HUMBI: A Large Multiview Dataset of Human Body Expressions
Zhixuan Yu, Jae Shin Yoon, In Kyu Lee, Prashanth Venkatesh, Jaesik Park, Jihun Yu, Hyun Soo Park
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.
BibTeX
@inproceedings{cvpr2020_humbialargemulti,
title = {HUMBI: A Large Multiview Dataset of Human Body Expressions},
author = {Zhixuan Yu and Jae Shin Yoon and In Kyu Lee and Prashanth Venkatesh and Jaesik Park and Jihun Yu and Hyun Soo Park},
booktitle = {CVPR 2020},
year = {2020}
}