Anime Avatar Generation Based on Generative Adversarial Networks (GANs)

• 1、School of information and electronic engineering, Zhejiang Gongshang University,Hangzhou 310018

Abstract：With the rapid development of artificial intelligence technology, Generative Adversarial Networks (GANs), as a breakthrough in deep learning, have been widely applied in fields such as image generation, restoration, and style transfer. This paper focuses on the application of GANs in generating anime avatars, analyzing the adversarial mechanism between the generator and discriminator, as well as the network architecture and training process of GANs. Through experiments, the paper compares the performance of different GAN models (such as DCGAN, WGAN, and StyleGAN) in generating anime avatars. The comparative analysis demonstrates the effectiveness and potential of GANs in anime avatar generation, highlighting their broad application prospects in personalized avatar design, virtual character generation, and other fields. Finally, the paper concludes that the application of GANs in anime avatar generation holds significant value, and with the continuous advancement of GAN technology, its application in the digital cultural industry and intelligent art creation will bring new opportunities for innovation and development in the industry.

Keywords： Deep Learning Generative Adversarial Networks (GAN) Anime Avatar Generation