  Image virtual try-on replaces the clothes on a person image with a desired
in-shop clothes image. It is challenging because the person and the in-shop
clothes are unpaired. Existing methods formulate virtual try-on as either
in-painting or cycle consistency. Both of these two formulations encourage the
generation networks to reconstruct the input image in a self-supervised manner.
However, existing methods do not differentiate clothing and non-clothing
regions. A straight-forward generation impedes virtual try-on quality because
of the heavily coupled image contents. In this paper, we propose a Disentangled
Cycle-consistency Try-On Network (DCTON). The DCTON is able to produce
highly-realistic try-on images by disentangling important components of virtual
try-on including clothes warping, skin synthesis, and image composition. To
this end, DCTON can be naturally trained in a self-supervised manner following
cycle consistency learning. Extensive experiments on challenging benchmarks
show that DCTON outperforms state-of-the-art approaches favorably.
