GAN-based Facial Attribute Manipulation
It serves as a reference for researchers in computer vision, offering a systematic review to aid newcomers and the community, but it is incremental as it surveys existing work without introducing new methods.
This paper provides a comprehensive survey of GAN-based methods for Facial Attribute Manipulation (FAM), summarizing motivations, technical details, and categorizing approaches to modify face images for applications like digital entertainment and biometric forensics.
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.