Pan, Xiaoyu;
Zheng, Bowen;
Jiang, Xinwei;
Zeng, Zijiao;
Kou, Qilong;
Wang, He;
Jin, Xiaogang;
(2024)
RoMo: A Robust Solver for Full-body Unlabeled Optical Motion Capture.
In: Igarashi, Takeo and Shamir, Ariel and Zhang, Hao (Richard), (eds.)
SA '24: SIGGRAPH Asia 2024 Conference Papers.
(pp. pp. 1-11).
ACM: New York, NY, USA.
Preview |
Text
2410.02788v1.pdf - Accepted Version Download (11MB) | Preview |
Abstract
Optical motion capture (MoCap) is the "gold standard" for accurately capturing full-body motions. To make use of raw MoCap point data, the system labels the points with corresponding body part locations and solves the full-body motions. However, MoCap data often contains mislabeling, occlusion and positional errors, requiring extensive manual correction. To alleviate this burden, we introduce RoMo, a learning-based framework for robustly labeling and solving raw optical motion capture data. In the labeling stage, RoMo employs a divide-and-conquer strategy to break down the complex full-body labeling challenge into manageable subtasks: alignment, full-body segmentation and part-specific labeling. To utilize the temporal continuity of markers, RoMo generates marker tracklets using a K-partite graph-based clustering algorithm, where markers serve as nodes, and edges are formed based on positional and feature similarities. For motion solving, to prevent error accumulation along the kinematic chain, we introduce a hybrid inverse kinematic solver that utilizes joint positions as intermediate representations and adjusts the template skeleton to match estimated joint positions. We demonstrate that RoMo achieves high labeling and solving accuracy across multiple metrics and various datasets. Extensive comparisons show that our method outperforms state-of-the-art research methods. On a real dataset, RoMo improves the F1 score of hand labeling from 0.94 to 0.98, and reduces joint position error of body motion solving by 25%. Furthermore, RoMo can be applied in scenarios where commercial systems are inadequate. The code and data for RoMo are available at https://github.com/non-void/RoMo.
| Type: | Proceedings paper |
|---|---|
| Title: | RoMo: A Robust Solver for Full-body Unlabeled Optical Motion Capture |
| Event: | SA '24: SIGGRAPH Asia 2024 Conference Papers |
| ISBN-13: | 9798400711312 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3680528.3687615 |
| Publisher version: | https://doi.org/10.1145/3680528.3687615 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10201161 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
