Abstract: Existing pose guidance-based virtual try-on methods suffer from excessive deformation of clothing texture and occlusion of generated images. To address these issues, this paper proposes a pose-guided virtual try-on network (PG-VTON), an improved virtual try-on network based on the Downto network. First, a U-Net-based network is used to transform the pose of a figure image and generate the figure parsing image under the target pose. An information-enhancement module is introduced to improve the accuracy of the parsing image. Then, a thin plate spline (TPS) is used to transform the target cloth into a shape that fits the body of the target figure, and grid warping regularization is introduced to preserve the texture and detail features of the target cloth. The final virtual try-on image is generated by combining the parsing image with a warped cloth. Experimental results indicate that the proposed method improves the average structural similarity (SSIM) of the virtual try-on image by 2.83% and inception score (IS) by 6.74%compared with the Downto network. Further, as compared to other virtual try-on methods, the proposed method reduces false occlusion in the process and generates clearer and more realistic results.

Key words: virtual try-on, thin plate spline (TPS), information enhancement module, grid warping regularization