Generating High-Resolution Fashion Model Images Wearing Custom Outfits

Visualizing an outfit is an essential part of shopping for clothes. On fashion e-commerce platforms, only a limited number of outfits are visually represented, as it is impractical to photograph every possible outfit combination, even with a small assortment of garments. In this paper, we broaden the set of articles that can be combined into visualizations by training two Generative Adversarial Network (GAN) architectures on a dataset of outfits, poses, and fashion model images. Our first approach employs vanilla StyleGAN that is trained only on fashion model images. We show that this method can be used to transfer the style and the pose of one randomly generated outfit to another. In order to control the generated outfit, our second approach modifies StyleGAN by adding outfit/pose embedding networks. This enables us to generate realistic, high-resolution images of fashion models wearing a custom outfit under an input body pose.