Generative adversarial networks and their application to 3D face generation: A survey
作者：
Highlights：
• Deep generative models are a combination of generative models and neural networks.
• 3D Face generation as a task of interpolating new faces from existing datasets.
• Deep generative models are exploited as generative models among Researchers.
• Generative Adversarial Networks excitingly generates images with high quality.
• Adversarial training has gained remarkable results not only in face generation.
摘要
•Deep generative models are a combination of generative models and neural networks.•3D Face generation as a task of interpolating new faces from existing datasets.•Deep generative models are exploited as generative models among Researchers.•Generative Adversarial Networks excitingly generates images with high quality.•Adversarial training has gained remarkable results not only in face generation.
论文关键词：Generative adversarial networks,3D face generation,Generator,Discriminator,Deep neural network,Deep learning
论文评审过程：Received 24 December 2020, Revised 17 January 2021, Accepted 28 January 2021, Available online 18 February 2021, Version of Record 11 March 2021.
论文官网地址：https://doi.org/10.1016/j.imavis.2021.104119
