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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

RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks

Ruiying Li, Yunlang Zhou, Yuyao Zhu, Kylin Chen, Sukai Wang, Kongtao Hu, Minhui Yu, Bowen Jiang, Jiayao Ma, Zhan Su, Yongjian Shen, Yang Yang, Guanghui Ren, Maoqing Yao, Wenhao Wang, Yao Mu

Vision-Language-Action (VLA) systems have shown strong potential for language-driven robotic manipulation. However, scaling them to long-horizon tasks remains challenging. Existing pipelines typically separate data collection, policy learning, and deployment, resulting in heavy reliance on manual environment resets and brittle multi-policy execution. We present RoboClaw, an agentic robotics framework that unifies data collection, policy learning, and task execution under a single VLM-driven controller. At the policy level, RoboClaw introduces Entangled Action Pairs (EAP), which couple forward manipulation behaviors with inverse recovery actions to form self-resetting loops for autonomous data collection. This mechanism enables continuous on-policy data acquisition and iterative policy refinement with minimal human intervention. During deployment, the same agent performs high-level reasoning and dynamically orchestrates learned policy primitives to accomplish long-horizon tasks. By maintaining consistent contextual semantics across collection and execution, RoboClaw reduces mismatch between the two phases and improves multi-policy robustness. Experiments in real-world manipulation tasks demonstrate improved stability and scalability compared to conventional open-loop pipelines, while significantly reducing human effort throughout the robot lifecycle, achieving a 25% improvement in success rate over baseline methods on long-horizon tasks and reducing human time investment by 53.7%. Code is available at: https://github.com/RoboClaw-Robotics/RoboClaw.

ECCV 2026

DIVE-Bench: Codec-Style High-FPS Video Understanding

Haichao Zhang, Wenhao Chai, Shwai He, Ang Li, Yun Fu

High temporal resolution is essential for capturing fine-grained dynamics in video understanding, yet current video large language models (VLLMs) and benchmarks predominantly rely on low-frame-rate sampling, discarding dense temporal information. This design compromise is driven by the high cost of frame-wise tokenization, which incurs redundant computation and linear growth in token count with video length. While effective for slowly changing content, such engineering trade-offs severely limit tasks where information is distributed across nearly every frame, such as educational or high-motion videos that require frameby-frame reasoning. To address this gap, we introduce the novel task of High-FPS Video Understanding, the first task specifically designed to evaluate video comprehension at high frame rates, where information is present in nearly every frame. We further propose the first benchmark, DIVE-Bench (Dense Information Video Evaluation Benchmark), to expose the limitations of existing benchmarks whose question– answer pairs are not sensitive to dense temporal variations. To enable efficient high-FPS processing, we propose Gated Residual Tokenization (GRT), a codec-style inter-tokenization framework that operates not only after but also during tokenization. First, Motion-Compensated Gated Inter-Tokenization applies pixel-level motion estimation to gate static regions during tokenization, achieving sub-linear growth in both tokenization time and token count. Second, Semantic-Scene Token Merging further reduces redundancy by merging semantically similar static tokens while preserving dynamic content. Extensive experiments on DIVEBench show that GRT improves high-FPS efficiency while preserving or improving answer quality under dense temporal perception tasks. These results highlight the importance of dense temporal modeling and establish codec-style inter-tokenization as a practical direction for scalable high-FPS video understanding.

ECCV 2026

City-Level 3D Surface Reconstruction with Viewpoint Orientation Partitioning and Scene Completion

Liang Han, Wenyuan Zhang, Junsheng Zhou, Yushen Liu, Zhizhong Han

Multi-view 3D surface reconstruction is a longstanding challenge in computer vision. Although recent large-scale reconstruction methods based on 3D Gaussian Splatting (3DGS) achieve impressive novelview synthesis, producing high-quality surfaces over large scenes remains difficult, due to complex geometry, long optimization, and limited memory. In this paper, we propose a novel yet simple partitioning method to efficiently and faithfully reconstruct large-scale scene surfaces. Our key insight lies in a scene partitioning method based on viewpoint orientation. This partitioning approach ensures that views with similar orientations are jointly involved for more accurate depth estimations, leading to precise surface reconstructions and balanced computation on multiple GPUs in parallel. In addition, we propose a strategy to detect and repair missing regions in the initial point cloud caused by sparse viewpoints or insufficient textures, thereby further improving the geometric quality. Extensive experiments on the GauU-Scene, MatrixCity, and UrbanScene3D datasets demonstrate that our method outperforms the state-of-the-art approaches in surface reconstruction for large-scale scenes. Project page: https://hanl2010.github.io/VOP-GS. 2 L. Han et al.

ECCV 2026

MeanTalker: Efficient and Expressive Speech-Driven 3D Facial Animation via Geometric-Aware Mean Flow

Zhongyuan Zhao, Zhihao Li, Qing Li, Leidong Fan, Kanglin Liu

Speech-driven 3D facial animation aims to synthesize natural and synchronized facial motions from arbitrary speech audio. Deterministic models struggle with the complex many-to-many mapping, thus yielding over-smoothed motions and lacking expression nuances. In comparison, diffusion-based approaches excel in capturing expressive distributions but suffer from significant latency due to iterative sampling. To this end, we propose MeanTalker, a novel one-step speech-to-motion generative framework built upon Mean Flow. It utilizes a Dual-Time Denoising Transformer (DTDT) for training stability and Geometric-Aware Trajectory Learning (GATL) for precise manifold alignment. Specifically, DTDT introduces an interleaved dual-time encoding to support a progressive curriculum from modeling instantaneous to average velocities, effectively circumventing the instability of direct mean flow training. Furthermore, GATL rectifies the generation trajectory through synergistic dual-space supervision. By applying explicit vertex constraints and projecting latent velocity errors onto the surface manifold, it strictly regularizes the flow direction to guarantee precisely synchronized one-step synthesis. Extensive experiments demonstrate that MeanTalker establishes state-of-the-art lip-sync accuracy while accelerating inference by over 200× compared to iterative diffusion models. Achieving an exceptional Real-Time Factor (RTF) of 0.004, our method effectively bridges the gap between precise motion generation and real-time deployment.

ECCV 2026

DisentangledTMR: Privacy-Preserving Skeleton Motion Retargeting via Factorized Transformers

Thomas Carr, Depeng Xu, Shuhan Yuan, Aidong Lu

Skeleton-based motion data leak personally identifiable information through both static skeletal structure and dynamic motion patterns, enabling re-identification even without facial features. We present DisentangledTMR, a Transformer Motion Retargeting (TMR) architecture that achieves privacy through explicit architectural disentanglement. Two encoders with complementary inductive biases, temporal convolutions for action and spatial graph convolutions for identity, feed a factorized decoder that fuses their representations through separate crossattention streams and adaptive gating. A three-stage training curriculum progressively establishes disentanglement, reconstruction, and endto-end refinement, and a tunable partial-retargeting ratio trades privacy for compatibility with pre-trained downstream models. On three benchmarks, DisentangledTMR substantially reduces re-identification while preserving action recognition, outperforming single-encoder baselines.

ECCV 2026

Leveraging Cross-Modal Knowledge Transfer for Knowledge-Aware Concept Customization

Chenyang Zhu, Hongxiang Li, Xiu Li, Long Chen

Concept customization typically binds rare tokens to a target concept. Unfortunately, these approaches often suffer from unstable performance as the pretraining data seldom contains these rare tokens. Meanwhile, these rare tokens fail to convey the inherent knowledge of the target concept. Consequently, we introduce Knowledgeaware Concept Customization, a novel task aiming at binding diverse textual knowledge to target visual concepts. This task requires the † Corresponding author. 2 C Zhu et al. model to identify the knowledge within the text prompt to perform highfidelity customized generation. Meanwhile, the model should efficiently bind all the textual knowledge to the target concept. Therefore, we propose MoKus, a novel framework for knowledge-aware concept customization. Our framework relies on a key observation: cross-modal knowledge transfer, where modifying knowledge within the text modality naturally transfers to the visual modality during generation. Inspired by this observation, MoKus contains two stages: (1) In visual concept learning, we first learn the anchor representation to store the visual information of the target concept. (2) In textual knowledge updating, we update the answer for the knowledge queries to the anchor representation, enabling high-fidelity customized generation. To further comprehensively evaluate our proposed MoKus on the new task, we introduce the first benchmark for knowledge-aware concept customization: KnowCusBench. Extensive evaluations have demonstrated that MoKus outperforms state-of-the-art methods. Moreover, the cross-model knowledge transfer allows MoKus to be easily extended to other knowledge-aware applications like virtual concept creation and concept erasure. We also demonstrate the capability of our method to achieve improvements on world knowledge benchmarks.

ECCV 2026

Generating a Paracosm for Training-Free Zero-Shot Composed Image Retrieval

Tong Wang, Yunhan Zhao, Shu Kong

Composed Image Retrieval (CIR), a task towards personalizing visual intelligence, aims to retrieve a target image from a database based on users’ multimodal query, which contains a reference image and a modification text. The text specifies how to alter the reference image to form a “mental image”, based on which CIR should find the target image in the database. The fundamental challenge of CIR is that this “mental image” is not physically available and is only implicitly defined by the query. The contemporary literature pursues zero-shot methods and uses a Large Multimodal Model (LMM) to generate a textual description for a given multimodal query, and then employs a Vision-Language Model (VLM) for textual-visual matching to search for the target image. In contrast, we address CIR from first principles by directly generating the “mental image” for more accurate matching. Particularly, we prompt an LMM to generate a “mental image” for a given multimodal query and propose to use this “mental image” to search for the target image. As the “mental image” has a synthetic-to-real domain gap with real images, we also generate a synthetic counterpart for each real image in the database to facilitate matching. In this sense, our method uses LMM to construct a “paracosm”, where it matches the multimodal query and database images. Hence, we call this method Paracosm. Notably, Paracosm is a training-free zero-shot CIR method. It significantly outperforms existing zero-shot methods on challenging benchmarks, achieving state-of-the-art performance for zero-shot CIR.

ECCV 2026

Unbalanced Optimal Transport for Efficient Visual Document Retrieval

Hoyeon Shin, Jeongyeon Kim, Yeong Jun Koh, Yeoneung Kim, Hanul Kim

Vision-Language Models (VLMs) have advanced Visual Document Retrieval (VDR) by enabling OCR-free, multi-vector representations. However, these models generate excessive tokens to preserve dense visual details, which severely limits retrieval scalability. Existing compression methods mitigate this overhead but rely on isolated heuristics that fail to balance token importance, semantic redundancy, and coverage capacity. We propose a visual token compression framework formulated as an Unbalanced Optimal Transport (UOT) problem. Our approach unifies these three objectives into a single alternating optimization process. Furthermore, we theoretically show that minimizing the transport cost upper-bounds the MaxSim retrieval error, ensuring ranking stability. Extensive experiments on the ViDoRe V1 and V3 benchmarks demonstrate that our method preserves fine-grained document semantics and improves the efficiency-effectiveness trade-off over state-of-the-art baselines. Code is available at https://github.com/shhhhhyy/UnbalancedOptimal-Transport-for-EVDR.

ECCV 2026

Towards Effective Long Video Understanding: Dynamic MAS Construction via Meta-Agent

Jing Huang, Lidong Zhang, Yadong Li, Xingzhong Xu, Siye Chen, Jie Liu, Ming Kong, Qiang Zhu

Long video understanding is fundamentally constrained by massive data volume and high information density. While agent-based methods mitigate context length bottlenecks, they predominantly rely on static, "one-size-fits-all" multi-agent systems (MAS). These rigid architectures are computationally redundant and lack the flexibility to handle diverse, instance-level reasoning demands. To address this, we propose DyMAC (Dynamic MAS Construction), a novel framework that dynamically generates instance-specific MAS for each video-question pair. By logically abstracting the MAS into a directed computational graph via automated code generation, DyMAC exhibits exceptional adaptability, seamlessly tailoring its collaborative architecture to the specific complexity of each input. Furthermore, we employ a streamlined two-stage training strategy—Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR)—to enhance the model’s dynamic construction and decision-making capabilities. Extensive experiments across four mainstream benchmarks demonstrate that DyMAC achieves a new state-of-the-art, outperforming the second-best method by 3.3% on average. Notably, it significantly improves inference efficiency compared to existing static approaches, further highlighting its superior adaptability and robustness in complex video scenarios.

ECCV 2026

SVGEval: A Vision-Grounded Framework for Perceptual-Quality Benchmarking and Evaluation in Text-to-SVG Generation

Yiming Wang, Ye Chen, Hanqi Chen, Bingbing Ni

Multimodal large models are increasingly used to generate scalable vector graphics (SVG), but reliable evaluation remains underexplored. Existing protocols are often code-centric or borrow raster-image metrics after rendering SVGs, which fail to reflect human perception and overlook SVG-specific qualities such as geometry and spatial composition. We introduce SVGEval, a vision-grounded multimodal benchmark for human-aligned SVG quality assessment. SVGEval explicitly incorporates visual renderings to evaluate whether models can judge the rendered outcome rather than only inspect SVG code, and provides highquality annotations obtained via multi-round human labeling with expert refinement. Systematic evaluations across representative multimodal models reveal a clear gap: models perform relatively well on semantic alignment and aesthetics, yet struggle on geometryand layout-related judgments. Building on SVGEval, we train an explainable SVG quality scorer that outputs multi-aspect scores with textual rationales. Ablations show that explicit visual grounding and reasoning supervision are crucial, especially for spatial and geometric assessment. SVGEval offers a reliable testbed and practical scorer for evaluating and improving SVG generation in the era of multimodal models.

ECCV 2026

LARY: A Latent Action Representation Yielding Benchmark

Dujun Nie, FengJiao Chen, Jun Kuang, Qi Lv, Xiaoyu Li, Xuezhi Cao

While the shortage of explicit action data limits VisionLanguage-Action (VLA) models, human action videos offer a scalable yet unlabeled data source. A critical challenge in utilizing large-scale human video datasets lies in transforming visual signals into ontologyindependent representations, known as latent actions. However, the capacity of latent action representation to derive robust control from visual observations has yet to be rigorously evaluated. We introduce the Latent Action Representation Yielding (LARY) Benchmark, a unified framework for evaluating latent action representations on both highlevel semantic actions (what to do) and low-level robotic control (how to do). The comprehensively curated dataset encompasses over one million videos (1,000 hours) spanning 151 action categories, alongside 620K image pairs and 595K motion trajectories across diverse embodiments and environments. Our experiments reveal two crucial insights: (i) General visual foundation models, trained without any action supervision, consistently outperform specialized embodied latent action models. (ii) Latent-based visual space is fundamentally better aligned to physical action space than pixel-based space. These results suggest that general visual representations inherently encode action-relevant knowledge for physical control, and that semantic-level abstraction serves as a fundamentally more effective pathway from vision to action than pixel-level reconstruction.

ECCV 2026

Benchmarking Federated Learning & Knowledge Distillation for Point Cloud Classification

Aizierjiang Aiersilan

Deploying 3D point cloud analysis in privacy-sensitive and resource-constrained settings faces two coupled barriers: data cannot be centralized for training, and the trained model must run on limited edge hardware. We present a multi-seed benchmark that jointly evaluates federated learning (FL) and knowledge distillation (KD) for 3D point cloud classification. It spans thirteen FL algorithms and ten KD objectives, supporting their full 130-pair teacher–objective cross-product per dataset; every standardized configuration is repeated over three random seeds for 504 training runs in total, with the complete combined grid evaluated at multi-seed scale on the clinical dataset. We characterize federated degradation and the combined-pipeline pitfall on ModelNet40, then validate them on a real-world clinical craniosynostosis dataset of patient head shapes, where the privacy and edge-deployment stakes are concrete. We report three findings. First, under extreme non-independent and identically distributed (non-IID) label skew, standalone FL degrades sharply: on ModelNet40 the strongest method reaches only 76.32% against a 92.26% centralized reference, on the clinical data the best reaches 75.83% against 100%, and the four server-side optimizers collapse to near the chance level; the best algorithm differs by dataset, so none is universally robust. Second, distillation compresses the teacher into a student 74.51% smaller and roughly twice as fast at inference, with five of the seven objectives evaluated on ModelNet40 matching or surpassing the 92.44% teacher. Third, the combined pipeline exposes an evaluation pitfall: when distillation keeps a hard-label cross-entropy term on a labeled proxy split, a collapsed federated teacher at 8.50% paired with Logit-MSE still yields a 92.94% student. This 84.4-point gap reflects the proxy labels rather than the federated model, and the hard-label term reuses the very labels whose privacy motivated federation. Objectives without a hard-label term instead track teacher quality (r ≈ 0.99 on the clinical grid) and collapse when the teacher does. We therefore recommend evaluating FL-KD pipelines with label-free distillation, so that the reported accuracy reflects the federated teacher rather than the proxy.

ECCV 2026

MoMCE: Mixture of Modality and Cue Experts for Multimodal Deception Detection

Dongliang Zhu, Ruimin Hu, Zitong Yu, Xiaobao Guo, Mei Wang, Shuo Ye, Fei Ma, Xiaochun Cao

Audio-visual deception detection aims to predict whether a person is lying by integrating visual and acoustic modalities, which has two main challenges: 1) the modality conflict problem and 2) heterogeneous cue representation difficulty. However, existing approaches 1) often overlook the differences across modalities for different individuals; and 2) typically rely on a single encoder to handle diverse and individual-specific cues, which limits models’ representation capacity for heterogeneous cues. To address these challenges, we propose MoMCE, a novel model with mixture of modality and cue experts for deception detection. It consists of two key components: 1) Prompt-aware Mixture of Modality Experts, which employs a learnable prompt routing mechanism to generate adaptive instance-aware modality weight distributions for dynamic modality adjustment. In addition, we propose a consistency-aware expert weighting loss. For samples with high crossmodal consistency, it encourages balanced contributions across modalities. In contrast, for samples with strong conflicts, it reduces the entropy of the modality weight distribution to focus on more reliable modalities. 2) Prompt-aware Mixture of Cue Experts, which captures heterogeneous and diverse deceptive cues within each modality. This module introduces multiple experts with distinct semantic biases on top of a shared backbone to model different deceptive patterns. Additionally, we introduce a cue expert diversity loss to balance learning across multiple cue experts, promoting effective representation of diverse deceptive cues. Extensive experiments demonstrate that MoMCE adapts to variations in both cross-modal contributions and cue heterogeneity, achieving substantial improvements in deception detection performance. The code is available at this link.

ECCV 2026

QVAM: Query-guided View-aware Adaptive Modulation for Aerial-Ground Person Re-Identification

Mengfei Zhou, Lin Wan

Aerial–Ground Person Re-Identification (AGPReID) matches identities across unmanned aerial vehicles (UAVs) and ground cameras but suffers from extreme viewpoint gaps. Most existing methods rely on coarse binary aerial/ground labels and rigid orthogonality to disentangle view and identity features. We identify an important limitation of prior methods: the oversimplified binary-label design cannot fully capture continuous aerial viewpoint shifts. Moreover, rigid orthogonality constraints may further suppress identity cues. To address these issues, we propose Query-guided View-aware Adaptive Modulation (QVAM) for identity-preserving cross-view matching. Specifically, a View-aware Decoder (VAD) distills fine-grained viewpoint cues from patch tokens using learnable view queries. Guided by these cues, Adaptive Feature Modulation (AFM) predicts query-conditioned masks to suppress view-biased responses while preserving identity discrimination. A Cross-View Prototype Alignment (CVPA) loss further aligns modulated features at batch and memory levels with dual-view memory banks. Extensive experiments on AG-ReID, AG-ReIDv2, and CARGO show that QVAM achieves stateof-the-art performance, improving the previous best results by +10.26% Rank-1/+10.83% mAP on CARGO-ALL and +2.62% Rank-1/+2.37% mAP on AG-ReID A→G. The code is available at https://github.com/ Sakuraandroxy/QVAM.

ECCV 2026

Towards Reconfigurable Visual Feature Compression

Jiahang Zhang, Wenhan Yang, minghao liu, Jiaying Liu

Recently, vision foundation models (VFMs) have demonstrated strong power for versatile downstream task analysis. This paper starts from the problem of how to efficiently deploy VFMs at the cloud to support various user requests with arbitrary multi-task combinations that end in any scalable fashion. To achieve feature data transmission between frontend-cloud, traditional feature coding employs the direct compression-reconstruction paradigm. However, this can result in potential redundancy: (1) task-irrelevant information, and (2) repeated coding of cross-task shared knowledge, leading to an inflexible and redundant scheme. To this end, we propose a novel feature coding paradigm, termed reconfigurable multi-task feature compression, which aims to efficiently and adaptively compress the intermediate features to support the requested targeted tasks. Correspondingly, we propose a unified Reconfigurable Feature Compression framework, RFC, by feature factorization and recomposition. Specifically, the original feature is first factorized into multiple task-specific features with light-weight adapters. Then to efficiently compress these separate features, we consolidate them in a task-conditional auto-regressive manner, leveraging previously encoded task features as hyperprior conditions to reduce the redundancy of shared information in the current task feature. At the cloud side, a task-attentive recomposition module is further developed to fulfill the multi-task inference power within a single forward pass. Finally, we construct a comprehensive benchmark of reconfigurable feature compression to verify the effectiveness of RFC. Our project page can be found at https://jhang2020.github.io/Projects/RFC/RFC.html.

ECCV 2026

Video-HolmesV2: Can MLLMs Reason with Spatio-Temporal Audio-Visual Evidence in Long Videos?

Zhaoyang Wei, Zipeng Wang, Yushe Cao, Chenhui Qiang, Shuaibing Cheng, Xuesong Yang, Sen Nie, Bowen Jiang, Wenchao Ding, Yanchao Hao, Zheng Wei, Xuehui Yu, Zhenjun Han

Multimodal Large Language Models have demonstrated impressive video understanding, yet their ability to reason over long-form narratives is often masked by visual-centric evaluations and inefficient context processing. Existing benchmarks over-rely on visual heuristics while marginalizing auditory cues, effectively reducing models to "silent observers" that bypass genuine cross-modal reasoning. Moreover, standard dense sampling creates an evidence–context trade-off: increasing frames to capture evidence inevitably leads to attention distraction and token explosion. To bridge these gaps, we present VideoHolmesV2, a novel benchmark designed for Deep Audio-Visual Coupling. Unlike previous works, it enforces an Evidence-Based Evaluation, requiring models to justify answers with precise spatio-temporal audio-visual evidence, thereby reducing confounding effects of guessing and hallucinated evidence. To support this, we introduce: (1) a MultiModel Cross-Verification pipeline to ensure task rigor; (2) a Spatiotemporal Evidence-Aware Metric for fine-grained calibration. Furthermore, we propose an Audio-Text Guided Token Compression framework. By fusing task intent with auditory anchors, our method distills highvalue reasoning cues to mitigate long-context noise. In our evaluation, even strong proprietary models achieve below 60% accuracy, while our approach outperforms over comparable open-source omni-models.

ECCV 2026

REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling

Huazhong Zhang, Xiaowen Fu, Yang Zhang, Linlin Shen, Jinbao Wang

Open-World Object Detection (OWOD) requires detectors to identify previously unseen objects as unknown and incrementally incorporate them into the set of known categories, while preserving previously acquired knowledge. Existing frameworks rely heavily on exemplar replay to mitigate catastrophic forgetting, but in some real applications, storing raw data conflicts with data access restrictions and leads to data exposure risks, while incurring significant memory overhead. In this paper, we propose REAL-OW, a novel rehearsal-free framework that decouples incremental knowledge through a collaborative adapter architecture based on Low-Rank Adaptation (LoRA). Specifically, we deploy General Adapters (GAs) in the backbone to enable the significance-aware refinement of cross-task universal representations, while Specific Adapters (SAs) in the decoder provide orthogonal storage for task-specific expertise. To resolve representation drift in objectness modeling under rehearsal-free constraints, we introduce Dual-Stage Objectness Modeling (DSOM), which alternates between feature aggregation and boundary consolidation to stabilize objectness distributions while maintaining the separation between known and unknown categories. Furthermore, DSOM is supported by a Calibrated Gaussian Negative Log-Likelihood (CG-NLL) distance tailored for the dispersed feature distributions inherent in rehearsal-free settings. Extensive evaluations demonstrate that REAL-OW achieves state-of-the-art performance, surpassing existing exemplar replay methods in both detection precision and unknown discovery. Our approach establishes a new baseline for rehearsal-free OWOD.

ECCV 2026

LogFA: Efficient Feature-Space Data Augmentation for Egocentric Temporal Action Segmentation

Zijia Lu, Ehsan Elhamifar

Egocentric AI assistants seek to infer user actions and intentions from long first-person videos to facilitate real-world tasks ranging from daily activities to industrial operations. However, collecting large-scale annotated egocentric videos that capture variations in objects, tools, environments and procedures is costly and impractical, motivating the need for data-efficient learning. Existing data augmentation approaches either offer limited visual diversity through simple transformations or rely on computationally expensive generative models that may introduce artifacts. We propose LogFA (Local-global Featurespace Augmentation), a novel framework that performs data augmentation directly in the feature space for Temporal Action Segmentation (TAS). LogFA efficiently generates semantically consistent variations without pixel-level synthesis by modifying pre-extracted video features at both local and global levels. Locally, LogFA leverages visionlanguage models with a Prompt-based Feature Enhancement strategy to create diverse action-level variations through text-guided feature modifications. Globally, LogFA constructs a Generalized Directed Acyclic Graph to model procedural dependencies and sample alternative action sequences. Experiments on egocentric video benchmarks show LogFA significantly improves model generalization to unseen environments while maintaining low computational and data collection costs. Code will be released at https://github.com/ZijiaLewisLu/ECCV2026- LogFA.

ECCV 2026

LiDAR-EVS: Enhance Extrapolated View Synthesis for 3D Gaussian Splatting with Pseudo-LiDAR Supervision

Yiming Huang, Xin Kang, Sipeng Zhang, Hongliang Ren, Weihua Zhang, Junjie Lai

3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time LiDAR and camera synthesis in autonomous driving simulation. However, simulating LiDAR with 3DGS remains challenging for extrapolated views beyond the training trajectory, as existing methods are typically trained on single-traversal sensor scans, suffer from severe overfitting and poor generalization to novel ego-vehicle paths. To enable reliable simulation of LiDAR along unseen driving trajectories without external multi-pass data, we present LiDAR-EVS, a lightweight framework for robust extrapolated-view LiDAR simulation in autonomous driving. Designed to be plug-and-play, LiDAR-EVS readily extends to diverse LiDAR sensors and neural rendering baselines with minimal modification. Our framework comprises two key components: (1) pseudo extrapolated-view point cloud supervision with multi-frame LiDAR fusion, view transformation, occlusion curling, and intensity adjustment; (2) spatially-constrained dropout regularization that promotes robustness to diverse trajectory variations in real-world driving. Extensive experiments demonstrate that LiDAR-EVS achieves SOTA performance on extrapolated-view LiDAR synthesis across three datasets, making it a promising tool for data-driven simulation, closed-loop evaluation, and synthetic data generation in autonomous driving. Code and demos are available at: https://lastbasket.github.io/LiDAR-EVS_page/.

ECCV 2026

ProGVC: Progressive-based Generative Video Compression via Auto-Regressive Context Modeling

Daowen Li, Ruixiao Dong, Ying Chen, Kai Li, Ding Ding, Li Li

Perceptual video compression leverages generative priors to reconstruct realistic textures and motions at low bitrates. However, existing perceptual codecs often lack native support for variable bitrate and progressive delivery, and their generative modules are weakly coupled with entropy coding, limiting bitrate reduction. Inspired by the next-scale prediction in the Visual Auto-Regressive (VAR) models, we propose ProGVC, a Progressive-based Generative Video Compression framework that unifies progressive transmission, efficient entropy coding, and detail synthesis within a single codec. ProGVC encodes videos into hierarchical multi-scale residual token maps, enabling flexible rate adaptation by transmitting a coarse-to-fine subset of scales in a progressive manner. A Transformer-based multi-scale autoregressive context model estimates token probabilities, utilized both for efficient entropy coding of the transmitted tokens and for predicting truncated fine-scale tokens at the decoder to restore perceptual details. Extensive experiments demonstrate that as a new coding paradigm, ProGVC delivers promising perceptual compression performance at low bitrates while offering practical scalability at the same time.

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