  High-fidelity clothing reconstruction is the key to achieving photorealism in
a wide range of applications including human digitization, virtual try-on, etc.
Recent advances in learning-based approaches have accomplished unprecedented
accuracy in recovering unclothed human shape and pose from single images,
thanks to the availability of powerful statistical models, e.g. SMPL, learned
from a large number of body scans. In contrast, modeling and recovering clothed
human and 3D garments remains notoriously difficult, mostly due to the lack of
large-scale clothing models available for the research community. We propose to
fill this gap by introducing Deep Fashion3D, the largest collection to date of
3D garment models, with the goal of establishing a novel benchmark and dataset
for the evaluation of image-based garment reconstruction systems. Deep
Fashion3D contains 2078 models reconstructed from real garments, which covers
10 different categories and 563 garment instances. It provides rich annotations
including 3D feature lines, 3D body pose and the corresponded multi-view real
images. In addition, each garment is randomly posed to enhance the variety of
real clothing deformations. To demonstrate the advantage of Deep Fashion3D, we
propose a novel baseline approach for single-view garment reconstruction, which
leverages the merits of both mesh and implicit representations. A novel
adaptable template is proposed to enable the learning of all types of clothing
in a single network. Extensive experiments have been conducted on the proposed
dataset to verify its significance and usefulness. We will make Deep Fashion3D
publicly available upon publication.
