Synergetic Reconstruction from 2D Pose and 3D Motion for Wide-Space Multi-Person Video Motion Capture in the Wild

Takuya Ohashi1,2 Yosuke Ikegami2 Yoshihiko Nakamura2
1NTT DOCOMO 2The Univerisity of Tokyo


Although many studies have investigated markerless motion capture, the technology has not been applied to real sports or concerts. In this paper, we propose a markerless motion capture method with spatiotemporal accuracy and smoothness from multiple cameras in wide-space and multi-person environments. The proposed method predicts each person's 3D pose and determines the bounding box of multi-camera images small enough. This prediction and spatiotemporal filtering based on human skeletal model enables 3D reconstruction of the person and demonstrates high-accuracy. The accurate 3D reconstruction is then used to predict the bounding box of each camera image in the next frame. This is feedback from the 3D motion to 2D pose, and provides a synergetic effect on the overall performance of video motion capture. We evaluated the proposed method using various datasets and a real sports field. The experimental results demonstrate that the mean per joint position error (MPJPE) is 31.5 mm and the percentage of correct parts (PCP) is 99.5% for five people dynamically moving while satisfying the range of motion (RoM).


[Journal] [arXiv]


YNL-MP dataset, Version 1.1 [12 Oct, 2020]
Copyright 2020, The University of Tokyo

Latest Update (12 Oct, 2020)

  • add test9 (hug motion)
  • change the calibration parameters [mm] to [m]

  • This dataset contains eight test data. In each test, 1-5 subjects were recorded by 8 cameras (4 view points) at 60Hz. For the 3D ground-truth, 1-2 of them were measured by optical motion capture system at 200Hz simultaneously. In addition, to evaluate our work, we share the results. The results include joint position (trc), joint angle (bvh), and joint position without considering range of motion.
    The dataset is shared only for research purposes. Redistribution of the dataset or its modified version is strictly prohibited without a written permission by the copyright holder.

    Download YNL-MP dataset (18.9GB)


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      title = {{Synergetic Reconstruction from 2D Pose and 3D Motion for Wide-Space Multi-Person Video Motion Capture in the Wild}},
      author = {Takuya Ohashi and Yosuke Ikegami and Yoshihiko Nakamura},
      journal = {Image and Vision Computing},
      volume = {104},
      pages = {104028},
      year = {2020}