Description

Generating a virtual try-on image from in-shop clothing images and a model person’s snapshot is a challenging task because the human body and clothes have high flexibility in their shapes. Recently proposed Image-based virtual try-on (VTON) approaches have several challenges regarding diverse human poses and clothing styles. First, clothing warping networks often generate highly distorted and misaligned warped clothes, due to the erroneous clothing agnostic human representations, mismatches in input images for clothing-human matching, and improper regularization transform parameters. Second, blending networks can fail to retain the remaining clothes due to the wrong representation of humans and improper training loss for the composition-mask generation. We propose CP-VTON+ (Clothing shape and texture Preserving VTON) to overcome these issues, which significantly outperforms the state-of the-art methods, both quantitatively and qualitatively. Image-based virtual try-on systems for fitting a new in-shop clothes into a person image have attracted increasing research attention, yet is still challenging. The system is developed the different techniques such as GMM and TOM and DNN for fitting the input dress to input person. Then, the experimental results shows that some performance metrics such as accuracy, ROC and Confusion matrix.