3D Human Pose Estimation

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13 papers in the last four weeks, up 86% on the four weeks before. 0.1% of all new papers.

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Learning Human-Object Interaction for 3D Human Pose Estimation from LiDAR Point Clouds
Mar 17, 2026Daniel Sungho Jung, Dohee Cho, Kyoung Mu Lee3D Human Pose Estimation Point Cloud Learning
IPAI · Dept. of ECE&ASRI, Seoul National University
Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors
Mar 17, 2026Ryosuke Hori, Jyun-Ting Song, Zhengyi Luo +43D Human Pose Estimation Multimodal Sensor Fusion
Carnegie Mellon University · Keio University · Keio AI Research Center
Enhancing Hands in 3D Whole-Body Pose Estimation with Conditional Hands Modulator
Mar 16, 2026Gyeongsik Moon3D Human Pose Estimation 3D Hand Pose Estimation
Korea University
HARMONI: Aligning Human and Scene Priors for Multi-View 4D Reconstruction
Mar 13, 2026Sangmin Kim, Minhyuk Hwang, Geonho Cha +23D Reconstruction Multi-View 3D Reconstruction
Seoul National University · NAVER Cloud
Improving 3D Foot Motion Reconstruction in Markerless Monocular Human Motion Capture
Mar 10, 2026Tom Wehrbein, Bodo RosenhahnMarkerless Motion Capture 3D Human Reconstruction
L3S - Leibniz University Hannover, Germany
mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud
Mar 9, 2026Abdullah Al Masud, Shi Xintong, Mondher Bouazizi +1Human Pose Estimation 3D Human Pose Estimation
Graduate School of Science and Technology, Keio University, Kanagawa, 223-8522, Japan · Faculty of Science and Technology, Keio University, Kanagawa, 223-8522, Japan
PCFEx: Point Cloud Feature Extraction for Graph Neural Networks
Mar 9, 2026Abdullah Al Masud, Shi Xintong, Mondher Bouazizi +1Point Cloud Learning 3D Human Pose Estimation
Graduate School of Science and Technology, Keio University, Kanagawa, 223-8522, Japan · Faculty of Science and Technology, Keio University Kanagawa, 223-8522, Japan
Scalable Injury-Risk Screening in Baseball Pitching From Broadcast Video
Mar 5, 2026Jerrin Bright, Justin Mende, John ZelekMarkerless Motion Capture Sports Video Analysis
Vision and Image Processing Lab · University of Waterloo, Canada
EgoPoseFormer v2: Accurate Egocentric Human Motion Estimation for AR/VR
Mar 4, 2026Zhenyu Li, Sai Kumar Dwivedi, Filip Maric +11Human Pose Estimation 3D Human Pose Estimation
Meta · KAUST · Max Planck Institute for Intelligent Systems
Stereo-Inertial Poser: Towards Metric-Accurate Shape-Aware Motion Capture Using Sparse IMUs and a Single Stereo Camera
Mar 2, 2026Tutian Tang, Xingyu Ji, Yutong Li +33D Human Pose Estimation Markerless Motion Capture
School of Computer Science, Shanghai Jiao Tong University · Meta Robotics Institute, SJTU · Shanghai Innovation Institute
SIMSPINE: A Biomechanics-Aware Simulation Framework for 3D Spine Motion Annotation and Benchmarking
Feb 24, 2026Muhammad Saif Ullah Khan, Didier Stricker3D Human Pose Estimation
German Research Center for Artificial Intelligence (DFKI)
SEAL-pose: Enhancing 3D Human Pose Estimation via a Learned Loss for Structural Consistency
Feb 23, 2026Yeonsung Kim, Junggeun Do, Seunguk Do +33D Human Pose Estimation
Seoul National University, South Korea · Texas A&M University, College Station, Texas, USA
SAM 3D Body: Robust Full-Body Human Mesh Recovery
Feb 17, 2026Xitong Yang, Devansh Kukreja, Don Pinkus +11Human Mesh Recovery 3D Reconstruction
Meta Superintelligence Labs
Superman: Unifying Skeleton and Vision for Human Motion Perception and Generation
Feb 2, 2026Xinshun Wang, Peiming Li, Ziyi Wang +5Cross-Modal Representation Learning Unified Multimodal Models
State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School · Sony R&D Center 3Nanyang Technological University
On the Role of Rotation Equivariance in Monocular 2D-to-3D Human Pose Lifting
Jan 20, 2026Pavlo Melnyk, Cuong Le, Urs Waldmann +2Data Augmentation Equivariant Neural Networks
CVL, Linköping University · Independent Researcher
SKEL-CF: Coarse-to-Fine Biomechanical Skeleton and Surface Mesh Recovery
Nov 25, 2025Da Li, Jiping Jin, Xuanlong Yu +63D Reconstruction Human Mesh Recovery
1Intellindust AI Lab · 3ShanghaiTech University · 5Didi Chuxing Co.Ltd +1
MonoMSK: Monocular 3D Musculoskeletal Dynamics Estimation
Nov 24, 2025Farnoosh Koleini, Hongfei Xue, Ahmed Helmy +1Markerless Motion Capture 3D Human Pose Estimation
University of North Carolina at Charlotte
TrackStudio: An Integrated Toolkit for Markerless Tracking
Nov 10, 2025Hristo Dimitrov, Viktorija Pavalkyte, Giulia Dominijanni +1Markerless Motion Capture 3D Hand Pose Estimation
1MRC Cognition and Brain Sciences Unit, University of Cambridge · Center for Neuroprosthetics and School of Engineering, ´Ecole Polytechnique F´ed´erale de Lausanne
PRGCN: A Graph Memory Network for Cross-Sequence Pattern Reuse in 3D Human Pose Estimation
Oct 22, 2025Zhuoyang Xie, Yibo Zhao, Hui Huang +2Memory-Augmented Neural Networks Graph Neural Networks
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, P.R China. · the Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, 300384, China. · School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, P.R China. +1
SAP-CoPE: Social-Aware Planning using Cooperative Pose Estimation with Infrastructure Sensor Nodes
Apr 8, 2025Minghao Ning, Yufeng Yang, Shucheng Huang +5Social Robot Navigation Motion Planning
Mechanical and Mechatronics Eng. Department, University of Waterloo, 200 University Ave W, Waterloo, ON N2L3G1, Canada · Department of Data and Systems Engineering, University of Hong Kong, Pok Fu Lam, Hong Kong, China · Mechanical Engineering Department, University of Alberta, Alberta, T6G1H9, Canada

Mar 17, 2026J/K move · Enter open · S save

Learning Human-Object Interaction for 3D Human Pose Estimation from LiDAR Point Clouds

Daniel Sungho Jung, Dohee Cho, Kyoung Mu Lee

IPAI · Dept. of ECE&ASRI, Seoul National University

3D Human Pose Estimation Point Cloud Learning Human-Object Interaction

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Understanding humans from LiDAR point clouds is one of the most critical tasks in autonomous driving due to its close relationships with pedestrian safety, yet it remains challenging in the presence of diverse human-object interactions and cluttered backgrounds. Nevertheless, existing methods largely overlook the potential of leveraging human-object interactions to build robust 3D human pose estimation frameworks. There are two major challenges that motivate the incorporation of human-object interaction. First, human-object interactions introduce spatial ambiguity between human and object points, which often leads to erroneous 3D human keypoint predictions in interaction regions. Second, there exists severe class imbalance in the number of points between interacting and non-interacting body parts, with the interaction-frequent regions such as hand and foot being sparsely observed in LiDAR data. To address these challenges, we propose a Human-Object Interaction Learning (HOIL) framework for robust 3D human pose estimation from LiDAR point clouds. To mitigate the spatial ambiguity issue, we present human-object interaction-aware contrastive learning (HOICL) that effectively enhances feature discrimination between human and object points, particularly in interaction regions. To alleviate the class imbalance issue, we introduce contact-aware part-guided pooling (CPPool) that adaptively reallocates representational capacity by compressing overrepresented points while preserving informative points from interacting body parts. In addition, we present an optional contact-based temporal refinement that refines erroneous per-frame keypoint estimates using contact cues over time. As a result, our HOIL effectively leverages human-object interaction to resolve spatial ambiguity and class imbalance in interaction regions. Codes will be released.