Markerless Motion Capture

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Overview

Markerless motion capture reconstructs how people move in 3D from ordinary video, multi-camera recordings, or wearable sensors, enabling measurement and animation without laboratory marker suits. Common approaches detect 2D keypoints and lift them to 3D, fit articulated body meshes, or fuse views and sensor signals; temporal models and biomechanical constraints help produce coherent, physically plausible motion. Researchers apply these methods to clinical gait and rehabilitation, sports, hand tracking, animation, and robot learning, where accessible capture can extend measurement beyond specialized labs. Accuracy is improving, but occlusion, camera and sensor variation, and differences across populations remain difficult, and many systems still need stronger clinical validation—especially for out-of-plane motion and kinetics.

Latest papers

CardsList

68 papers9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

  1. Automating Timed Up and Go Phase Segmentation and Gait Analysis via the tugturn Markerless 3D Pipeline

    Feb 24, 2026Abel Gonçalves Chinaglia, Guilherme Manna Cesar, Paulo Roberto Pereira SantiagoGait AnalysisMarkerless Motion Capture

    Biomechanics and Motor Control Laboratory, School of Physical Education and Sport of Ribeirao Preto, University of Sao Paulo, Brazil · Graduate Program in Rehabilitation and Functional Performance, Ribeirao Preto Medical School, University of Sao Paulo, Brazil · Department of Physical Therapy, Brooks College of Health, University of North Florida, USA

  2. BigMaQ: A Big Macaque Motion and Animation Dataset Bridging Image and 3D Pose Representations

    Feb 23, 2026Lucas Martini, Alexander Lappe, Anna Bognár +2Video Action RecognitionMarkerless Motion Capture

    Hertie Institute, University of Tübingen · IMPRS-IS · KU Leuven

  3. MorphGS: Morphology-Adaptive Articulated 3D Motion Transfer from Videos

    Jan 6, 2026Taeyeon Kim, Youngju Na, Jumin Lee +3Markerless Motion CaptureMotion Retargeting

    KAIST, Republic of Korea

  4. OmniEgoCap: Device-Agnostic Sequence-Level Egocentric Motion Reconstruction

    Dec 22, 2025Kyungwon Cho, Jeonghyeon Na, Hanbyul Joo3D Human Motion ReconstructionMarkerless Motion Capture

    Seoul National University

  5. MonoMSK: Monocular 3D Musculoskeletal Dynamics Estimation

    Nov 24, 2025Farnoosh Koleini, Hongfei Xue, Ahmed Helmy +1Markerless Motion CaptureMusculoskeletal Modeling

    University of North Carolina at Charlotte

  6. TrackStudio: An Integrated Toolkit for Markerless Tracking

    Nov 10, 2025Hristo Dimitrov, Viktorija Pavalkyte, Giulia Dominijanni +1Markerless Motion Capture3D 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

  7. Joint Optimization for 4D Human-Scene Reconstruction in the Wild

    Jan 4, 2025Zhizheng Liu, Joe Lin, Wayne Wu +1Markerless Motion Capture4D Reconstruction

    Department of Computer Science, University of California, Los Angeles

  8. Multi-view Hand Reconstruction with a Point-Embedded Transformer

    Aug 20, 2024Lixin Yang, Licheng Zhong, Pengxiang Zhu +43D ViTsMarkerless Motion Capture

    School of Artificial Intelligence (SAI), Shanghai Jiao Tong University, Shanghai 200230, China · School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China +1