Human Pose and Action Recognition

Últimos artículos

• Engram-E2VID: Reference-Based Event-to-Video Reconstruction via Generative Activation of Appearance Engrams

  Feiyu Ji, Xiang Li, Hao Ma, Tianxiang Huang, Qingxin Lu, Mengqi Ji, Lei Han, Xiaokang Yang, Xiaoyun Yuan · 7 de agosto de 2026

  Reference-based event-to-video reconstruction aims to recover target RGB frames from a reference frame and the event stream captured over the reference-to-target interval. Although events provide fine-grained temporal cues, they encode sparse and asynchronous log-intensity changes rather than absolu…

• D-CLOT: Double Closed Loop Optimal Transport for Unsupervised Action Segmentation

  Elena Bueno-Benito, Mariella Dimiccoli · 7 de agosto de 2026

  Optimal transport (OT) has emerged as an effective framework for unsupervised action segmentation. Yet, in existing OT-based methods, the latent action prototypes that define the OT costs are not re-estimated from the refined frame geometry. Instead, they evolve solely through gradients from the pse…

• Equipment-centric workpiece localization in near real-time using deep learning-based vision and event-driven finite state machines

  Dohyeon Kong, Jaebong Cho, Hyunbo Cho · 7 de agosto de 2026

  Continuous workpiece localization is essential for traceability and process coordination in hot forging, but direct tracking is unreliable because of extreme temperatures, surface degradation, and irregular routing. This study presents an equipment-centric framework that infers workpiece locations f…

• Robust and Efficient Motion Reasoning for Privacy-Aware Classroom Incident Recognition

  Paritosh Parmar, Landy Lan, Hong Yang, Chen Yi, Chiat Pin Tay · 6 de agosto de 2026

  Can computer vision help make classrooms safer? In this pilot study, we investigate privacy-aware and computationally efficient classroom incident recognition from CCTV-style observations. This setting remains underexplored, with limited benchmarks and few methods designed for the privacy, efficienc…

• The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering

  Yuqian Fu, Tianwen Qian, Yanjun Li, Yu Li, Kunyu Peng, Xu Zheng, Yongqin Xian, Alessio Tonioni, Yanwei Fu, Xiaoling Wang, Danda Paudel, Federico Tombari, Luc Van Gool, Leyi Wu, Yifan Zhao, Jinjie Zhang, Yinchuan Li, Yingcong Chen, Zixu Li, Zhiwei Chen, Zhiheng Fu, Wenbo Wang, Yupeng Hu, Weili Guan, Liqiang Nie, Takuya Murakawa, Toru Tamaki, Yi Wen, Zhenglin Du, Zhengyang Li, Lingling Li, Licheng Jiao, Wenping Ma · 6 de agosto de 2026

  EgoCross is a cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scenarios. The first EgoCross Challenge was hosted at the Third EgoVis Workshop at CVPR 2026 and evaluated models on first-pe…

• Entity-Aware Sequence Transduction for Player-Centric Ball Action Spotting

  Ruifeng Wang, Di Yang, Jiangtao Wang · 4 de agosto de 2026

  Player-centric ball action spotting requires temporally precise event detection together with actor attribution in crowded, partially observed multi-agent sports videos. Existing Denoising Sequence Transduction (DST) baselines treat the player-role dimension as part of a flattened frame-level repres…

• RF-HOI: Recognize Human-Object Interaction with Radio Frequency Signals

  Lihao Wang, Linlu Gao, Jiacan Yu, Yanyu Lin, Yifan Yin, Jianxin Wang, Tianmin Shu, Renjie Zhao · 4 de agosto de 2026

  Recognizing Human-Object Interactions (HOI) is essential for intelligent systems, underpinning applications in virtual and augmented reality, embodied AI, and assistive robotics. However, vision-based HOI methods face challenges in privacy concerns and poor light conditions. In this work, we introdu…

• Faster-WAM: Do World Action Models Need Deep Action Modules?

  Liheng Ma, Rui Heng Yang, Zhanguang Zhang, Mateo Clemente, Ziwen Hu, Tongtong Cao, Yingxue Zhang · 4 de agosto de 2026

  World Action Models (WAMs) couple robot action prediction with video world models. Existing WAMs with shared-backbone and Mixture-of-Transformers designs generally tie the depth of the action module to that of the video backbone, resulting in substantial computational overhead and high inference lat…

• Latent Actions from Factorized Transition Effects under Agent Ambiguity

  Heejeong Nam, Chandradithya S Jonnalagadda, Harshit Aggarwal, Eric Xu, Randall Balestriero · 3 de agosto de 2026

  Latent Action Models (LAMs) learn action-like proxies from observation. However, in multi-object or distractor-rich scenes, observations contain not only agent motion but also distractors, camera dynamics, and background changes, making recovery of the underlying action intrinsically ambiguous witho…

• Mirror Learning

  Yunpeng Liu, Matthew Niedoba, Oluwanifemi A. Adekanye, Jason Yoo, Yingchen He, Berend Zwartsenberg, Frank Wood · 3 de agosto de 2026

  We investigate imitation learning through the lens of third-person observation and propose a framework for mirror learning: acquiring actionable policies from passive observation. While behavior cloning (BC) excels under dense, well-aligned first-person data, it fundamentally fails to leverage the r…

• Dual-Force: Enhanced Offline Diversity Maximization under Imitation Constraints

  Pavel Kolev, Marin Vlastelica, Georg Martius · 3 de agosto de 2026

  Offline diversity maximization under imitation constraints can transform demonstration data into a set of distinct behavioral policies, improving robustness to distribution shift without additional environment interaction. In practice, however, existing offline approaches often rely on mutual-inform…

• Reproducing Human Individual Motor Signatures: A Data-Driven Approach for Repetitive Motion

  Angelo Di Porzio, Marco Coraggio · 3 de agosto de 2026

  The deployment of autonomous virtual avatars (in extended reality) and robots in human group activities---such as rehabilitation therapy, sports, and manufacturing---is expected to increase as these technologies become more pervasive. Designing cognitive architectures and control strategies to drive…

• DLAM: Distributional Latent Actions with Temporal Constraints

  Zuojin Tang, Feifan Luo, Haoyun Liu, Botai Yuan, Dekang Qi, Ronghan Chen, Yandan Yang, Tong Lin, Xinyuan Chang, Mu Xu, Bin Liu, De Ma, Zhiheng Ma · 30 de julio de 2026

  Vision-language-action (VLA) models remain constrained by scarce action-labeled robot data, whereas action-free videos offer abundant observations of physical change. Latent action models can extract such priors, but reconstruction-trained codes may predict future observations without the structure …

• Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not

  Kavya Bhand, Aadi Joshi · 27 de julio de 2026

  Multi-horizon latent consistency is a common training knob in video predictors and world models, but practitioners rarely know what it does to transition geometry. We treat lambda, the weight on multi-step latent agreement, as a diagnostic control and measure an empirical expansion proxy L20,q95 tog…

• Multi-Task Learning for Heterogeneous Prediction from Video Game State with Transfer Learning

  Jonas Pech\'e, Aliaksei Tsishurou, Alexander Zap, G\"unter Wallner · 24 de julio de 2026

  Multi-task learning (MTL) is a promising approach for prediction tasks derived from video game state data, as modern game telemetry provides multiple related supervision signals from the same structured observations. We study whether a shared model trained jointly across tasks in team-based multipla…

• Quality Action Assurance: Multimodal Verification of Examiner Claims in VR OSCEs

  Harry Rogers, Sally Shiels, Ashley Tomlinson, James Thomas, James Aylward, Nathan Gauge, Helen Higham, Alison Noble · 22 de julio de 2026

  Objective Structured Clinical Examinations (OSCEs) are the gold standard for assessing clinical competence, yet scoring remains vulnerable to examiner subjectivity, fatigue, and cognitive bias. Standard examiner validation via inter-rater statistics lacks explanatory power regarding the source of er…

• STAR: Skeletal Token Alignment and Rearrangement for Interaction Recognition

  Yuhang Wen, Mengyuan Liu, Zixuan Tang, Junsong Yuan, Sirui Li, Beichen Ding · 21 de julio de 2026

  Understanding physical human-robot and human-human interactions is a challenging yet emerging topic in 3D vision. While most existing methods rely on skeleton sequences--effective in low-light and privacy-sensitive environment--they face two major challenges: 1) learning and effectively exploiting i…

• ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video

  Xiaozhong Lyu, Gen Li, Zhiyin Qian, Xucong Zhang, Marc Pollefeys, Siyu Tang · 21 de julio de 2026

  Egocentric devices, such as wearable front-facing cameras, provide a unique perspective for capturing the continuous interaction between a human viewer and the surrounding environment. A holistic and efficient multimodal model capable of reconstructing this 4D representation is therefore highly desi…

• EgoExoMoCap: Distributed Ego-Exo Human Motion Capture

  Jiaxi Jiang, Bharat Lal Bhatnagar, Nan Yang, Lingni Ma, Sebastian Starke, Robin Kips, Nadine Bertsch, Christian Holz, Federica Bogo · 20 de julio de 2026

  Human motion capture from head-mounted devices (HMDs) offers a scalable way to acquire real-world human motion and interaction data, which is crucial for applications in embodied AI and VR/AR. Existing approaches focus on either egocentric body tracking, estimating the motion of the subject wearing …

• On the Geometry of Learned Representations in Event-Based Multi-Modal Egomotion Estimation

  Stefano Silvestrini, Michele Ceresoli · 20 de julio de 2026

  Classical approaches to event-based egomotion estimation, including those adopted by the top-performing teams of the ELOPE challenge, rely on geometric optimization frameworks such as contrast maximization, homography estimation, or dense optical flow combined with analytic motion inversion. This wo…

• A Standardized Benchmark for Skeleton-Based Rehabilitation Assessment Using Deep Learning

  Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier · 20 de julio de 2026

  Automated assessment of human motion plays a vital role in rehabilitation, enabling objective evaluation of patient performance and progress. Unlike general human activity recognition, rehabilitation motion assessment focuses on analyzing the quality of movement within the same action class, requiri…

• Augmentations for Robust and Efficient Imitation Learning in Streamed Video Games

  Somjit Nath, Abdelhak Lemkhenter, Pallavi Choudhury, Chris Lovett, Katja Hofmann, Sergio Valcarcel Macua, Lukas Sch\"afer · 17 de julio de 2026

  Imitation learning is an appealing way to scale game-playing agents to complex 3D environments by training policies to map visual observations to actions from human demonstrations. However, these demonstrations are expensive to collect and modern game-playing is often done through streaming in which…

• Classifying daily activities needs posture, reconstructing them needs motion

  Arefeh Farahmandi, Gunnar Blohm · 16 de julio de 2026

  Humans recognize movements effortlessly, even from noisy and complex visual input. But what information in the stimulus allows humans to rapidly classify movements? No framework has systematically compared different strategies of movement analysis to address this question. Here, we used videos of 16…

• Human4K: A Large-Scale 4K Multi-View Mocap Dataset for Whole-Body 3D Human Reconstruction

  Tianshun Han, Ziyu Shi, Lijian Liu, Ajian Liu, Benjia Zhou, Hugo Jair Escalante, Yanyan Liang, Sergio Escalera, Zhen Lei, Jun Wan · 16 de julio de 2026

  Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenarios. They often produce unstable geometry, inaccurate limb articulation and unreliable predictions under depth ambiguity or self-occlusion. A key reaso…

• On Occlusions in Video Action Detection: Benchmark Datasets And Training Recipes

  Rajat Modi, Vibhav Vineet, Yogesh Singh Rawat · 14 de julio de 2026

  This paper explores the impact of occlusions in video action detection. We facilitate this study by introducing five new benchmark datasets namely O-UCF and O-JHMDB consisting of synthetically controlled static/dynamic occlusions, OVIS-UCF and OVIS-JHMDB consisting of occlusions with realistic motio…