  In this paper, we consider the problem to automatically reconstruct garment
and body shapes from a single near-front view RGB image. To this end, we
propose a layered garment representation on top of SMPL and novelly make the
skinning weight of garment independent of the body mesh, which significantly
improves the expression ability of our garment model. Compared with existing
methods, our method can support more garment categories and recover more
accurate geometry. To train our model, we construct two large scale datasets
with ground truth body and garment geometries as well as paired color images.
Compared with single mesh or non-parametric representation, our method can
achieve more flexible control with separate meshes, makes applications like
re-pose, garment transfer, and garment texture mapping possible. Code and some
data is available at https://github.com/jby1993/BCNet.
