The rise of 3D anime-style avatars in gaming, virtual reality, and other digital media has driven significant interest in automated generation methods capable of capturing their distinctive visual characteristics. These include stylized proportions, expressive features, and non-photorealistic rendering. This paper reviews the advancements and challenges in using deep learning in 3D anime-style avatar generation. We analyze the strengths and limitations of these methods in capturing the aesthetics of anime characters and supporting customization and animation. Additionally, we identify and discuss open problems in the field, such as difficulties in resolution and detail preservation, and constraints regarding the animation of hair and loose clothing. This article aims to provide a comprehensive overview of the current state-of-the-art and identify promising research directions for advancing 3D anime-style avatar generation.

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