3D Human Scan With A Moving Event Camera
<|reference_start|>3D Human Scan With A Moving Event Camera: Capturing a 3D human body is one of the important tasks in computer vision with a wide range of applications such as virtual reality and sports analysis. However, conventional frame cameras are limited by their temporal resolution and dynamic range, which imposes constraints in real-world application setups. Event cameras have the advantages of high temporal resolution and high dynamic range (HDR), but the development of event-based methods is necessary to handle data with different characteristics. This paper proposes a novel event-based method for 3D pose estimation and human mesh recovery. Prior work on event-based human mesh recovery require frames (images) as well as event data. The proposed method solely relies on events; it carves 3D voxels by moving the event camera around a stationary body, reconstructs the human pose and mesh by attenuated rays, and fit statistical body models, preserving high-frequency details. The experimental results show that the proposed method outperforms conventional frame-based methods in the estimation accuracy of both pose and body mesh. We also demonstrate results in challenging situations where a conventional camera has motion blur. This is the first to demonstrate event-only human mesh recovery, and we hope that it is the first step toward achieving robust and accurate 3D human body scanning from vision sensors. https://florpeng.github.io/event-based-human-scan/<|reference_end|>
@article{kohyama20243d,
    title={3D Human Scan With A Moving Event Camera},
    author={Kai Kohyama, Shintaro Shiba, Yoshimitsu Aoki},
    journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  Workshop On Computer Vision For Mixed Reality (CV4MR), Seattle, 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08504},
    primaryClass={cs.CV}
}