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Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors

Yu Zhang, Songpengcheng Xia, Lei Chu, Jiarui Yang, Qi Wu, Ling Pei
CVPR 2024 pp. 1889-1899
doi:10.1109/CVPR52733.2024.00185 /cvpr/2024/zhang2024cvpr-dynamic/

Abstract

This paper introduces a novel human pose estimation approach using sparse inertial sensors addressing the shortcomings of previous methods reliant on synthetic data. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and model generalization. This method features two innovative components: a pseudo-velocity regression model for dynamic motion capture with inertial sensors and a part-based model dividing the body and sensor data into three regions each focusing on their unique characteristics. The approach demonstrates superior performance over state-of-the-art models across five public datasets notably reducing pose error by 19% on the DIP-IMU dataset thus representing a significant improvement in inertial sensor-based human pose estimation. Our codes are available at https://github.com/dx118/dynaip

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Text

Zhang et al. "Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.00185

Markdown

[Zhang et al. "Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/zhang2024cvpr-dynamic/) doi:10.1109/CVPR52733.2024.00185

BibTeX

@inproceedings{zhang2024cvpr-dynamic,
  title     = {{Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors}},
  author    = {Zhang, Yu and Xia, Songpengcheng and Chu, Lei and Yang, Jiarui and Wu, Qi and Pei, Ling},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2024},
  pages     = {1889-1899},
  doi       = {10.1109/CVPR52733.2024.00185},
  url       = {https://mlanthology.org/cvpr/2024/zhang2024cvpr-dynamic/}
}