Improved multi-reference makeup transfer with localized attention mechanism

Abstract

Makeup transfer refers to the methodology of transferring the makeup style of a reference image to a source image. Previous works have achieved satisfactory results of transferring the entire style, but multi-reference localized makeup transfer is still challenging due to the diversity of makeup styles as well as a large variety of image content. Our method builds upon image segmentation in order to detect the facial silhouette of the portraits. In this study, an end-to-end multireference makeup transfer framework that generates the output image given multiple reference images. The deep learning (DL) network successfully applies the style from the desired regions of the target reference image to the source image without damaging the original facial features. As demonstrated in the experiment results, the makeup transfer utilizing partial style transfer, and achieve state-of-the-art performance on a wide range of makeup styles.