  Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face
image to render desired attributes, which has received significant attention
due to its broad practical applications ranging from digital entertainment to
biometric forensics. In the last decade, with the remarkable success of
Generative Adversarial Networks (GANs) in synthesizing realistic images,
numerous GAN-based models have been proposed to solve FAM with various problem
formulation approaches and guiding information representations. This paper
presents a comprehensive survey of GAN-based FAM methods with a focus on
summarizing their principal motivations and technical details. The main
contents of this survey include: (i) an introduction to the research background
and basic concepts related to FAM, (ii) a systematic review of GAN-based FAM
methods in three main categories, and (iii) an in-depth discussion of important
properties of FAM methods, open issues, and future research directions. This
survey not only builds a good starting point for researchers new to this field
but also serves as a reference for the vision community.
