基于StyleGAN的人脸生成与编辑：综述
Face Generation and Editing with StyleGAN: A Survey
AI总结：
本综述系统梳理了基于StyleGAN的人脸生成与编辑技术，涵盖从PGGAN到StyleGAN3的演进，并探讨了GAN反演、图像编辑、跨域风格化、人脸修复及Deepfake等核心主题，为深度学习读者提供入门指南。
摘要：
本综述的目标是概述使用StyleGAN进行人脸生成和编辑的最先进深度学习方法。综述涵盖了StyleGAN的演进，从PGGAN到StyleGAN3，并探讨了相关主题，如适用于训练的指标、不同的潜在表示、GAN反演到StyleGAN的潜在空间、人脸图像编辑、跨域人脸风格化、人脸修复，甚至Deepfake应用。我们旨在为具备深度学习基础知识并寻找易于理解的入门和概述的读者提供进入该领域的切入点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
