Vectorization Based Color Transfer for Portrait Images. (October 2019)

• Record Type: Journal Article
• Authors: Fu, Qian; He, Ying; Hou, Fei; Zhang, Juyong; Zeng, Anxiang; Liu, Yong-Jin
• Is Part Of: Computer aided design. Volume 115(2019)
• Page Start: 111
• Page End: 121
• Publication Date: 2019-10
• Subjects: Color transfer -- Portrait images -- Optimal mass transportation -- Diffusion curves -- Poisson vector graphics; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2019.05.005
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: This paper introduces a method for transferring colors between portrait images. Using a trained neural network to extract facial mask, we vectorize each image with a set of sparse diffusion curves to encode the low-frequency colors, and use the Laplacian of residual colors to represent the high-frequency details. Then we apply optimal mass transport to transfer the boundary colors between the diffusion curves of the source and reference images. Finally, the original or modified Laplacians of colors are added to the transferred diffusion curve image. Unlike the existing methods that either require 3D information or assume the source and reference images have similar poses and dense correspondence, our method is computationally efficient and flexible, which can work for portrait images with large pose and color differences. Highlights: An efficient method for transferring skin and hair colors using Poisson vector graphics. We apply optimal mass transport to transfer the boundary colors between extracted diffusion curves. It works for portrait images with large pose and color differences.