RAMT-GAN: Realistic and accurate makeup transfer with generative adversarial network

Abstract

Automatic makeup transfer aims to precisely transfer the makeup style from a given reference makeup face image to a source image while preserving face identity and background information. Current works have achieved promising results in makeup transfer by using deep learning techniques. However, existing methods are focused on only one or two requirements, thus cannot simultaneously achieve face identity preservation, background retention, and accurate makeup transfer. Besides, it is also difficult to acquire a pair of well-aligned face images with different makeup styles. Aimed at these problems, we propose RAMT-GAN (Realistic and Accurate Makeup Transfer Generative Adversarial Network), a GAN-based image transformation framework for achieving realistic and accurate makeup style transfer. Specifically, we utilize a dual input/output network that builds on the BeautyGAN architecture to achieve cross-domain image transformation. Then, identity preservation loss and background invariant loss are introduced in RAMT-GAN to help synthesize realistic and accurate face makeup images. Extensive experiments demonstrate that the proposed makeup transfer model can synthesize makeup faces with accurate reference style as well as maintaining the identity information and the background information.