Performance evaluation of generative adversarial networks for anime face synthesis using deep learning approaches

Abstract

Anime characters have transcended their traditional animation role, permeating entertainment, video games, and social media. Online platforms, including prominent blogs and forums, are filled with references and adaptations of these characters. This digital ubiquity has spurred creators to develop innovative storytelling methods through the internet, fostering new avenues for anime character creation. Notably, advanced systems like Generative Adversarial Networks (GANs) have emerged as powerful tools for this purpose. This research investigates the capabilities of various GAN variants in generating anime faces. The Fréchet Inception Distance (FID) serves as a key metric for performance comparison. The analysis focuses on Deep Convolutional GAN (DCGAN), Progressively Growing GAN (ProGAN), and Style GAN2. Additionally, the performance metrics of the DCGAN model are examined. The findings show demonstrate significant differences in FID scores across these GANs. Notably, Style GAN2 exhibits superior performance in generating anime faces, achieving a significantly lower FID score (31) compared to DCGAN (625) and ProGAN (218.3). This research underscores Style GAN2's superior effectiveness in creating anime characters within the digital landscape.