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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

SegFly: A 2D-3D-2D Paradigm for Aerial RGB-Thermal Semantic Segmentation at Scale

Markus Gross, Sai Bharadhwaj Matha, Rui Song, Viswanathan Muthuveerappan, Conrad Christoph, Julius Huber, Daniel Cremers

Semantic segmentation for uncrewed aerial vehicles (UAVs) isfundamental for aerial scene understanding, yet existing RGB and RGB-Tdatasets remain limited in scale, diversity, and annotation efficiencydue to the high cost of manual labeling and the difficulties of accurateRGB-T alignment on off-the-shelf UAVs. To address these challenges, wepropose a scalable geometry-driven 2D-3D-2D paradigm that leveragesmulti-view redundancy in high-overlap aerial imagery to automaticallypropagate labels from a small subset of manually annotated RGB imagesto both RGB and thermal modalities within a unified framework. Bylifting less than 3% of RGB images into a semantic 3D point cloud andrendering it into all views, our approach enables dense pseudo ground-truth generation across large image collections, automatically producing97% of RGB labels and 100% of thermal labels while achieving 91% and88% annotation accuracy without any 2D manual refinement. We furtherextend this 2D–3D–2D paradigm to cross-modal image registration, using3D geometry as an intermediate alignment space to obtain fully automatic,strong pixel-level RGB-T alignment with 87% registration accuracy and nohardware-level synchronization. Applying our framework to existing geo-referenced aerial imagery, we construct SegFly, a large-scale benchmarkwith over 20,000 high-resolution RGB images and more than 15,000geometrically aligned RGB-T pairs spanning diverse urban, industrial, andrural environments across multiple altitudes and seasons. On SegFly, weestablish the Firefly baseline for RGB and thermal semantic segmentationand show that both conventional architectures and vision foundationmodels benefit substantially from SegFly supervision, highlighting thepotential of geometry-driven 2D-3D-2D pipelines for scalable multi-modalaerial scene understanding. The SegFly dataset and our Firefly baselineare available at https://github.com/markus-42/SegFly.

ECCV 2026

SceneOrchestra: Efficient Agentic 3D Scene Synthesis via Full Tool-Call Trajectory Generation

Yun He, Kelin Yu, Matthias Zwicker

Recent agentic frameworks for 3D scene synthesis have ad-vanced realism and diversity by integrating heterogeneous generationand editing tools. These tools are organized into workflows orchestratedby an off-the-shelf LLM. Current approaches typically adopt an exe-cute–review–reflect loop: at each step, the orchestrator executes a tool,renders intermediate results for review, and then decides on the tooland its parameters for the next step. However, this design has two keylimitations. First, next-step tool selection and parameter configurationare driven by heuristic rules, which can lead to suboptimal executionflows, unnecessary tool invocations, degraded output quality, and in-creased runtime. Second, rendering and reviewing intermediate resultsafter each step introduces additional latency. To address these issues, wepropose SceneOrchestra, a trainable orchestration framework that opti-mizes the tool-call execution flow and eliminates the step-by-step reviewloop, improving both efficiency and output quality. SceneOrchestra con-sists of an orchestrator and a discriminator, which we fine-tune witha two-phase training strategy. In the first phase, the orchestrator learnscontext-aware tool selection and complete tool-call trajectory generation,while the discriminator is trained to assess the quality of full trajectories,enabling it to select the best trajectory from multiple candidates. In thesecond phase, we perform interleaved training, where the discriminatoradapts to the orchestrator’s evolving trajectory distribution and distillsits discriminative capability back into the orchestrator. At inference, weonly use the orchestrator to generate and execute full tool-call trajec-tories from instructions, without requiring the discriminator. Extensiveexperiments show that our method achieves state-of-the-art scene qualitycompared to previous work.

ECCV 2026

Reasoning Path and Latent State Analysis for Multi-view Visual Spatial Reasoning: A Cognitive Science Perspective

Qiyao Xue, Haoming Wang, Weichen Liu, Shiqi Wang, Yuyang Wu, Wei Gao

Spatial reasoning is a core aspect of human intelligence forperception and inference in 3D environments. However, current vision-language models (VLMs) struggle to maintain geometric coherence andcross-view consistency for spatial reasoning in multi-view settings. Inthis paper, we aim to understand the successes and failure of VLMs inmulti-view spatial reasoning, via structured analysis of how VLMs en-code perceptual evidence, integrate relational information, construct in-termediate spatial representations and perform perspective transforma-tion. Guided by cognitive science theories of human spatial reasoning,our analysis examines VLM’s consistency and representational persis-tence across reasoning phases that align with human’s spatial cognition,in explicit and implicit manners. Using a self-constructed spatial bench-mark, namely ReMindView-Bench, which consists of >50,000 VQA sam-ples with complementary views and controlled viewpoint configurations,spatial patterns and query types, our explicit analysis using an LLM-as-graph-parser workflow and self-consistency prompting shows that VLMsremain reliable for in-frame perceptual encoding but degrade sharplywhen integrating information across views. Our implicit analysis of linearprobing and entropy dynamics further reveals progressive loss of task-relevant spatial information and increasing uncertainty over reasoningphases. These findings provide important insights of how multi-view spa-tial mental models are formed, destabilized, and degraded across reason-ing phases in VLMs. The ReMindView-Bench benchmark is available athttps://huggingface.co/datasets/Xue0823/ReMindView-Bench, andthe source codes of benchmark construction and VLM reasoning analy-sis are available at https://github.com/pittisl/ReMindView-Bench.

ECCV 2026

PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation

Cao Duy, Phong Nguyen

Recent advances in 3D content generation from text or im-ages have achieved impressive results, yet view inconsistency from 2Dgenerators and the scarcity of high-quality 3D data remain significantbottlenecks. Existing solutions [21, 28] typically adapt large-scale pre-trained text-to-image latent diffusion models to generate 3D GaussianSplats (3DGS). However, these approaches often rely on training complexcascade pipelines that are computationally expensive and scalability-limited. Most critically, the quality of generated 3D assets is inherentlyconstrained by each component capacity and compressed latent space,leading to decoding artifacts and accumulated errors. To address theselimitations, we propose PixGS, a single-stage pipeline for direct high-quality 3DGS generation, which leverages recent advances in pixel-spacediffusion to bypass lossy latent compression while still benefiting from thevast 2D generative priors. By directly denoising 3D Gaussian attributesat each timestep, our method enables precise, splat-level regularizationof both appearance and geometry. Furthermore, we introduce a compre-hensive supervision strategy that incorporates surface normals, depth,and high-frequency structural information, which is often overlooked inprior works. Experiments demonstrate that PixGS outperforms currentstate-of-the-art methods while maintaining a fast inference speed (≈ 1son a single A100 GPU), offering a robust and efficient alternative tomulti-stage generation pipelines.

ECCV 2026

Personalize Your Large Vision-language Models With In-context Prompt Tuning

Yanshu Li, Jiaqian Li, Kuai Yu, Xi Xiao, Dongfang Liu, Tianyang Wang, Ruixiang Tang

Large vision-language models (LVLMs) have demonstratedstrong general multimodal capability and are increasingly deployed indownstream systems. This trend has driven growing interest in LVLMpersonalization, which aims to enable models to quickly and effectivelylearn out-of-distribution multimodal concepts to meet user-specific needs.However, many existing methods rely on inference-time training, whichreduces efficiency. They also struggle to maintain accuracy in complexmulti-image, multi-concept settings. These limitations restrict the broaderdeployment of LVLM-based systems. Therefore, this paper proposes in-context prompt tuning (ICPT). Specifically, ICPT employs a lightweightprojection module capable of operating in complex scenarios to extractfine-grained visual semantics from multiple reference images, seamlesslytransforming these features alongside identity-label mappings into con-tinuous prompts. To maximize computational efficiency, this moduleadaptively determines the prompt length based on the intrinsic visualcomplexity of each concept. Crucially, to overcome the environmental bi-ases and cross-concept interference prevalent in real-world applications,we introduce two novel geometric regularizations. These constraints re-fine prompt representations by decoupling key identities from transientenvironmental states and separating concepts to avoid semantic confu-sion. Extensive experiments show that ICPT achieves state-of-the-artpersonalization accuracy across diverse tasks and LVLM backbones.

ECCV 2026

Persistent Robot World Models: Stabilizing Multi-Step Rollouts via Reinforcement Learning

Jai Bardhan, Patrik Drozdík, Josef Sivic, Vladimir Petrik

Action-conditioned robot world models generate future videoframes of the manipulated scene given a robot action sequence, o!eringa promising alternative for simulating tasks that are di"cult to modelwith traditional physics engines. However, these models are optimized forshort-term prediction and break down when deployed autoregressively:each predicted clip feeds back as context for the next, causing errors tocompound and visual quality to rapidly degrade. We address this throughthe following contributions. First, we introduce a reinforcement learning(RL) post-training scheme that trains the world model on its own au-toregressive rollouts rather than on ground-truth histories. We achievethis by adapting a recent contrastive RL objective for di!usion modelsto our setting and show that its convergence guarantees carry over ex-actly. Second, we design a training protocol that generates and comparesmultiple candidate variable-length futures from the same rollout state,reinforcing higher-fidelity predictions over lower-fidelity ones. Third, wedevelop e"cient, multi-view visual fidelity rewards that combine com-plementary perceptual metrics across camera views and are aggregatedat the clip level for dense, low-variance training signal. Fourth, we showthat our approach establishes a new state-of-the-art for rollout fidelityon the DROID dataset, outperforming the strongest baseline on all met-rics (e.g., LPIPS reduced by 14% on external cameras, SSIM improvedby 9.1% on the wrist camera), winning 98% of paired comparisons, andachieving an 80% preference rate in a blind human study.

ECCV 2026

OREO: Fidelity Alignment in 3D Generation via On-the-fly Rendering-Editing Optimization

Zhiyuan Ma, Wenbo Hu, Wang Zhao, Pengfei Wang, Ying Shan, Lei Zhang

Despite recent advancements in 3D generation, models oftenstruggle to produce assets with high visual fidelity. To bridge this gap,we propose OREO, an alignment framework that enhances the realismof 3D generators by leveraging rich 2D diffusion priors. Instead of relyingon static datasets, OREO establishes a dynamic optimization loop thatproduces on-the-fly pseudo ground truths. At its core, OREO combinestwo components: (i) Reinforced Editing, which uses an image editor torefine rendered views of the 3D output, improving the visual fidelity ofrendered views while preserving geometric structure; and (ii) ContrastiveDistillation, which treats the edited views as positive and the originalrenderings as negative anchors, distilling the fidelity gap into the 3Dgenerator via a latent contrastive objective. Experiments demonstratethat OREO effectively improves upon pre-trained baselines, producing3D assets with enhanced visual realism. Our project page is at https://theericma.github.io/oreo/.

ECCV 2026

OneVAE: Joint Discrete and Continuous Optimization Helps Discrete Video VAE Train Better

Yupeng Zhou, Zhen Li, Yuming Chen, Ziheng Ouyang, Ruoyi Du, Daquan Zhou, Bin Fu, Yihao Liu, Peng Gao, Ming-Ming Cheng, Qibin Hou

Encoding videos into discrete tokens can enable concise andunified multi-modal LLMs. Previous discrete video VAEs suffer from un-stable training, long training time, and degraded reconstruction quality.We revisit the relationship between continuous and discrete VAEs andfind that bridging discrete and continuous representations improves dis-crete token learning. Based on this insight, we propose a unified progres-sive training framework that (i) jointly optimizes continuous and discretereconstructions within a single network, and (ii) progressively derives afamily of VAEs at different compression ratios, leading to faster conver-gence and better final performance. Furthermore, leveraging this unifiedtraining, a single VAE can achieve competitive performance for bothcontinuous and discrete representations. Meanwhile, we propose two ar-chitectural improvements to further boost the performance of discreteVAEs. First, inspired by the use of enlarging continuous latent dimensionto boost reconstruction, we propose multi-token quantization, improvingPSNR by nearly 1 dB at the same token compression ratio. Second, weintroduce first-frame enhancement, which uses a lower-compression firstframe as an anchor in causal VAEs, alleviates the limited-context is-sue of the first frame and significantly improves reconstruction underhigh-compression settings (e.g., 4 × 16 × 16). The code is available athttps://github.com/HVision-NKU/OneVAE.

ECCV 2026

On the Reliability of Cue Conflict and Beyond

Pum Jun Kim, Seung-Ah Lee, Seongho Park, Dongyoon Han, Jaejun Yoo

Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes. The cueconflict benchmark has been influential in probing shape-texture preference and in motivating the insight that stronger, human-like shape bias is often associated with improved in-domain performance. However, we find that the current stylization-based instantiation can yield unstable and ambiguous bias estimates. Specifically, stylization may not reliably instantiate perceptually valid and separable cues nor control their relative informativeness, ratio-based bias can obscure absolute cue sensitivity, and restricting evaluation to preselected classes can distort model predictions by ignoring the full decision space. Together, these factors can confound preference with cue validity, cue balance, and recognizability artifacts. We introduce REFINED-BIAS, an integrated dataset and evaluation framework for reliable and interpretable shape–texture bias diagnosis. REFINED-BIAS constructs balanced, humanand modelrecognizable cue pairs using explicit definitions of shape and texture, and measures cuespecific sensitivity over the full label space via a ranking-based metric, enabling fairer cross-model comparisons. Across diverse training regimes and architectures, REFINED-BIAS enables fairer cross-model comparison, more faithful diagnosis of shape and texture biases, and clearer empirical conclusions, resolving inconsistencies that prior cue-conflict evaluations could not reliably disambiguate. Our code is available at REFINED-BIAS.

ECCV 2026

OmniFace: Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer

Xu Guo, Fulong Ye, Xinghui Li, Pengqi Tu, Pengze Zhang, Qichao Sun, Songtao Zhao, Xiangwang Hou, Qian HE

Video Face Swapping (VFS) requires seamlessly injecting asource identity into a target video while meticulously preserving the orig-inal pose, expression, lighting, background, and dynamic information.Existing methods struggle to maintain identity similarity and attributepreservation while preserving temporal consistency. To address the chal-lenge, we propose a comprehensive framework to seamlessly transfer thesuperiority of Image Face Swapping (IFS) to the video domain. Wefirst introduce a novel data pipeline SyncID-Pipe that pre-trains anIdentity-Anchored Video Synthesizer and combines it with IFS modelsto construct bidirectional ID quadruplets for explicit supervision. Build-ing upon paired data, we propose a powerful Diffusion Transformer-basedframework OmniFace, employing a core Modality-Aware Conditioningmodule to discriminatively inject multi-model conditions. Meanwhile,we propose a Synthetic-to-Real Training mechanism and an Identity-Coherence Reward Weighting strategy to enhance visual realism andidentity consistency under challenging scenarios. To address the issue oflimited benchmarks, we introduce IDBench-V, a comprehensive bench-mark encompassing diverse scenes. Extensive experiments demonstrateOmniFace outperforms state-of-the-art methods and further exhibits ex-ceptional versatility, which can be seamlessly adapted to various swap-related tasks.

ECCV 2026

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis

Zicheng Kong, dehua ma, Zhenbo Xu, Anwen Yang, Yiwei Ru, Haoran Wang, Zixuan Zhou, Fuqing Bie, Liuyu Xiang, Huijia Wu, Jian Zhao, Zhaofeng He

Multimodal large language models (MLLMs) struggle withalignment due to the limitations of existing reward models (RMs), whichare predominantly vision-centric, dependent on costly human labels, andprovide opaque scalar scores that fail to capture nuanced reasoning,leading to brittle alignment. We present Omni-RRM, an Omni-modalRubric-grounded Reward Model that generates multi-dimensional re-ward signals across text, image, video, and audio. To overcome the highcost and inherent inconsistency of human-centric evaluation in multi-dimensional reasoning, we introduce Omni-Preference, a high-qualitydataset constructed via automatic rubric-grounded preference synthe-sis. In this pipeline, teacher models reconcile raw preferences into ex-plicit justifications, ensuring that the synthesized supervision is bothhigh-fidelity and interpretable. Omni-RRM is trained using a progres-sive SFT + GRPO regimen, specifically optimized to sharpen rewarddiscrimination on low-margin, hard preference pairs. It achieves state-of-the-art accuracy on video (80.2% on ShareGPT-Video) and audiobenchmarks (66.8% on Audio-HH-RLHF and 65.0% on TA2T), yield-ing a five-benchmark Overall accuracy of 70.4% and a +17.0% relativegain over its backbone. Furthermore, Omni-RRM effectively guides Best-of-N selection and exhibits robust transfer to text-only alignment. Allresources, including the dataset, training and inference code, and modelcheckpoints are available at https://tmfk418.github.io/Omni-RRM.

ECCV 2026

RoomPlanner: Reachability-Aware View Sampling for Text-to-Room 3D Gaussian Splatting

Wenzhuo Sun, Mingjian Liang, Wenxuan Song, Xuelian Cheng, Zongyuan Ge

In this paper, we propose RoomPlanner, the first fully au-tomatic 3D room generation framework for painlessly creating realisticindoor scenes with only short text as input. Without any manual layoutdesign or panoramic image guidance, our framework can generate explicitlayout criteria for rational spatial placement. We begin by introducing ahierarchical structure of language-driven agent planners that can auto-matically parse short and ambiguous prompts into detailed scene descrip-tions. These descriptions include raw spatial and semantic attributes foreach object and the background, which are then used to initialize 3Dpoint clouds. To position objects within bounded environments, we im-plement two arrangement constraints that iteratively optimize spatialarrangements, ensuring a collision-free and accessible layout solution.In the final rendering stage, we propose a novel ReachView Samplingstrategy for camera trajectory, along with the Interval Timestep FlowSampling (ITFS) strategy, to efficiently optimize the coarse 3D Gaussianscene representation. These approaches help reduce the total generationtime to under 30 minutes. Extensive experiments demonstrate that ourmethod can produce geometrically rational 3D indoor scenes, surpass-ing prior approaches in both rendering speed and visual quality whilepreserving editability. The code will be available at https://kaitlina-s.github.io/RoomPlanner/.

ECCV 2026

NGPS: Structure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling

Jaehyun Cho, YoungJoon Yoo

Neighboring-slice self-supervised denoising is attractive forvolumetric medical imaging, yet inter-slice misalignment breaks anatom-ical correspondence and often yields ghosting and blurred margins whenadjacent slices are used naïvely as targets. We propose Neighbor-GuidedPatch Sampling (NGPS), a lightweight framework that constructs neigh-boring supervision under local inter-slice misalignment. To avoid learn-ing from misleading targets, prior methods commonly mask discrepantregions, but this stabilizes training at the cost of leaving a non-trivialportion of neighboring evidence unexploited, particularly around high-frequency anatomical boundaries. NGPS addresses this by decouplingstructure matching from signal retrieval: for each masked location, itsearches a local neighborhood for structurally similar candidate patchesusing a simple guide image (e.g., fast bilateral filtering), while retriev-ing the supervision signal directly from the raw noisy neighbor at thematched coordinates. By matching on a noise-attenuated guide whileretrieving raw values from neighboring slices, NGPS constructs localpseudo targets without dense deformation-field estimation or spatial re-sampling. Across the evaluated CT and synthetic-Rician MRI settings,NGPS improves fidelity and structure-sensitive metrics. Code is availableat https://github.com/cv-cho/NGPS.

ECCV 2026

Narrative-Driven Paper-to-Slide Generation via ArcDeck

Tarik Can Ozden, SACHIDANAND VISHNUKUMAR SARMINI, Furkan Horoz, Ozgur Kara, Junho Kim, James Rehg

We introduce ArcDeck, a multi-agent framework that for-mulates paper-to-slide generation as a structured narrative reconstruc-tion task. Unlike existing methods that directly summarize raw text intoslides, ArcDeck explicitly models the source paper’s logical flow. It firstparses the input to construct a discourse tree and establish a global com-mitment document, ensuring the high-level intent is preserved. Thesestructural priors then guide an iterative multi-agent refinement process,where specialized agents iteratively critique and revise the presentationoutline before rendering the final visual layouts and designs. To evaluateour approach, we also introduce ArcBench, a newly curated benchmarkof academic paper-slide pairs. Experimental results demonstrate that ex-plicit discourse modeling, combined with role-specific agent coordination,significantly improves the narrative flow and logical coherence of the gen-erated presentations. The ArcDeck codebase and ArcBench benchmarkare available on our project webpage.

ECCV 2026

C3-Bench: A Context-Aware Change Captioning Benchmark

JAEWOO KIM, Hyeongbeom Kim, Ue-Hwan Kim

While Change Captioning systems have garnered substantialattention to respond to our evolving world, their true performance on di-verse real-world change contexts remains largely unexplored due to thelack of comprehensive evaluation frameworks. To fill this gap, we proposeC3-Bench, a comprehensive benchmark for evaluating Context-awareChange Captioning. C3-Bench features: (1) 4,996 human-labeled imagepairs of 51 real-world change contexts across four domains (e.g., naturalscenes, remote sensing imagery, image editing, and anomalies), each withdiverse, carefully curated scenarios derived from multiple change-centriccommunities; and (2) the first LLM-as-Judge evaluation framework in thechange captioning task that measure fine-grained dimensions (e.g., cor-rectness, specificity, fluency, and relevance), along with a novel reversibil-ity metric exploring whether models understand changes with symmetricconsistency. Based on C3-Bench, we benchmark 32 models—including* Corresponding authorconventional change captioning models, proprietary Large MultimodalModels (LMMs), and 2B-90B open-source LMMs. We reveal a fundamen-tal blind spot in the prevailing change captioning paradigm: Once thechange context departs from training-style regimes, conventional mod-els collapse, and even state-of-the-art LMMs such as GPT-5.2 exhibitsystematic domain- and position-dependent errors that distort reliablechange understanding. By making these hidden failure modes explicitand measurable, we delineate the next frontier for building generalizableand trustworthy change captioning systems. All codes and datasets arepublicly available on the project page.

ECCV 2026

NanoGS: Training-Free and Lightweight Gaussian Splat Simplification

Butian Xiong, Rong Liu, Tiantian Zhou, Meida Chen, Zhiwen Fan, Andrew Feng

3D Gaussian Splat (3DGS) enables high-fidelity, real-timenovel view synthesis by representing scenes with large sets of anisotropicprimitives, but often requires millions of Splats, incurring significant stor-age and transmission costs. Most existing compression methods rely onGPU-intensive post-training optimization with calibrated images, limit-ing practical deployment. We introduce NanoGS, a training-free andlightweight framework for Gaussian Splat simplification. Instead of re-lying on image-based rendering supervision, NanoGS formulates sim-plification as local pairwise merging over a sparse spatial graph. Themethod approximates a pair of Gaussians with a single primitive usingmass preserved moment matching and evaluates merge quality througha principled merge cost between the original mixture and its approx-imation. By restricting merge candidates to local neighborhoods andselecting compatible pairs efficiently, NanoGS produces compact Gaus-sian representations while preserving scene structure and appearance.NanoGS operates directly on existing Gaussian Splat models, runs ef-ficiently on CPU, and preserves the standard 3DGS parameterization,enabling seamless integration with existing rendering pipelines. Experi-ments demonstrate that NanoGS substantially reduces primitive countwhile maintaining high rendering fidelity, providing an efficient and prac-tical solution for Gaussian Splat simplification. Our project website isavailable at https://saliteta.github.io/NanoGS/ .

ECCV 2026

MessyKitchens: Contact-rich object-level 3D scene reconstruction

Junaid Ahmed Ansari, Ran Ding, Fabio Pizzati, Ivan Laptev

Monocular 3D scene reconstruction has recently seen signif-icant progress. Powered by the modern neural architectures and large-scale data, recent methods achieve high performance in depth estimationfrom a single image. Meanwhile, reconstructing and decomposing com-mon scenes into individual 3D objects remains a hard challenge due tothe large variety of objects, frequent occlusions and complex object rela-tions. Notably, beyond shape and pose estimation of individual objects,applications in robotics and animation require physically-plausible scenereconstruction where objects obey physical principles of non-penetrationand realistic contacts. In this work we advance object-level scene re-construction along two directions. First, we introduce MessyKitchens,a new dataset with real-world scenes featuring cluttered environmentsand providing high-fidelity object-level ground truth in terms of 3D ob-ject shapes, poses and accurate object contacts. Second, we build onthe recent SAM 3D approach for single-object reconstruction and ex-tend it with Multi-Object Decoder (MOD) for joint object-level scenereconstruction. To validate our contributions, we demonstrate MessyK-itchens to significantly improve previous datasets in registration accuracyand inter-object penetration. We also compare our multi-object recon-struction approach on three datasets and demonstrate consistent andsignificant improvements of MOD over the state of the art. Our newbenchmark, code and pre-trained models are publicly available on ourproject website: https://messykitchens.github.io/.

ECCV 2026

LumiTokens: 3D Relighting via Token-Space Lighting Transformation

YiWen Chen, Matheus Gadelha, Huaizu Jiang

Existing 3D relighting methods operate through either ex-plicit material decomposition, diffusion-based view-space generation, ora combination of both, requiring full recomputation for each new light-ing condition. We observe that recent latent scene representations, whichencode multi-view images into a set of compact tokens with no fixed phys-ical semantics, open up a novel design space for relighting. We presentLumiTokens, a framework that formulates 3D relighting as a directtransformation on latent scene tokens, without explicit 3D representa-tions, rendering equations, or physics-based decomposition. Our modelintroduces a Scene Token Editor that processes scene tokens jointly withlight-ray tokens through self-attention, producing updated tokens thatcan be decoded into multi-view-consistent relit images. To support di-verse lighting types through a unified interface, all lighting signals, in-cluding environment maps, point lights, and area lights, are parameter-ized as Plücker ray tokens, enabling native 3D user interaction with arepresentation that carries no explicit spatial structure. Crucially, thisdesign supports progressive relighting: because the editor’s output re-mains in the same latent space as its input, a user can incrementallybuild up illumination one light source at a time, with each edit compos-ing in token space. Experiments demonstrate that LumiTokens achievescomparable or superior relighting quality to other methods and supportsprogressive, composable lighting edits.

ECCV 2026

KineBench: Benchmarking Embodied World Models via IDM-Free Kinematic Grounding

Zeyu Liu, Zhangzhe Zhu, Yang Zhang, Chenyou Fan, Chenjia Bai, Xuelong Li

Evaluating the physical consistency of embodied world mod-els (EWMs) is a critical open challenge. While closed-loop evaluationvia simulator rollouts offers a more faithful assessment of physical plau-sibility than open-loop alternatives, existing frameworks almost exclu-sively rely on Inverse Dynamics Models (IDMs) for action extraction.Due to the intricate mapping from 2D pixel space to 3D kinematicspace, the learned IDMs can be brittle to data outside their trainingdistribution, resulting in unreliable action extraction from the gener-ated videos with novel objects and scenarios. This creates an unavoid-able attribution ambiguity between world model inaccuracies and ex-tractor errors. To reduce this ambiguity, we present KineBench, anIDM-free closed-loop benchmark for EWMs, built upon an explicit kine-matic grounding pipeline. Given a generated video, KineBench employscascaded visual foundation models to directly extract 6D end-effectorposes from individual frames, which are then executed in a physics sim-ulator for closed-loop validation. This explicit grounding directly teststhe physical feasibility rather than visual plausibility, while remainingsensitive to general physical hallucinations such as gripper vanishing orspatial inconsistency. Beyond execution-based task success, KineBenchincorporates two classical 3D kinematic metrics—Spectral Arc Length(SPARC) and the Maruyama Manipulability Index—to characterize tra-jectory smoothness and kinematic feasibility from a robot-centric per-spective. Across the evaluated models and tasks, these metrics exhibittask- and model-dependent associations with physical success rates, sug-gesting that they provide complementary diagnostic signals for assessingembodied generation quality. Built on 20 diverse manipulation tasks inManiSkill3, KineBench evaluates EWMs across four progressive suites:basic execution, task transfer, visual out-of-distribution generalization,and complexity-conditioned scaling. Evaluation across frontier models re-veals task-complexity-bounded nonlinear scaling in embodied video gen-eration, providing empirical guidance for future data-scaling strategies.The code and datasets are available on GitHub at https://github.com/minecraft-zzz/KineBench and on Hugging Face at https://huggingface.co/datasets/Zorkzak/KineBenchDatasets.

ECCV 2026

JSON: Jigsaw Self-play Optimization for Normalizing Flows

Fengxiang Yang, Tianyi Zheng, Jinwei Chen, Bo Li

Normalizing Flows (NFs) is a principle generative framework,which learns to generate images by establishing a bijective mapping tra-jectory between noise and data with likelihood maximization. In thispaper, we leverage the self-play paradigm, a general trajectory fine-tuningmethod to improve generative capability of NFs without auxiliary fine-tuning data. The general self-play mechanism formulates competitionbetween model and its historical counterpart in favoring real data trajec-tory (i.e., “wining trajectory”) while forgetting trajectory of self-generatedsynthesized data (i.e., “losing trajectory”), improving model capabilitythrough preference alignment. However, NF-based self-play faces twochallenges. (1) Enforcing NF to forget data synthesized with historicalmodel contradicts with NF’s original training goal, leading to potentialtraining collapse. (2) Self-play is naturally plausible for generative modelwith pre-defined data trajectory (e.g., diffusion models), but faces com-patibility problem with NFs due to its implicit wining data trajectorydefinition. We thus propose jigsaw self-play optimization for NFs (JSON)to overcome these challenges. Our core contribution is the integration ofa jigsaw puzzle reassembly task to serve as an intrinsic spatial coherenceevaluator, identifying “winning” trajectories that exhibit superior spatiallogic for self-play fine-tuning. We further propose a bounded self-play lossand a nested-loop optimization strategy to mitigate training collapse andensure stable fine-tuning. By aligning the model with high-quality struc-tural anchors mined during self-play, JSON achieves promising results onImageNet-1K, providing a robust framework for high-fidelity NF-basedgeneration.

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