded and ro-bust error metric. Furthermore, we introduce a geometry-aware samplingstrategy that filters out low-texture and non-semantic regions, focusingevaluation on geometrically meaningful areas with reliable correspon-dences to improve robustness. We apply this reward model to align videodiffusion models through two complementary pathways: post-training ofa bidirectional model via SFT or Reinforcement Learning and inference-time optimization of a Causal Video Model (e.g., Streaming video genera-tor) via test-time scaling with our reward as a path verifier. Experimentalresults validate the effectiveness of our design, demonstrating that ourgeometry-based reward provides superior robustness compared to othervariants. By enabling efficient inference-time scaling, our method offersa practical solution for enhancing open-source video models without re-quiring extensive computational resources for retraining.

ECCV 2026

VideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning

Zhe Gao, Shiyu Shen, Taifeng Chai, Weinong Wang, Haotian Xu, Xing W, Wenbin Li, Qi Fan, Yang Gao, Dacheng Tao

Existing Multimodal Large Language Models (MLLMs) of-ten suffer from hallucinations in long video understanding (LVU), pri-marily due to the imbalance between textual and visual tokens. Ob-serving that MLLMs handle shorter but more accurate visual inputswell, recent LVU works alleviate hallucinations by automatically parsingthe vast visual data into manageable segments that can be effectivelyprocessed by MLLMs. SFT-based tool-calling methods can serve thispurpose, but they typically require vast amounts of fine-grained, high-quality data and suffer from constrained tool-calling trajectories. Wepropose a novel VideoTIR that leverages Reinforcement Learning (RL)to encourage proper usage of comprehensive multi-level toolkits for ef-ficient long video understanding. VideoTIR explores both Zero-RL andSFT cold-starting to enable MLLMs to retrieve and focus on meaning-ful video segments/images/regions, enhancing long video understandingboth accurately and efficiently. To reduce redundant tool-calling in theearly RL-stage and accelerate convergence, we propose Toolkit ActionGrouped Policy Optimization (TAGPO), which enhances the efficiencyof the calling process through the finer stepwise reward assignment. Ad-ditionally, we develop a sandbox-based trajectory synthesis frameworkto generate high-quality trajectory data. Extensive experiments on threelong-video QA benchmarks demonstrate the effectiveness and efficiencyof our method.

ECCV 2026

Local Spacing-Aware Hungarian Matching for Stable Point-Supervised Crowd Counting

Kai Jiang, Yiming Lin, Zurui Ao

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

ECCV 2026

WAPR: A Foundation Model for Wide-Angle Refinement in Unseen Object Pose Estimation

Yulin Wang, Jianghao Zhou, Hongli Li, Mengting Hu, Chen Luo

Real-world applications require 6D pose estimation to beaccurate, fast, and scalable to unseen objects. This paper introducesWAPR, a zero-shot wide-angle pose re_x001C_nement model that re_x001C_nes candi-date poses with rotational deviations up to 90^0013._Weather-Conditioned_Depth_Anything_1490_Weather_Conditioned_Depth_Anything_ECCV_2026___C....abstract.txt

ECCV 2026

TouchAnything: Diffusion-Guided 3D Reconstruction from Sparse Robot Touches

Langzhe Gu, Hung-Jui Huang, Mohamad Qadri, Michael Kaess, Wenzhen Yuan

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

ECCV 2026

Domain Adaptive Object Detection via Dual-Stream Bilevel-Cycle Optimization

Yannan Chen, Wei Wang, Ruoyu Chen, Wenqiang Wang, Jiancheng Wang, Mingbo Yang, Yaowei Wang, Xiaochun Cao

Cycle self-training (CST) breaks the shared classifier assumption of the standard self-training framework, which is effective for unsupervised domain adaptation and exploits unlabeled target data by training with target pseudo-labels. CST introduces a target classifier and employs an inner-outer loop updating strategy, addressing the issue of unreliable pseudo-labels and enabling pseudo-labels to generalize across domains. Despite its success in image classification, extending CST to object detection faces three main challenges. First, the upper bound of CST in object detection is constrained by three types of unreliable pseudo-labels, such as classification error alone, localization error alone, and their combination. Second, since object detection involves detecting multiple target objects, directly applying CST leads to training instability. Third, a wider numerical range of regression coordinates leads to exploding losses. To this end, we apply CST to both classification and regression and propose the Dual-Stream Bilevel-Cycle Optimization framework. Specifically, we construct CST upon Mean Teacher to prevent training instability and use extra normalization to map the regression bounding box into a standardized space, effectively addressing exploding losses. Also, we provide a theoretical derivation of the regression bound. Extensive experiments across four cross domain standard scenarios demonstrate that our framework achieves considerable results.

ECCV 2026

MultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learning

Wenhao Wang, Franziska Boenisch, Michael Backes, Adam Dziedzic

Memorization in machine learning models enables high per-formance on rare in-distribution samples by capturing their atypicalpatterns. However, it also causes harmful retention of noise and out-liers, degrading generalization. While memorization has been extensivelystudied in both supervised and self-supervised learning in the visiondomain, it remains unexplored in multi-modal contrastive learning. Weaddress this gap by introducing MultiMem, the first metric designed toquantify memorization in multi-modal contrastive learning. Through oursystematic analysis, we demonstrate that cross-modal semantic misalign-ment has the strongest influence on memorization, with text being thedominant modality driving memorization, followed by video, image, andaudio. We show that targeted augmentations applied across all modalitieseffectively reduce memorization as measured by our MultiMem metricand improve model performance. Overall, this work establishes the firstframework for measuring and mitigating memorization in multi-modalcontrastive learning, preventing harmful data retention and contributingto higher-performing models. Full version with appendix available athttp://arxiv.org/abs/2606.22220

ECCV 2026

FuDU: A Fuzzy Dual-dimension Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection

Zhaoyang Wang, Haiyong Chen, Binyi Su, Xinwei Lyu

Ensuring the reliability of deep learning models in real-timeindustrial defect detection is critical for high-stakes quality inspection.To mine uncertain samples within continuous industrial media streams,thereby enhancing the reliability of the detection system, this paperproposes a streaming active learning method based on the Fuzzy Dual-dimensional Uncertainty (FuDU) framework. Specifically, we first designa Prototype-based Global Uncertainty Quantification (PGUQ) moduleon the backbone to evaluate image-level uncertainty via normal/defectivefeature prototypes. A Dual-entropy defect Uncertainty Evaluator (DeUE)is then integrated into the detection head to quantify box-level uncer-tainty. Finally, by modeling uncertainty as systematic error, we pro-pose a fuzzy dual-dimensional uncertainty-aware strategy that lever-ages fuzzy inference to fuse dual-dimensional uncertainties, enabling ex-pert knowledge-driven adaptive sampling decisions. Comprehensive ex-periments demonstrate that FuDU is efficient and flexible, making itwell-suited for challenging industrial inspection tasks such as the de-tection of nuclear fuel rod defects. Our code is publicly available at:https://github.com/wangzhaoyang-508/FuDU.

ECCV 2026

The Language of Visual Attention: Modeling Scanpaths via Autoregressive Token Prediction

Susmit Agrawal, Matthias Bethge, Matthias Kuemmerer

Understanding human visual attention on a scene over timehas applications in domains such as autonomous driving, interface de-sign, inferring cognitive states. Modeling human visual scanpaths has his-torically relied on specialized architectures with hand-crafted geometricpriors. While these architectures successfully model _x001C_xation sequences,their rigid structural biases restrict easy extendability and _x001D_exible con-ditioning. For instance, integrating task-speci_x001C_c instructions or adaptingto distinct viewer identities traditionally requires custom, disjoint ar-chitectural additions. We address this limitation by framing scanpathprediction purely as a discrete sequence modeling task. By mapping spa-tial coordinates into an autoregressive vocabulary, we leverage the pre-trained representations of Large Vision-Language Models (LVLMs). Thisformulation naturally absorbs diverse factors of variation: simple prompt-ing modi_x001C_cations allow for global conditioning, such as providing vieweridentities to capture personalized biases, or task-speci_x001C_c objectives (likefree-viewing versus visual search). The framework can also easily inte-grate the modeling of per-_x001C_xation attributes, such as individual _x001C_xa-tion durations alongside spatial locations. Crucially, this autoregressivealignment enables the scalable, exact computation of per-_x001C_xation log-likelihoods, directly equivalent to the commonly used Information Gain(IG) metric. Our model, DeepGaze3.5-VL, establishes a new state-of-the-art across multiple datasets, achieving 2.18 bits of IG on MIT1003,a 46% improvement over matched-backbone DeepGaze III. This advan-tage persists even when baselines use identical high-capacity vision en-coders. Beyond predictive performance, our generative framework servesas a powerful computational tool for direct behavioral interventions, al-lowing for controlled in-silico simulations that would be experimentallydi_x001E_cult or impossible to conduct in vivo. We demonstrate this abilityby performing controlled interventions on the durations of pre-saccadic_x001C_xations, recovering known oculomotor phenomena purely from data.

ECCV 2026

Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation

Shengqi Xu, Yang Liu, Guojin Zhong, Fanjie Wang, Hu Luo, Hanyu Zhou, Weiyao Zhang, Ziyi Ye, Zuxuan Wu, Yu-Gang Jiang

Visuo-Tactile policies leveraging optical tactile sensors haveshown great promise in contact-rich manipulation. These sensors achievehigh spatial resolution and multi-dimensional force sensing by utilizingan internal camera to monitor the deformation of their elastic gel sur-face, thereby indirectly inferring tactile cues. Despite their advantages,extracting fine-grained contact states necessary for contact-rich manipu-lation remains an open challenge. Existing methods typically use eitherraw images or cumulative motion fields to represent tactile cues. However,both are prone to perception ambiguity. Raw tactile images mainly cap-ture appearance changes, while cumulative motion fields only reflect theaggregate gel deformation. Consequently, distinct fine-grained contactstates can exhibit highly similar patterns, making it difficult to explicitlydistinguish subtle contact variations. To address this issue, we explorethe dynamic priors of tactile motion and discover that the correlationbetween transient and cumulative motion can explicitly distinguish fine-grained contact states. Based on this insight, we propose a motion-awaretactile representation to facilitate contact-rich manipulation. Beyond tac-tile representation, effective fusion of tactile and visual modalities is alsocritical. Most existing fusion methods either directly concatenate featuresfrom each modality or train modality-specific networks separately andfuse their outputs. However, these strategies struggle to simultaneouslymodel cross-modal interactions and preserve modality-specific character-istics. In this work, we take advantage of the Mixture-of-Transformersarchitecture and propose a unified modality-aware visuo-tactile policythat captures cross-modal complementarity while maintaining modality-specific properties. Extensive experiments on four challenging contact-rich manipulation tasks indicate the superior performance of our method.

ECCV 2026

SafeSAE-VLA: Interpreting OpenVLA Progress Dynamics with Sparse Feature Analysis

Socrates Osorio, Joy Yang

Understanding whether modern vision-language-action poli-cies internally represent task progress is central to interpretable robotcontrol. This paper presents a progress-based SafeSAE-VLA analysis ofOpenVLA on 750 LIBERO episodes, replacing a degenerate binary targetwith a suite-normalized high-vs.-low split over relative geometric progressand testing layer-20 sparse-autoencoder (SAE) features (dsae = 16,384)with non-parametric differential analysis and false-discovery-rate (FDR)control. The recovered signal is strong and sparse. Of 1,881 active fea-tures, 1,117 are significant (FDR < 0.05), a linear probe reaches 0.918AUROC (area under the ROC curve), and top-20 features alone retain0.894 AUROC. Performance is stable across suites (up to 0.985 on goaland 0.947 on object), while motion-only controls are markedly weaker(0.572–0.711), indicating separability is not explained by gross move-ment magnitude. Dense raw-activation probes are equal or stronger thanSAE readouts (e.g., raw LR 0.975 vs. full-SAE 0.947 under a pooledanalysis with bootstrap confidence intervals). We therefore position thecontribution not as predictive dominance but as showing that nearly allof the progress signal survives in a compact set of named, inspectable,intervention-compatible directions. Layer sweeps, split-robustness checks,and a success-labeled audit (in which the progress-tuned top-20 correctlyscores an at-chance 0.471 while the broader SAE basis reaches 0.968)support this scoping, and direct on-OpenVLA feature-setting interven-tions produce specific, task-local behavioral shifts rather than generalclosed-loop repair.

ECCV 2026

RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning

Yelin Wang, Zijia Song, Shuo Ye, Chuanguang Yang, Miaoyu Wang, Yong Xu, Zhulin An, Yongjun Xu, Zitong Yu

Remote Sensing Image Change Captioning (RSICC) aimsto describe changes between bi-temporal remote sensing images andholds significant research and application value. However, most exist-ing methods rely on conventional deep learning architectures, and thelimited model capacity constrains performance. Although large-modelpost-training techniques have achieved great success in general domains,their direct transfer to RSICC remains challenging due to data scarcityand the need for fine-grained change understanding. To address this, wepropose RSICCLLM, the first post-training framework for large vision-language models in RSICC. Specifically, we design a data genera-tion paradigm, release the instruction dataset RSICI, and establish atask-specific RSICC benchmark. We further introduce Difference-aware Supervised Fine-tuning to explicitly extract change repre-sentations and guide the model in perceiving and understanding tem-poral differences. In addition, we propose Dual-Negative PreferenceOptimization (DNPO), which employs two complementary negative-sample construction strategies to construct the preference dataset RSICPand further refine model performance. Extensive experiments validatethe superior capability of RSICCLLM, which achieves outstanding re-sults with only 7B parameters, surpassing models of substantially largerscales. The code and dataset will be made publicly available at https://github.com/keaill/RSICCLLM.

ECCV 2026

R2M: Real-Aware Residual Model Merging for Robust and Generalizable Deepfake Detection

Jinhee Park, Guisik Kim, Choongsang Cho, Junseok Kwon

Deepfake generators evolve rapidly, making exhaustive datacollection and repeated retraining impractical. Unlike generic multi-tasksettings, deepfake specialists share a common binary objective (Real vs.Fake) and mainly differ in generator-specific artifacts. However, naiveparameter arithmetic can induce unintended decision-boundary shifts,causing unstable ranking behavior and degraded AUC under domainshift. We propose R2 M, a training-free merging framework that decom-poses task vectors into a shared component and generator-specific resid-uals, linking parameter-space updates to logit-space behavior. Offlinespectral construction identifies shared and residual subspaces, and on-line routing selects residuals via first-order gradient-residual alignment,predicting per-sample logit updates under linearization. R2 M is bothefficient and interpretable: expensive computations are performed of-fline, while online inference requires only a single forward–backward passwith lightweight inner-product routing. The same formulation enablesdiagnostic analysis through margin and routing statistics. Experimentsdemonstrate consistently strong performance across in-domain, cross-domain, and unseen settings, highlighting R2 M as an interpretable andscalable approach to training-free deepfake model merging.

ECCV 2026

Evidence Triangulation for Multimodal Fact-Checking in the Wild

Stefanos-Iordanis Papadopoulos, Zacharias Chrysidis, Christos Koutlis*, Symeon Papadopoulos, Panagiotis Petrantonakis

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

ECCV 2026

PWM-ArtGen: Part World Model for Articulated Object Generation

Wentao Zheng, Ancong Wu

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

ECCV 2026

PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design

Jiazhe Wei, Ken Li, Tianyu Lao, Haofan Wang, Yueming Lyu, Liang Wang, Caifeng Shan, Chenyang Si

Graphic design forms the cornerstone of modern visual com-munication, serving as a vital medium for promoting cultural and com-mercial events. Recent advances have explored automating this processusing Large Multimodal Models (LMMs), yet existing methods often pro-duce geometrically inaccurate layouts and lack the iterative, layer-specificediting required in professional workflows. To address these limitations,we present PosterCopilot, a framework that advances layout reasoningand controllable editing for professional graphic design. Specifically, weintroduce a progressive three-stage training strategy that equips LMMswith geometric understanding and aesthetic reasoning for layout design,consisting of Perturbed Supervised Fine-Tuning, Reinforcement Learn-ing for Visual-Reality Alignment, and Reinforcement Learning from Aes-thetic Feedback. Furthermore, we develop a complete workflow thatcouples the trained LMM-based design model with generative models,enabling layer-controllable, iterative editing for precise element refine-ment while maintaining global visual consistency. Extensive experimentsdemonstrate that PosterCopilot achieves geometrically accurate and aes-thetically superior layouts, offering fine-grained, layer-wise controllabilityfor professional iterative design.

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