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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

No Place to Hide: Benchmarking Video Hallucination with Background-Controlled Pairs

Haojian Huang, Harold Haodong Chen, Meng Luo, Junjia Du, Shanqing Xu, Ziheng Chen, Yanxiang Huang, Yinchuan Li, Yingcong Chen

We introduce VidPair-Halluc, a new benchmark for evaluating video hallucination in large video models (LVMs) under rigorous and controlled conditions. Unlike previous benchmarks that primarily rely on text-based perturbations or adversarial questions while neglecting the consistency of visual backgrounds, VidPair-Halluc features video pairs with highly similar backgrounds but distinctly different foreground semantics, enabling precise attribution of model errors to genuine hallucination rather than background variation. The benchmark is constructed through PairFlow, a pipeline that leverages recent advances in text-to-image and video generation to systematically compose stories, generate coherent video clips, and assemble them into adversarial pairs. Covering both spatial and temporal reasoning across ten semantic aspects, VidPair-Halluc comprises 1K high-quality adversarial video pairs and 11K spatio-temporal QA pairs with control over background and foreground variations. Evaluations on mainstream LVMs show persistent difficulty with robust fine-grained video understanding in adversarial settings, and code and data are available at the project page.

ECCV 2026

Multi-view Multi-vehicle Driving Dataset for Novel View Synthesis

Sanjay Dharavath, Hanvitha Mukkamala, Faizan Khan, Ioannis Kakogeorgiou, Aditya Arun, Zakaria Laskar, C. V. Jawahar

Di!erentiable rendering has advanced novel view synthesis(NVS), yet applying it to real-world driving remains di"cult due tosparse capture viewpoints, dynamic objects, and limited multi-trajectorydata. We introduce the Multi-View Multi-Vehicle (MV2 ) datasetand benchmark for evaluating NVS models under large viewpoint changesin dynamic urban scenes. MV2 features synchronized captures from a car,scooter, and drone, each following distinct yet synchronized trajectories.Training NVS methods on one vehicle’s camera stream and testing on an-other enables evaluation under substantially larger viewpoint variationsthan existing single-trajectory datasets. All sequences are registered viaStructure-from-Motion and camera poses verified using manual pixel-level correspondence annotations, yielding 50 high-quality scenes with12000 images. Benchmarking recent NVS and camera pose estimationmethods shows that NVS performance degrades with increasing view-point disparity, and that feed-forward pose estimators notably lag behindoptimization-based approaches, highlighting MV2 as a rigorous testbedfor NVS in driving. The dataset, benchmark protocol, and project re-sources are available at https://mv2-dataset.github.io/.

ECCV 2026

mmIR: Frequency-Space Inverse Rendering for 3D Millimeter-Wave Radar ADC Synthesis

Adnan Armouti, Yixuan Gao, Rajalakshmi Nandakumar

High-resolution 3D radar data is scarce. Commodity mmWavesensors use small antenna arrays that limit angular resolution to several de-grees, and existing datasets provide only 2D range–azimuth maps or sparsepoint clouds rather than raw analog-to-digital converter (ADC) signals.Hardware scaling is expensive, synthetic-aperture scanning is impracticalat fleet scale, and learned synthesis methods are bottlenecked by the verydata shortage they aim to address. We present mmIR, an open-sourcedifferentiable frequency-modulated continuous-wave (FMCW) radar in-verse renderer that fits a physics-based forward model to real capturesand re-renders from dense virtual apertures to synthesize high-resolution3D radar data. Because radar resolution is too coarse to recover geom-etry directly, mmIR performs LiDAR-assisted inverse rendering: usingLiDAR-derived meshes as a geometric scaffold, mmIR optimizes per-vertex International Telecommunication Union (ITU) physics materials,vertex normals, and antenna beam patterns through end-to-end auto-matic differentiation of a phase-coherent multiple-input multiple-output(MIMO) forward model with multi-bounce propagation, polarization,and free-space diffraction. On seven outdoor and six indoor ColoRadarscenes, mmIR achieves 0.914 mean Pearson correlation on range–azimuthmaps versus 0.307 for Sionna-RT. Scenes trained on a cascaded imag-ing radar transfer to a co-located single-chip radar without re-training(0.554 correlation), and dense virtual arrays (100×100 elements) pro-duce single-frame 3D occupancy validated against LiDAR. Project page:https://mmwave-inverse-rendering.github.io/.

ECCV 2026

MiNQVIS: Mitigating Noisy Queries for Robust Online Video Instance Segmentation

Jie Qiao, Jianxu Chen, Xiaowei Xu

Online Video Instance Segmentation (VIS) relies on per-frame instance queries for causal detection, segmentation, and tempo-ral association. However, in realistic videos with occlusion, blur, andclutter, these predictions often become noisy—exhibiting false positives,missed instances, or inaccurate masks. Such noisy queries not only desta-bilize cross-frame association but also contaminate contrastive supervi-sion and propagate drift through memory-based matching. Despite ad-vances in recent online VIS methods, none explicitly identify or addressnoisy query contamination, leaving a fundamental weakness unexam-ined. We introduce MiNQVIS, the first noise-aware framework for on-line VIS that stabilizes both training and inference. Our approach con-sists of three complementary components: (1) query-level gating that fil-ters unreliable predictions using classification-guided thresholding andembedding-norm stabilization; (2) video-level prototypical contrastivelearning that aggregates only reliable matches into stable, noise-robustprototypes; (3) dual-memory association that fuses momentum embed-dings with history-best retrieval to prevent drift from corrupted frames.Experiments on YouTubeVIS 2019/2021 and OVIS show consistent im-provements over strong online VIS baselines, with large gains in challeng-ing scenes. Project Page: https://nothing898.github.io/MiNQVIS.

ECCV 2026

LumiDepth: Stable Monocular Depth in Multi-Illumination Scenes

Anqi Cheng, Zhiyuan Yang, Tianjiao Li, Haiyue Zhu, Kezhi Mao

Depth estimation in multi-illumination scenes with multi-ple, spatially varying light sources remains a crucial yet less-exploredproblem. Illumination changes introduce shadows, specular highlights,and exposure shifts that distort local appearance cues, causing severedepth inconsistency or even failure. Existing depth foundation models,trained predominantly on uniformly lit data, degrade sharply under suchconditions. However, direct adaptation is challenging because groundtruth depth is typically limited for multi-illumination datasets, whilesynthetic relighting often incurs geometric distortions. To address thesechallenges, we propose LumiDepth, a framework that learns from multi-illumination RGB images. First, a Disagreement-Calibrated ProbabilisticPseudo Supervision (DCPS) module constructs high-quality pseudo la-bels while preserving diversity. Second, a Frequency-aware Consistencyand Distillation (FaCD) module improves cross-illumination stabilitywithout over-smoothing by enforcing low-frequency geometric consis-tency and distilling high-frequency structural details bi-directionally. Toenable systematic evaluation, we introduce ReMID, a real-world multi-illumination RGB-D benchmark, together with stability metrics thatquantify average and worst-case depth variation. Experiments acrossdiverse datasets demonstrate that LumiDepth achieves state-of-the-artoverall performance, markedly improving both consistency and accuracyby reducing depth variation by 30.2% and absolute relative error by24.8%. We further show our target-domain label-free design remains ef-fective for depth under other appearance shifts such as weather and sen-⋆sor noise.

ECCV 2026

Learning Generatable Mutual Distance for Scene-Aware Human Motion Generation

Zonglin Yang, Chaoyue Xing*, Yixuan Yin, miaomiao Liu, Liyuan Pan

Generating human motion from language in 3D scenes re-quires both semantic alignment with textual intent and physically plau-sible human-scene interactions. Existing methods struggle because im-plicit language-scene fusion entangles motion synthesis with scene rea-soning, while explicit grounding via fixed-vocabulary detectors or affor-dance maps lacks generalization and fails to capture full-body, temporallycoherent interactions. We address this representation gap by identifyingmutual distance as an effective interaction representation for modelingfull-body spatiotemporal human-scene relations, and propose MDNet,a two-stage diffusion framework that explicitly generates and groundssuch interactions. Since mutual distance encodes only relative geometryand is ambiguous for stochastic generation, we introduce VLM-based se-mantic anchors to provide absolute spatial grounding. We further modelmutual distance in the frequency domain to enhance temporal stabilitybefore synthesizing final motions. Experiments on HUMANISE demon-strate state-of-the-art semantic alignment, physical plausibility, and tem-poral coherence, with cross-dataset generalization to unseen scenes.

ECCV 2026

LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection

Johannes Meier, Jonathan Michel, Oussema Dhaouadi, Yung-Hsu Yang, Christoph Reich, Zuria Bauer, Stefan Roth, Marc Pollefeys, Jacques Kaiser, Daniel Cremers

Real-time monocular 3D object detection remains challeng-ing due to severe depth ambiguity, viewpoint shifts, and the high compu-tational cost of 3D reasoning. Existing approaches either rely on LiDARor geometric priors to compensate for missing depth or sacrifice efficiencyto achieve competitive accuracy. We introduce LeAD-M3D, a monocu-lar 3D detector that achieves state-of-the-art accuracy and real-time in-ference without extra modalities. Our method is enabled by three keycomponents. Asymmetric Augmentation Denoising Distillation (A2D2)transfers geometric knowledge from a clean-image teacher to a MixUp-noised student via a quality- and importance-weighted depth-feature loss,enabling stronger depth reasoning without LiDAR. 3D-aware ConsistentMatching (CM3D ) improves prediction-to-ground truth assignment by in-tegrating 3D MGIoU into the matching score, yielding stable and precisesupervision. Finally, Confidence-Gated 3D Inference (CGI3D ) acceleratesinference by restricting expensive 3D regression to confident regions. To-gether, these contributions set a new Pareto frontier for monocular 3Ddetection: LeAD-M3D achieves state-of-the-art accuracy on KITTI andWaymo, and the best reported car AP on Rope3D, while running up to3.6 × faster than prior high-accuracy models (e.g., MonoDiff). LeAD-M3D demonstrates that high fidelity and real-time monocular 3D de-tection is simultaneously attainable, without LiDAR, stereo, or stronggeometric assumptions.

ECCV 2026

InstanceControl: Controllable Complex Image Generation without Instance Labeling

Xiaoyu Liu, Huan Wang, Fan Li, Zhixing Wang, Jiaqi Xu, Ming Liu, Wangmeng Zuo

Controllable image generation methods, such as ControlNet,have demonstrated a remarkable capacity to introduce visual conditions(e.g., depth maps) to guide image generation. However, these methodsoften struggle with complex multi-instance scenes, frequently leading toattribute confusion among instances. While recent approaches attemptto mitigate this via manual instance labeling, such requirements arelabor-intensive. In this paper, we propose InstanceControl, a novel multi-instance controllable generation method that eliminates the need for in-stance labeling. We identify the primary bottleneck in existing meth-ods as the inability to accurately associate instance descriptions withtheir corresponding regions within visual conditions. To address this, weleverage the Vision-Language Model (VLM) to establish instance-levelcorrespondences between text prompts and visual conditions. Specifi-cally, the VLM automatically parses instance descriptions from the textprompts and simultaneously predicts instance masks based on the visualconditions. Furthermore, since the predicted masks may contain noise,we introduce an adaptive mask refinement strategy that dynamically re-fines these instance masks during the generation process. Extensive ex-periments demonstrate that our approach outperforms state-of-the-artmethods, achieving superior fidelity and precise instance-level control.The code and dataset will be made publicly available.

ECCV 2026

Hierarchical and Holistic Open-Vocabulary Functional 3D Scene Graphs for Indoor Spaces

Xinggang Hu, Chenyangguang Zhang, Alexandros Delitzas, Xiangkui Zhang, Marc Pollefeys, Francis Engelmann, Xiangyang Ji

Functional 3D scene graphs offer a versatile and flexible rep-resentation for 3D scene understanding and robotic manipulation, de-fined by object nodes, interactive elements, and functional relationshipedges. However, their potential remains underexplored due to the limitedcoverage of existing benchmarks and the overly straightforward designof previous pipelines, which primarily focus on large-scale furniture butlack of hierarchical structures. Therefore, in this work, we extend thebenchmark coverage by introducing dense tabletop objects and explicitmulti-level functional relationships. This expansion introduces criticalchallenges involving small-scale, dense, and similar instances, with lack ofvisual anchoring in relational reasoning, instance confusion during cross-frame fusion, and attribution uncertainty under dynamic viewpoints. Toaddress these issues, we propose an open-vocabulary pipeline based on 2Dvisual grounding and 3D graph optimization. Specifically, we anchor fine-grained functional edges from 2D visual evidence, and associate nodesacross frames in 3D using multiple cues. Furthermore, edge associationis formulated as temporal graph optimization, integrating evidence ac-cumulation, entropy regularization, and temporal smoothing to robustlydetermine the functional connections of each node. Finally, global hierar-chy shaping is performed to recover the hierarchical graph structure. Ex-tensive experiments demonstrate that the proposed method can reliablyinfer functional 3D scene graphs in challenging real-world scenes, therebyfurther unlocking their potential for practical applications. Code is avail-able at https://github.com/Hbelief1998/HHOpenFunGraph-ECCV26.

ECCV 2026

GenRecal: Generation after Recalibration from Large to Small Vision-Language Models

Byung-Kwan Lee, Ryo Hachiuma, Yong Man Ro, Yu-Chiang Frank Wang, Yueh-Hua Wu

Recent advancements in vision-language models (VLMs) haveleveraged large language models (LLMs) to achieve performance on parwith closed-source systems like GPT-4V. However, deploying these mod-els in real-world scenarios, particularly on resource-constrained devices,remains challenging due to their substantial computational demands.This has spurred interest in distilling knowledge from large VLMs intosmaller, more efficient counterparts. A key challenge arises here from thediversity of VLM architectures, which are built on different LLMs andemploy varying token types—differing in vocabulary size, token splits,Changing Teacher VLMs for Distillation MMMU70GenRecalMMB GPT-4oInternVL2.5-78B → InternVL2.5-8B60 Qwen2-VLAccuracy (%)MM-Vet InternVL2.550 MolmoMMMU40NVLM-72B → InternVL2.5-8B LLaVA-OneVisionInternVL2-76B → InternVL2.5-8BMMMU-Pro Qwen2-VL-72B → InternVL2.5-8BInternVL2.5-78B → InternVL2.5-8B300 20 40 60 80 1 2 4 7 8 … 72 78Accuracy (%) Model Size (B)Fig. 2: (Left) Comparison of the challenging benchmark performances, MMB [36], MM-Vet [63], MMMU [65], and MMMU-Pro [66] by changing large VLMs. The more pow-erful large VLMs we select, the greater the performance improvement we can achieve.(Right) Comparing the performance of the challenging benchmark: MMMU [65], withGenRecal and various VLMs across model sizes. Note that all the experiments in Fig. 1and Fig. 2 are conducted on the equal training dataset.and token index ordering. To address this challenge of limitation to aspecific VLM type, we present Generation after Recalibration (Gen-Recal), a general-purpose distillation framework for VLMs. GenRecalincorporates a Recalibrator that aligns and adapts feature representa-tions between heterogeneous VLMs, enabling effective knowledge trans-fer across different types of VLMs. Through extensive experiments onmultiple challenging benchmarks, we demonstrate that GenRecal signif-icantly improves baseline performances, eventually outperforming large-scale open- and closed-source VLMs.

ECCV 2026

DINO-SLAM: DINO-Informed RGB-D SLAM for Neural Implicit and Explicit Representations

Ziren Gong, Xiaohan Li, Fabio Tosi, Youmin Zhang, Stefano Mattoccia, Jun Wu, Matteo Poggi

This paper presents DINO-SLAM, a DINO-informed designstrategy to enhance implicit (Neural Radiance Field – NeRF) and explicitrepresentations (Gaussian Splatting – GS) in SLAM systems through themore comprehensive semantic understanding enabled by DINO. This lat-ter alone, however, lacks proper 3D geometry understanding, allowingonly for marginal improvements. Therefore, we rely on a Scene Geom-etry Encoder (SGE) to lift DINO features into geometry-aware DINOfeatures (geoDINO), to better understand those geometric relationshipsthat vanilla DINO features fail to capture. Building upon it, we pro-pose two foundational paradigms for NeRF and GS SLAM systems in-tegrating geoDINO features. Compared to state-of-the-art methods, ourDINO-informed pipelines achieve superior performance on the Replica,ScanNet, and TUM datasets.

ECCV 2026

NeuralDMD: Interpretable Untrained Neural Network for Imaging from Sparse and Noisy Observations

Ali SaraerToosi, Renbo Tu, Esther Lin, Kamyar Azizzadenesheli, Aviad Levis

Many challenges in scientific imaging involve solving ill-posedinverse problems, where the goal is to recover spatio-temporal fields fromindirect, noisy, and highly sparse measurements — often without accessto ground truth data or reliable simulators. To address this challengingscenario, we present NeuralDMD, an interpretable, untrained (per-instance) reconstruction framework that combines neural implicit rep-resentations with Dynamic Mode Decomposition (DMD) to reconstructcontinuous spatio-temporal dynamics directly from measurements. Neu-ralDMD parameterizes DMD modes as continuous neural fields, andimposes a low-rank linear dynamics prior with spectral time evolutionto enforce temporal continuity. This formulation enables both forecast-ing under sparsity, and yields interpretable modes and spectra. We findthat NeuralDMD outperforms baselines on a wide variety of tasks: fromweather data assimilation from sparse station observations to interfero-metric (Fourier domain) observations of Sagittarius A*, the black hole atthe center of our galaxy. Moreover, NeuralDMD remains stable whenextrapolating into the future. While this framework is most naturallysuited to linear dynamics, we show that it can be applied to nonlinearregimes, though with extrapolation performance that degrades with in-creasing nonlinearity. Together, these results show that NeuralDMDenables interpretable reconstruction and forecasting of spatio-temporaldynamics directly from sparse and indirect measurements without rely-ing on numerical simulators or training data.

ECCV 2026

DiffuPrompt: Adapting Video Foundation Models to 3D Medical Volumes via Latent Trajectory Priors

Guowei Dai, Duwei Dai, Yulong Ji, Chen Hu, Yi Zhang

Adapting video foundation models to volumetric medicalsegmentation by treating axial depth as a temporal dimension is attrac-tive, but the resulting slice propagation can be unstable when anatom-ical structures branch, disappear, or become poorly contrasted acrossslices. Static geometric prompts provide little guidance for newly ap-pearing or disconnected components, and SAM2-style streaming mem-ory may accumulate errors in these regions. We propose Di_x001B_uPrompt,a backbone-frozen, single-prompt framework that augments SAM2 withlatent trajectory priors for volumetric propagation. Given a user prompton a representative slice, an LLM-conditioned Neural ODE predicts acontinuous, patient-conditioned latent trajectory and converts it into apersistent prior memory. A dual-stream spatial gate then fuses this priormemory with the standard observation-driven working memory, allowingthe model to use image evidence when it is reliable and trajectory pri-ors when local propagation becomes ambiguous. Experiments on MSD,BTCV, and BraTS show improved promptable volumetric segmentationover SAM/SAM2-based baselines, especially on boundary-sensitive met-rics.

ECCV 2026

DG-Force: Disentangling and Gathering Forensic Cues is Needed for Image Manipulation Localization

Yong Yao, Zhenyu Cui, Lei Chen, Jiwen Lu, Jiahuan Zhou

Image Manipulation Localization (IML) aims to achieve pixel-level localization of locally manipulated images. Its core challenge is howto distinguish the inconsistencies between authentic and tampered ar-eas. To this end, existing IML methods typically exploit patch-level andedge-level forensic cues for accurate localization. However, these methodsextract forged information at both levels with fixed parameters, leadingto interference from real information that is detrimental to localizationas more realistic manipulated textures are enhanced by the upgrading oftampering techniques. To tackle the above issue, this paper proposes aDisentangling and Gathering Forensic Cues method, called DG-Force,which explicitly disentangles and adaptively aggregates region and edgecues to preserve the most informative evidence. Specifically, we proposea Patch-based Forensic Disentangling (PFD) module and an Edge-basedForensic Disentangling (EFD) module to decompose forensic cues of dif-ferent granularities to explicitly suppress the imbalance between forensictraces in both patch-level and edge-level areas. In addition, a Multi-scale Forensic Transfer (MFT) module is further designed to aggregateand balance multi-granularity information through the interaction acrossdifferent scales for robust key inconsistencies in forensic traces discovery.Extensive experiments on multiple benchmarks demonstrate the superi-ority of our method in the image manipulation localization task.

ECCV 2026

On Geometric Understanding and Learned Priors in Feed-forward 3D Reconstruction Models

Jelena Bratulić, Sudhanshu Mittal, Thomas Brox, Christian Rupprecht

Feed-forward 3D reconstruction models such as DUSt3R, VGGT, and Depth Anything 3 (DA3) are transformer–based foundation models that infer camera geometry and dense scene structure in a single forward pass. Trained at scale in a supervised fashion, they raise a central question: do these models build upon geometric principles akin to traditional multi–view pipelines, or do they primarily rely on learned priors arising from the large–scale training setup? We find that epipolar geometry emerges within the intermediate layers of all three models and is causally linked to correspondence patterns in attention heads. To study this, we perform a systematic analysis of their internal representations across three real–world datasets and a controlled synthetic dataset. We quantify geometric understanding by probing intermediate features, analyzing attention patterns to identify correspondence matching patterns, and performing targeted interventions at the attention level. Further, we assess the role of learned priors by applying challenging input–level perturbations, such as occlusions, scene ambiguities, and varying camera configurations, and compare them against classical multi–stage reconstruction pipelines.

ECCV 2026

Denoising-Enhanced Coarse-to-Fine Infrared Small Target Detection with Attention Prior-Guided Knowledge Distillation

Houzhang Fang, Ruixuan Huang, Qiuhuan Chen, Xiaolin Wang, Yi Chang, Luxin Yan

Infrared small target detection (IRSTD) in high-resolutionimages is crucial for many practical applications, such as surveillanceof unmanned aerial vehicles (UAVs) and UAV-based ground monitoring.However, IRSTD remains challenging due to the small size and weakfeatures of targets, as well as significant interference from complex dy-namic backgrounds. Existing detection methods often suffer from redun-dant computations on non-target background regions and insufficient ex-ploitation of target context information, which limits their performancein complex backgrounds. To address these issues, we propose an efficientcoarse-to-fine infrared small target detection framework with attentionprior-guided knowledge distillation, termed ECFNet. In the coarse stage,we design a region binary classification network (RBCN) on grid-basedmulti-scale feature maps to efficiently recognize target-containing con-text region proposals. Moreover, we introduce a novel denoising-assistedtraining strategy that incorporates noisy ground-truth (GT) masks intoRBCN feature maps. By training the network to reconstruct the GTmasks via a denoising task, this strategy encourages the model to ex-plicitly capture target-background context, thereby enhancing its abil-ity to distinguish target proposals from background regions. In the finestage, we customize a lightweight target detector to the coarse stage’sregion proposals for balancing accuracy and efficiency. Furthermore, wepropose a knowledge distillation strategy guided by the teacher-studentcross-attention prior. This mechanism directs the student to focus oncritical target regions, thereby enhancing the discriminative feature rep-resentation for infrared small targets. Extensive experiments on threereal infrared datasets demonstrate that our method outperforms bothexisting single-stage and two-stage approaches while maintaining highreal-time processing efficiency.

ECCV 2026

Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models

Hongyu Li, Wanjia Fu, Xiaoyan Cong, Zekun Li, Binghao Huang, Hanxiao Jiang, Xintong He, Yiqing Liang, Rao Fu, Tao Lu, Srinath Sridhar, Kevin Smith, George Konidaris, Yunzhu Li

Predicting object dynamics (i.e., world modeling) is a fun-damental challenge for robotic manipulation, and modeling deformableobjects presents a particularly difficult case due to their high-dimensionalstate spaces and complex material properties. While current world mod-els approach this through two distinct paradigms: learning the dynamicsover the 2D pixel space or more explicit 3D geometric space. A systematicunderstanding of their relative strengths and limitations remains elusivedue to the lack of diverse, large-scale real-world data. To address this,we present Deform360, a large-scale visuotactile dataset featuring 198daily-life objects, 1,980 interaction sequences, and over 215 hours of ob-servations from 41 surround-view cameras and bimanual tactile grippersto capture both global motion and contact-induced local deformations.Leveraging a novel markerless visuotactile 3D tracking pipeline to ex-tract dense geometry and motion, we systematically evaluate currentstate-of-the-art world models, comparing 2D video models against 3Dparticle models. Finally, we provide a preliminary demonstration indi-cating the real-world applicability of our dataset by performing robotplanning tasks on deformable objects. Our analysis reveals key insightsinto the trade-offs between structural priors and scalability, providing asolid benchmark for future research in generalizable deformable object-centric world modeling. Project website: https://deform360.lhy.xyz

ECCV 2026

Category-Level 3D Correspondence in Camera Space via Morphable Object Priors

Leonhard Sommer, Artur Jesslen, Basavaraj Sunagad, Adam Kortylewski

Understanding 3D objects from images is fundamental to robotics and AR/VR applications. While recent work has made progress in category-level pose estimation, current representations fail to capture the fine-grained semantics needed for reasoning about object parts, functions, and interactions. In this work, we study category-level 3D correspondence in camera space—predicting, from a single image, 3D locations that remain consistent across instances within a category—and show that it can emerge without explicit correspondence supervision by learning a shared morphable object prior. To enable research in this direction, we introduce HouseCorr3D, the first large-scale benchmark for single-view category-level 3D correspondence with 178k images across 50 household object categories, 280 unique instances, and 3D keypoint annotations directly on CAD models. Crucially, HouseCorr3D provides amodal correspondence labels for occluded regions and explicit symmetry annotations, addressing key limitations of existing datasets. We further propose Morpheus, a method that learns morphable category-level shape priors by disentangling canonical shape, deformation, and object pose. Through this shared canonical grounding, semantically meaningful 3D correspondences in camera space emerge implicitly. These emerging 3D correspondences set a new state of the art on HouseCorr3D, demonstrating that semantic 3D object understanding can arise without direct correspondence supervision. Data and code: §/GenIntel/HouseCorr3D.

ECCV 2026

Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning

Shilin Hu, Jingyi Xu, Sagnik Das, Dimitris Samaras, Hieu Le

Shadows encode rich information about scene geometry andillumination, yet existing methods either predict a unified shadow maskor overlook attached shadows entirely. We address this gap by propos-ing a framework for jointly detecting cast and attached shadows throughexplicit physical modeling of light direction and surface geometry un-der a dominant directional-light setting. Our approach is grounded in asimple observation: surfaces facing away from the light source tend tofall into shadow. We exploit the reciprocal relationship between shadowformation and light estimation to construct a closed feedback loop, adual-module architecture in which a shadow detection module and alight estimation module iteratively refine each other. At each pass, up-dated light estimates, together with surface normals, produce partial at-tached shadow maps that guide detection, while improved shadow pre-dictions sharpen light estimation. To support training and evaluation,we introduce a dataset of 1,458 images with manually annotated castand attached shadow masks sourced from three existing benchmarks.Experiments demonstrate that our proposed method outperforms priormethods, with at least a 33% reduction in attached-shadow BER, whilemaintaining strong full-shadow and cast-shadow performance. Projectpage: https://shilin21.github.io/attached_detection/

ECCV 2026

Benchmarking Scientific Understanding and Reasoning for Video Generation using VideoScience-Bench

Lanxiang Hu, Abhilash Shankarampeta, Yixin Huang, Zilin Dai, Haoyang Yu, Yujie Zhao, Haoqiang Kang, Daniel Zhao, Tajana Rosing, Hao Zhang

The next frontier for video generation lies in developing models capable of zero-shot reasoning, where understanding real-world scientific laws is crucial for accurate modeling of physical outcomes under diverse conditions. However, existing video benchmarks are physical commonsense-based, offering limited insight into video models’ scientific reasoning capability. We introduce VideoScience-Bench1, a benchmark for evaluating undergraduate-level scientific understanding in video models. Each prompt encodes a composite scientific scenario that requires understanding and reasoning across multiple scientific concepts to generate the correct phenomenon. The benchmark comprises 200 evaluation items built from 160 expert-curated test cases (160 text-to-video prompts; 40 also include image conditioning) spanning 14 topics and 103 concepts. We conduct expert-annotated evaluations across seven state-of-the-art video models in T2V and I2V settings, along five dimensions: Prompt Consistency, Phenomenon Congruency, Dynamism, Immutability, and Spatio-Temporal Coherence. Using a VLM-as-a-Judge to assess video generations, we observe strong correlation with human assessments. To the best of our knowledge, VideoScience-Bench is the first benchmark to evaluate video models not only as generators but also as reasoners, requiring their generations to demonstrate scientific understanding consistent with expected physical and chemical phenomena.

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