Our goal with this survey is to provide an overview of the state of the art deep learning technologies for face generation and editing. We will cover popular latest architectures and discuss key ideas that make them work, such as inversion, latent representation, loss functions, training procedures, editing methods, and cross domain style transfer. We particularly focus on GAN-based architectures that have culminated in the StyleGAN approaches, which allow generation of high-quality face images and offer rich interfaces for controllable semantics editing and preserving photo quality. 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：本综述旨在概述基于深度学习的人脸生成与编辑技术现状。我们将涵盖主流最新架构，并讨论使其有效运作的核心思想，例如逆向映射、潜在表示、损失函数、训练流程、编辑方法及跨域风格迁移。我们特别关注以StyleGAN为代表的生成对抗网络架构——该架构不仅能生成高质量人脸图像，还提供了丰富的可控语义编辑接口，同时保持照片级真实感。本文旨在为具备深度学习基础知识、希望获得易于理解的入门指导和全面概述的读者，提供进入该领域的切入点。