StylishGAN: Toward Fashion Illustration Generation
Xingxing Zou, Wai Keung Wong · AATCC Journal of Research · 2023
In this article, we propose StylishGAN, a generative adversarial network that generates a fashion illustration sketch given an actual photo of a human model. The generated stylish sketches not only capture the image style from real photos to hand drawings with a cleaner background, but also adjust model’s body into a perfectly proportioned shape. StylishGAN learns proportional transformation and texture information through a proposed body-shaping attentional module. Furthermore, we introduce a contextual fashionable loss that augments the design details, especially the fabric texture, of the clothing. To implement our method, we prepare a new fashion dataset, namely, StylishU, that consists of 3578 paired photo–sketch images. In each pair, we have one real photo collected from the fashion show and one corresponding illustration sketch created by professional fashion illustrators. Extensive experiments show the performance of our method qualitatively and quantitatively.