4.6 Article

Synthesising 2D Video from 3D Motion Data for Machine Learning Applications

期刊

SENSORS
卷 22, 期 17, 页码 -

出版社

MDPI
DOI: 10.3390/s22176522

关键词

synthesising video images; pose estimation; machine learning; biomechanics; 3D motion data

资金

  1. Australian Institute of Sport
  2. University of Western Australia's Minderoo Tech and Policy Lab

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This study proposes and validates a method to synthesize 2D video frames from historic 3D motion data, and demonstrates its utility in human pose estimation and ground reaction force estimation tasks, showing improved accuracy by enlarging the dataset with synthetic views.
To increase the utility of legacy, gold-standard, three-dimensional (3D) motion capture datasets for computer vision-based machine learning applications, this study proposed and validated a method to synthesise two-dimensional (2D) video image frames from historic 3D motion data. We applied the video-based human pose estimation model OpenPose to real (in situ) and synthesised 2D videos and compared anatomical landmark keypoint outputs, with trivial observed differences (2.11-3.49 mm). We further demonstrated the utility of the method in a downstream machine learning use-case in which we trained and then tested the validity of an artificial neural network (ANN) to estimate ground reaction forces (GRFs) using synthesised and real 2D videos. Training an ANN to estimate GRFs using eight OpenPose keypoints derived from synthesised 2D videos resulted in accurate waveform GRF estimations (r > 0.9; nRMSE < 14%). When compared with using the smaller number of real videos only, accuracy was improved by adding the synthetic views and enlarging the dataset. The results highlight the utility of the developed approach to enlarge small 2D video datasets, or to create 2D video images to accompany 3D motion capture datasets to make them accessible for machine learning applications.

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