[4M1] [General Session] 10. Vision / Speech

[4M1-03] SwapGAN: Cloth-Region Aware Generative Adversarial Networks toword Virtual Try-On System

〇Shizuma Kubo¹, Yusuke Iwasawa¹, Yutaka Matsuo¹ (1. The university of Tokyo)

Keywords:Deep Generative Model, GANs, Fashion

We propose a novel virtual try-on method based on generative adversarial networks (GANs), which we call SwapGAN. Conditional Analogy GAN (CAGAN) has already been proposed as a virtual try-on method based on GANs, though this method is not good at generating with complex patterns of clothing. By considering clothing regions, SwapGAN enables us to reflect the pattern of clothes better than CAGAN. Our method first obtains the clothing region on a person by using a human parsing model trained with a large-scale dataset. Next, using the acquired region, the clothing part is removed from a human image. A desired clothing image is added to the blank area. The network learns how to apply new clothing to the area of people's clothing. Furthermore, an image of the clothes that the p erson is originally wearing becomes unnecessary during testing.