  In this paper, we present a simple yet effective method to automatically
transfer textures of clothing images (front and back) to 3D garments worn on
top SMPL, in real time. We first automatically compute training pairs of images
with aligned 3D garments using a custom non-rigid 3D to 2D registration method,
which is accurate but slow. Using these pairs, we learn a mapping from pixels
to the 3D garment surface. Our idea is to learn dense correspondences from
garment image silhouettes to a 2D-UV map of a 3D garment surface using shape
information alone, completely ignoring texture, which allows us to generalize
to the wide range of web images. Several experiments demonstrate that our model
is more accurate than widely used baselines such as thin-plate-spline warping
and image-to-image translation networks while being orders of magnitude faster.
Our model opens the door for applications such as virtual try-on, and allows
for generation of 3D humans with varied textures which is necessary for
learning.
