Makeup transfer: A review

Makeup transfer (MT) aims to transfer the makeup style from a given reference makeup face image to a source image while preserving face identity and background information. In recent years, MT has attracted the attention of many scholars, and it has a wide range of application prospects and research value. Since then, many methods have been proposed to accomplish MT, most of which are based on Generative Adversarial Network methods. A taxonomy of existing algorithms in the field of MT is first proposed. Then, evaluation methods are proposed, existing methods are analysed, and existing datasets are introduced. This paper finally discusses the current problems in the field of MT and the trend of future research.

Automated summary

Makeup transfer (MT) is a technique that aims to transfer the makeup style from a given reference image to a source image while preserving face identity and background information. It has gained significant attention from scholars in recent years due to its wide range of applications and research value. Many methods based on Generative Adversarial Network (GAN) have been proposed, but there are still some challenges that need to be addressed in this field.