# OpenMoCap: Rethinking Optical Motion Capture under Real-world Occlusion

arXiv: https://arxiv.org/abs/2508.12610 · HTML: https://mlsanity.com/paper/2508.12610/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [3D Human Pose Estimation via Intuitive Physics](https://mlsanity.com/paper/2303.18246/) (2023)
- [SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos](https://mlsanity.com/paper/2104.11452/) (2021)
- [RoMo: A Robust Solver for Full-body Unlabeled Optical Motion Capture](https://mlsanity.com/paper/2410.02788/) (2024)
- [A Locality-based Neural Solver for Optical Motion Capture](https://mlsanity.com/paper/2309.00428/) (2023)
- [Towards Unstructured Unlabeled Optical Mocap: A Video Helps!](https://mlsanity.com/paper/2407.06114/) (2024)

## Built on by (most cited first, 1 of 1)

- [DirtyMoCap: Robust Motion Capture from Unconstrained Markers](https://mlsanity.com/paper/2609.19927/) (2026)

## Abstract (arXiv)

Optical motion capture is a foundational technology driving advancements in cutting-edge fields such as virtual reality and film production. However, system performance suffers severely under large-scale marker occlusions common in real-world applications. An in-depth analysis identifies two primary limitations of current models: (i) the lack of training datasets accurately reflecting realistic marker occlusion patterns, and (ii) the absence of training strategies designed to capture long-range dependencies among markers. To tackle these challenges, we introduce the CMU-Occlu dataset, which incorporates ray tracing techniques to realistically simulate practical marker occlusion patterns. Furthermore, we propose OpenMoCap, a novel motion-solving model designed specifically for robust motion capture in environments with significant occlusions. Leveraging a marker-joint chain inference mechanism, OpenMoCap enables simultaneous optimization and construction of deep constraints between markers and joints. Extensive comparative experiments demonstrate that OpenMoCap consistently outperforms competing methods across diverse scenarios, while the CMU-Occlu dataset opens the door for future studies in robust motion solving. The proposed OpenMoCap is integrated into the MoSen MoCap system for practical deployment. The code is released at: https://github.com/qianchen214/OpenMoCap.