Encoded Marker Clusters for Auto-Labeling in Optical Motion Capture

Abstract

Marker-based optical motion capture (MoCap) is a vital tool in applications such as virtual production, and movement sciences. However, reconstructing scattered MoCap data into real motion sequences is challenging, and data processing is time-consuming and labor-intensive. Here we propose a novel framework for MoCap auto-labeling and matching. In this framework, we designed novel clusters of reflective markers called auto-labeling encoded marker clusters (AEMCs), including clusters with an explicit header (AEMCs-E) and an implicit header (AEMCs-I). Combining cluster design and coding theory gives each cluster a unique codeword for MoCap auto-labeling and matching. Moreover, we provide a method of mapping and decoding for cluster labeling. The labeling results are only determined by the intrinsic characteristics of the clusters instead of the skeleton structure or posture of the subjects. Compared with commercial software and data-driven methods, our method has better labeling accuracy in heterogeneous targets and unknown marker layouts, which demonstrates the promising application of motion capture in humans, rigid or flexible robots.