GenAI4ELab/papercli-papers huggingface dataset:
EgoXtreme: A Dataset for Robust Object Pose Estimation in Egocentric Views under Extreme Conditions
Authors: Taegyoon Yoon; Yegyu Han; Seojin Ji; Jaewoo Park; Sojeong Kim; Taein Kwon; Hyung-Sin Kim
Venue: CVPR 2026 (main)

Abstract: Smart glass is emerging as an useful device since it provides plenty of insights under hands-busy, eyes-on-task situations. To understand the context of the wearer, 6D object pose estimation in egocentric view is becoming essential. However, existing 6D object pose estimation benchmarks fail to capture the challenges of real-world egocentric applications, which are often dominated by severe motion blur, dynamic illumination, and visual obstructions. This discrepancy creates a significant gap between controlled lab data and chaotic real-world application. To bridge this gap, we introduce EgoXtreme, a new large-scale 6D pose estimation dataset captured entirely from an egocentric perspective. EgoXtreme features three challenging scenarios--industrial maintenance, sports, and emergency rescue--designed to introduce severe perceptual ambiguities through extreme lighting, heavy motion blur, and smoke. Evaluations of state-of-the-art generalizable pose estimators on EgoXtreme indicate that their generalization fails to hold in extreme conditions, especially under low light. We further demonstrate that simply applying image restoration (e.g., deblurring) offers no positive improvement for extreme conditions. While performance gain has appeared in tracking-based approach, implying using temporal information in fast-motion scenarios is meaningful. We conclude that EgoXtreme is an essential resource for developing and evaluating the next generation of pose estimation models robust enough for real-world egocentric vision. The dataset and code are available at https://taegyoun88.github.io/EgoXtreme/
Hyper-PCN: Hypergraph-Based Point Cloud Completion via High-Order Correlation Modeling
Authors: Linfei Li; Pei Tan; Siqi Li; Changqing Zou; Yue Gao
Venue: CVPR 2026 (main)

Abstract: Point cloud completion is an important yet challenging problem in 3D computer vision, which aims to reconstruct complete and dense 3D shapes from partial point clouds. Although transformer-based and geometry-based approaches have made significant progress, they often struggle to capture the complex, high-order correlations inherent in point clouds. To address this limitation, we propose Hyper-PCN, a point cloud completion framework that leverages hypergraphs to explicitly model complex, higher-order correlations within incomplete inputs for more accurate completion. It comprises two key modules: Hyper Refinement Stack, designed to progressively capture coarse-to-fine high-order correlations through a series of hypergraph learning stages, and Anchor-based Hypergraph Neural Network, which employs a two-stage sampling strategy to construct collaborative hypergraphs, ensuring robust modeling of global structures. Extensive experiments on multiple datasets demonstrate that our approach consistently outperforms state-of-the-art methods.
PAVAS: Physics-Aware Video-to-Audio Synthesis
Authors: Oh Hyun-Bin; Yuhta Takida; Toshimitsu Uesaka; Tae-Hyun Oh; Yuki Mitsufuji
Venue: CVPR 2026 (main)

Abstract: Recent advances in Video-to-Audio (V2A) generation have achieved impressive perceptual quality and temporal synchronization, yet most models remain appearance-driven, capturing visual-acoustic correlations without considering the physical factors that shape real-world sounds. We present Physics-Aware Video-to-Audio Synthesis (PAVAS), a method that incorporates physical reasoning into a latent diffusion-based V2A generation through the Physics-Driven Audio Adapter (Phy-Adapter). The adapter receives object-level physical parameters estimated by the Physical Parameter Estimator (PPE), which uses a Vision-Language Model (VLM) to infer the moving-object mass and a segmentation-based dynamic 3D reconstruction module to recover its motion trajectory for velocity computation. These physical cues enable the model to synthesize sounds that reflect underlying physical factors. To assess physical realism, we curate VGG-Impact, a benchmark focusing on object-object interactions, and introduce Audio-Physics Correlation Coefficient (APCC), an evaluation metric that measures consistency between physical and auditory attributes. Comprehensive experiments show that PAVAS produces physically plausible and perceptually coherent audio, outperforming existing V2A models in both quantitative and qualitative evaluations. Visit https://physics-aware-video-to-audio-synthesis.github.io.
From 3D Pose to Prose: Biomechanics-Grounded Vision-Language Coaching
Authors: Yuyang Ji; Yixuan Shen; Shengjie Zhu; Yu Kong; Feng Liu
Venue: CVPR 2026 (main)

Abstract: We present BioCoach, a biomechanics-grounded vision-language framework for fitness coaching from streaming video. BioCoach fuses two signals, visual appearance and 3D skeletal kinematics, through a novel three-stage pipeline: an exercise-specific degree-of-freedom selector that focuses analysis on salient joints; a structured biomechanical context that pairs individualized morphometrics with cycle and constraint analysis; and a vision-biomechanics conditioned feedback module that applies cross-attention to generate precise, actionable text. Using parameter-efficient training that freezes the vision and language backbones, BioCoach yields transparent, personalized reasoning rather than pattern matching. To enable learning and fair evaluation, we augment QEVD-fit-coach with biomechanics-oriented feedback to create QEVD-bio-fit-coach, and we introduce a biomechanics-aware LLM judge metric. BioCoach delivers clear gains on QEVD-bio-fit-coach across lexical and judgment metrics while maintaining temporal triggering; on the original QEVD-fit-coach, it improves text quality and correctness with near-parity timing, demonstrating that explicit kinematics and constraints are key to accurate, phase-aware coaching.
FISHuman: Fine-grained Single-image 3D Human Reconstruction via Multi-view 4D Remeshing
Authors: Hanxi Liu; Yifang Men; Zhouhui Lian
Venue: CVPR 2026 (main)

Abstract: Single-image 3D human reconstruction holds significant promise due to its convenience and high demand in various applications. Previous methods have garnered tremendous progress by employing 2D multi-view diffusion models to generate auxiliary views as reconstruction priors, but they struggle with 3D inconsistencies and limited generalization capabilities. In this paper, we present FISHuman, which aims to generate fine-grained, high-fidelity, and content-wise diverse 3D humans from a single-view input, providing production-ready 3D assets. We propose an elaborately designed workflow that reconstructs dynamic 3D meshes from multi-view inconsistent guidance. Specifically, we adapt a dual-stream transformer-based video diffusion model to generate cross-modally aligned multi-view RGB and normal sequences. We find that naively employing static 3D reconstruction can lead to geometric distortions and texture blurriness, due to the lack of 3D awareness within the generated frames. To address this, we introduce a novel 4D remeshing module that explicitly disentangles the learning of the globally shared canonical mesh and transient variations by tracking per-vertex deformations under different viewpoints. The topological consistency of the deformed meshes inherently enables the optimization of a unified UV representation that effectively integrates appearance attributes across frames. Both qualitative and quantitative experimental results demonstrate the superiority of our method over prior works in terms of appearance realism, geometric fineness, and generalization diversity. We also showcase the applicability of our reconstructed avatars for downstream applications including animation and 3D editing.
Exploring Adaptive Masked Reconstruction for Self-Supervised Skeleton-Based Action Recognition
Authors: Shengkai Sun; Zhiyong Cheng; Zefan Zhang; Jianfeng Dong; Zhihui Li; Meng Wang
Venue: CVPR 2026 (main)

Abstract: Recently, masked skeleton reconstruction models have emerged as strong action representation learners, driving significant progress in self-supervised skeleton-based action recognition. However, existing state-of-the-art methods must predict an exceedingly large number of spatiotemporal patches, significantly prolonging training time. Besides, by treating all spatiotemporal regions equally during reconstruction, these models are distracted from learning the critical motion patterns that underlie action semantics. To address these challenges, we propose Adaptive Masked Reconstruction (AMR), a faster and stronger pre-training framework. We first decouple the decoder from the encoder, enabling flexible prediction of larger spatiotemporal patches and dramatically reducing reconstruction complexity. Given that larger patches contain more complex information, which is challenging to predict and consequently degrades performance, we accordingly introduce an adaptive guidance module. This module identifies regions of high motion informativeness, guiding the model to focus on the most discriminative parts of each patch and alleviating reconstruction difficulty. Experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD datasets demonstrate that AMR not only accelerates pre-training substantially but also improves downstream recognition accuracy, surpassing current state-of-the-art approaches.
Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation
Authors: Zaijing Li; Bing Hu; Rui Shao; Gongwei Chen; Dongmei Jiang; Pengwei Xie; Jianye Hao; Liqiang Nie
Venue: CVPR 2026 (main)

Abstract: Hierarchical Vision-Language-Action (VLA) models have rapidly become a dominant paradigm for robotic manipulation. It typically comprising a Vision-Language backbone for perception and understanding, together with a generative policy for action generation. However, its performance is increasingly bottlenecked by the action generation proceess. (i) Low inference efficiency. A pronounced distributional gap between isotropic noise priors and target action distributions, which increases denoising steps and the incidence of infeasible samples. (ii) Poor robustness. Existing policies condition solely on the current observation, neglecting the constraint of history sequence and thus lacking awareness of task progress and temporal consistency. To address these issues, we introduce OptimusVLA, a dual-memory VLA framework with Global Prior Memory (GPM) and Local Consistency Memory (LCM). GPM replaces Gaussian noise with task-level priors retrieved from semantically similar trajectories, thereby shortening the generative path and reducing the umber of function evaluations (NFE). LCM dynamically models executed action sequence to infer task progress and injects a learned consistency constraint that enforces temporal coherence and smoothness of trajectory. Across three simulation benchmarks, OptimusVLA consistently outperforms strong baselines: it achieves 98.6% average success rate on LIBERO, improves over pi_0 by 13.5% on CALVIN, and attains 38% average success rate on RoboTwin 2.0 Hard. In Real-World evaluation, OptimusVLA ranks best on Generalization and Long-horizon suites, surpassing pi_0 by 42.9% and 52.4%, respectively, while delivering 2.9x inference speedup.
VGGT-Ω
Authors: Jianyuan Wang; Minghao Chen; Shangzhan Zhang; Nikita Karaev; Johannes Schönberger; Patrick Labatut; Piotr Bojanowski; David Novotny; Andrea Vedaldi; Christian Rupprecht
Venue: CVPR 2026 (main)

Abstract: We introduce VGGT-O, a feed-forward model for 3D reconstruction that improves accuracy, efficiency, and capabilities for both static and dynamic scenes. Prior models such as VGGT have shown that feed-forward 3D reconstruction is, in many cases, competitive with traditional optimization-based methods. Here, we show that the accuracy and robustness of these models scale predictably with model capacity and data size. To enable training 3D reconstruction models at an unprecedented scale, we introduce architectural changes that improve training efficiency and scalability, a high-quality data annotation pipeline that supports dynamic scenes, and a self-supervised learning protocol. We significantly simplify VGGT's architecture by using a single dense prediction head with multi-task supervision, removing expensive high-resolution convolutional layers, and introducing efficient scene tokens for feature aggregation in lieu of global attention. These changes allow us to train VGGT-O with 15 x  more supervised data than prior work and to leverage vast amounts of unlabeled videos, while requiring only ~30% of VGGT's training memory. VGGT-O achieves strong results for 3D reconstruction of static and dynamic scenes across multiple benchmarks, e.g., improving over the previous best camera estimation accuracy by 77% on Sintel.
3D-IDE: 3D Implicit Depth Emergent
Authors: Chushan Zhang; Ruihan Lu; Jinguang Tong; Yikai Wang; Hongdong Li
Venue: CVPR 2026 (main)

Abstract: Leveraging 3D information within Multimodal Large Language Models (MLLMs) has recently shown significant advantages for indoor scene understanding. However, existing methods, including those using explicit ground-truth 3D positional encoding and those grafting external 3D foundation models for implicit geometry, struggle with the trade-off in 2D-3D representation fusion, leading to suboptimal deployment. To this end, we propose 3D-Implicit Depth Emergence, a method that reframes 3D perception as an emergent property derived from geometric self-supervision rather than explicit encoding. Our core insight is the Implicit Geometric Emergence Principle: by strategically leveraging privileged geometric supervision through mechanisms like a fine-grained geometry validator and global representation constraints, we construct an information bottleneck. This bottleneck forces the model to maximize the mutual information between visual features and 3D structures, allowing 3D awareness to emerge naturally within a unified visual representation. Unlike existing approaches, our method enables 3D perception to emerge implicitly, disentangling features in dense regions and, crucially, eliminating depth and pose dependencies during inference with zero latency overhead. This paradigm shift from external grafting to implicit emergence represents a fundamental rethinking of 3D knowledge integration in visual-language models. Extensive experiments demonstrate that our method surpasses SOTA on multiple 3D scene understanding benchmarks. Our approach achieves a 55% reduction in inference latency while maintaining strong performance across diverse downstream tasks, underscoring the effectiveness of meticulously designed auxiliary objectives for dependency-free 3D understanding. Source code can be found at \href https://chushanzhang.github.io/3D-IDE/  \textcolor[HTML] E8477A  \small\texttt github.com/ChushanZhang/3D-IDE   .
CoRiM: Conflict-driven Risk Minimization for Dynamic Multimodal Fusion
Authors: Shihao Zou; Wei Wei
Venue: CVPR 2026 (main)

Abstract: Dynamic multimodal fusion methods lack robust theoretical guidance for handling modal conflicts and inconsistent data quality. While recent theoretical work has associated modal weights with scalar quantities such as loss or confidence, this paradigm struggles to comprehensively address the risks caused by inconsistent predictive distributions across modalities. In this paper, we propose a  **Co**nflict-driven **Ri**sk **M**inimization (**CoRiM**) dynamic fusion paradigm. Specifically, we redefine dynamic fusion as a principled, per-sample, direct risk minimization task. To this end, we first design a novel, differentiable Modality Conflict Risk (MCR) function, \mathcal R (w), which quantifies risk by directly modeling fused uncertainty and inter-modal consistency. Second, we identify that minimizing \mathcal R (w) is fundamentally a non-convex constrained optimization problem over the probabilistic simplex. To efficiently solve this specific challenge, we innovatively introduce the projection free Frank-Wolfe (FW) algorithm, as it is perfectly suited for optimization on the simplex.We prove that our designed \mathcal R (w) possesses L-smoothness, which provides theoretical guarantees for the convergence of the FW algorithm on our non-convex objective. Extensive experiments on multiple benchmark datasets demonstrate that CoRiM outperforms current state-of-the-art methods in high-conflict and noisy environments, validating the robustness of our method.
Sculpt4D: Generating 4D Shapes via Sparse-Attention Diffusion Transformers
Authors: Minghao Yin; Wenbo Hu; Jiale Xu; Ying Shan; Kai Han
Venue: CVPR 2026 (main)

Abstract: Recent breakthroughs in 3D generative modeling have yielded remarkable progress in static shape synthesis, yet truly dynamic 4D generation remains elusive, hindered by temporal artifacts and prohibitive computational demand. We present Sculpt4D, a native 4D generative framework that seamlessly integrates efficient temporal modeling into a pretrained 3D Diffusion Transformer (Hunyuan3D 2.1), thereby mitigating the scarcity of 4D training data. At its core lies a Block Sparse Attention mechanism that preserves object identity by anchoring to the initial frame while capturing rich motion dynamics via a time-decaying sparse mask. This design faithfully models complex spatiotemporal dependencies with high fidelity, while sidestepping the quadratic overhead of full attention and reducing network total computation by 56%. Consequently, Sculpt4D establishes a new state-of-the-art in temporally coherent 4D synthesis, charting a path toward efficient and scalable 4D generation.
StreamVLO: Streaming Visual-LiDAR Odometry with Cumulative Drift Compensation
Authors: Mengmeng Liu; Jiuming Liu; Michael Ying Yang; Chaokang Jiang; Jiangtao Li; Yunpeng Zhang; Hesheng Wang; Francesco Nex; Hao Cheng
Venue: CVPR 2026 (main)

Abstract: We propose StreamVLO, a streaming visual-LiDAR odometry framework that performs unified spatio-temporal correlation with Mamba models and tackles the long-standing cumulative drift problem via an online Cumulative Drift Compensation scheme for localization in 4D dynamic environments. Specifically, StreamVLO introduces a unified spatio-temporal correlation module built on Mamba to fuse heterogeneous visual and LiDAR cues across multi-frame clips, overcoming the limited temporal exploration of prior pairwise methods. Furthermore, a Cumulative Drift Compensation module minimizes cumulative drift by iteratively learning residual corrections from multiple historical frames in a causal manner. To strengthen spatial feature representation on salient regions, we adopt a Keypoint-Aware Auxiliary Loss with a winner-takes-all strategy. StreamVLO achieves state-of-the-art performance on two commonly used autonomous driving datasets, reducing errors by 19% t_rel and 22% r_rel on KITTI, and by 18% ATE and 16% RPE on Argoverse, while remaining suitable for real-time deployment.
FedARA: Resource-adaptive Low-rank Personalized Federated Learning via Anchor-driven Representation Alignment on Heterogeneous Edge Devices
Authors: Ruonan Zhao; Zheng Wang; Debin Liu; Shijie Lv; Laurence Tianruo Yang
Venue: CVPR 2026 (main)

Abstract: Personalized Federated Learning (PFL) has gained significant attention for enabling participating clients to train customized personalized models on non-IID local data. However, current PFL methods mainly suffer from two limitations: 1) Only the personalized part supports heterogeneous design, while the shared part must remain homogeneous. 2) The semantic representations of models generated on different clients with non-IID data characteristics inevitably tend to be inconsistent, negatively impacting model performance. To conquer them, this paper proposes a novel resource-adaptive personalized Federated Learning via Anchor-driven Representation Alignment (FedARA). Concretely, we design a low-rank decomposition and reconstruction fusion scheme for shared feature extractors based on the matrix decomposition technology, where each client can autonomously set the rank value based on its locally available resources, controlling the complexity of extractors and naturally reducing communication and computational costs. Moreover, to address the inconsistency of feature spaces across clients, an anchor-driven representation consistency learning mechanism is developed, which can guide client models to learn unified feature representations and alleviate global knowledge forgetting, thereby improving personalized model performance. Extensive experimental results demonstrate that our method significantly outperforms seventeen state-of-the-art baselines in diverse heterogeneous scenarios with less communication and computational costs.
Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective
Authors: Maoxun Yuan; Duanni Meng; Ziteng Xi; Tianyi Zhao; Shiji Zhao; Yimian Dai; Xingxing Wei
Venue: CVPR 2026 (main)

Abstract: Infrared small target detection and segmentation (IRSTDS) is a critical yet challenging task in defense and civilian applications, owing to the dim, shapeless appearance of targets and severe background clutter. Recent CNN-based methods have achieved promising target perception results, but they only focus on enhancing feature representation to offset the impact of noise, which results in the increased false alarm problem. In this paper, through analyzing the problem from the frequency domain, we pioneer in improving performance from noise suppression perspective and propose a novel noise-suppression feature pyramid network (NS-FPN), which integrates a low-frequency guided feature purification (LFP) module and a spiral-aware feature sampling (SFS) module into the original FPN structure. The LFP module suppresses the noise features by purifying high-frequency components to achieve feature enhancement devoid of noise interference, while the SFS module further adopts spiral sampling to fuse target-relevant features in feature fusion process. Our NS-FPN is designed to be lightweight yet effective and can be easily plugged into existing IRSTDS frameworks. Extensive experiments on the IRSTD-1k and NUAA-SIRST datasets demonstrate that our method significantly reduces false alarms and achieves superior performance on IRSTDS task.
MV2UV: Generating High-quality UV Texture Maps with Multiview Prompts
Authors: Zheng Zhang; Qinchuan Zhang; Yuteng Ye; Zhi Chen; Penglei Ji; Mengfei Li; Wenxiao Zhang; Yuan Liu
Venue: CVPR 2026 (main)

Abstract: Generating high-quality textures for 3D assets is a challenging task. Existing multiview texture generation methods suffer from the multiview inconsistency and missing textures on unseen parts, while UV inpainting texture methods do not generalize well due to insufficient UV data and cannot well utilize 2D image diffusion priors. In this paper, we propose a new method called MV2UV that combines 2D generative priors from multiview generation and the inpainting ability of UV refinement to get high-quality texture maps. Our key idea is to adopt a UV space generative model that simultaneously inpaints unseen parts of multiview images while resolving the inconsistency of multiview images. Experiments show that our method enables a better texture generation quality than existing methods, especially in unseen occluded and multiview-inconsistent parts.