3D Human Scan With A Moving Event Camera

Kai Kohyama, Shintaro Shiba, Yoshimitsu Aoki; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5586-5596

Capturing the 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.