Virtual Try-On Networks Based on Images with Super Resolution Model

The main job of a virtual imaging try-on is to transfer a garment to a specific area of an individual’s body part. Trying to deform said garment so that it fits in a part of the desired body. Despite some research, the vast majority use a low-quality image resolution of 192 × 256 pixels, limiting the visual satisfaction of online users. Analyzing this visual limitation, we find that the vast majority of the algorithms use these mentioned measures to obtain better performance and optimization during their training, since while the number of pixels is smaller, in the same way, their execution time will be less in the generation. of segments or masks of garments and body parts. Despite having better performance and optimization, quality and pixel size are also of the utmost importance, since it is in the final resolution that the result for the user is appreciated. To address this challenge, we propose a super-resolution extension module, added to the ACGPN model. Such a module gets the resulting image from the ACGPN model, and then with the help of computer vision aims to increase the resolution to 768 × 1024 pixels with minimal loss of quality. For this, a comparison of models that perform this task of increasing the resolution is made. Finally, it is quantitatively shown that the proposal obtains better results.