GAN-based Detailed Clothing Generation System

Abstract

The implementation of Generative Adversarial Networks (GAN) in the fashion domain has been researched for various applications such as virtual-try-on, fashion item recommendation, and design generation. In this paper, we propose a GAN-based fashion design generation system that reduces the workload of the labor-intensive design creation task. Our system consists of two generative models: one that produces images of fashion items without any clothing patterns using conditioned StyleGAN2-ADA, and one that is a style transfer model reflecting the fine texture of the fashion item. The system also allows users to edit images of garments by manipulating the latent code of the generator. We demonstrate through qualitative and quantitative experiments that the proposed system trained on a dataset of real clothing inventory images can generate realistic and diverse images that reflect the input conditions in detail.