  Virtual try-on methods aim to generate images of fashion models wearing
arbitrary combinations of garments. This is a challenging task because the
generated image must appear realistic and accurately display the interaction
between garments. Prior works produce images that are filled with artifacts and
fail to capture important visual details necessary for commercial applications.
We propose Outfit Visualization Net (OVNet) to capture these important details
(e.g. buttons, shading, textures, realistic hemlines, and interactions between
garments) and produce high quality multiple-garment virtual try-on images.
OVNet consists of 1) a semantic layout generator and 2) an image generation
pipeline using multiple coordinated warps. We train the warper to output
multiple warps using a cascade loss, which refines each successive warp to
focus on poorly generated regions of a previous warp and yields consistent
improvements in detail. In addition, we introduce a method for matching outfits
with the most suitable model and produce significant improvements for both our
and other previous try-on methods. Through quantitative and qualitative
analysis, we demonstrate our method generates substantially higher-quality
studio images compared to prior works for multi-garment outfits. An interactive
interface powered by this method has been deployed on fashion e-commerce
websites and received overwhelmingly positive feedback.
