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：我们的目标是提供关于人脸生成和编辑的最新深度学习技术的综述。我们将涵盖流行的最新架构，并讨论使它们工作的关键思想，如反演、潜在表示、损失函数、训练过程、编辑方法和跨领域风格转移。我们特别关注基于GAN架构的最新进展，即StyleGAN方法，它们可以生成高质量的人脸图像，并提供丰富的界面进行可控的语义编辑并保持照片的质量。我们旨在为对深度学习有基本了解并寻求可访问的介绍和概述的读者提供一个进入该领域的入口。