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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

Pointer-CAD v2: Plan-Then-Construct CAD Generation with Dimension-Aware Parametric Precision

Dacheng Qi, Chenyu Wang, Jingwei Xu, Yi Ma, Shenghua Gao

Computer-aided design (CAD) plays a fundamental role inmodern manufacturing by providing the high precision required for in-dustrial production. Recent large language model based approaches for-mulate CAD generation as a sequence prediction problem and haveachieved promising results. However, existing methods and evaluationprotocols primarily emphasize visual similarity, while overlooking precisegeometric parameters and correct metric scale. Small numerical devia-tions that are negligible at the shape-level may still violate industrial tol-erance requirements, a problem further compounded by current autore-gressive paradigms that utilize command sequence representations, ag-gressively quantize numerical parameters to ease LLM prediction. In thiswork, we present Pointer-CAD v2. Compared with v1 [36], this versiondirectly predicts continuous values, bypassing the need for quantized nu-merical parameters and thereby eliminating quantization errors. Specif-ically, we propose a unified framework that decouples parameter rea-soning from geometric construction through a Plan-Then-Constructparadigm. Our method first produces a structured design plan withexplicit metric scale parameters. These parameters are organized intoa dictionary and directly referenced during sequence generation via apointer mechanism, eliminating discretization errors and ensuring dimen-sionally consistent execution. In addition, we construct a new large-scaledataset with plan-level annotation and introduce three hierarchical geom-etry accuracy metrics to evaluate parametric fidelity at the vertex, edge,and face levels. Extensive experiments demonstrate that Pointer-CADv2 consistently outperforms existing baselines and achieves substantialimprovements in geometric accuracy, enabling reliable CAD generationfor precision-critical engineering applications. Our code is available athttps://github.com/Snitro/Pointer-CAD-v2.

ECCV 2026

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation

Yang Chen, Xiaowei Xu, Shuai Wang, Xinwen Zhang, Qiushi Guo, Tiezheng Ge, Limin Wang

Normalizing Flows (NFs) are powerful generative models ca-pable of exact density estimation and sampling. However, their strictinvertibility often forces the model to exhaust its capacity on low-levelpixel details, hindering the capture of high-level semantic structures.While Masked Image Modeling (MIM) has excelled in representationlearning, its integration into generative pipelines has remained largelymodular and disjointed. In this paper, we propose MIMFlow, a unifiedend-to-end framework that jointly optimizes latent semantics, pixel re-construction, and generative flow. By employing a VAE encoder to infersemantic latent from masked images, MIMFlow achieves a principleddecoupling of the generative task: the Normalizing Flow focuses on mod-eling a simplified, low-frequency semantic manifold, while a specializeddecoder handles high-frequency synthesis. This design effectively resolvesthe inherent capacity bottleneck of NFs, allowing the model to prioritizeglobal structural coherence over redundant noise. Empirical results onImageNet 256×256 show that MIMFlow-L reaches 71.3% linear prob-ing accuracy and an FID of 2.50. Despite using only 128 tokens (50%fewer than standard models), it yields a 32.8% performance gain oversimilar-scale NF baselines.

ECCV 2026

LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models

Arpita Nema, Hanwei Zhu, Xi Zhang, Weisi Lin

The evaluation of long-term video quality understanding re-mains an open challenge for large vision-language models (LVLMs). Ex-isting video quality benchmarks predominantly focus on short clips andisolated distortions, overlooking the temporal continuity, cumulative degra-dation, and reasoning complexity inherent in long-duration content. Toaddress these limitations, we present LongVQUBench, a comprehen-sive benchmark for long-term video quality understanding. LongVQUBenchcontains over 1,200 diverse videos spanning movies, documentaries, surveil-lance footage, egocentric recordings, and animated content, accompa-nied by 1,500 multiple-choice and open-ended questions for validationand testing. To assess perceptual reasoning across different temporalscopes, we introduce three progressively complex evaluation levels: (i)local event quality understanding (LQU) for analyzing localized distor-tions; (ii) cross-event quality reasoning (CQR) for integrating multipledegraded events; and (iii) global quality understanding (GQU) for holis-tic perceptual evaluation over extended durations. Furthermore, a needledistortion question-answering (NDQA) paradigm is embedded across allthree levels, where spatial or temporal artifacts are sparsely inserted toprobe fine-grained detection and reasoning capabilities. Extensive ex-periments on 14 state-of-the-art LVLMs reveal significant performancedegradation with increasing video length and reasoning depth, highlight-ing their limited capacity for long-range temporal integration and percep-tual attribution. We envision LongVQUBench as a foundational step to-ward the systematic, hierarchical, and explainable evaluation of LVLMs’long-term video quality understanding.

ECCV 2026

ShellMaker: Language-Guided Exterior Completion under Structural Constraints

Ruiqi Xu, Daniel Aliaga

Despite advances in indoor scene generation, synthesizingcoherent building exteriors consistent with generated interiors remainslargely unexplored. Existing methods can generate floor plans and walllayouts but typically stop at a structural shell, lacking stylistically con-sistent facades and roofs. Completing these exteriors is challenging be-cause the footprint, wall geometry, and opening semantics must remainfixed—constraints that unconstrained generative models often violate.We introduce ShellMaker, a language-guided exterior completion frame-work that operates under these structural constraints. Given a build-ing scaffold and a text style prompt, ShellMaker generates a completeexterior mesh with PBR materials by combining parametric roof gen-eration, LLM-based part-aware prompt refinement, joint wall–roof ma-terial retrieval, and geometry-aware assembly. Operating on a format-agnostic scaffold representation, ShellMaker generalizes to indoor gener-ators, CityGML, and CAD inputs, while maintaining structural consis-tency and improving architectural coherence over retrieval and uncon-strained generative baselines. The project page is available at https://ruiqixu37.github.io/ShellMaker_web/.

ECCV 2026

SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving

Wenchao Sun, Xuewu Lin, Keyu Chen, Zixiang Pei, Xiang Li, Yining Shi, Sifa Zheng

End-to-end multi-modal planning has been widely adopted to model the uncertainty of driving behavior, typically by scoring candidate trajectories and selecting the optimal one. Existing approaches generally fall into two categories: scoring a large static trajectory vocabulary, or scoring a small set of dynamically generated proposals. While static vocabularies often suffer from coarse discretization of the action space, dynamic proposals provide finer-grained precision and have shown stronger empirical performance on existing benchmarks. However, it remains unclear whether dynamic generation is fundamentally necessary, or whether static vocabularies can already achieve comparable performance when they are sufficiently dense to cover the action space. In this work, we start with a systematic scaling study of Hydra-MDP, a representative scoring-based method, revealing that performance consistently improves as trajectory anchors become denser, without exhibiting saturation before computational constraints are reached. Motivated by this observation, we propose SparseDriveV2 to push the performance boundary of scoring-based planning through two complementary innovations: (1) a scalable vocabulary representation with a factorized structure that decomposes trajectories into geometric paths and velocity profiles, enabling combinatorial coverage of the action space, and (2) a scalable scoring strategy with coarse factorized scoring over paths and velocity profiles followed by fine-grained scoring on a small set of composed trajectories. By combining these two techniques, SparseDriveV2 scales the trajectory vocabulary to be 32× denser than prior methods, while still enabling efficient scoring over such super-dense candidate set. With a lightweight ResNet-34 as backbone, SparseDriveV2 achieves 92.0 PDMS and 90.1 EPDMS on NAVSIM, with 89.15 Driving Score and 70.00 Success Rate on Bench2Drive. Code and model are released at https://github.com/swc-17/SparseDriveV2.

ECCV 2026

Logit Refiner: Improving Visual Autoregressive Models via Intra-Scale Dependency Modeling

Meimingwei Li, Stefan Andreas Baumann, Felix Krause, Bjorn Ommer

Visual Autoregressive Models (VAR) generate images throughnext-scale prediction, producing all tokens within each scale in paral-lel. We show that this parallel decoding constitutes a mean-field-styleapproximation that discards spatial dependencies among same-scale to-kens, causing locally incoherent samples regardless of backbone capacity– a limitation of the decoding rule. Addressing this limitation, we intro-duce the Logit Refiner, a lightweight autoregressive module that restoresintra-scale dependencies by sequentially sampling tokens conditioned onfrozen backbone features. Adding only ∼10% parameters and less than5% of the base model’s training compute, it plugs into any pretrainedVAR checkpoint without retraining. Controlled ablations isolate jointintra-scale sampling – rather than additional capacity or training – asthe critical ingredient. Across backbones from 310M to 2B parameterson class-conditional ImageNet 256×256, the refiner consistently improvesgeneration quality, enabling a 1.1B-parameter model to surpass one twiceits size. The approach further generalizes to text-to-image generation,confirming that the mean-field bottleneck persists across VAR variantsand is effectively alleviated by our method.Project page: https://compvis.github.io/logit-refiner/.

ECCV 2026

Don’t Mask Out the Background! Natural-Light Photometric Stereo via Illumination Reconstruction

Taiga Hashida, Hiroaki Santo, Fumio Okura

This paper introduces an inverse-rendering framework fornatural-light, uncalibrated photometric stereo (PS) that leverages back-ground cues captured alongside the target object. Natural-light PS (NaPS)acquires shading variations by moving or rotating the camera and objectunder fixed, uncontrolled illumination, such as indoor lighting, whilemaintaining their relative geometry. However, uncalibrated NaPS, inwhich the lighting conditions are unknown, remains inherently ill-posed.To tackle this challenge, we propose explicitly reconstructing the lightingenvironment from the image background, which is typically masked outin prior work, thereby converting uncalibrated NaPS into a tractableinverse-rendering problem. Specifically, we move and rotate the camera-object pair while keeping their relative pose fixed, and reconstruct thesurrounding illumination directly from the observed background usinga 3D Gaussian Splatting (3DGS) representation. We then optimize thetarget object’s shape and reflectance via inverse rendering under thereconstructed illumination. Experimental results demonstrate that ourinverse-rendering-based approach yields more accurate estimates of bothgeometry and reflectance than existing learning-based PS methods, espe-cially under realistic near-field indoor conditions.

ECCV 2026

Global-Local Dual Perception for MLLMs in High-Resolution Text-Rich Image Translation

junxin lu, Tengfei Song, Zhanglin Wu, Lipengfei Lipengfei, Xiaowei Liang, Hui Yang, Kun Chen, Ning Xie, Yunfei Lu, Jing Zhao, Shiliang Sun, Daimeng Wei

Text Image Machine Translation (TIMT) aims to translatetext embedded in images in the source-language into target-language,requiring synergistic integration of visual perception and linguistic un-derstanding. Existing TIMT methods, whether cascaded pipelines orend-to-end multimodal large language models (MLLMs), struggle withhigh-resolution text-rich images due to cluttered layouts, diverse fonts,and non-textual distractions, resulting in text omission, semantic drift,and contextual inconsistency. To address these challenges, we proposeGLoTran, a global-local dual visual perception framework for MLLM-based TIMT. GLoTran integrates a low-resolution global image withmulti-scale region-level text image slices through pre-local region trans-lation replay and an instruction-guided alignment strategy, conditioningMLLMs to maintain scene-level contextual consistency while faithfullycapturing fine-grained textual details. Moreover, to realize this dual-perception paradigm, we construct GLoD, a large-scale text-rich TIMTdataset comprising 510K high-resolution global-local image-text pairscovering diverse real-world scenarios. Extensive experiments demonstratethat GLoTran substantially improves translation completeness and ac-curacy over state-of-the-art MLLMs.

ECCV 2026

Following the Flow: Advection-Consistent Modeling for Event-based Small Object Detection

Wen Guo, Fulong Cai, Wuzhou Quan

Event cameras enable high-frequency visual perception withmicrosecond latency, offering advantages for dynamic scenes. However,event-based small object detection remains challenging due to sparseasynchronous measurements and weak object responses that are easilydisrupted by noise. Limited spatial support causes small-object signalsto lose temporal continuity, resulting in fragmented and unstable pre-dictions. To address this issue, we propose a physics-guided advection-consistent modeling framework, termed PACT, which formulates eventevolution as a motion-driven feature transport process. Instead of rely-ing solely on local spatio-temporal aggregation, PACT propagates fea-tures along estimated velocity fields and enforces trajectory-level consis-tency through advection constraints. This design preserves weak eventresponses over time and prevents their degradation under complex back-ground interference. Technically, PACT integrates motion-aware featureextraction with a differentiable advection-based transport operator, en-abling coherent motion representation and effective noise suppressionduring temporal evolution. Extensive experiments on benchmark event-based datasets demonstrate that PACT consistently outperforms state-of-the-art methods, achieving improvements of 20.72% in IoU and 15.03%in accuracy while maintaining comparable computational efficiency. Thecode will be made publicly available.

ECCV 2026

CtrlCoMo: Controllable Co-Speech Motion Generation with Gesture–Action Disentanglement

Xinghan Wang, Ming Zhou, Yanbo Zheng, Youjiang Xu, Yuan Zhang, Mingyuan Gao, Nan Zhuang, Yadong Mu

Generating human motion synchronized with speech is es-sential for creating realistic virtual avatars. While recent work has madestrides in generating gestures from speech, these approaches often fallshort in enabling avatars to perform meaningful, context-specific actions.This paper presents CtrlCoMo, a motion generation framework that usestextual input alongside speech audio to provide explicit control over ac-tions in co-speech scenarios. To address the interference between ges-tures and actions in co-speech motions, we introduce Pyramid-VQ, anautoencoder that separates gestures from actions through hierarchicalquantization, with shallow layers capturing global semantics and deeplayers encoding localized gestures, guided by layer-wise CLIP-based se-mantic regularization. Additionally, the model provides explicit controlover gesture intensity via an AdaLN mechanism. For evaluation, we intro-duce CoHuMo, a large-scale co-speech motion dataset of 370 hours withrich human actions. This dataset encompasses a wide range of scene typesand languages, providing a robust benchmark for future research. Exten-sive evaluations are conducted on CoHuMo which validate our model’seffectiveness.

ECCV 2026

Caption Bottleneck Models

Seref Baris Cagliyan, Umut Ozdemir, Merve Tapli, Emre Akbas

Concept Bottleneck Models (CBMs) provide interpretabilityby routing predictions through a layer of human-understandable con-cepts. However, defining an optimal concept set for a specific datasetremains an open challenge. Existing approaches rely on expensive ex-pert annotations or LLM-generated lists based solely on class names.Even “open-vocabulary” variants typically depend on static concept sets,which restrict discovery and introduce label bias. Furthermore, tradi-tional CBMs often suffer from information leakage, where unmodeledvisual features bypass the bottleneck and compromise the integrity ofthe explanations. To overcome these limitations, we propose CaptionBottleneck Models (CaBM), a framework that circumvents the need forpredefined concept sets by replacing rigid concept layers with free-formnatural language. By representing images via LMM-generated captionsand training a classifier strictly on this text, CaBM ensures a leakage-freearchitecture by construction. Additionally, by analyzing the text classi-fier post-training, CaBM autonomously discovers high-quality, dataset-specific concepts. Our results across fine- and coarse-grained benchmarksdemonstrate that CaBM achieves competitive accuracy while preservinginterpretability without the constraints of external dictionaries or manuallabeling. Our code is available at https://github.com/bariscagliyan/CaptionBottleneckModels.

ECCV 2026

Holo360D: A Large-Scale Real-World Dataset with Continuous Trajectories for Advancing Panoramic 3D Reconstruction and Beyond

Jing OU, Zidong Cao, Yinrui Ren, Zhuoxiao Li, Jinjing Zhu, Tongyan Hua, Shuai Zhang, Hui Xiong, Wufan Zhao

While feed-forward 3D reconstruction models have advancedrapidly, they still exhibit degraded performance on panoramas due tospherical distortions. Moreover, existing panoramic 3D datasets are pre-dominantly collected with 360◦ cameras fixed at discrete locations, re-sulting in discontinuous trajectories. These limitations critically hin-der the development of panoramic feed-forward 3D reconstruction, espe-cially for the multi-view setting. In this paper, we present Holo360D,a comprehensive dataset containing 109,495 panoramas paired with reg-istered point clouds, meshes, and aligned camera poses. To our knowl-edge, Holo360D is the first large-scale dataset that provides continuouspanoramic sequences with accurately aligned high-completenessdepth maps. The raw data are initially collected using a 3D laser scan-ner coupled with a 360◦ camera. Subsequently, the raw data are pro-cessed with both online and offline SLAM systems. Furthermore, to en-hance the 3D data quality, a post-processing pipeline tailored for the 360◦dataset is proposed, including geometry denoising, mesh hole filling, andregion-specific remeshing, etc. Finally, we establish a new benchmark byfine-tuning 3D reconstruction models on Holo360D, providing key in-sights into effective fine-tuning strategies. Our results demonstrate thatHolo360D delivers superior training signals and provides a comprehensivebenchmark for advancing panoramic 3D reconstruction models. Datasetsand Code will be made publicly available. Dataset Page: Holo360D

ECCV 2026

Temporally Aware Densification for Dynamic 3D Gaussian Splatting

VIKRAM SANDU, Mayurdeep Pathak, Rajiv Soundararajan

Despite modeling temporal motion, dynamic 3D GaussianSplatting (3DGS) methods still inherit a static densification strategy ill-suited for dynamic scenes. This neglect of temporal behavior leads tounder-reconstructed and blurry dynamic regions, as short-lived Gaus-sians receive sparse supervision and fail to densify effectively. We pro-pose a Visibility-Aware Densification (VAD) framework that integratestemporal visibility into the densification process, ensuring that Gaus-sians are refined based on their actual temporal presence. A Temporally-Adaptive Thresholding (TAT) mechanism further adjusts each Gaussian’sdensification threshold according to its temporal lifespan, promoting bal-anced refinement of both static and dynamic regions. Finally, a TemporalOffset Warping (TOW) design enhances deformation capacity aroundtemporal centers, extending the lifespan of highly dynamic Gaussiansand facilitating more effective densification. Our approach achieves sub-stantial improvements in the visual quality of dynamic regions, outper-forming existing methods across three dynamic multi-view benchmarkdatasets. Moreover, the proposed VAD module generalizes across diversedynamic 3DGS methods, consistently improving dynamic reconstructionas a plug-and-play component. The project page is available here.

ECCV 2026

HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark

Dairu Liu, Zekun Qi, Jiayu Zeng, Yu Guan, Chenghuai Lin, Xuchuan Chen, XinQiang Yu, Wenyao Zhang, He Wang, Li Yi

Humanoid motion tracking is central to teleoperation andwhole-body imitation, yet evaluation often disagrees with what peo-ple perceive in videos. Kinematic errors average per-frame pose differ-ences but miss the physical artifacts that matter most, such as unstablesupport and incorrect contacts (e.g., foot skating and mistimed touch-downs). Meanwhile, widely used test suites are small and lack the diver-sity needed to stress contact-rich, long-horizon behaviors. We introduce# Corresponding author.HumanTracker to make humanoid tracking evaluation both perceptu-ally aligned and scalable. HumanTracker contributes 150 hours of newlycaptured optical motion from 24 professional performers, organized intofour motion families with text labels for fine-grained diagnosis. We fur-ther propose HumanScore, a preference-aligned metric trained from 12Khuman-labeled motion pairs on synchronized tracking videos via a trajec-tory reward model. Across representative state-of-the-art trackers, Hu-manScore better predicts held-out human preferences and reveals contactand stability failures that kinematic metrics often miss. The project pageis available at https://dairuliu.github.io/humantracker.

ECCV 2026

Towards Video Anomaly Detection from Event Streams: A Baseline and Benchmark Datasets

Peng Wu, Yuting Yan, Guansong Pang, Yujia Sun, Qingsen Yan, Peng Wang, Yanning Zhang

Event-based vision, characterized by low redundancy, focuson dynamic motion, and inherent privacy-preserving properties, natu-rally fits the demands of video anomaly detection (VAD). However, theabsence of dedicated event-stream anomaly detection datasets and effec-tive modeling strategies has significantly hindered progress in this field.In this work, we take the first major step toward establishing event-based VAD as a unified research direction. We first construct multi-ple simulated event-stream based benchmarks for video anomaly de-tection, featuring synchronized event and RGB recordings. Leveragingthe unique properties of events, we then propose an EVent-centric spa-tiotemporal Video Anomaly Detection framework, namely EWAD, withthree key innovations: an event density aware dynamic sampling strategyto select temporally informative segments; a density-modulated tempo-ral modeling approach that captures contextual relations from sparseevent streams; and an RGB-to-event knowledge distillation mechanismto enhance event-based representations under weak supervision. Exten-sive experiments on three benchmarks demonstrate that our EWADachieves significant improvements over existing approaches, highlight-ing the potential and effectiveness of event-driven modeling for videoanomaly detection. The benchmark datasets and code are available athttps://github.com/kanyutingfeng/EWAD.

ECCV 2026

When the City Teaches the Car: Label-Free 3D Perception from Infrastructure

Zhen Xu, Jinsu Yoo, Cristian Bautista, Zanming Huang, Tai-Yu Pan, Zhenzhen Liu, Katie Luo, Mark Campbell, Bharath Hariharan, Wei-Lun Chao

Building robust 3D perception for self-driving still relies heav-ily on large-scale data collection and manual annotation, yet this paradigmbecomes impractical as deployment expands across diverse cities andregions. Meanwhile, modern cities are increasingly instrumented withroadside units (RSUs), static sensors deployed along roads and at inter-sections to monitor traffic. This raises a natural question: can the cityitself help train the vehicle? We propose infrastructure-taught, label-free3D perception, a paradigm in which RSUs act as stationary, unsuper-vised teachers for ego vehicles. Leveraging their fixed viewpoints and re-peated observations, RSUs learn local 3D detectors from unlabeled dataand broadcast predictions to passing vehicles, which are aggregated aspseudo-label supervision for training a standalone ego detector. The re-sulting model requires no infrastructure or communication at test time.We instantiate this idea as a fully label-free three-stage pipeline and con-duct a concept-and-feasibility study in a CARLA-based multi-agent en-vironment. With CenterPoint, our pipeline achieves 82.3% AP for detect-ing vehicles, compared to a fully supervised ego upper bound of 94.4%.We further systematically analyze each stage, evaluate its scalability, anddemonstrate complementarity with existing ego-centric label-free meth-ods. Together, these results suggest that city infrastructure itself canpotentially provide a scalable supervisory signal for autonomous vehi-cles, positioning infrastructure-taught learning as a promising orthogonalparadigm for reducing annotation cost in 3D perception.

ECCV 2026

Rethinking Training and Inference for Trajectory Forecasting: Linking Winner-Take-All back to GMMs

Qiyuan Wu, Katie Luo, Bharath Hariharan, Wei-Lun Chao, Mark Campbell

Trajectory forecasting for autonomous driving has advancedrapidly, yet representative models often produce uninformative posteri-ors over forecast modes, causing problems for mode pruning. We tracethis to a modeling-training mismatch: forecasters are typically modeledas conditional Gaussian mixture models (GMMs) but trained with awinner-take-all (WTA) loss that assigns each sample to its nearest mode.We argue that this K-means-like hard assignment (one-hot), while pre-venting mode collapse, is the source of uninformative mode probabil-ities: it over-segments the trajectory space, ignores relatedness amongnearby modes, and yields assignment instability under small perturba-tions. Guided by this lens, we introduce two post-hoc treatments: (1)test-time posterior-weighted merging that aggregates nearby candidatetrajectories; and (2) a one-step expectation-maximization (EM) updatethat replaces hard labels with soft responsibilities, sharing probabilitymass across neighboring modes. Across several WTA-trained architec-tures, these lightweight steps produce more informative, faithfully rankedmode posteriors and strengthen final forecasts on popular displacementmetrics—without retraining. Our analysis unifies recent design choicesthrough a GMM-vs-K-means perspective and offers principled, practicalcorrections that better align training objectives with inference.

ECCV 2026

InstantHDR: Single-forward Gaussian Splatting for High Dynamic Range 3D Reconstruction

Dingqiang Ye, Jiacong Xu, Jianglu Ping, Yuxiang Guo, Chao Fan, Vishal Patel

High dynamic range (HDR) novel view synthesis (NVS) aimsto reconstruct HDR scenes from multi-exposure low dynamic range (LDR)images. Existing HDR pipelines heavily rely on known camera poses,well-initialized dense point clouds, and time-consuming per-scene op-timization. Current feed-forward alternatives overlook the HDR prob-lem by assuming exposure-invariant appearance. To bridge this gap, wepropose InstantHDR, a feed-forward network that initializes 3D HDRscenes from uncalibrated multi-exposure LDR collections in a fast sin-gle forward pass. Specifically, we design a geometry-guided appearancemodeling for multi-exposure fusion, and a meta-network for generaliz-able scene-specific tone mapping. Due to the lack of HDR scene data,we build a pre-training dataset, called HDR-Pretrain, for generalizablefeed-forward HDR models, featuring 168 Blender-rendered scenes, di-verse lighting types, and multiple camera response functions. Compre-hensive experiments show that our InstantHDR delivers a single-forwardHDR initialization at ∼ 700× the speed of SoTA optimization-basedmethods, and reaches comparable quality in real settings after lightweightpost-optimization while remaining ∼ 20× faster. All code, models, anddatasets: https://github.com/Bugjudger/InstantHDR.

ECCV 2026

World Models for Learning Dexterous Hand-Object Interactions from Human Videos

Raktim Goswami, Amir Bar, David Fan, Tsung-Yen Yang, Gaoyue Zhou, Prashanth Krishnamurthy, Michael Rabbat, Farshad Khorrami, Yann LeCun

Modeling dexterous hand–object interactions is challengingas it requires understanding how subtle finger motions influence theenvironment through contact with objects. While recent world mod-els address interaction modeling, they typically rely on coarse actionspaces that fail to capture fine-grained dexterity. We, therefore, intro-duce DexWM, a Dexterous Interaction World Model that predicts futurelatent states of the environment conditioned on past states and dexterousactions. To overcome the scarcity of finely annotated dexterous datasets,DexWM represents actions using finger keypoints extracted from ego-centric videos, enabling training on over 900 hours of human and non-dexterous robot data. Further, to accurately model dexterity, we find thatpredicting visual features alone is insufficient; therefore, we incorporatean auxiliary hand consistency loss that enforces accurate hand config-urations. DexWM outperforms prior world models conditioned on text,navigation, or full-body actions in future-state prediction and demon-strates strong zero-shot transfer to unseen skills on a Franka Panda armwith an Allegro gripper, surpassing Diffusion Policy by over 50% on av-erage across grasping, placing, and reaching tasks. Codes and dataset areavailable on the project page: https://raktimgg.github.io/dexwm/.

ECCV 2026

Bootstrapping Articulated 3D Reconstruction from 2D Image Collections

Jakub Zadrozny, Oisin Mac Aodha, Hakan Bilen

3D reconstruction of articulated objects from a single im-age is challenging because large training datasets with paired image and3D supervision are difficult to obtain. Recent point map–based methodsachieve strong performance but rely on synthetic datasets rendered frommanually created articulated 3D assets with carefully curated pose dis-tributions. While camera viewpoints can be easily sampled, generatingrealistic object articulations remains costly and labor-intensive. We pro-pose a training framework that reduces this requirement by leveragingunannotated 2D images collections with only a single rigged canonicalmesh per category. Starting from a weak 3D shape predictor trained oncanonical-pose renders, we iteratively estimate object articulation andcamera pose by fitting the mesh to predicted point maps. The recov-ered articulations and viewpoints are then used to render updated syn-thetic training data, progressively improving the predictor. Despite us-ing substantially weaker 3D supervision, our models achieve performancecomparable with DualPM, which requires manually curated articulatedtraining datasets.

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