TaiChi Action Capture and Performance Analysis with Multi-view RGB Cameras
This work addresses the lack of vision-based motion capture for TaiChi, enabling performance analysis for different groups, but it is incremental as it combines existing methods for a specific domain.
The paper tackles the problem of markerless motion capture and analysis for professional TaiChi movements by proposing a framework using multi-view geometry and AI, achieving sparse 3D skeleton fusion and dense 3D surface reconstruction with demonstrated efficiency in experiments.
Recent advances in computer vision and deep learning have influenced the field of sports performance analysis for researchers to track and reconstruct freely moving humans without any marker attachment. However, there are few works for vision-based motion capture and intelligent analysis for professional TaiChi movement. In this paper, we propose a framework for TaiChi performance capture and analysis with multi-view geometry and artificial intelligence technology. The main innovative work is as follows: 1) A multi-camera system suitable for TaiChi motion capture is built and the multi-view TaiChi data is collected and processed; 2) A combination of traditional visual method and implicit neural radiance field is proposed to achieve sparse 3D skeleton fusion and dense 3D surface reconstruction. 3) The normalization modeling of movement sequences is carried out based on motion transfer, so as to realize TaiChi performance analysis for different groups. We have carried out evaluation experiments, and the experimental results have shown the efficiency of our method.