DF-VTON: Dense Flow Guided Virtual Try-On Network

Virtual try-on system that transfers clothes onto the target person has attracted rapidly. Previous works use the affine or Thin Plate Spline (TPS) transformation for clothes warping and directly learn a composition mask to fuse the warped clothes and the person image, which usually causes rough shape and blurry details due to the poor warping mechanism and lack of human structure. In this paper, we propose a novel Dense Flow guided Virtual Try-On Network (DF-VTON), which contains a progressive warping network for clothes deformation, and a personalized fitting network for the fusion of the clothes and the person image. Specifically, given a target clothes image and a reference person image, the progressive warping network generates the dense flow in a progressive way and multi-scale views. The personalized fitting network aims to fuse warped clothes and person image seemly by using multi-scale composition masks. Extensive experiments on two challenging benchmarks demonstrate the superiority of our proposed DF-VTON over existing strong baselines with realistic high-resolution try-on results.