3D Human Scan With A Moving Event Camera

• Published in: arXiv.org
• Main Authors: Kohyama, Kai, Shiba, Shintaro, Aoki, Yoshimitsu
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 16.04.2024
• Subjects: Blurring; Cameras; Computer vision; Dynamic range; Finite element method; Human body; Pose estimation; Temporal resolution; Virtual reality
• ISSN: 2331-8422

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/