Virtual Try-On System using Machine Learning

Abstract

Abstract: This paper proposes a virtual try-on system forapparel shopping that generates high-resolution virtualization without pixel disruption. The system employs a Parser Free Appearance Flow Network, which simultaneously warps clothes and generates segmentation while exchanging information. The proposed methodologyoutperforms existing virtual fitting methods at 192 x 256 resolution, as demonstrated by the Fréchet inception distance (FID) performance metric. The system's technical specifications, software and hardware requirements, and user interface design are presented in detail. The proposed methodology achieves realistic try-on images and high- quality virtual try-on images in real-time. Future research areas include the incorporation of personalized measurements and the development of more accurate bodymodels.