ECCV 2026 Papers

Driven by Artificial Intelligence-Generated Content (AIGC),the authenticity of audio-visual content is facing severe challenges. Tem-poral Forgery Localization (TFL) aims to precisely identify manipulatedsegments within untrimmed sequences. However, existing methods arelimited by CNNs’ local receptive fields or Transformers’ quadratic com-plexity, while emerging linear models often struggle to balance globalauthentic context compression with local abrupt forgery perception. Toaddress this, we propose MG-RWKV, a multi-granularity framework thatleverages the data-dependent state evolution of RWKV to achieve ef-ficient full-sequence processing with O(T ) complexity. Our frameworkfeatures three core innovations: (1) a Bidirectional RWKV architec-ture that captures bidirectional temporal contexts without quadraticoverhead; (2) a Multi-Granularity Mixture of Experts (MG-MoE) that performs dynamic routing over explicit temporal recep-tive fields, adaptively selecting granularities based on forgery dura-tion to significantly enhance decision interpretability; and (3) Cross-Granularity Consistency (CGC), which aligns adjacent feature pyra-mid levels through hierarchical scale-wise pairing and spatial boundary-aware weighting, effectively reducing false positives in authentic regions.Extensive experiments on Lav-DF, TVIL, and Psynd datasets demon-strate that MG-RWKV achieves state-of-the-art performance with lowcomputational cost.

Event cameras have recently shown promising capabilities ininstantaneous motion estimation due to their robustness to low light andfast motions. However, computing wide-baseline correspondence betweentwo arbitrary views remains a significant challenge, since event appear-ance changes substantially with motion, and learning-based approachesare constrained by both scalability and limited wide-baseline supervi-sion. We therefore introduce the first event matching model that achievescross-dataset wide-baseline correspondence in a zero-shot manner:a single model trained once is deployed on unseen datasets withoutany target-domain fine-tuning or adaptation. To enable this capabil-ity, we introduce a motion-robust and computationally efficient atten-tion backbone that learns multi-timescale features from event streams,augmented with sparsity-aware event token selection, making large-scaletraining on diverse wide-baseline supervision computationally feasible.To provide the supervision needed for wide-baseline generalization, wedevelop a robust event motion synthesis framework to generate large-scale event-matching datasets with augmented viewpoints, modalities,and motions. Extensive experiments across multiple benchmarks showthat our framework achieves a 37.7% improvement over the previousbest event feature matching methods. Code and data are available at:https: // github. com/ spikelab-jhu/ Match-Any-Events .

Generating talking avatars is a fundamental task in videogeneration. Although existing methods can generate full-body talkingavatars with simple human motion, extending this task to groundedhuman-object interaction (GHOI) remains an open challenge, requiringthe avatar to perform text-aligned interactions with surrounding objects.This challenge stems from the need for environmental perception and thecontrol-quality dilemma in GHOI generation. To address this, we proposea novel dual-stream framework, InteractAvatar, which decouples per-ception and planning from video synthesis for grounded human-object in-teraction. Leveraging detection to enhance environmental perception, weintroduce a Perception and Interaction Module (PIM) to generate text-aligned interaction motions. Additionally, an Audio-Interaction Aware† ⋆Equal contribution. Corresponding author.Generation Module (AIM) is proposed to synthesize vivid talking avatarsperforming object interactions. With a specially designed motion-to-video aligner, PIM and AIM share a similar network structure and enableparallel co-generation of motions and plausible videos, effectively miti-gating the control-quality dilemma. Finally, we establish a benchmark,GroundInter, for evaluating GHOI video generation. Extensive exper-iments and comparisons demonstrate the effectiveness of our method ingenerating grounded human-object interactions for talking avatars.

Egocentric videos capture rich and diverse human–object in-teractions and have emerged as a fundamental resource for understand-ing human activities related to objects. In this context, Video ReferringExpression Comprehension (Video REC), the task of localizing the tem-poral and spatial extent of a referred object in video frames given anatural language query, plays a key role in linking textual descriptionsto observed objects in untrimmed egocentric recordings. However, exist-ing egocentric Video REC benchmarks primarily focus on short videoclips, where some target object appears densely within frames. Suchsettings do not reflect real-world egocentric recordings, which are long-form, untrimmed, and characterized by sparse object occurrences andcomplex activity transitions. To address this limitation, we introduceLongEgoRefer, a novel and challenging benchmark constructed fromlong-form videos in the Ego4D dataset. LongEgoRefer contains 1,498referring expressions with an average video duration of 45 minutes. Thebenchmark exhibits extreme target sparsity, detailed linguistic descrip-tions, and complex human–object interactions embedded in long, dy-namic egocentric narratives. Consequently, it defines a demanding spatio-temporal grounding problem that requires models to identify both whenan event occurs and where the referred object appears within extendedvideo sequences. We evaluate existing Video REC approaches, includ-ing training-free baselines based on vision–language models combinedwith Grounded SAM2. Extensive experiments show that even advancedbaselines and current state-of-the-art models struggle significantly onLongEgoRefer. These results highlight the intrinsic difficulty of long-form egocentric spatio-temporal grounding and emphasize the need formore robust video understanding models. Our benchmark and code areavailable at https://github.com/shunya-kato/LongEgoRefer.LongEgoRefer0s 4345sA transparent, round bowl containing a bakedvegetable dish is shown being removed from anoven. The bowl, encased in a blue protective cloth,is then placed on the Induction stove. Its contents,a creamy vegetable and mushroom mixture, aresubsequently subjected to a brief external inspection.Human-Object Interaction3067s 3111sRefEgo Long-form video Sparse appearance Linguistic complexity0s 5sThere have white colored hot box in theshelf of the room.Fig. 1: Data comparison of LongEgoRefer and RefEgo. (a) Video durations in LongE-goRefer are orders of magnitude longer than those in RefEgo. (b) The appearance rateis significantly lower and sparser in LongEgoRefer compared to RefEgo. (c) To handlethe complexity of long-form videos, captions in LongEgoRefer are substantially longerand more descriptive than those in RefEgo. Dashed lines indicate the mean value foreach distribution.

Despite rapid progress in learning-based stereo matching,high accuracy is often achieved at the cost of heavy backbones andcomputationally intensive 3D cost volume processing, resulting in sub-stantial memory and runtime overhead. More critically, these methodsfrequently struggle to generalize across domains, limiting their practi-cal deployment. We present LiteMatch, a lightweight stereo matchingframework that achieves strong zero-shot generalization through costvolume stabilization-without expensive 3D convolutions. LiteMatch em-ploys two complementary encoders: a Cross-View Correspondence En-coder (CVCE) to capture global cross-view interactions, and a High-Frequency Encoder (HFE) that enhances fine structural details via FFT-based frequency cues. To stabilize the cost volume, we introduce the CostVolume Consistency Loss (CVC-Loss), a voxel-wise binary cross-entropyobjective applied to softmax-normalized cost distributions. By encour-aging sharp and unimodal disparity probabilities, CVC-Loss promotesstable cost distributions and enables rapid convergence. A lightweightrefinement module further produces sharp full-resolution disparities withlow-iteration updates, avoiding heavy recurrent refinement. With a flexi-ble design ranging from 3.36M to 9.58M parameters, LiteMatch achievesexceptional zero-shot generalization, delivering competitive EPE and D1performance across Scene Flow, KITTI, Middlebury, ETH3D, and Driv-ingStereo. Our results establish that lightweight architectures can indeedgeneralize across domains without sacrificing accuracy. Code

General Salient Object Detection (SOD) aims to identify andsegment visually interesting objects from uni-modality or multi-modalityscenes, recently advanced by cutting-edge State Space Models (SSMs).However, a critical limitation of current approaches is their neglect of theinherent spectral biases exhibited by different neural network paradigms.By digging to the dataset-level spectral analysis of Convolutional Neu-ral Networks (CNNs) and SSMs, their semantic representations are in-herently complementary based on their complementary frequency prefer-ences. Inspired by this, we harmonize heterogeneous representations fromSSMs and CNNs to bridge their spectral biases for general salient objectdetection. To this end, inspired by the dynamic information propaga-tion of Liquid Neural Networks (LNNs), we introduce a liquid fusion todynamically integrates features from two backbones, including VMambaand ConvNeXt, referred to Liquid Fusion Network (LFNet). Concretely,by treating the continuous VMamba features and ConvNeXt features asevolving states and exogenous stimulus, respectively, LFNet employs adynamic gating mechanism for content-aware feature aggregation. Cru-cially, this state-stimulus paradigm enables to scale to multi-modal cues,resulting in flexibility in general SOD. Besides, a Saliency-Guided Up-sampling (SGU) operator to propagate the features to the shallow layer,which leverages a spectral-spatial co-design to suppress upsampling ar-tifacts while preserving semantics. Extensive experiments across five di-verse tasks (RGB, RGB-D, RGB-T, VSOD, and VDT) demonstrate thatLFNet achieves state-of-the-art performance, offering a superior trade-offbetween detection accuracy and model efficiency. Code has been releasedat https://github.com/cke520/LFNet.

With the advancement of video generation foundation mod-els (VGFMs), customized generation, particularly subject-to-video (S2V),has attracted growing attention. However, a key challenge lies in balanc-ing the intrinsic priors of a VGFM, such as motion coherence, visualaesthetics, and prompt alignment, with its newly derived S2V capa-bility. Existing methods often neglect this balance by enhancing oneaspect at the expense of others. To address this, we propose Libra-Gen, a novel framework that views extending foundation models forS2V generation as a balance game between intrinsic VGFM strengthsand S2V capability. Specifically, guided by the core philosophy of “Rais-ing the Fulcrum, Tuning to Balance,” we identify data quality as thefulcrum and advocate a quality-over-quantity approach. We constructa hybrid pipeline that combines automated and manual data filteringto improve overall data quality. To further harmonize the VGFM’s na-tive capabilities with its S2V extension, we introduce a Tune-to-Balancepost-training paradigm. During supervised fine-tuning, both cross-pairand in-pair data are incorporated, and model merging is employed toachieve an effective trade-off. Subsequently, two tailored direct prefer-ence optimization (DPO) pipelines, namely Consis-DPO and Real-FakeDPO, are designed and merged to consolidate this balance. During in-ference, we introduce a time-dependent dynamic classifier-free guidancescheme to enable flexible and fine-grained control. Experimental resultsdemonstrate that LibraGen outperforms both open-source and commer-cial S2V models using only thousand-scale training samples. ProjectPage: https://github.com/Phantom-video/LibraGen

Streaming autoregressive (AR) video generators combined with few-step distillation achieve low-latency, high-quality synthesis, yet remain difficult to align via reinforcement learning from human feedback (RLHF). Existing SDE-based GRPO methods face challenges in this setting: few-step ODEs and consistency model samplers deviate from standard flow-matching ODEs, and their short, low-stochasticity trajectories are highly sensitive to initialization noise, rendering intermediate SDE exploration ineffective. We propose AR-CoPO (AutoRegressive Contrastive Policy Optimization), a framework that adapts the Neighbor GRPO contrastive perspective to streaming AR generation. ARCoPO introduces chunk-level alignment via a forking mechanism that constructs neighborhood candidates at a randomly selected chunk, assigns sequence-level rewards, and performs localized GRPO updates. We further propose a semi-on-policy training strategy that complements onpolicy exploration with exploitation over a replay buffer of reference rollouts, improving generation quality across domains. Experiments on SelfForcing demonstrate that AR-CoPO improves both out-of-domain generalization and in-domain human preference alignment over the baseline, providing evidence of genuine alignment rather than reward hacking.

Achieving both global semantic coherence and precise in-stance level control in wide-aspect-ratio panorama generation is an un-resolved challenge. Existing methods that synchronize independent viewsto generate panoramas often lack semantic coherence and struggle withfine-grained multi-instance placement, resulting in contextual artifactsand fragmented objects. We introduce InstaPano, a training-free frame-work for zero-shot instance-controlled panorama generation. InstaPanointegrates a Global Attention Fusion mechanism into a pre-trained layout-to-image model. Through Sync-Fuse-Dispatch workflow, it periodicallyaggregates latent features from all local views to construct a unifiedglobal context, performs multi-level attention computation over this con-text to achieve true fusion, and then dispatches the enriched global fea-tures back to each view. This enables the coherent rendering of complexpanoramas with multiple specified instances. Furthermore, we introducea conditional positional mask to resolve object repetition artifacts thatmay arise in large bounding boxes. On a newly constructed yet challeng-ing benchmark for panoramic instance layout control, InstaPano achievessuperior performance in both layout fidelity and semantic coherence,faithfully generating complex panoramic scenes.

Multimodal learning robust to missing modalities is essen-tial for real-world applications. Existing methods mainly focus on inter-modality missing, where entire modalities are absent, while overlookingintra-modality degradation, where modalities are present but severelycorrupted. In practice, these two types of missing often coexist, mak-ing existing approaches ineffective. To address this limitation, we pro-pose General Incomplete Multimodal Learning (GIML), a unified frame-work that simultaneously handles both inter-modality missing and intra-modality degradation through dynamic quality perception. Specifically,GIML models heterogeneous missing patterns as continuous modality in-formation degradation, enabling degradation-aware adaptive fusion. Toachieve reliable quality perception, we introduce a Noise-aware QualityEstimator that learns the mapping from corrupted features to noise in-tensity through controlled noise injection. Furthermore, we propose aNoise–Semantic Decoupled module that separates semantic informationfrom noise interference. This improves robustness and generalization tounseen corruption patterns. Extensive experiments across datasets withdiverse modality types demonstrate the effectiveness and generality ofGIML. Code is available at: https://github.com/Yu-Five/GIML.

Geometric distortion breaks the projective mapping and ge-ometric relations in images, which degrades the performance and relia-bility of computer vision tasks. Although deep network-based methodsfor single-image distortion correction have achieved remarkable progress,most existing approaches optimize only for 2D pixel-level alignment, suchas optical flow losses or image reconstruction errors. Without explicit 3Dgeometric constraints, such models may produce visually plausible recti-fications yet fail to enforce 3D-consistent structure. To address this lim-itation, this paper proposes a 3D geometry guided framework for singleimage distortion correction, named Geometry-Grounded Distortion Cor-rection (G2DC). G2DC introduces a pretrained 3D foundation model toextract multi-dimensional geometric representations as supervision, andincorporates projective geometry priors into feature learning. Specifically,G2DC adds a geometric constraint scheme and jointly optimizes a struc-tural alignment loss and a physical alignment loss. The former minimizesfeature mismatch to ensure that the corrected output is structurally closeto the undistorted scene. The latter enforces strict 3D constraints usingthe predicted point map, depth topology, and camera parameter regular-ization, so the model preserves 3D structure consistency and plausibilitywhile restoring 2D appearance. Experiments on several widely used dis-tortion benchmark datasets show that G2DC outperforms state-of-the-art methods, especially on metrics that are sensitive to 3D geometricconsistency. Project webpage: https://gitee.com/VR_NAVE/g2dc.git.

3D object detection is a core task in autonomous driving per-ception, with mainstream methods primarily based on LiDAR. However,its robustness deteriorates significantly in adverse weather conditions.While 4D radar offers all-weather robustness and unique Doppler veloc-ity, it is hindered by its inherent sparsity and noise interference. There-fore, fusing LiDAR and 4D radar is highly promising. However, due to thesignificant differences in semantics and density between the data of thesetwo modalities, single feature space fusion cannot fully leverage the ad-vantages of each modality and the complementarity between modalities.To address this, we propose a two-stage fusion detection framework—HVA-Fusion. Specifically, motion velocity is first estimated through aMotion Velocity Estimation and Encoding module to enhance dynamicperception. Then, in the pillar feature space, a Cross-Modal Progres-sive Adaptation module is introduced for local feature alignment andfusion (Stage-I fusion) to enrich representations and mitigate semanticdifferences between modalities. After that, to alleviate the sparsity of4D radar point cloud, a Radar Cross-Section (RCS) Guided GaussianGeneration module is developed to densify the feature representations.Subsequently, in the Bird’s Eye View (BEV) space, the enhanced multi-modal features are adaptively fused through a Multi-Scale Bi-DirectionalDeformable Attention Gate module (Stage-II fusion) to address featuremisalignment and sensor degradation caused by adverse weather. Exten-sive experiments demonstrate that HVA-Fusion achieves highly compet-itive performance across the K-Radar, VoD, and TJ4DRadSet datasets,while exhibiting robustness in adverse weather conditions.

Federated learning (FL) enables collaborative model trainingwithout sharing raw data, but practical deployments must balance sta-tistical heterogeneity, resource-constrained clients, and communicationefficiency. While conventional FL requires clients to train full modelslocally, split federated learning (SFL) reduces client-side computationby partitioning models across tiers, at the cost of transmitting high-dimensional activations and gradients during training. We propose Hier-archical Split-Federated Prototyping (H-SFP), a communication-efficientframework for hierarchical cloud–edge–client environments. Instead ofexchanging sample-level activations or gradients, H-SFP communicatescompact class-wise feature statistics and uses them to synthesize repre-sentative feature distributions at upper tiers. This statistical prototyp-ing mechanism enables independent training of model segments whilepreserving the computational advantages of split-model execution andsubstantially reducing synchronization and communication overhead. Akey finding of this work is that lightweight first- and second-order fea-ture statistics are sufficient to support hierarchical split-model train-ing, providing an effective communication interface without requiringsample-level feature exchange. We analyze the communication proper-ties and stability of the proposed statistical interface under hierarchi-cal aggregation. Extensive experiments on CIFAR-10/100, HAM10000,ImageNet-1K, and ISIC-2018 demonstrate that H-SFP remains robustunder heterogeneous data distributions while reducing communicationoverhead by up to two orders of magnitude compared with federatedand split-learning baselines.

Generating complete 3D objects under partial occlusions (i.e.,amodal scenarios) is a practically important yet challenging problem,as large portions of object geometry are unobserved in real-world sce-narios. Existing approaches either operate directly in 3D, which en-sures geometric consistency but often lacks generative expressiveness,or rely on 2D amodal completion, which provides strong appearancepriors but does not guarantee reliable 3D structure. This raises a keyquestion: how can we achieve both generative plausibility and geometriccoherence in amodal 3D modeling? To answer this question, we intro-duce GENA3D (GENerative Amodal 3D), a framework that integrateslearned 2D generative priors with explicit 3D geometric reasoning withina conditional 3D generation paradigm. The 2D priors enable the modelto plausibly infer diverse occluded content, while the 3D representationenforces multi-view consistency and spatial validity. Our design incorpo-rates a novel View-Wise Cross-Attention for multi-view alignment and aStereo-Conditioned Cross-Attention to anchor generative predictions in3D relationships. By combining generative imagination with structuralconstraints, GENA3D generates complete and coherent 3D objects fromlimited observations without sacrificing geometric fidelity. Experimentsdemonstrate that our method outperforms existing approaches in bothsynthetic and real-world amodal scenarios, highlighting the effectivenessof bridging 2D priors and 3D coherence in generating plausible and geo-metrically consistent 3D structures in complex environments.

Recent advances in Gaussian Splatting-based inverse rendering extend Gaussian primitives with shading parameters and physically grounded light transport, enabling high-quality material recovery from dense multi-view captures. However, the accuracy of these methods degrades under sparse-view settings, where limited observations lead to severe ambiguity between geometry, reflectance, and lighting. We introduce GAINS (Gaussian-based Inverse rendering from Sparse multi-view captures), a two-stage inverse rendering framework that leverages foundation models as priors to stabilize geometry and material estimation. The core technical contribution of this paper is an inverse rendering framework that unifies foundation model priors with physically-based representations in an optimization scheme. GAINS first refines geometry using monocular depth, normal, and diffusion priors, and then employs segmentation, intrinsic image decomposition (IID), and diffusion priors to regularize material recovery. Extensive experiments on synthetic and real-world datasets show that GAINS significantly improves material parameter accuracy, relighting quality, and novel-view synthesis compared to state-of-the-art Gaussian-based inverse rendering methods. While GAINS outperforms and remains competitive across a wide range of objects captured with 4 to 32 cameras, the improvement is particularly pronounced under sparse-view settings, where ambiguity is high and learning-based priors become especially beneficial. Project page: https://patrickbail.github.io/gains/

Zero-shot object counting aims to estimate the number ofobjects specified by a text query without category-specific training. Re-cent approaches primarily rely on density regression or detection-styleinstance prediction. While effective, density-based models often sufferfrom spatial ambiguity and background leakage due to weakly regulatedmass allocation, leading to fragmented or part-biased representationsthat increase counting error in complex scenes. In this work, we proposean instance-aware dual-decoder framework that structurally couples den-sity and point representations for zero-shot object counting. Instead oftreating density estimation as independent pixel-wise regression, we in-terpret it as a structured mass allocation problem over a latent set ofobject instances. Predicted instance centers induce a soft instance-wisedecomposition of the density map, upon which we enforce two geometricconstraints: (1) per-instance mass conservation, ensuring each object con-tributes approximately one unit of density mass, and (2) center-of-massalignment, encouraging each density component to concentrate aroundits corresponding predicted center. These constraints introduce instance-level geometric consistency and lead to more accurate mass allocation,thereby reducing counting error. Extensive experiments on FSC-147,PUCPR+, and CARPK show that our approach consistently reducescounting error and establishes new state-of-the-art performance in zero-shot object counting.

Human driving behavior is inherently diverse, yet most end-to-end autonomous driving (E2E-AD) systems learn a single averagedriving style, neglecting individual differences. Achieving personalizedE2E-AD faces challenges across three levels: limited real-world datasetswith individual-level annotations, a lack of quantitative metrics for eval-uating personal driving styles, and the absence of algorithms that canlearn stylized representations from users’ trajectories. To address thesegaps, we propose Person2Drive, a comprehensive personalized E2E-ADplatform and benchmark. It includes an open-source, flexible data col-lection system that simulates realistic scenarios to generate scalable, di-verse personalized driving datasets; style vector–based evaluation metricswith Maximum Mean Discrepancy and KL divergence to comprehen-sively quantify individual driving behaviors; and a personalized E2E-ADframework with a style reward model that efficiently adapts E2E mod-els for safe and individualized driving. Crucially, our framework enablesplug-and-play personalization by fine-tuning only the trajectory predic-tion head, preserving the pretrained base model and ensuring safety. Ex-tensive experiments demonstrate that Person2Drive enables fine-grainedanalysis and effective personalization, while preserving driving perfor-mance and success rate even in challenging scenarios.

Recent advances in Large Vision Language Models (LVLMs)have led to interest in alignment methods such as Direct PreferenceOptimization (DPO), which improves multi-modal instruction followingwithout costly supervision or explicit reward modeling. However, pre-vious attempts to improve DPO through data augmentation and pref-erence ranking fall short of bridging vision-language perceptual gaps,limiting consistent multi-modal alignment. In this paper, we proposea novel Vision-Language Consistency based Direct PreferenceOptimization (VLC-DPO), which automatically constructs prefer-ence data and leverages consistent information from visual inputs andcorresponding textual descriptions as alignment signals. The VLC-DPOconsists of three key components: (1) an initial response generation thatproduces image- and description-based responses to bridge the modal-ity gap in LVLMs by capturing complementary aspects of visual un-derstanding, (2) a preference data construction via vision-language con-sistency score that automatically selects high-quality preference pairsbased on response consistency, and (3) a VLC-DPO training that usesconsistency-based implicit rewards to optimize preferences, assigning dy-namic weights to less image-related or distant samples to improve DPOlearning. Extensive experiments on hallucination mitigation and zero-shot VQA demonstrate the e↵ectiveness of our method, which outper-forms existing approaches.

Reflection removal aims to recover clean images from pho-tographs degraded by glass, a crucial problem in mobile photography.Single-image methods suffer from the fundamental ill-posedness of de-composition and often rely on specialized hardware to enhance robust-ness. We propose a user-friendly dual-view diffusion framework that ef-fectively leverages complementary viewpoint information for ubiquitousdual-camera devices. Our approach introduces frame-aware spatial at-tention to capture implicit cross-view correlations and integrates camerapose as conditional embeddings for geometric guidance. We contributeDualRef, a dataset for reflection removal comprising semi-syntheticpairs and real-world dual-camera sequences with ground truth. Exper-iments demonstrate over 10% PSNR improvement on real-world data,significantly outperforming state-of-the-art methods.