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 |
Abstract
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).
Paper
Dataset
YNL-MP dataset, Version 1.1 [12 Oct, 2020] Copyright 2020, The University of Tokyo Latest Update (12 Oct, 2020)
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Citation
@article{ohashi20vmocap,
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}
}
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