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

arXiv: https://arxiv.org/abs/1907.13580 · HTML: https://mlsanity.com/paper/1907.13580/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [SOMA: Solving Optical Marker-Based MoCap Automatically](https://mlsanity.com/paper/2110.04431/) (2021)
- [RoMo: A Robust Solver for Full-body Unlabeled Optical Motion Capture](https://mlsanity.com/paper/2410.02788/) (2024)
- [Towards Unstructured Unlabeled Optical Mocap: A Video Helps!](https://mlsanity.com/paper/2407.06114/) (2024)
- [Noise-in, Bias-out: Balanced and Real-time MoCap Solving](https://mlsanity.com/paper/2309.14330/) (2023)

## Abstract (arXiv)

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.