3D Human Scan With A Moving Event Camera
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929315207184384 |
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| author | Kohyama, Kai Shiba, Shintaro Aoki, Yoshimitsu |
| author_facet | Kohyama, Kai Shiba, Shintaro Aoki, Yoshimitsu |
| contents | 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/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_08504 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3D Human Scan With A Moving Event Camera Kohyama, Kai Shiba, Shintaro Aoki, Yoshimitsu Computer Vision and Pattern Recognition 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/ |
| title | 3D Human Scan With A Moving Event Camera |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.08504 |