Abstract
Nowadays, deep learning and neural network-related research play a very important role in the widely use of artificial intelligence -related technologies, Among them, the hot development in the direction of generative adversarial networks (GAN) has given birth to many generation-related techniques. For example, MoCoGAN is based on the implementation principle of GAN, which enables video generation of different actions of the same character or the same action of different characters, through the method that decompose video into actions and content. This paper introduces the history and principles of MoCoGAN, starting from the prospect of using MoCoGAN in artificial intelligence (AI) industry and the technical challenges that need to be overcome in the future application of action generation. Besides, this paper also discusses the two main issues of how to improve the quality of video generation using MoCoGAN and the input conditions that are the most central problem to GAN networks. By summarizing the optimization solutions of other researchers in these two areas in recent years, this paper searches the core problems need to be solved and propose a broad prospect for future video generation techniques that can be implemented by using MoCoGAN in human-computer interaction (HCI) area.
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