@inproceedings{shape_under_cloth:CVPR17,
  title = {Detailed, accurate, human shape estimation from clothed {3D} scan sequences},
  booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  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.},
  pages = {5484-5493},
  publisher = {IEEE Computer Society},
  address = {Washington, DC, USA},
  month = jul,
  year = {2017},
  note = {Spotlight},
  author = {Zhang, Chao and Pujades, Sergi and Black, Michael and Pons-Moll, Gerard},
  doi = {10.1109/CVPR.2017.582},
  month_numeric = {7}
}