Quality Dynamic Human Body Modeling Using a Single Low-cost Depth Camera

Qing Zhang, Bo Fu, Mao Ye, Ruigang Yang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 676-683

Abstract


In this paper we present a novel autonomous pipeline to build a personalized parametric model (pose-driven avatar) using a single depth sensor. Our method first captures a few high-quality scans of the user rotating herself at multiple poses from different views. We fit each incomplete scan using template fitting techniques with a generic human template, and register all scans to every pose using global consistency constraints. After registration, these watertight models with different poses are used to train a parametric model in a fashion similar to the SCAPE method. Once the parametric model is built, it can be used as an animitable avatar or more interestingly synthesizing dynamic 3D models from single-view depth videos. Experimental results demonstrate the effectiveness of our system to produce dynamic models.

Related Material


[pdf]
[bibtex]
@InProceedings{Zhang_2014_CVPR,
author = {Zhang, Qing and Fu, Bo and Ye, Mao and Yang, Ruigang},
title = {Quality Dynamic Human Body Modeling Using a Single Low-cost Depth Camera},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}