Design and Implementation of Virtual Try-On System Using Machine Learning

Virtual try-on systems have become increasingly popular in the e-commerce industry, allowingcustomers to virtually try on clothes and accessories before making a purchase. However, current virtual fitting methods often suffer from pixel disruption and low resolution, leading to unrealistic try-on images. To solve this problem, we propose a Parser Free Appearance Flow Network (PFAFN) methodology that generates try-on images by simultaneously warping clothes and generating segmentation maps while exchanging information. Our experimental results show that PFAFN outperforms existing methods at a resolution of 192 x 256. The proposed virtual try-on system was implemented using Python and TensorFlow. The system's testing and validation were alsodiscussed. Our research contributes to the development of more realistic virtual try-on systems that could enhance customer experience and satisfaction in the e-commerce industry