Auto-labelling of Markers in Optical Motion Capture by Permutation Learning
Abstract
Optical marker-based motion capture is a vital tool in applications such as motion and behavioural analysis, animation, and biomechanics. Labelling, that is, assigning optical markers to the pre-defined positions on the body is a time consuming and labour intensive postprocessing part of current motion capture pipelines. The problem can be considered as a ranking process in which markers shuffled by an unknown permutation matrix are sorted to recover the correct order. In this paper, we present a framework for automatic marker labelling which first estimates a permutation matrix for each individual frame using a differentiable permutation learning model and then utilizes temporal consistency to identify and correct remaining labelling errors. Experiments conducted on the test data show the effectiveness of our framework.
Cite
@article{arxiv.1907.13580,
title = {Auto-labelling of Markers in Optical Motion Capture by Permutation Learning},
author = {Saeed Ghorbani and Ali Etemad and Nikolaus F. Troje},
journal= {arXiv preprint arXiv:1907.13580},
year = {2019}
}