paperlist.ai

AI conference papers

AI conference paper library

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

AirZoo: A Unified Large-Scale Dataset for Grounding Aerial Geometric 3D Vision

Xiaoya Cheng, Rouwan Wu, Xinyi Liu, Zeyu Cui, Yan Liu, Na Zhao, Yu Liu, Maojun Zhang, Shen Yan

Despite the rapid progress in data-driven 3D vision, aerialgeometric 3D vision remains a formidable challenge due to the severescarcity of large-scale, high-fidelity training data. Existing benchmarks,predominantly biased toward ground-level or object-centric views, donot account for complex viewpoint transformations and diverse envi-ronmental conditions in UAV-based sensing. To bridge this critical gap,we propose AirZoo, a unified large-scale dataset and benchmark forgrounding aerial geometric 3D vision. AirZoo possesses three appeal-ing properties: 1) Scalable Generation Pipeline: Leveraging freelyavailable, world-scale photogrammetric 3D meshes, it renders vast out-door environments with customizable UAV flight trajectories and config-urable weather/illumination. 2) Comprehensive Scene Diversity: Itprovides extensive global coverage (22 countries, 95 base flight sequences,and 377 weather-conditioned trajectories), systematically encompassingboth highly structured urban landscapes and complex unstructured nat-ural environments. 3) Rich Geometric Annotations: Each frameprovides synchronized, pixel-level metric depth and precise 6-DoF geo-referenced poses, essential for geometry-aware learning. Through threerigorous evaluation tracks—aerial image retrieval, cross-view matching,and multi-view 3D reconstruction—we demonstrate that AirZoo servesas a powerful "pre-training engine." Extensive experiments on both pub-lic and newly collected real-world benchmarks reveal that fine-tuning onAirZoo yields substantial performance gains for state-of-the-art models(e.g., MegaLoc, RoMa, VGGT, and Depth Anything 3), establishing anew performance upper bound for aerial spatial intelligence.

ECCV 2026

When Rubrics Fail: Error Enumeration as Reward for Reference-Free RL Post-Training

Wisdom Ikezogwo, Mehmet Saygin Seyfioglu, Ranjay Krishna, Karim Bouyarmane

Reinforcement learning with verifiable rewards (RLVR) and Rubrics as Rewards (RaR) have driven strong gains in domains with clear correctness signals and even in subjective domains by synthesizing evaluation criteria from ideal reference answers. But many real-world tasks admit multiple valid outputs and lack the single ideal answer that rubric generation depends on. We identify this reference-free setting as a gap in current post-training methods and propose Implicit Error Counting (IEC) to fill it. Instead of checking what a response gets right against a rubric, IEC enumerates what it gets wrong, applying severity-weighted scores across task-relevant axes and converting them into calibrated per-aspect rewards. We show that naïve explicit enumeration is too noisy for stable optimization, and that two design choices: implicit score emission and group calibration are necessary to make error counting a reliable reward. As a case study, we validate IEC on virtual try-on (VTO), a domain that is simultaneously too constrained for holistic scoring and too permissive for rubric-based evaluation: subtle garment errors are unacceptable, yet many output variations are correct. We introduce Cascaded Error Counting (CEC) as an evaluation metric, which tracks human preferences well (60% top-1 vs. 30% others), and curate Mismatch-DressCode (MDressBench), a benchmark with maximal attribute mismatch to stress-test reward designs. On MDressBench, IEC outperforms RaR across all metrics (CEC: 5.31 vs. 5.60 on flat references; 5.20 vs. 5.53 on non-flat). On VITON-HD and DressCode, IEC matches or surpasses six baselines on 6 of 8 perceptual metrics. These results suggest that when ideal answers are unavailable, counting errors provide a stronger signal than constructing rubrics.

ECCV 2026

VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting

Daeun Lee, Shoubin Yu, Yue Zhang, Mohit Bansal

Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) withverifiable rewards improves accuracy, it still struggles to achieve reliablespatio-temporal grounding during the reasoning process. Moreover, im-proving grounding typically relies on scaled training data or inference-timeperception tools, which increases annotation cost or computational cost.To address this challenge, we propose VisionCoach, an input-adaptiveRL framework that improves spatio-temporal grounding through visualprompting as training-time guidance. During RL training, visual promptsare selectively applied to challenging inputs to amplify question-relevantevidence and suppress distractors. The model then internalizes theseimprovements through self-distillation, enabling grounded reasoning di-rectly on raw videos without visual prompting at inference. VisionCoachconsists of two components: (1) Visual Prompt Selector, which predictsappropriate prompt types conditioned on the video and question, and(2) Spatio-Temporal Reasoner, optimized with RL under visual promptguidance and object-aware grounding rewards that enforce object identityconsistency and multi-region bounding-box IoU. Extensive experimentsdemonstrate that VisionCoach achieves state-of-the-art performanceacross diverse video reasoning, video understanding, and temporal ground-ing benchmarks (V-STAR, VideoMME, World-Sense, VideoMMMU, Per-ceptionTest, and Charades-STA), while maintaining a single efficientinference pathway without external tools. Our results highlight the effec-tiveness of training-time visual prompting as a lightweight mechanismfor improving grounded video reasoning.

ECCV 2026

V-HOLD: Stabilizing Flow Trajectories to Rethink the Edit–Preservation Trade-off

Gahyeon Kim, Dong-oh Kang

Recent diffusion- and flow-based image editing methods en-able high-quality text-guided image editing. However, improving editstrength often degrades identity and background preservation, a phe-nomenon known as the edit—preservation trade-off. This trade-off iswidely observed and commonly regarded as an inherent limitation ofgenerative editing. In this work, we revisit this assumption and iden-tify trajectory instability as a previously overlooked factor behind theedit–preservation trade-off. Through empirical analysis across multipleediting methods and backbones, we find that trajectory instability isclosely associated with degraded semantic preservation while showinglittle association with edit alignment. We characterize instability by us-ing directional consistency and update variability of latent update ve-locities during integration, revealing that unstable integration dynamicscontribute substantially to the edit–preservation trade-off. Motivated bythis insight, we propose V-HOLD, a simple training-free method thatstabilizes editing trajectories by maintaining a target-oriented updatedirection across multiple integration steps instead of recomputing veloc-ities at every step. Experiments on PIE-Bench and the FlowEdit-Datashow that our method consistently improves semantic preservation whilemaintaining competitive edit alignment across multiple backbones. Inaddition, V-HOLD reduces the number of velocity-field evaluations dur-ing editing, improving computational efficiency. These results highlighttrajectory stability as a key factor for improving semantic preservationwithout sacrificing edit alignment.

ECCV 2026

Unsupervised Pixel-Level Semantic Left-Right Understanding of In-the-Wild Images

Weikang Wang, Tobias Weißberg, Florian Bernard

While various works address reflective symmetry understand-ing in 3D data and images, pixel-level semantic left-right prediction ofin-the-wild images remains challenging, due to certain difficulties includ-ing the lack of 3D information, occlusion, object pose variation, partial-ity, etc. In this work, we propose an unsupervised learning framework totackle this challenge. Leveraging recent advances in vertex-wise semanticleft-right understanding of 3D data, our unsupervised learning methodjointly utilises 3D shape and image datasets to infer pixel-wise seman-tic left-right predictions in single-view images. In particular, we showthat a medium-scale 3D shape dataset comprising mainly of human- andquadruped animal-like shapes, combined with diverse in-the-wild imagedata, are sufficient to achieve high-quality semantic left-right predictionin images, even for entirely unseen 3D object categories, such as carsor trains. Overall, our approach achieves superior performance in densepixel-wise semantic left-right predictions on both rendered and in-the-wild image datasets when compared to existing state-of-the-art methods.

ECCV 2026

Tricam-rPPG: A Multimodal Multispectral Dataset for remote Photoplethysmography

Abhijit Sarkar, Surendrabikram Thapa, Ishtiaque Ahmed Khan, Yogesh Deshpande, Amos Abbott

This paper introduces Tricam-rPPG, a multimodal datasetdesigned to support systematic studies of remote photoplethysmography(rPPG) using multispectral imaging. Remote (noncontact) monitoringoffers the potential for unobtrusive measurement of physiological signalsrelated to health, cognitive load, and affect. However, most existing rPPGdatasets rely primarily on RGB imaging, limiting the study of spectraleffects, illumination variability, and sensing biases associated with differ-ences in optical properties of the skin. Tricam-rPPG provides synchro-nized recordings of 31 human subjects from three co-located cameras: astandard RGB camera and two near-infrared (NIR) cameras operatingat 850 nm and 940 nm, all captured under controlled illumination con-ditions. For fourteen participants, simultaneous recordings from MetaAria glasses are also included. All cases are supplemented by referencewaveforms of blood volume pulses (BVP) measured using a fingertipPPG sensor. In addition to presenting the dataset, we establish base-line benchmarks with widely used rPPG algorithms to evaluate heart-rate estimation performance across different combinations of spectralchannels. By providing synchronized RGB and NIR video together withphysiological ground truth, Tricam-rPPG enables new research directionsin multispectral physiological sensing, fairness-aware rPPG algorithms,and robust remote cardiovascular monitoring. The dataset is availableunder a data use license and mutual agreement (check https://tricam-rppg.github.io).

ECCV 2026

Transferability Between Understanding and Generation in Unified Multimodal Models

Jiwon Kang, Heeji Yoon, Jaewoo Jung, Jaewon Min, Minkyeong Jeon, Biyeon Hwang, Sangwon Jung, Seungryong Kim

Unified Multimodal Models (UMMs) integrate image under-standing and generation within a single architecture, yet how the twotasks interact remains understudied. We investigate transferability inUMMs: whether training a capability on one task improves the samecapability on the other without explicit supervision. Through controlledexperiments, we empirically find that transferability depends on archi-tecture—models with fully shared transformer backbone and a unifiedvisual encoder exhibit consistent cross-task transfer, while loosely cou-pled designs show little or none. Leveraging this transferability, we pro-pose a practical training strategy. The most straightforward way to im-prove a target generative capability (e.g., counting) is to fine-tune gen-eration directly, but this can degrade visual quality due to distributionshift. Instead, we train the corresponding understanding task and letit transfer into generation, which improves capability-specific genera-tive performance while minimizing distribution shift. We validate thisacross three capabilities—counting, spatial relation, and text recogni-tion/generation—showing that cross-task transferability can be system-atically exploited in UMMs.

ECCV 2026

Towards Metric-Agnostic Trajectory Forecasting

Markus Knoche, Daan de Geus, Bastain Leibe

Accurate trajectory forecasting of surrounding traffic par-ticipants is a core capability for autonomous driving, enabling vehiclesto anticipate behavior and plan safe maneuvers. We observe that cur-rent state-of-the-art forecasting models on Argoverse 2 and the WaymoOpen Motion Dataset tailor their training objectives to the differentbenchmark metrics. Because these metrics encourage conflicting behav-ior, we propose a paradigm change for trajectory forecasting: trainingmodels with metric-agnostic probabilistic objectives and treating metricoptimization as a downstream task applied to the predictive distribution.Concretely, we introduce Trajectory Distribution Evaluation (TraDiE)policies, metric-specific policies that map a predictive distribution to theset of K trajectories and confidences required by trajectory forecast-ing metrics. We evaluate this framework by introducing DONUT-NLL,which adapts the training objective of the state-of-the-art trajectory fore-casting model DONUT to directly optimize the predictive distribution.Using our policies, DONUT-NLL achieves state-of-the-art results on allmetrics of the Waymo motion prediction benchmark.

ECCV 2026

Swap the Right Identity: Spatio-Temporal Preference Optimization for Identity Swapping

Hongdeng Shen, Xiongzheng Li, Jikang Cheng, Duo Li, Yunlong Feng, Jing Li

Character swapping aims to faithfully transfer a target iden-tity while preserving the source scene layout and pose, yielding visuallyseamless composites. However, existing general image editing methodsoften struggle to simultaneously achieve identity consistency, backgroundpreservation, and pose or structural stability. This challenge is further ex-acerbated by the lack of high-quality character swapping datasets. To ad-dress these challenges, we propose Spatio-Temporal Identity PreferenceAlignment (SIPA) framework, that systematizes diffusion-based pref-erence optimization from both spatial and temporal perspectives. Spa-tially, we introduce Region-Weighted Preference (RWP), which increasesthe weighting of critical regions (e.g., the face or body) during DPOtraining, focusing preference signals on the desired local differences andthereby substantially reducing data dependence while improving train-ing stability. Temporally, we propose Time-Injected Identity Preference(TIP): during low-noise steps, we explicitly inject face-difference cues intothe DPO preference term to further enhance facial identity consistency.In addition, we build and plan to release a two-stage dataset tailoredfor character swapping. Extensive quantitative and qualitative exper-iments demonstrate that SIPA consistently outperforms prior methodsacross identity fidelity, structure preservation, and visual quality, achiev-ing state-of-the-art performance.

ECCV 2026

Spectral Gradient Orthogonalization Improves Differentially Private Training at Scale

Sabari Shanmugam, Nick Barnes, Kerry Taylor

Differentially private training adds isotropic Gaussian noiseto clipped gradients, corrupting every singular direction equally. In vi-sion models, where spatial correlation concentrates gradient energy intoa low-rank subspace, most of this noise falls in directions that carrylittle signal. Spectral gradient orthogonalization via polar decomposi-tion is introduced as a post-processing step that recovers directionalsignal from the noisy gradient’s low-rank structure at zero additionalprivacy cost. A phase transition governs the utility of this approach:orthogonalization improves accuracy only when the per-direction spec-tral signal-to-noise ratio (SNR) suffices for singular vector recovery; inlow-SNR regimes, the directional bias of the gradient is replaced by anearly random orthogonal update, and the transformation is harmful.The recovery threshold is determined by the spectral gap of the gradientand is surpassed at large batch sizes. Empirically, the benefit scales withmodel capacity: spectral orthogonalization achieves a +20.9% improve-ment over DP-SGD on WRN-28-10 (B = 4096) and +14.9% on ResNet-18, while reducing inter-run variance by a factor of two to three. In thefine-tuning regime, spectral orthogonalization matches the stability ofDP-Adam while maintaining a first-order memory footprint. Combiningspectral with temporal denoising yields 50.3% on CIFAR-10 (ε = 4), thehighest accuracy in any tested configuration. These gains are specific tomoderate-to-high-SNR regimes such as large-batch training of higher-capacity models. Small-batch or low-SNR settings are better served byDP-SGD or temporal denoising.

ECCV 2026

SIGMA-Lane: Scale-pyramId Gated MAmba for Temporally Consistent Video Lane Detection

Zhang Tiancheng, Mengmeng Wang, Yan Gao, Xiangjie Kong, Guojiang Shen, Jiaxin Du

Video lane detection requires predictions that remain sta-ble across frames, yet severe vehicle occlusions can break temporal cues.In streaming recurrent models, corrupted observations may enter thehidden state and produce errors that persist into later frames. Existingocclusion-aware refinements usually provide obstacle masks as auxiliaryinputs, so the state-update path is only indirectly protected. We proposeSIGMA-Lane, which treats this failure mode as state contamination inState Space Model (SSM)-based temporal modeling. SIGMA-Lane placesocclusion-aware gates on the SSM write and residual-fusion paths, con-trolling how current observations enter temporal memory and are fusedback after temporal propagation. After coordinate-consistent affine align-ment, the model combines two complementary paths: SSM-consistentdual-gating for temporal filtering and Structural Spatial Retrieval (SSR)for recovering missing lane structure from aligned historical priors. Ex-periments on VIL-100 and OpenLane-V show improved temporal stabil-ity under heavy occlusion, with competitive F1 and mIoU scores.

ECCV 2026

Self-Evolving MCP-GUI Agents via Automated Environment Generation and Experience Learning

Tiantian He, Yihang Chen, Keyue Jiang, Ka Lee, Kaiwen Zhou, Kun Shao, Shuai Wang

Computer-use agents that combine GUI interaction withstructured API calls via the Model Context Protocol (MCP) show promisefor automating software tasks. However, existing approaches lack a prin-cipled understanding of how agents should balance these two modalitiesand how to enable iterative self-improvement across diverse applica-tions. We formulate MCP-GUI interplay as a unified hybrid policylearning problem where the agent learns when each modality providescomplementary advantages, and show that distillation and experienceaugmentation target fundamentally different failure modes—requiringapplication-aware mechanism selection. Built on this formulation, wepropose a self-evolving framework with a fully automatic pipelinethat orchestrates automatic environment generation and validation, tra-jectory collection, gap-driven task synthesis, and quality-filtered training—all without manual intervention. A key innovation is our experiencebank, which accumulates LLM-learned rules from trajectory comparison,enabling inference-time improvement without fine-tuning. Systematiccross-application analysis across three desktop applications revealsthat the optimal strategy depends on MCP–GUI composition and tool-chain complexity: distillation achieves 77.8% pass rate on MCP-dominanttasks (+17.8pp), while the experience bank excels on GUI-intensive tasks(+10.0pp). Code available at: https://github.com/Tiantian-H/EE-MCP

ECCV 2026

Safe Responses Matter: Output-Aware Safety Guardrail Mitigate Over-Refusal in MLLMs

Jiayi Li, Kun Zhan

Existing safety mechanisms for multimodal large languagemodels (MLLMs) face a fundamental trade-off between safety and util-ity. Model fine-tuning achieves robust safety but compromises generalutility. Input-side safety guardrails offer a lightweight alternative, yetthey suffer from severe over-refusal, indiscriminately blocking benignqueries or those the model could have safely answered through refusalor advisory responses. We identify that the root cause of over-refusallies in the input-aware paradigm: safety guardrails make safety decisionswithout considering whether the model itself is capable of generatingsafe responses. Usually, MLLMs already possess intrinsic safety mech-anisms that can transform harmful inputs into harmless outputs, butinput-side safety guardrails override this capability, degrading user ex-perience. Motivated by this insight, we propose a paradigm shift towardoutput-aware safety guardrails. Our method operates within the model’shidden state space to predict whether the forthcoming generation willbe unsafe before it is fully produced. By training a lightweight classifiervia multi-instance contrastive learning on hidden state representations,our approach distinguishes between inputs that will lead to unsafe out-puts and those that will not, even when the inputs themselves containrisky elements. This enables precise intervention only when the model’sactual response would be harmful. Extensive experiments demonstratethat our output-aware safety guardrail matches the safety performance ofexisting methods while drastically reducing over-refusal, preserving themodel’s utility and built-in safety capabilities. Our code is available athttps://github.com/kunzhan/OutGuard.

ECCV 2026

Rethinking IRSTD: Single-Point Supervision Guided Encoder-only Framework is Enough for Infrared Small Target Detection

Rixiang Ni, Boyang Li, Chen Jun, Zhijie Chen, Feiyu Ren, Yuji Wang, Haoyang Yuan, Wujiao He, Wei An

Infrared small target detection (IRSTD) aims to localizesmall targets in cluttered infrared scenes. Extensive research follows thepixel-level supervision-guided ‘encoder–decoder’ segmentation paradigm.Although these methods have achieved promising performance, they of-ten overlook that infrared small targets occupy only a few pixels andare usually surrounded by blurred, low-contrast boundaries caused bycluttered backgrounds. Based on this observation, we argue that thefirst principle of IRSTD should be target localization rather than densereconstruction of target regions entangled with indistinguishable back-ground noise. In this paper, we reformulate IRSTD as a centroid re-gression task and propose a novel Single-Point Supervision guided In-frared Probabilistic Response Encoding method (namely, SPIRE), whichis non-trivial because point-level supervision must produce detectionoutputs comparable to dense supervision. Specifically, we design Point-Response Prior Supervision (PRPS) to transform single-point annota-tions into probabilistic response maps consistent with infrared point-target response characteristics, and combine it with a High-ResolutionProbabilistic Encoder (HRPE) that performs encoder-only probabilis-tic regression followed by lightweight peak-based inference, without de-coder reconstruction. By preserving high-resolution features and increas-ing effective supervision density, SPIRE alleviates optimization instabil-ity under sparse target distributions. Finally, extensive experiments ontwo public IRSTD benchmarks, SIRST-UAVB and SIRST4, demonstratethat SPIRE achieves competitive target-level detection performance withconsistently low false alarm rate (Fa) and significantly reduced compu-tational cost.

ECCV 2026

RePlan: Reasoning-Guided Region Planning for Complex Instruction-Based Image Editing

Tianyuan Qu, Lei Ke, Xiaohang Zhan, Longxiang Tang, Yuqi Liu, Bohao PENG, Bei Yu, Dong Yu, Jiaya Jia

Instruction-based image editing enables natural-language con-trol over visual modifications, yet existing models falter under Instruc-tion–Visual Complexity (IV-Complexity), where intricate instructionsmeet cluttered or ambiguous scenes. We introduce RePlan (Region-alignedPlanning), a plan-then-execute framework that couples a vision–languageplanner with a diffusion editor. The planner decomposes instructions viastep-by-step reasoning and explicitly grounds them to target regions;the editor then applies changes using a training-free attention-region in-jection mechanism, enabling precise, parallel multi-region edits withoutiterative inpainting. To strengthen planning, we apply GRPO-based rein-forcement learning using 1K instruction-only examples, yielding substan-tial gains in reasoning fidelity and format reliability. We further presentIV-Edit, a benchmark focused on fine-grained grounding and knowledge-intensive edits. Across IV-Complex settings, RePlan consistently outper-forms strong baselines trained on far larger datasets, improving regionalprecision and overall consistency. Project page: https://replan- iv-edit.github.io/

ECCV 2026

RefAlign: Representation Alignment for Reference-to-Video Generation

Lei Wang, YuXin Song, Ge Wu, Haocheng Feng, Hang Zhou, Jingdong Wang, Yaxing Wang, Jian Yang

Reference-to-video (R2V) generation is a controllable videosynthesis paradigm that constrains the generation process using bothtext prompts and reference images, enabling applications such as per-sonalized advertising and virtual try-on. In practice, existing R2V meth-ods typically introduce additional high-level semantic or cross-modal fea-tures alongside the VAE latent representation of the reference image andjointly feed them into the diffusion Transformer (DiT). These auxiliaryrepresentations provide semantic guidance and act as implicit alignmentsignals, which can partially alleviate pixel-level information leakage inthe VAE latent space. However, they may still struggle to address copy–paste artifacts and multi-subject confusion caused by modality mismatchacross heterogeneous encoder features. In this paper, we propose Re-fAlign, a representation alignment framework that explicitly aligns DiTreference-branch features to the semantic space of a visual foundationmodel (VFM). The core of RefAlign is a reference alignment loss thatpulls the reference features and VFM features of the same subject closerto improve identity consistency, while pushing apart the correspond-ing features of different subjects to enhance semantic discriminability.This simple yet effective strategy is applied only during training, incur-ring no inference-time overhead, and achieves a better balance betweentext controllability and reference fidelity. Extensive experiments on theOpenS2V-Eval benchmark demonstrate that RefAlign outperforms cur-rent state-of-the-art methods in TotalScore, validating the effectivenessof explicit reference alignment for R2V tasks.

ECCV 2026

RadarGen: Automotive Radar Point Cloud Generation from Cameras

Tomer Borreda, Fangqiang Ding, Sanja Fidler, Shengyu Huang, Or Litany

We present RadarGen, a diffusion model for synthesizing re-alistic automotive radar point clouds from multi-view camera imagery.RadarGen adapts efficient image-latent diffusion to the radar domain byrepresenting radar measurements in bird’s-eye-view form that encodesspatial structure together with radar cross section (RCS) and Dopplerattributes. A lightweight recovery step reconstructs point clouds fromthe generated maps. To better align generation with the visual scene,RadarGen incorporates BEV-aligned depth, semantic, and motion cuesextracted from pretrained foundation models, which guide the stochasticgeneration process toward physically plausible radar patterns. Condition-ing on images makes the approach broadly compatible, in principle, withexisting visual datasets and simulation frameworks, offering a scalable di-rection for multimodal generative simulation. Evaluations on large-scaledriving data show that RadarGen captures characteristic radar measure-ment distributions and reduces the gap to perception models trained onreal data, marking a step toward unified generative simulation acrosssensing modalities.

ECCV 2026

Physically Grounded 3D Generative Reconstruction under Hand Occlusion using Proprioception and Multi-Contact Touch

Gabriele Mario Caddeo, Pasquale Marra, Lorenzo Natale

We propose a multimodal, physically grounded approach formetric-scale amodal object reconstruction and pose estimation undersevere hand occlusion. Unlike prior occlusion-aware 3D generation meth-ods that rely only on vision, we leverage physical interaction signals:proprioception provides the posed hand geometry, and multi-contacttouch constrains where the object surface must lie, reducing ambigu-ity in occluded regions. We represent object structure as a pose-aware,camera-aligned signed distance field (SDF) and learn a compact latentspace with a Structure-VAE. In this latent space, we train a condi-tional flow-matching diffusion model, pretraining on vision-only imagesand finetuning on occluded manipulation scenes while conditioning onvisible RGB evidence, occluder/visibility masks, the hand latent rep-resentation, and tactile information. Crucially, we incorporate physics-based objectives and differentiable decoder-guidance during finetuningand inference to reduce hand–object interpenetration and to align thereconstructed surface with contact observations. Because our methodproduces a metric, physically consistent structure estimate, it integratesnaturally into existing two-stage reconstruction pipelines, where a down-stream module refines geometry and predicts appearance. Simulation ex-periments show that adding proprioception and touch substantially im-proves completion under occlusion and yields physically plausible recon-structions at correct real-world scale compared to vision-only baselines;we further validate transfer by deploying the model on a real humanoidrobot with an end-effector different from those used during training. Seehttps://github.com/hsp-iit/physical-generative-reconstruction

ECCV 2026

PhysFlowNet: Learning Canonical Latent Manifolds via Spatio-Spectral Physics Priors for Underwater Object Detection

Xueting Liu, Haoyu Ji, Wenze Huang, Zhihao Yang, Yu Gao, Weihong Ren, Zhiyong Wang, Honghai LIU

Underwater object detection is fundamentally ill-posed dueto severe light attenuation and scattering. These physical degradationsinduce highly non-linear geometric distortions in the visual feature space,deviating significantly from the canonical manifold of clear images andrendering standard Euclidean convolutions suboptimal. To address this,we propose PhysFlowNet, that reformulates representation rectificationas a physics-guided geometric evolution on a Riemannian manifold. Phys-FlowNet first extracts a joint spatial-frequency physical prior to encap-sulate macroscopic scattering mechanics. Conditioned on this prior, weintroduce the Parallel Physics-Residual Bottleneck (PPRB), which exe-cutes a metric-preconditioned Riemannian feature retraction to transportdistorted features back to their canonical states. Moreover, to counter-act cascaded smoothing during scale transitions, we introduce a Physics-Guided Manifold Downsampling (PMD) strategy to strictly preserve finedetail textures and structural boundaries. Ultimately, the network is op-timized via a Unified Evidential-Contrastive Objective (UECO). Com-prising a Supervised Contrastive Manifold Loss (SCML) and an Eviden-tial Uncertainty Regulation (EUR), UECO jointly ensures latent geo-metric alignment and reliable uncertainty calibration. Extensive evalua-tions across challenging underwater benchmarks demonstrate that Phys-FlowNet achieves state-of-the-art performance, establishing a principledgeometric paradigm for physically degraded vision tasks.

ECCV 2026

MV-STRIDE: Enabling MLLMs to Master Multi-View Spatial Reasoning via Hierarchical Capability Modeling

Jin Xu, Xiaojian Huang, zhang zhihong, Luo Zhuodong, Xuejin Chen, Jie Zhao, Xin Liu, Wang Xinzhi, Jiansheng Wei

Despite the rapid progress of Multimodal Large LanguageModels (MLLMs) in 2D vision-language tasks, robust multi-view spatialreasoning remains a fundamental bottleneck due to the lack of struc-tured 3D cognitive pathways in existing datasets. To address this, weintroduce MV-STRIDE, a Multi-View hierarchical SpaTial Reasoningdataset with Interdependent and Decomposed capabilitiEs. Moving be-yond _x001D_at data structures, MV-STRIDE explicitly models the dependencyrelationships between foundational perception, scene understanding, andcomplex contextual reasoning, providing a coherent learning pathwayaligned with human spatial cognition. We develop a systematic QA gen-eration pipeline leveraging diverse 3D scene sources that enforces cross-view dependency constraints to prevent single-view solvability, generat-ing multi-level spatial reasoning tasks supported by cognitively groundedchain-of-thought supervision for complex inference. Extensive evalua-tions demonstrate that our multi-stage training framework based on ourhierarchical dataset achieves state-of-the-art performance across multiplespatial reasoning benchmarks, notably the multi-view oriented MMSI-Bench. Our approach enables MLLMs to maintain robust, 3D-consistentspatial reasoning across diverse viewpoints. The code and dataset areavailable at https://co1dspring.github.io/MV-STRIDE/.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.