by Dan Song, Ruofeng Tong, Jian Chang, Xiaosong Yang, Min Tang, and Jian Jun Zhang.

Overview: Our novel automatic framework experiences two stages: 3D landmarks regression and 3D body reconstruction. We create training samples using our database which contains 3D naked and dressed body pairs. Regressors are trained to learn the predictive relationship between dressed-human silhouettes and target 3D naked body landmarks. The regressed landmarks are used as constraints to optimize SCAPE model which is trained with a database of 3D naked bodies.

 

Abstract

Estimation of 3D body shapes from dressed-human photos is an important but challenging problem in virtual fitting. We propose a novel automatic framework to efficiently estimate 3D body shapes under clothes. We construct a database of 3D naked and dressed body pairs, based on which we learn how to predict 3D positions of body landmarks (which further constrain a parametric human body model) automatically according to dressed-human silhouettes. Critical vertices are selected on 3D registered human bodies as landmarks to represent body shapes, so as to avoid the time-consuming vertices correspondences finding process for parametric body reconstruction. Our method can estimate 3D body shapes from dressed-human silhouettes within 4 seconds, while the fastest method reported previously need 1 minute. In addition, our estimation error is within the size tolerance for clothing industry. We dress 6042 naked bodies with 3 sets of common clothes by physically based cloth simulation technique. To the best of our knowledge, We are the first to construct such a database containing 3D naked and dressed body pairs and our database may contribute to the areas of human body shapes estimation and cloth simulation.

Contents

Paper (PDF 12.7 MB)

Appendix (PDF 3.7 MB)

Dan Song, Ruofeng Tong, Jian Chang, Xiaosong Yang, Min Tang, and Jian Jun Zhang, 3D Body Shapes Estimation from Dressed-human Silhouettes, To appear in Pacific Graphics 2016.

Video (54 MB)

Acknowledgements

The research is supported in part by NSFC (61572424) and the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme FP7 (2007-2013) under REA grant agreement No.612627-"AniNex". It is partially supported by the grant of the "Sino-UK Higher Education Research Partnership for Ph.D. Studies" Project funded by the Department of Business, Innovation and Skills of the British Government and Ministry of Education of P.R. China. Min Tang is supported in part by NSFC (61572423), Zhejiang Provincial NSFC (LZ16F020003), and the Doctoral Fund of Ministry of Education of China (20130101110133).