VTON-IT: Virtual Try-On using Image Translation
This addresses the challenge of realistic virtual try-on for e-commerce and fashion applications, though it appears incremental as it builds on existing GAN-based methods.
The paper tackles the problem of generating photo-realistic virtual try-on images by transferring clothing items onto human bodies despite variations in body size, pose, and occlusions, resulting in high-resolution natural images with detailed textures on real-life test images.
Virtual Try-On (trying clothes virtually) is a promising application of the Generative Adversarial Network (GAN). However, it is an arduous task to transfer the desired clothing item onto the corresponding regions of a human body because of varying body size, pose, and occlusions like hair and overlapped clothes. In this paper, we try to produce photo-realistic translated images through semantic segmentation and a generative adversarial architecture-based image translation network. We present a novel image-based Virtual Try-On application VTON-IT that takes an RGB image, segments desired body part, and overlays target cloth over the segmented body region. Most state-of-the-art GAN-based Virtual Try-On applications produce unaligned pixelated synthesis images on real-life test images. However, our approach generates high-resolution natural images with detailed textures on such variant images.