Estimating body shape under clothing (CVPR 2017)

21 July 2017

06:53

Detailed, accurate, human shape estimation from clothed 3D scan sequences Abstract: We address the problem of estimating human body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual try-on, health monitoring, and avatar creation for virtual reality. Scanning bodies in minimal clothing, however, presents a practical barrier to these applications. We address this problem by estimating body shape under clothing from a sequence of 3D scans. Previous methods that have exploited statistical models of body shape produce overly smooth shapes lacking personalized details. In this paper we contribute a new approach to recover not only an approximate shape of the person, but also their detailed shape. Our approach allows the estimated shape to deviate from a parametric model to fit the 3D scans. We demonstrate the method using high quality 4D data as well as sequences of visual hulls extracted from multi-view images. We also make available a new high quality 4D dataset that enables quantitative evaluation. Our method outperforms the previous state of the art, both qualitatively and quantitatively. Authors: Chao Zhang https://sites.google.com/site/cschaozhang/ Sergi Pujades https://ps.is.tuebingen.mpg.de/person/spujades Michael J. Black https://ps.is.tuebingen.mpg.de/person/black Gerard Pons-Moll https://ps.is.tuebingen.mpg.de/person/gpons pdf: https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/369/buff_minshape_estimation.pdf dataset: http://buff.is.tue.mpg.de/ related work on clothing capture: https://youtu.be/dVxj8tzx04U bibtex: @inproceedings{shape_under_cloth:CVPR17, title = {Detailed, accurate, human shape estimation from clothed {3D} scan sequences}, author = {Zhang, Chao and Pujades, Sergi and Black, Michael and Pons-Moll, Gerard}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2017} }