Makeup transfer based on Laplacian pyramid network

Abstract

Makeup transfer aims to extract a specific makeup from a face and transfer it to another face, which can be widely used in portrait beauty, and cosmetics marketing. At present, existing methods can achieve the transfer of the entire facial makeup, but the quality of makeup transfer is not excellent because there may be a mismatch between the two images. In this paper, we propose a facial makeup transfer network based on the Laplacian pyramid, which can better preserve the facial structure from the source image and achieve high-quality transfer results. The model consists of three parts: makeup feature extraction, facial structure feature extraction, and makeup fusion. The makeup extraction part is used to extract the facial makeup from the reference image. And the facial structure feature extraction part is used to extract facial structure from the source image, in order to solve the loss of facial details when extracting facial structural features, we used the method based on Laplacian Pyramid. The makeup fusion part will fuse the facial makeup with facial structure features. Many experiments on the MT dataset have shown that this method can transfer makeup successfully without changing the original facial structure, and achieve advanced performance in various makeup styles.