Vectorization based color transfer for portrait images

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.