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.

翻译：在游戏、虚拟现实及其他数字媒体中，3D动漫风格头像的兴起推动了人们对能够捕捉其独特视觉特征的自动生成方法的浓厚兴趣。这些特征包括风格化的比例、富有表现力的面部特征以及非写实渲染。本文综述了深度学习在3D动漫风格头像生成方面的进展与挑战。我们分析了这些方法在捕捉动漫角色美学特性以及支持定制化与动画制作方面的优势与局限性。此外，我们识别并讨论了该领域存在的未解问题，例如分辨率和细节保持方面的困难，以及头发和宽松衣物动画化所面临的限制。本文旨在全面概述当前最先进技术，并指出推进3D动漫风格头像生成的有前景的研究方向。