AI-driven automatic generation and rendering of game characters

Abstract

Abstract. This paper provides a comprehensive review of AI-driven techniques for the automatic generation and rendering of game characters, with a particular focus on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). GANs, using adversarial training approaches, have revolutionized character development by producing incredibly realistic and aesthetically pleasing gaming characters. VAEs, despite frequently encountering issues like image blurriness, provide an alternate strategy that emphasizes diversity and originality in the generated content. Additionally, conditional models that enable more individualized and regulated character generation are explored, as well as hybrid models that combine the best features of both GANs and VAEs. The difficulties with mode collapse in GANs and the requirement for big datasets for both GANs and VAEs are also covered, along with some possible fixes like transfer learning and semi-supervised learning strategies. This analysis emphasizes the growing significance of AI-driven game character generation in the gaming industry by highlighting its current state, problems, and future directions.