GAN-based Facial Attribute Manipulation

TL;DR

This survey reviews GAN-based facial attribute manipulation methods, highlighting their motivations, technical approaches, and discussing open issues and future directions in the field.

Contribution

It provides a comprehensive overview and systematic categorization of GAN-based FAM techniques, serving as a foundational reference for researchers.

Findings

Categorization of GAN-based FAM methods into three main groups

Analysis of key properties and challenges in FAM methods

Identification of open issues and future research directions

Abstract

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.