Auto-labelling of Markers in Optical Motion Capture by Permutation Learning
This work addresses a time-consuming postprocessing problem in motion capture for applications like animation and biomechanics, offering an incremental improvement over manual methods.
The paper tackles the labor-intensive task of labeling optical markers in motion capture by proposing an automatic framework that estimates permutation matrices per frame and applies temporal consistency to correct errors, achieving effective results on test data.
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.