VCFN: Virtual Cloth Fitting Try-On Network

Abstract

Virtual cloth fitting network has an increasing demand with a growing online shopping trend to map target clothes on reference subject. Previous research depicts limitations in the generation of promising deformed clothes on the wearer's body while retaining the design features of cloth-like logo, text and wrinkles. The proposed model first learns thin-plate spline transformations to warp images according to body shape, followed by a try-on module. The former model combines deformed cloth with a rendered image to generate composition mask and outputs target body without blurry clothes while preserving critical requirements of the wearer. Experiments are performed on the Zalando dataset and the model produces fine richer details and promised generalized results.