# Auto-labelling of Markers in Optical Motion Capture by Permutation Learning

## TL;DR

This paper introduces a novel framework for automatic optical marker labelling in motion capture, leveraging permutation learning and temporal consistency to improve accuracy and reduce manual effort.

## Contribution

It proposes a differentiable permutation learning model combined with temporal consistency for automatic marker labelling, a significant advancement over manual and heuristic methods.

## Key findings

- Effective permutation matrix estimation for each frame
- Improved labelling accuracy through temporal correction
- Demonstrated success on test motion capture data

## 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.