  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.
