Multi-Scene Virtual Try-on Network Guided by Attributes
Xiaoyang Lv, Bo Zhang, Jie Li, Yangjie Cao, Cong Yang · 2021
Existing image-based virtual try-on methods often require personal photos, causing privacy violations. We thus propose a novel Multi-Scene Virtual Try-on Network (MSVTON), which generates try-on images while only giving clothes images and attribute texts consisting of view angle, skin color, etc. With our model, various scene try-on images can be created by manipulating the attributes. In order to improve semantic consistency and visual realism of the generated images, MSVTON decomposes the virtual try-on process into two stages. MS-VTON consists of Scene Learning Network (SLN) and Content Learning Network (CLN). SLN learns the semantics of try-on scenes and generates coarse try-on images in the first stage, and CLN refines minutiae such as clothes textures, etc. in the second stage. Experiments show that the FID score of MSVTON reaches 9. S, which outperforms the existing image-toimage translation methods.