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HUMBI: A Large Multiview Dataset of Human Body Expressions

Cited 31 time in Web of Science Cited 51 time in Scopus
Authors

Yu, Zhixuan; Shin Yoon, Jae; Lee, In Kyu; Venkatesh, Prashanth; Park, Jaesik; Yu, Jihun; Park, Hyun Soo

Issue Date
2020
Publisher
IEEE
Citation
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2987-2997
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.
ISSN
1063-6919
URI
https://hdl.handle.net/10371/201306
DOI
https://doi.org/10.1109/CVPR42600.2020.00306
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  • College of Engineering
  • Dept. of Computer Science and Engineering
Research Area Computer Graphics, Computer Vision, Machine Learning, Robotics

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