filename     : Dib16b.pdf
entry        : inproceedings
conference   : Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA October 25-28, 2016
pages        : 108--117
year         : 2016
month        : October
title        : HS-Nets: Estimating Human Body Shape from Silhouettes with Convolutional Neural Networks
subtitle     :
author       : Endri Dibra and Himanshu Jain and Cengiz {\"{O}}ztireli and Remo Ziegler and Markus Gross
booktitle    : Fourth International Conference on 3D Vision, 3DV 2016
ISSN/ISBN    :
editor       : 
publisher    : {IEEE} Computer Society
publ.place   : 
volume       : 
issue        : 
language     : english
keywords     : human shape estimation, deep networks, shape from silhouettes, CNNs
abstract     : We represent human body shape estimation from binary silhouettes or shaded images as a regression problem, and describe a novel method to tackle it using CNNs. Utilizing a parametric body model, we train CNNs to learn a global mapping from the input to shape parameters used to reconstruct the shapes of people, in neutral poses, with the application of garment fitting in mind. This results in an accurate, robust and automatic system, orders of magnitude faster than methods we compare to, enabling interactive applications. In addition, we show how to combine silhouettes from two views to improve prediction over a single view. The method is extensively evaluated on thousands of synthetic shapes and real data and compared to stateof-art approaches, clearly outperforming methods based on global fitting and strongly competing with more expensive local fitting based ones.