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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

Policy-Based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards

Orhun Bugra Baran, Melih Kandemir, Ramazan Gokberk Cinbis

Autoregressive (AR) models are highly effective for imagegeneration, yet their standard maximum-likelihood estimation traininglacks direct optimization for sample quality and diversity. While rein-forcement learning (RL) has been used to align diffusion models, thesemethods typically suffer from output diversity collapse. Similarly, concur-rent RL methods for AR models rely strictly on instance-level rewards,often trading off distributional coverage for quality. To address these lim-itations, we propose a lightweight RL framework that casts token-basedAR synthesis as a Markov Decision Process, optimized via Group Rela-tive Policy Optimization (GRPO). Our core contribution is the introduc-tion of a novel distribution-level Leave-One-Out FID (LOO-FID) reward;by leveraging an exponential moving average of feature moments, it ex-plicitly encourages sample diversity and prevents mode collapse duringpolicy updates. We integrate this with composite instance-level rewards(CLIP and HPSv2) for strict semantic and perceptual fidelity, and stabi-lize the multi-objective learning with an adaptive entropy regularizationterm. Extensive experiments on LlamaGen and VQGAN architecturesdemonstrate clear improvements across standard quality and diversitymetrics within only a few hundred tuning iterations. The results alsoshow that the model can be updated to produce competitive sampleseven without Classifier-Free Guidance, and bypass its 2x inference cost.

ECCV 2026

RT-DETRv4: Painlessly Furthering Real-Time Object Detection with Vision Foundation Models

Zijun Liao, Yian Zhao, Xin Shan, Yu Yan, Chang Liu, LEI LU, Xiangyang Ji, Jie Chen

Real-time object detection has achieved substantial progress through meticulously designed architectures and optimization strategies. However, the pursuit of high-speed inference via lightweight network designs often leads to degraded feature representation, which hinders further performance improvements and practical on-device deployment. In this paper, we propose a cost-effective and highly adaptable distillation framework that harnesses the rapidly evolving capabilities of Vision Foundation Models (VFMs) to enhance lightweight object detectors. Given the significant architectural and learning objective disparities between VFMs and resource-constrained detectors, achieving stable and task-aligned semantic transfer is challenging. To address this, on one hand, we introduce a Deep Semantic Injector (DSI) module that facilitates the integration of high-level representations from VFMs into the deep layers of the detector. On the other hand, we devise a Gradient-guided Adaptive Modulation (GAM) strategy, which dynamically adjusts the intensity of semantic transfer based on gradient norm ratios. Without increasing deployment and inference overhead, our approach painlessly delivers striking and consistent performance gains across diverse DETR-based models, underscoring its practical utility for real-time detection. Our new model family, RT-DETRv4, achieves state-of-the-art results on COCO, attaining AP scores of 49.8/53.7/55.4/57.0 at corresponding speeds of 273/169/124/78 FPS.

ECCV 2026

OTCache: Optimal Transport for Geometry-Aware Caching in Diffusion Models

Huanlin Gao, Fang Zhao, Qiang Hui, Fuyuan Shi, Shaoan Zhao, Yantao Li, Chao Tan, Ting Lu, Yuren You, Kai Wang, Shiguo Lian

We propose OTCache, a training-free framework for ac-celerating diffusion sampling via caching schedule prediction. Existinggraph-based caching methods reduce redundant computation by opti-mizing shortest-path objectives, but rely on an additive independenceassumption, which often breaks down in the low NFE regime. To addressthis issue, OTCache models caching schedules across inference budgetsas a smooth evolution in policy space, inspired by Optimal Transport(OT). The framework consists of three stages: (1) obtaining a high-fidelity reference schedule using a graph-based caching method un-der a conservative budget; (2) performing a lightweight anchor searchunder an extreme low-budget setting via Optuna optimization with anend-to-end perceptual objective; and (3) predicting schedules for tar-get budgets via quantile interpolation between the reference and an-chor policies using continuous warping representations. Experiments onFLUX.1 [dev], Qwen-Image, and HunyuanVideo show that OTCacheachieves 4.5×, 4.7×, and 3.66× acceleration, respectively, while consis-tently improving generation fidelity over state-of-the-art caching base-lines. This work provides a new perspective on accelerating diffusionmodels through Optimal-Transport-inspired schedule modeling. Code:https://github.com/UnicomAI/OTCache

ECCV 2026

Open-Vocabulary 3D Object Detection with Co-Distillation Discovery and Dual Guidance Robust Training

Shangbo Yuan, Jie Xu, Xiaofeng Zhu, Na Zhao

Recently, open-vocabulary 3D object detection (3D-OVD)has gained increasing attention for its ability to detect unseen objects in3D scenes. Existing approaches typically adopt a two-stage pipeline thatfirst discovers novel objects using foundation models and then trains a3D-OVD model based on these discovered objects. Although effective,this pipeline often suffers from inaccurate localization and mismatchedclassification during the discovery stage, which subsequently limits theperformance of the model training stage. To address these limitations,we advocate for improving both the reliability of novel object discov-ery and the robustness of model training, and propose an innovativeframework. Specifically, for reliable discovery, our co-distillation strategydistills high-quality novel objects by applying Hungarian matching overa comprehensive score that incorporates geometric consistency, struc-tural objectness, and semantic certainty. To enhance robust model train-ing, we further propose a dual-guidance learning scheme, incorporatinga scene-awareness-guided uncertainty regularization for the regressionhead and an LLM-guided hierarchical alignment for the classificationhead, effectively mitigating the negative effects of imprecise 3D boundingboxes and semantic ambiguity. Extensive experiments on SUN RGB-Dand ScanNetV2 demonstrate that our method achieves significant per-formance gains over state-of-the-art approaches. Code is available athttps://github.com/shangboyuan/Co-3DGT.

ECCV 2026

On the Plasticity Collapse in Continual Machine Unlearning

Yingdan Shi, Xiang Xu, Kaize Ding, Alfred Hero, Ren Wang

Machine unlearning enables deep neural networks to selec-tively remove the influence of specific data in response to privacy andregulatory requirements. While prior work largely studies single-shot un-learning, real-world systems must accommodate continual unlearning,where multiple unlearning requests occur sequentially over time. In thiswork, we identify a fundamental limitation of this setting: plasticity col-lapse, a progressive breakdown in a model’s ability to effectively forget.Through theoretical analysis of continual unlearning dynamics, we showthat continual unlearning operations accumulate geometric constraintsin parameter space, leading to saturated subspaces that restrict futureupdates. This structural effect induces two distinct failure modes: (1)Forward failure – diminishing forgetting quality for subsequent tasks,and (2) Backward failure – spontaneous re-memorization of previouslyforgotten information. Extensive experiments across multiple architec-tures, datasets, and methods in image classification confirm that plastic-ity collapse is not an artifact of specific implementations, but a pervasivephenomenon inherent to continual unlearning. Our findings reveal a criti-cal barrier to the long-term reliability of machine unlearning systems andmotivate the development of plasticity-preserving unlearning algorithms.Our code is available at https://github.com/TIML-Group/Continual-Machine-Unlearning-Plasticity-Collapse.

ECCV 2026

OmniDS: Dual-Stream Context Fusion for Omnidirectional Depth from Fisheye Cameras

Chaesong Park, Jihyeon Hwang, Muyeol Sung, Jongwoo Lim

Omnidirectional depth estimation from multi-fisheye camerarigs is complicated by visibility conflicts: wide baselines cause differentcameras to observe different portions, or even different faces, of the sameobject, so aggregating their features into a unified equirectangular (ERP)representation under fixed projection produces ambiguous matching ev-idence near occlusion boundaries and thin structures. Although existingmethods mitigate this by down-weighting unreliable views, they do notresolve the underlying discrepancy because context formation and cross-view fusion remain tied to rigid fisheye-to-ERP sampling. We presentOmniDS, an iterative depth refinement framework that replaces rigidaggregation by combining dynamic context fusion with consensus-awaremulti-view similarity. A dual-stream encoder pairs a lightweight CNNfor geometric detail with a frozen DINOv3 for semantic priors; their fea-tures are reprojected into ERP space at each refinement step via learnedview weighting and deformable cross-attention with geometric distortionbias. In parallel, a multi-view consensus volume captures global cross-camera agreement through group-wise correlation and feature variance,regularized by a 3D U-Net. For efficient deployment, we distill the dual-stream representation into a single MobileNet-based encoder. OmniDSachieves state-of-the-art performance on the OmniThings, OmniHouse,and Sunny benchmarks while maintaining competitive inference speed.Project page and codes are available here.

ECCV 2026

Noise-Robust Facial Expression Recognition via Mamba-driven Neighbor Weight Refinement

Yuzhuang Yang, Xiaolin Tian, Qigong Sun

Facial Expression Recognition (FER) in the wild is inher-ently affected by annotation ambiguity and noisy label. Recent label dis-tribution learning (LDL) methods alleviate this issue by exploiting neigh-borhood information. However, existing approaches typically rely onstatic or heuristic mechanisms to estimate neighbor contribution weights,limiting their capacity to model complex dependencies among neighbor-ing samples. In this paper, we propose Mamba-driven Neighbor WeightRefinement (Mamba-NWR), a robust FER framework that integratescross-attention routing (CAR) for initial neighbor weight estimation andMamba-based iterative refinement (MIR) for distribution aggregation,effectively handling noisy labels. Specifically, our CAR mechanism intro-duces a multi-route local feature projection for both the input and neigh-bor samples. The subsequent cross-attention-based interaction amongthese features serves as the foundation for computing the initial neigh-bor weights. Then, we employ a Mamba-based state space model to iter-atively refine the contribution weights of neighboring label distributions,enabling dynamic balance between preserving historical states and ab-sorbing new neighborhood information. The optimized neighbor distribu-tions are then aggregated with the original logical label through a learn-able fusion factor to construct the final supervision information. Exten-sive experiments on multiple benchmark datasets demonstrate that ourapproach consistently improves robustness under ambiguous and noisyannotations, achieving state-of-the-art performance.

ECCV 2026

Residual-Guided Expert Specialization for Incomplete Multimodal Learning

Seunghun Baek, Jihwan Park, Jaeyoon Sim, Minjae Jeong, Hoseok Lee, Won Hwa Kim

As real-world prediction systems often face missing modali-ties at inference, incomplete multimodal learning (IML) remains a prac-tical challenge. While prior methods aim to learn representations robustto missing inputs, representations from incomplete modalities inevitablydeviate from their full-modality counterparts due to missing evidence. Toexplicitly leverage these deviations, we propose MARS (Missingness-Aware Residual-guided Specialization), a mixture-of-experts frameworkthat guides expert specialization based on how representations are re-shaped by missingness. By contrasting task representations derived fromincomplete inputs with their complete counterparts during training, wederive a privileged residual signal that captures this representational gap.The residual signal guides a residual router to assign samples to the ex-perts specialized for the corresponding deviation patterns. In parallel, afeature router learns to imitate this routing behavior using only incom-plete inputs, enabling deployment without access to full modalities. Tomitigate this train–test router gap, we develop a discrepancy-aware noiseregularization that adaptively perturbs the residual router’s decisionswhen the feature router deviates, enhancing the expert robustness underimperfect imitation. Experiments on multimodal classification (CASIA-SURF, CREMA-D, UPMC Food-101) and segmentation (MCubeS) un-der missing scenarios show that MARS consistently surpasses baselines,while remaining efficient and extensible to diverse backbones and tasks.

ECCV 2026

Path-level Hindsight Instructions for Semantic Exploration in Vision-Language Navigation

Sungjune Kim, Sangpil Kim, Honglak Lee

On-policy exploration is a crucial component for trainingrobust Vision-Language Navigation (VLN) agents, as it exposes the pol-icy to a broader state distribution. However, such exploration inevitablyleads to trajectories that deviate from expert demonstrations, resulting ina semantic mismatch between the executed visual stream and the originallanguage instruction. In this work, we address this challenge by introduc-ing Φ-Nav, a unified on-policy framework that leverages hindsight rea-soning to align instructions with the agent’s actual exploratory journey.Specifically, Φ-Nav operates through a three-stage dual-supervision cy-cle: 1) the agent performs oracle-guided on-policy exploration, samplinga trajectory while learning from expert action feedback, 2) a hindsightspeaker synthesizes a path-level hindsight instruction grounded in thecollected visual observations, and 3) the agent conducts a second imita-tion pass, treating the synthesized trajectory–instruction pair as an ad-ditional expert demonstration. Through this process, Φ-Nav bridges thecritical semantic supervision gap inherent in on-policy methods, trans-forming semantically unlabeled movement into dense training signals.Evaluations on the R2R-CE and RxR-CE benchmarks show that Φ-Navyields competitive performance while requiring only a fraction of the ex-pert demonstrations used by current baselines. These results underscorethe necessity of semantic exploration in VLN, positioning Φ-Nav as aeffective solution for training embodied agents with limited data.

ECCV 2026

From Local Windows to Adaptive Candidates via Individualized Exploratory: Rethinking Attention for Image Super-Resolution

Chunyu Meng, Wei Long, Shuhang Gu

Single Image Super-Resolution (SISR) is a fundamental com-puter vision task that aims to reconstruct a high-resolution (HR) im-age from a low-resolution (LR) input. Transformer-based methods haveachieved remarkable performance by modeling long-range dependenciesin degraded images. However, their feature-intensive attention compu-tation incurs high computational cost. To improve efficiency, most ex-isting approaches partition images into fixed groups and restrict atten-tion within each group. Such group-wise attention overlooks the inher-ent asymmetry in token similarities, thereby failing to enable flexibleand token-adaptive attention computation. To address this limitation,we propose the Individualized Exploratory Transformer (IET), which in-troduces a novel Individualized Exploratory Attention (IEA) mechanismthat allows each token to adaptively select its own content-aware andindependent attention candidates. This token-adaptive and asymmetricdesign enables more precise information aggregation while maintainingcomputational efficiency. Extensive experiments on standard SR bench-marks demonstrate that IET achieves state-of-the-art performance undercomparable computational complexity. The code is available at here.

ECCV 2026

Neural Harmonic Textures for High-Quality Primitive Based Neural Reconstruction

Jorge Condor, Nicolas Moënne-Loccoz, Merlin Nimier-David, Piotr Didyk, Zan Gojcic, Qi Wu

Primitive-based methods such as 3D Gaussian Splatting haverecently become the state-of-the-art for novel-view synthesis and relatedreconstruction tasks. Compared to neural fields, these representationsare more flexible, adaptive, and scale better to large scenes. However,the limited expressivity of individual primitives makes modeling high-frequency detail challenging. We introduce Neural Harmonic Textures, aneural representation approach that anchors latent feature vectors on avirtual scaffold surrounding each primitive. These features are interpo-lated within the primitive at ray intersection points. Inspired by Fourieranalysis, we apply periodic activations to the interpolated features, turn-ing alpha blending into a weighted sum of harmonic components. Theresulting signal is then decoded in a single deferred pass using a smallneural network, significantly reducing computational cost. Neural Har-monic Textures yield state-of-the-art results in real-time novel view syn-thesis while bridging the gap between primitive- and neural-field-basedreconstruction. Our method integrates seamlessly into existing primitive-based pipelines such as 3DGUT, Triangle Splatting, and 2DGS. We fur-ther demonstrate its generality with applications to 2D image fitting andsemantic reconstruction.

ECCV 2026

MotionEditGS: Editing Motion and Appearance of 4D Scenes from Monocular Video via Semantically Anchored Gaussians

Daehyun We, Youngho Yoon, Jiyong Boo, Kuk-Jin Yoon

Editing dynamic 3D scenes reconstructed from monocularvideos is important for applications such as content creation and dataaugmentation, but remains challenging. Prior works adapt image diffu-sion models across frames while enforcing spatial–temporal consistency,yet they often produce uniform, appearance-only edits. We instead lever-age a video diffusion model to enable photorealistic 4D edits and control-lable motion changes. In monocular settings, however, fitting dynamic3D Gaussians with pure photometric loss is under-constrained, lead-ing to shallow geometric reconstructions and temporal artifacts in theedited scene. We address this with a novel 4D editing pipeline that usescompact latents distilled from DINO features as additional supervisionduring scene optimization and preserves them during editing as a se-mantic identity signal. Such semantic consistency regularization providesstrong geometric and motion cues, enabling robust scene editing evenunder large or complex edits from the video diffusion model. Further-more, to promote semantically novel content generation during editing,we apply a gradient-based feature lifting method based on accumulatedfeature gradients during Gaussian densification. Experiments show im-proved spatial-temporal realism and text alignment over image editingbaselines, and demonstrate compositional motion-appearance edits withsignificant geometry changes that prior methods cannot achieve.

ECCV 2026

FedNASP: Federated Vision-Language Navigation with Adaptive Step-wise Personalization

Qingqian Yang, Hao Wang, Sai Qian Zhang, Jian Li, Yang Hua, Miao Pan, Tao Song, Zhengwei Qi, Haibing Guan

Federated learning (FL) protects sensitive (Vision-LanguageNavigation) VLN data without centralizing trajectories or instructions,but severe non-IID environments make personalized FL (pFL) necessary.Moreover, VLN poses several coupled challenges for personalized feder-ated learning, including environment heterogeneity, multimodal language-vision fusion, and long-horizon navigation with time-varying decisioncontexts. To address these challenges, we propose FedNASP, a step-wisepersonalized federated learning framework for VLN. The key idea is todynamically calibrate personalization strength along a navigation trajec-tory. Specifically, we introduce a lightweight Step-wise Personalized Mod-ulator (SPM) that predicts personalization strength at each navigationstep. We further design a structure-aware adapter-based personalizedprefix injection mechanism that enables client-specific grounding whilekeeping the backbone shared across clients. Experiments on three rep-resentative datasets show that FedNASP consistently outperforms state-of-the-art federated VLN methods under substantial cross-client hetero-geneity. Compared with the non-centralized baselines, FedNASP improvesRemote Grounding Success on REVERIE by 13.0% and Success Rateon CVDN by 22.6%. Extensive ablation studies and visualizations fur-ther validate the effectiveness of adaptive step-wise personalization forfederated VLN. Code is available at: https://github.com/IntelliSys-Lab/FedNASP.git

ECCV 2026

S2-FracMix: Self-Saliency Fractal Mixup

Khawar Islam, Arif Mahmood, Xin Jin, Naveed Akhtar

Data augmentation is known to improve generalization ofdeep visual models. Recent methods favor mixup strategies that gener-ate interpolated samples to improve model performance. However, thesetechniques not only incur significant computational overhead, they alsolead to semantic disruption of augmentation data due to cross-samplemixing. We first propose Self-Saliency (S 2 ) Mixup, which constructschallenging yet label-consistent samples by extracting multi-scale salientpatches and reinserting them into non-salient regions of the same im-age. This promotes scale-invariant feature learning while avoiding cross-sample interference. To further enhance model robustness, we intro-duce FracMix, a mixing scheme that injects self-similarity patterns intosalient regions using adaptive ratios. Collectively, our unified framework,S 2 -FracMix, enables simultaneous learning from fractal and non-fractalstructures within a single image, yielding a targeted and structurally co-herent augmentation strategy. We theoretically analyze the advantageof our technique, and empirically establish its superiority over the ex-isting methods by achieving state-of-the-art performance in extensiveevaluation with seven benchmarks across classification (coarse and fine-grained), robustness, calibration, object detection, and transfer learningtasks. Project page is available at fracmix-data-augmentation.github.io

ECCV 2026

MoCA3D: Monocular 3D Bounding Box Prediction in the Image Plane

Changwoo Jeon, Rishi Upadhyay, Achuta Kadambi

Monocular 3D object understanding has largely been cast asa 2D RoI-to-3D box lifting problem. However, emerging downstream ap-plications require image-plane geometry (e.g., projected 3D box corners)which cannot be easily obtained without known intrinsics, a problem forobject detection in the wild. We introduce MoCA3D, a Monocular,Class-Agnostic 3D model that predicts projected 3D bounding box cor-ners and per-corner depths without requiring camera intrinsics at in-ference time. MoCA3D formulates pixel-space localization and depthassignment as dense prediction via corner heatmaps and depth maps.To evaluate image-plane geometric fidelity, we propose Pixel-AlignedGeometry (PAG), which directly measures image-plane corner anddepth consistency. Extensive experiments under an oracle-2D box pro-tocol demonstrate that MoCA3D achieves state-of-the-art performance,improving image-plane corner PAG by 22.8% while remaining compa-rable on 3D IoU, using up to 57× fewer trainable parameters. Finally,we apply MoCA3D to downstream tasks which were previously imprac-tical under unknown intrinsics, highlighting its utility beyond standardbaseline models. Project page: https://jeoncwcw.github.io/moca3d/.

ECCV 2026

MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization

Jingchen Ni, Cangjin Yu, Zytang Jiang, Quan Zhang, Keyu Lv, Shannan Yan, Linyue Pan, Ke Zhang, Chun Yuan

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.

ECCV 2026

Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras

Ruijun Zhang, Hang Su, Kostas Daniilidis, Ziyun Wang

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 .

ECCV 2026

Making Avatars Interact: Towards Text-Driven Human-Object Interaction for Controllable Talking Avatars

Youliang Zhang, Zhengguang Zhou, Zhentao Yu, Ziyao Huang, Teng Hu, Sen Liang, Guozhen Zhang, Ziqiao Peng, Shunkai Li, Yi Chen, Zixiang Zhou, Yuan Zhou, Qinglin Lu, Xiu Li

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.

ECCV 2026

LongEgoRefer: A Benchmark for Long-Form Egocentric Video Referring Expression Comprehension

Shunya Kato, Taiki Miyanishi, Shuhei Kurita, Mahiro Ukai, Nakamasa Inoue, Chenhui Chu

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.

ECCV 2026

LiteMatch: Lightweight Zero-Shot Stereo Matching via Cost Volume Stabilization

Md Raqib Khan, Santosh Kumar Vipparthi, Subrahmanyam Murala

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

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