@inproceedings{568c1636dc604b5383bb1d8ed92c335d,
title = "BodyPrint: Pose invariant 3D shape matching of human bodies",
abstract = "3D human body shape matching has large potential on many real world applications, especially with the recent advances in the 3D range sensing technology. We address this problem by proposing a novel holistic human body shape descriptor called BodyPrint. To compute the bodyprint for a given body scan, we fit a deformable human body mesh and project the mesh parameters to a low-dimensional subspace which improves discriminability across different persons. Experiments are carried out on three real-world human body datasets to demonstrate that BodyPrint is robust to pose variation as well as missing information and sensor noise. It improves the matching accuracy significantly compared to conventional 3D shape matching techniques using local features. To facilitate practical applications where the shape database may grow over time, we also extend our learning framework to handle online updates.",
author = "Jiangping Wang and Kai Ma and Singh, {Vivek Kumar} and Thomas Huang and Terrence Chen",
year = "2015",
month = feb,
day = "17",
doi = "10.1109/ICCV.2015.186",
language = "English (US)",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1591--1599",
booktitle = "2015 International Conference on Computer Vision, ICCV 2015",
address = "United States",
note = "15th IEEE International Conference on Computer Vision, ICCV 2015 ; Conference date: 11-12-2015 Through 18-12-2015",
}