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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

R-ESC: Robustly Erasing Space Concepts via Stochastic Feature Remapping

Kang Eun Jeon, Yunsung Kang, Do Kang, Tae-Young Lee, Gyeong-Moon Park, Jong Hwan Ko

Concept erasure and machine unlearning have emerged asa critical paradigm for enabling models to remove unwanted knowledgefrom learned representations at the level of classes, attributes, or higher-level semantics, motivated by the safety concerns surrounding moderngenerative AI models. However, existing approaches face three persis-tent challenges: (1) they often degrade utility on the remain concepts,(2) they often leave residual feature-level signals that enable recoveryof the forget concepts via _x001C_ne-tuning or representation probing, and (3)they impose substantial computational or memory overhead, limitingscalability. Prior work, such as Erasing Space Concept (ESC), moves to-ward structured removal in representation space, but achieving robusterasure with low overhead remains a challenge. To address this issue, wepropose Robustly Erasing Space Concepts (R-ESC), a framework for ro-bustly erasing space concepts via stochastic feature remapping. R-ESCintroduces three components: (i) prototype-orthogonal projection thatreduces interference by decorrelating forget and remain prototypes priorto erasure, (ii) stochastic multi-target remapping that blends forget pro-totypes into multiple remain prototypes to prevent re-separation andhinder recovery, and (iii) activation-mean prototypes that compress theerasure procedure to a single forward pass, yielding linear-time computa-tion with constant additional memory. Across extensive experiments, R-ESC maintains remain data utility, strengthens resistance to feature-levelrecovery, and scales to large models and datasets, providing a practicalpathway toward robust and e_x001E_cient concept erasure.

ECCV 2026

PriorEye: Geospatial Visual Priors for End-to-End Autonomous Driving

Kyuhwan Yeon, Benjamin Ramtoula, Daniele De Martini

Most end-to-end autonomous driving methods rely solely oninstantaneous sensor observations, limiting them to reactive behaviorwithout the anticipatory foresight human drivers employ through priorexperience. We introduce geospatial visual priors, street-level visual con-text anchored to the intended driving route, providing visual-spatialforesight independent of real-time sensors. We propose a memory aug-mentation module featuring a dual-memory architecture and an adap-tive memory gate, which can be easily integrated into existing end-to-end approaches. This design pairs a contextual memory for retrievedpriors with a persistent fallback memory, and dynamically regulatesthe influence of memories based on current state compatibility. Eval-uated on the NAVSIM-v2 benchmark, our approach consistently im-proves performance across diverse end-to-end baselines. Furthermore,because these priors are independent of onboard sensors, our method in-herently improves robustness against sensor corruption, while the dual-memory design ensures safe fallback when the retrieved priors them-selves become unreliable. Our project page is available at https://ori-mrg.github.io/PriorEye.

ECCV 2026

PGCR: Pose–Geometry Coupled Reasoning for Image-to-Point Cloud Registration

Xinjun Li, Wenfei Yang, Yihan Chen, Zhixin Cheng, Shifeng Zhang, Xu Zhou, Tianzhu Zhang

Image-to-point cloud registration aims to estimate the 6-DoFcamera pose of a query image with respect to a 3D point cloud, whichremains challenging due to the heterogeneous nature of dense visual ap-pearance and sparse geometric structure. Most existing learning-basedmethods follow an overlap–then–registration paradigm, where pose esti-mation is restricted to a predicted overlapping region. Such a sequentialdecomposition is inherently fragile, as inaccurate overlap prediction maydiscard geometrically consistent regions and irreversibly constrain poseoptimization. In this work, we reformulate image-to-point cloud registra-tion as a pose–geometry coupled reasoning problem, where camera poseand scene geometry are treated as interdependent variables. Based onthis formulation, we propose PGCR, a unified framework that estimatescamera pose by explicitly reasoning over dense cross-modal geometry.PGCR jointly predicts camera pose together with dense geometric rep-resentations and grounds pose inference in global geometric consistency.To enable effective pose–geometry coupling, we introduce a Geometry-aware Pose Refinement mechanism to enforce prediction-level consistencybetween pose and geometry, and a Pose-guided Progressive Refinementstrategy to adapt cross-modal interaction according to intermediate poseestimates. Extensive experiments on two widely used outdoor and twoindoor benchmarks demonstrate that PGCR consistently outperformsprior state-of-the-art methods across all evaluation metrics while remain-ing computationally efficient.

ECCV 2026

PanoSAM2: Lightweight Distortion- and Memory-aware Adaptions of SAM2 for 360 Video Object Segmentation

Dingwen Xiao, Weiming Zhang, Shiqi Wen, Addison Wang

360 video object segmentation (360VOS) aims to predicttemporally-consistent masks in 360 videos, offering full-scene coverage,benefiting applications, such as VR/AR and embodied AI. Learning360VOS model is nontrivial due to the lack of high-quality labeled dataset.Recently, Segment Anything Models (SAMs), especially SAM2 – withits design of memory module – shows strong, promptable VOS capabil-ity. However, directly using SAM2 for 360VOS yields implausible resultsas 360 videos suffer from the projection distortion, semantic inconsis-tency of left-right sides, and sparse object mask information in SAM2’smemory. To this end, we propose PanoSAM2, a novel 360VOS frame-work based on our lightweight distortion- and memory-aware adaptationstrategies of SAM2 to achieve reliable 360VOS while retaining SAM2’suser-friendly prompting design. Concretely, to tackle the projection dis-tortion and semantic inconsistency issues, we propose a Pano-AwareDecoder with seam-consistent receptive fields and iterative distortionrefinement to maintain continuity across the 0°/360° boundary. Mean-while, a Distortion-Guided Mask Loss is introduced to weight pixelsby distortion magnitude, stressing stretched regions and boundaries. Toaddress the object sparsity issue, we propose a Long–Short Mem-ory Module to maintain a compact long-term object pointer to re-instantiate and align short-term memories, thereby enhancing temporalcoherence. Extensive experiments show that PanoSAM2 yields substan-tial gains over SAM2: +5.6 on 360VOTS and +6.7 on PanoVOS, show-ing the effectiveness of our method. Code is available at https://github.com/Eric-Bumaro/PanoSAM2.

ECCV 2026

Open Your Eyes: Benchmarking the Detection of Fabricated Realities and Weaponized Ethics in VLMs

Amit Pandey, Aditya Mohan, Phani Sankar

As Vision–Language Models (VLMs) increasingly power au-tonomous agents—from coding assistants that interpret terminal outputand execute shell commands, to Computer-Use Agents with full desktopcontrol, to HR screening and loan evaluation bots—these agents act di-rectly over visual artifacts from untrusted sources. While prior work hasstudied direct multimodal jailbreaks, text-based indirect prompt injec-tion, and HTML-embedded attacks, the security implications of Multi-modal Indirect Prompt Injection (M-IPI) in realistic agentic workflowsremain insufficiently understood. We ask a central question: does pro-cessing an artifact visually, rather than as text, degrade an agent’s abil-ity to resist embedded attacks? To study this, we introduce the M-IPIBenchmark—a suite of 2,600 high-fidelity visual artifacts encompass-ing two attack families: (1) Technically-Framed Attacks, where maliciouscommands are interwoven with genuine debugging workflows in terminalscreenshots, and (2) Ethics-Framed Attacks, a novel vector where adver-saries exploit alignment priors such as fairness mandates to override eval-uation policies—and use it to systematically analyze modality-dependentfailures. We evaluate five model families in paired vision and text-onlyconfigurations that receive identical content, isolating the modality effect.Our results reveal that VLMs consistently underperform text-only coun-terparts at detecting embedded attacks—a “visual authority” effect whererendered presentation suppresses critical evaluation. For end-to-end at-tack success, both modalities show comparable susceptibility (∼50% fortechnical, >80% for ethics-framed), with qualitatively distinct failureprofiles: VL models disproportionately trust visually-rendered commandswhile text-only models are more vulnerable to natural-language direc-tives. These findings indicate that the vulnerabilities are rooted in theshared language backbone and that alignment priors represent a dis-tinct, exploitable attack surface. Our benchmark artifacts are releasedunder gated, research-only access at https://github.com/adityakm24/OpenYourEyes to support future defensive research.

ECCV 2026

Obliviate: Erasing Concepts from Autoregressive Image Generation Models

Hossein Shakibania, Jonas Henry Grebe, Tobias Braun, Ege Aktemur, Saleh Aslani, Mehmet Görkem Yiğit, Marcus Rohrbach

The widespread adoption of generative AI models has inten-sified concerns about misuse, including the creation of unsafe or disturb-ing imagery. To mitigate such issues, several concept erasure approacheshave been proposed to remove harmful content from multimodal gen-erative models. Yet concept erasure for autoregressive image generationremains largely unexplored, despite the growing relevance of these modelsin recent trends toward unified multimodal architectures. In this work,we fill this gap by introducing Obliviate, a guidance-based concepterasure method for autoregressive image generation. Our method buildson three key design choices: KL-based supervision over visual tokendistributions, trajectory-level updates over full autoregressive rollouts,and aligned visual prefixes for stable target construction. We evaluateObliviate on three state-of-the-art autoregressive text-to-image mod-els, Liquid, Emu3-Gen, and Janus-Pro, covering the erasure of explicitcontent, graphic violence, and branded imagery. Obliviate consistentlyoutperforms current alternatives, reducing nudity on the defensive RABbenchmark from 91.58 to 3.15 while preserving overall model utility.

ECCV 2026

Scaling Whole-Slide Pathology Foundation Model Pretraining with Billions Off-the-Shelf Tokens

Honglin Li, Zhongyi Shui, Chenglu Zhu, Lin Yang

Pathology whole slide image (WSI) analysis is crucial fordisease diagnosis and understanding. While pathology foundation modelshave advanced WSI analysis, their scalability remains limited by thelarge resolution of individual WSIs and the small number of availableslides. Existing methods often use the [CLS] token from tile-level ViTsfor efficiency, but this design overlooks spatially informative tokens thatencode fine-grained pathological patterns. Incorporating all spatial tokenscould enrich representation but is computationally prohibitive, revealinga fundamental trade-off between efficiency and representational richness.We observe high redundancy among spatial tokens, suggesting that se-lectively retaining the most informative ones can better balance thesegoals. To address this, we propose TokRet, a token retention and com-pression module that preserves the most informative spatial tokens whileeliminating redundancy. Built upon TokRet, we develop PathTokScale, aslide-level self-supervised pretraining framework that leverages efficientlong-contextual transformer modeling to scale training to the billion-tokenlevel—over 20× larger than previous WSI studies—without requiring mas-sive WSI collections. Experiments show that PathTokScale consistentlyoutperforms existing pathology foundation models across diagnostic, prog-nostic, and biomarker prediction tasks. Remarkably, using only 10% ofthe number of WSIs as used by TITAN, it achieves comparable per-formance through efficient token scaling, paving the way for scalable,high-performing pathology foundation models.

ECCV 2026

MobileOcc: A Human-Aware Semantic Occupancy Dataset for Mobile Robots

Junseo Kim, Guido Dumont, Xinyu Gao, Gang Chen, Holger Caesar, Javier Alonso-Mora

Dense 3D semantic occupancy perception is critical for mo-bile robots operating in pedestrian-rich environments, yet it remains un-derexplored compared to its application in autonomous driving. To ad-dress this gap, we present MobileOcc, a semantic occupancy dataset formobile robots operating in crowded human environments. Our dataset isbuilt using an annotation pipeline that incorporates static object occu-pancy annotations and a novel mesh optimization framework explicitlydesigned for human occupancy modeling. It reconstructs deformable hu-man geometry from 2D images, then refines and optimizes it using as-sociated LiDAR point data. Using MobileOcc, we establish benchmarksfor two tasks: i) Occupancy prediction and ii) Pedestrian velocity predic-tion, using different methods, including monocular, stereo, and panopticoccupancy, with metrics and baseline implementations for reproduciblecomparison. Beyond occupancy prediction, we further assess our annota-tion method on 3D human pose estimation datasets. Results demonstratethat our method exhibits robust performance across different datasets.Our code and dataset are released at https://autonomousrobots.nl/paper_websites/mobileocc

ECCV 2026

MAC-Splat: Multi-Attribute Consistency for High-Fidelity Sparse-View Reconstruction

Jinqian Yang, Yichen Wu, Wanhua Li, Haokun Lin, Renzhen Wang, Xiangchu Feng, Xixi Jia

Reconstructing high-fidelity 3D scenes from sparse views re-mains a central problem in generalizable neural rendering. Existing gen-eralizable 3D Gaussian Splatting (3DGS) methods often exhibit geomet-ric artifacts in sparse-view settings, since supervision based solely on 2Dphotometric losses cannot resolve depth and correspondence ambigui-ties. To address this issue, we propose MAC-Splat, a training frameworkbuilt around direct 3D consistency supervision. MAC-Splat builds onthe MASt3R geometric backbone and a frozen DINOv3 encoder to ob-tain semantically informed 2D correspondences, which serve as geometricanchors for 3D supervision. Using these anchors, we define the Multi-Attribute Consistency (MAC) loss. This objective jointly regularizes the3D attributes of matched Gaussians, including their position, shape,and appearance, by enforcing agreement in a common world coordinateframe. The formulation is robust to outliers and respects the geometry ofcovariance matrices, which leads to stable training under sparse-view con-ditions. Experiments on ScanNet++ show that MAC-Splat outperformsstrong baselines, with particularly large gains under different overlapregimes. In particular, it improves average PSNR over Splatt3R by morethan 4.5 dB, reduces LPIPS, and maintains performance as the camerapose gap increases. These results indicate that a direct, multi-attribute3D consistency objective, when combined with high-quality correspon-dences, is effective for addressing the ill-posed sparse-view reconstructionproblem.

ECCV 2026

Learning to Mask: Cross-Modal Noise Modulation for Hallucination Mitigation in Multi-modal Large Language Models

Zijian Song, Chunlei Wang, Kun He

Multi-modal Large Language Models have demonstrated im-pressive capabilities in vision-language tasks, yet they remain highly sus-ceptible to hallucinations, in which generated text diverges from the ac-tual visual evidence. Current mitigation strategies, such as self-refinementand specialized decoding, often incur significant latency due to multi-round iterations or reliance on high-performance auxiliary models, limit-ing their real-time practical utility. In this paper, we systematically ana-lyze the hidden states of MLLMs across the entire inference pipeline andreveal that hallucination-inducing redundant features persist not onlyin image tokens during the prefill stage but also propagate into Imageand Answer Value Caches during decoding. Motivated by the observationthat Gaussian noise injection can effectively suppress these redundancies,we propose HalMask (Hallucination Feature Masking), a lightweightand real-time self-correction framework. HalMask employs specializedImage and Text Noise Modulation Modules, optimized via a dynamic reg-ularization term, to accurately discern and mask hallucination-triggeringfeatures. During inference, it applies a dynamic Value Cache maskingmechanism that continuously filters redundant information across bothgeneration phases without disrupting critical semantic cues. Extensiveexperiments on multiple benchmarks demonstrate that HalMask signifi-cantly reduces hallucinations while introducing only marginal computa-tional overhead.

ECCV 2026

Learning Sample-wise Rank-Aware Interpolation Weights for Composed Visual Data Retrieval

Boseung Jeong, Taegyu Park, Donghyeon Kwon, Hyunsouk Cho, Suha Kwak

At the heart of composed visual data retrieval is the fusionof a reference visual input and a textual modification into a single query.While current state-of-the-art methods utilize multimodal large languagemodels for this fusion, their complexity introduces prohibitive query-time latency, limiting their scalability. We instead revisit the efficacy ofsimple linear interpolation within an embedding space, and introduceSRAIN, the first framework that dynamically predicts query-specific in-terpolation weights. The key challenge lies in the fact that the qualityof an interpolation weight should be measured by the interpolated em-bedding’s discriminability from negatives as well as its proximity to truetargets; this makes collecting and predicting optimal weights intractable.We overcome this bottleneck through two key innovations: batch-wiserank-aware weight estimation during training, and a compact memorybank that synthesizes hard negatives during inference. SRAIN achievesthe best in composed video retrieval and matches the current state of theart in composed image retrieval, all while substantially reducing query-time latency compared to MLLM-based alternatives.

ECCV 2026

Instance Segmentation as Tracking: A New Paradigm for Multi-Small-Object Tracking with Event Cameras

Nuo Chen, Shiman He, Boyang Li, Yingqian Wang, Chao Xiao, QianYin QianYin, Ruojing Li, Yihang Luo, Wei An, Miao Li

Multiple small object tracking (MSOT) is critical for ap-plications such as anti-UAV systems and security surveillance, yet tra-ditional frame-based cameras struggle to track fast-moving small ob-jects in complex environments due to their low frame rates and lim-ited dynamic range. Event cameras, with their ability to continuouslyrecord subtle brightness changes, can naturally overcome these limita-tions. However, most existing event-based tracking methods follow a“convert-then-detect-and-track” pipeline. This pipeline sacrifices the in-herent high temporal resolution of event data, leading to fragmentedtrajectories of fast-moving objects. Moreover, it introduces significantbackground redundancy during framing, which reduces computational ef-ficiency. To handle these issues, we introduce “instance segmentationas tracking”, a novel paradigm that formulates event-based MSOT asan instance segmentation task in the spatio-temporal dimension. Follow-ing this paradigm, we first design Ev-ISNet, which leverages 3D sparseconvolutions to extract per-voxel features while simultaneously predict-ing object confidence and motion direction. Then we construct an eventgraph and progressively cluster trajectory instances by predicting edge-wise instance affinities. By leveraging intra-graph and inter-graph associ-ation modules, our method achieves highly efficient streaming inference.To address the lack of large-scale benchmarks for event-based MSOT,we build EV-UAV-Track, a comprehensive dataset featuring per-eventinstance-level annotations. Extensive experiments demonstrate that Ev-ISNet consistently outperforms state-of-the-art MSOT methods, achiev-ing over 30% improvement in MOTA score compared to frame-basedtracking methods.

ECCV 2026

Inference-Time Scaling of Diffusion Models via Progressive Seed Pruning

Rogerio Guimaraes, Pietro Perona

Di!usion and flow-matching models dominate conditionalimage generation, yet inference-time scaling for these models is far lessdeveloped than for autoregressive language models. Because final qual-ity is highly sensitive to the initial noise seed, many approaches spendextra compute on seed search or resampling under a black-box reward,but typically maintaining a constant memory footprint throughout in-ference. We show that relaxing this constraint enables an underexploredinference-time scaling axis: by front-loading exploration, evaluating manyseeds early, and pruning aggressively, we can use a fixed compute budgetmore e!ectively. Progressive Seed Pruning (PSP) scores intermediate de-noised estimates and progressively narrows the candidate set so that onlypromising trajectories are fully denoised, while keeping the total num-ber of model evaluations fixed. Across di!usion and flow-matching back-bones, PSP consistently improves reward-guided selection and achieveshigher GenEval scores (automated) and better human evaluation onprompt-alignment than best-of-N , importance-sampling, and tree-searchbaselines at matched compute. Project page: vision.caltech.edu/psp.Code: github.com/rogerioagjr/psp

ECCV 2026

InclusiveHuman-10K: Towards Inclusive Human Parsing Beyond the Intact-Limb Assumption

Heming Du, Jiaying Ying, Xiaofeng Cao, Zhu Li, Yuanyuan Liu, Kaihao Zhang, Xin Chen, Xin Yu

Human body parsing datasets and benchmarks define partlabels for intact anatomies and focus on body parts and clothing. Widelyused datasets implicitly assume intact anatomies and therefore do not in-clude residual-limb or prosthetic classes. This leaves people with limb de-ficiencies excluded from body parsing model training and standard evalu-ation, creating a fairness gap. To the best of our knowledge, InclusiveHuman-10K (IH-10K) is the first human parsing benchmark centered on in-dividuals with limb deficiencies, with explicit labels for residual limbs,prostheses, and mobility assistive devices. Specifically, IH-10K containsmore than 10k images and around 15k person instances from everyday,sports, and clinical scenes, each with pixel-wise body parsing masks. Thisbenchmark can support e-commerce, human-machine interaction, reha-bilitation and assistive technology, human-centric image understanding,and editing. Moreover, we extend conventional body parsing categorieswith explicit classes for residual limbs, prostheses, and mobility assis-tive devices. On our IH-10K, body parsing models can be trained andevaluated on disability-related regions for this population with limb de-ficiencies, instead of treating them as background or noise. To assesswhether disability-related regions are systematically underserved, we re-port group-wise evaluation metrics across disability-related and commonclasses. Our experiments show that supervised models struggle on theseregions due to large appearance variation and broken limb structure.Meanwhile, zero-shot methods perform poorly, suggesting that these con-cepts are absent from previous benchmarks and under-covered in open-vocabulary pretraining. This further highlights the representational andmeasurement gaps and signifies the necessity of our IH-10K benchmark.We hope this work helps build vision systems that serve people with limbdeficiencies more reliably and fairly, increasing the likelihood of mean-ingful social benefit.

ECCV 2026

High-speed Imaging through Turbulence with Event-based Light Fields

Yu-Hsiang Huang, Levi Burner, Sachin Shah, Ziyuan Qu, Adithya Pediredla, Christopher Metzler

This work introduces and demonstrates the first system capable of imaging fast-moving extended non-rigid objects through strong atmospheric turbulence at high frame rate. Event cameras are a novel sensing architecture capable of estimating high-speed imagery at thousands of frames per second. However, on their own, event cameras are unable to disambiguate scene motion from turbulence. In this work, we overcome this limitation using event-based light field cameras: By simultaneously capturing multiple views of a scene, event-based light field cameras and machine learning-based reconstruction algorithms are able to disambiguate motion-induced dynamics, which produce events that are strongly correlated across views, from turbulence-induced dynamics, which produce events that are weakly correlated across view. Tabletop experiments demonstrate event-based light field can overcome strong turbulence while imaging high-speed objects traveling at up to 16,000 pixels per second.

ECCV 2026

Hierarchical Hyperbolic Representation Learning for Aerial-Ground Person Re-Identification

QiWei Yang

Aerial-Ground Person Re-IDentification (AG-ReID) aims toretrieve the same person across heterogeneous aerial and ground cameraplatforms. Although great progress, existing methods remain subopti-mal due to the direct feature alignment across views, overlooking view-specific cues. To address this issue, we propose a novel Hierarchical Hy-perbolic Representation (HiHR) framework for AG-ReID. More specif-ically, we first extract multi-granularity features based on pre-trainedvisual-text encoders. Then, we propose a Text-guided Multi-granularityFusion (TMF) to fuse multi-granularity features and enhance the repre-sentation ability of identity features. Furthermore, we introduce the Hi-erarchical Hyperbolic Learning (HHL) to construct a hierarchical featurestructure in a hyperbolic space. This hierarchy includes a coarse level thatensures identity separability and cross-view consistency, and a fine levelthat preserves view-specific discriminative cues. A a result, our proposedframework can effectively aggregate view-invariant and view-specific dis-criminative features for AG-ReID. Extensive experiments on four AG-ReID benchmarks demonstrate the effectiveness of our framework. Thesource code is available at https://github.com/YangQiWei3/HiHR.

ECCV 2026

Generative Manifold Distillation: Aligning Restoration Trajectories with the Natural Image Prior

Yuyang Hu, Mojtaba Sahraee Ardakan, Kangfu Mei, Arpit Bansal, Chenyang Qi, Peyman Milanfar, Mauricio Delbracio

Pre-trained image restoration models often fail on out-of-distribution (OOD) real-world degradations. Adapting to these domainsis challenging as real-world data lacks paired ground truth, and unsuper-vised methods often require unstable architectural changes. We proposeGenerative Manifold Distillation (GMD), which reframes domain adapta-tion as geometric manifold alignment. GMD operates in a strictly unpairedsetting, requiring only low-quality (LQ) target observations. By leveragingthe flow-matching dynamics of a frozen text-to-image foundation model,GMD projects off-manifold restorations onto the natural image manifoldto generate high-quality pseudo-targets. To ensure stability, a quality-gated manifold filter rejects off-manifold samples, while source-anchoredtrajectory regularization prevents error accumulation. Ultimately, GMDdistills a powerful generative prior into an efficient restoration network.Experiments demonstrate that GMD seamlessly adapts to new distribu-tions using only LQ inputs, drastically improving perceptual quality withzero architectural modifications or added inference latency.

ECCV 2026

GEAR-Seg: A Grounded Explainable Agent for Reasoning Segmentation and Data Engine

Yanan Wang, Wen Li, Yibin Ying, Zhenghao Fei

Reasoning segmentation requires localizing targets based oncomplex, implicit queries. Current end-to-end models typically entangleperception and deduction into an opaque black box, severely limitinginterpretability and scalability. To address this, we propose GEAR-Seg(Grounded Explainable Agent for Reasoning Segmentation), an explic-itly decoupled agent that shifts the paradigm by translating visual pixelsinto dense, attribute-rich text. By decoupling class-agnostic segmenta-tion, semantic description, and Large Language Model (LLM) deduc-tion, GEAR-Seg transforms implicit reasoning into an explicit, track-able logic chain. As a zero-shot inference framework, it achieves highlycompetitive performance across diverse reasoning and fine-grained re-ferring segmentation benchmarks. Furthermore, GEAR-Seg inherentlyfunctions as a highly scalable data engine. Utilizing this engine, we con-struct GEAR-131K, a massive benchmark (over 38k images, 656k QA-mask pairs) introducing a multifaceted taxonomy tailored for complexreal-world manipulation-oriented reasoning. Finally, distillation exper-iments demonstrate that lightweight models supervised exclusively byour automated pipeline closely match the upper-bound performance ofcostly human-annotated baselines.

ECCV 2026

FlexAM: Flexible Appearance-Motion Decomposition for Versatile Video Generation Control

Mingzhi Sheng, Zekai Gu, Peng Li, Cheng Lin, Hao-Xiang Guo, Yingcong Chen, Yuan Liu

Effective and generalizable control in video generation re-mains a significant challenge. While many methods rely on ambiguousor task-specific signals, we argue that a fundamental disentanglement of"appearance" and "motion" provides a more robust and scalable path-way. We propose FlexAM, a unified framework built upon a novel 3Dcontrol signal. This signal represents video dynamics as a point cloud,introducing three key enhancements: multi-frequency positional encodingto distinguish fine-grained motion, depth-aware encoding, and a flexiblecontrol signal for balancing precision and generalization. This represen-tation allows FlexAM to effectively disentangle appearance and motion,enabling a wide range of tasks including I2V/V2V editing, camera con-trol, and spatial object editing. Extensive experiments demonstrate thatFlexAM achieves superior performance across all evaluated tasks. Codesare available at https://github.com/IGL-HKUST/FlexAM.

ECCV 2026

EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning

Chengjun Yu, XuHan Zhu, Chaoqun Du, Pengfei Yu, Wei Zhai, Yang Cao, Zheng-Jun Zha

Multimodal large language models (MLLMs) are increas-ingly considered as a foundation for embodied agents, yet it remainsunclear whether they can reliably reason about the long-term phys-ical consequences of actions from an egocentric viewpoint. We studythis gap through a new task, Egocentric Scene Prediction with LOng-horizon REasoning: given an initial-scene image and a sequence of atomicaction descriptions, a model is asked to predict the final scene afterall actions are executed. To enable systematic evaluation, we introduceEXPLORE-Bench, a benchmark curated from real first-person videosspanning diverse scenarios. Each instance pairs long action sequenceswith structured final-scene annotations, including object categories, vi-sual attributes, and inter-object relations, which supports fine-grained,quantitative assessment. Experiments on a range of proprietary andopen-source MLLMs reveal a significant performance gap to humans,indicating that long-horizon egocentric reasoning remains a major chal-lenge. We further analyze test-time scaling via stepwise reasoning andshow that decomposing long action sequences can improve performanceto some extent, while incurring non-trivial computational overhead. Over-all, EXPLORE-Bench provides a principled testbed for measuring andadvancing long-horizon reasoning for egocentric embodied perception.

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