Online Try-On: GANs and TPS

Abstract

Style transfer between images has been a research direction gaining considerable attention in the field of image generation. CycleGAN is widely used because it does not require paired image data to train, which greatly reduces the cost of collecting data. In 2018, based on CycleGAN, a new model structure, InstaGAN, was proposed and then applied in the style transfer algorithm in the special part of an image we called instance. From then on, style transfer can transform the instance in the image. Based on CycleGAN and InstaGAN, we transformed the pictures in different domains combined with shape context and thin plate splines (TPS) in the present study. Based on generative adversarial networks (GANs), we designed a fusion network to optimize the results. We combined style transfer with TPS in fashion and got convincing performance by experiments and a fusion net with good performance.