Abstract
This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing. The goal of HUMBI is to facilitate modeling view-specific appearance and geometry of gaze, face, hand, body, and garment from assorted people. 107 synchronized HD cam- eras are used to capture 772 distinctive subjects across gen- der, ethnicity, age, and physical condition. With the mul- tiview image streams, we reconstruct high fidelity body ex- pressions using 3D mesh models, which allows representing view-specific appearance using their canonical atlas. We demonstrate that HUMBI is highly effective in learning and reconstructing a complete human model and is complemen- tary to the existing datasets of human body expressions with limited views and subjects such as MPII-Gaze, Multi-PIE, Human3.6M, and Panoptic Studio datasets.
| Original language | English (US) |
|---|---|
| Article number | 9156401 |
| Pages (from-to) | 2987-2997 |
| Number of pages | 11 |
| Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| DOIs | |
| State | Published - 2020 |
| Event | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States Duration: Jun 14 2020 → Jun 19 2020 |
Bibliographical note
Funding Information:This work was partially supported by National Science Foundation (No.1846031 and 1919965), National Research Foundation of Korea, and Ministry of Science and ICT of Korea (No. 2020R1C1C1015260).
Publisher Copyright:
© 2020 IEEE.