Abstract
We address the problem of estimating human pose and
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 body models produce smooth
shapes lacking personalized details. We contribute a new
approach to recover a personalized shape of the person.
The estimated shape deviates 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
BUFF, a new 4D dataset that enables quantitative evaluation http://buff.is.tue.mpg.de/. Our method outperforms the state of the art in both pose estimation and
shape estimation, qualitatively and quantitatively.