Generative adversarial networks (GANs) have been extensively studied in recent years and have been used to address several problems in the fields of image generation and computer vision. Despite significant advancements in computer vision, applying GANs to real-world problems such as 3D face generation remains a challenge. Owing to the proliferation of fake images generated by GANs, it is important to analyze and build a taxonomy for providing an overall view of GANs. This, in turn, would facilitate many interesting applications, including virtual reality, augmented reality, computer games, teleconferencing, virtual try-on, special effects in movies, and 3D avatars. This paper reviews and discusses GANs and their application to 3D face generation. We aim to compare existing GANs methods in terms of their application to 3D face generation, investigate the related theoretical issues, and highlight the open research problems. Authors provided both qualitative and quantitative evaluations of the proposed approach. They claimed their results show the higher quality of the synthesized data compared to state-of-the-art ones.

生成对抗网络（GAN）近年来已被广泛研究，并已用于解决图像生成和计算机视觉领域中的若干问题。尽管计算机视觉技术已取得重大进步，但将GAN应用于现实世界的问题（如3D人脸生成）仍然是一个挑战。由于GANs生成的虚假图像的泛滥，分析和建立分类学以便提供GANs的总体观点很重要。反过来，这将促进许多有趣的应用程序，包括虚拟现实，增强现实，计算机游戏，电话会议，虚拟试戴，电影中的特殊效果和3D化身。本文回顾并讨论了GAN及其在3D人脸生成中的应用。我们旨在比较现有GAN方法在3D人脸生成中的应用，研究相关的理论问题，并强调开放的研究问题。作者对提议的方法进行了定性和定量评估。他们声称他们的结果表明，与最新技术相比，合成数据的质量更高。