Our goal with this survey is to provide an overview of the state of the art deep learning methods for face generation and editing using StyleGAN. The survey covers the evolution of StyleGAN, from PGGAN to StyleGAN3, and explores relevant topics such as suitable metrics for training, different latent representations, GAN inversion to latent spaces of StyleGAN, face image editing, cross-domain face stylization, face restoration, and even Deepfake applications. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview.

翻译：本综述旨在概述使用StyleGAN进行人脸生成与编辑的先进深度学习方法。文章梳理了StyleGAN从PGGAN到StyleGAN3的演进历程，并探讨了相关主题，包括适用于训练的评估指标、不同潜空间表征、针对StyleGAN潜空间的GAN反演、人脸图像编辑、跨域人脸风格化、人脸修复乃至深度伪造应用。我们旨在为具备深度学习领域基础知识的读者提供该领域的入门路径，使其获得易于理解的导论与全景式概览。