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HUMBI: A Large Multiview Dataset of Human Body Expressions
AbstractThis 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.
Related Material
[pdf] [supp] [arXiv][bibtex]@InProceedings{Yu_2020_CVPR,
author = {Yu, Zhixuan and Yoon, Jae Shin and Lee, In Kyu and Venkatesh, Prashanth and Park, Jaesik and Yu, Jihun and Park, Hyun Soo},
title = {HUMBI: A Large Multiview Dataset of Human Body Expressions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
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