VITON-GAN: Virtual Try-on Image Generator Trained with Adversarial Loss
This addresses the challenge of flexible human and clothing shapes in e-commerce applications, representing an incremental improvement in virtual try-on technology.
The paper tackles the problem of generating realistic virtual try-on images from in-shop clothing and model snapshots, particularly under occlusion conditions like crossed arms, by developing VITON-GAN, which improves image quality through an adversarial training mechanism.
Generating a virtual try-on image from in-shop clothing images and a model person's snapshot is a challenging task because the human body and clothes have high flexibility in their shapes. In this paper, we develop a Virtual Try-on Generative Adversarial Network (VITON-GAN), that generates virtual try-on images using images of in-shop clothing and a model person. This method enhances the quality of the generated image when occlusion is present in a model person's image (e.g., arms crossed in front of the clothes) by adding an adversarial mechanism in the training pipeline.