VTNFP: An Image-Based Virtual Try-On Network With Body and Clothing Feature Preservation

Image-based virtual try-on systems with the goal of transferring a desired clothing item onto the correspond- ing region of a person have made great strides recently, but challenges remain in generating realistic looking im- ages that preserve both body and clothing details. Here we present a new virtual try-on network, called VTNFP, to synthesize photo-realistic images given the images of a clothed person and a target clothing item. In order to bet- ter preserve clothing and body features, VTNFP follows a three-stage design strategy. First, it transforms the target clothing into a warped form compatible with the pose of the given person. Next, it predicts a body segmentation map of the person wearing the target clothing, delineating body parts as well as clothing regions. Finally, the warped cloth- ing, body segmentation map and given person image are fused together for fine-scale image synthesis. A key inno- vation of VTNFP is the body segmentation map prediction module, which provides critical information to guide image synthesis in regions where body parts and clothing inter- sects, and is very beneficial for preventing blurry pictures and preserving clothing and body part details. Experiments on a fashion dataset demonstrate that VTNFP generates substantially better results than state-of-the-art methods.