Fashion Attributes-to-Image Synthesis Using Attention-based Generative Adversarial Network

Abstract

In this paper, we present a method to generate fashion product images those are consistent with a given set of fashion attributes. Since distinct fashion attributes are related to different local sub-regions of a product image, we propose to use generative adversarial network with attentional discriminator. The attribute-attended loss signal from discriminator leads generator to generate more consistent images with given attributes. In addition, we present a generator based on Product-of-Gaussian to encode the composition of fashion attributes in effective way. To verify the proposed model whether it generates consistent image, an oracle attribute classifier is trained and judge the consistency of given attributes and the generated images. Our model significantly outperforms the baseline model in terms of correctness measured by the pre-trained oracle classifier. We show not only qualitative performance but also synthesized images with various combinations of attributes, so we can compare them with baseline model.