生成式AI在角色动画中的应用：技术、应用与未来方向的全面综述

Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions

April 27, 2025

作者: Mohammad Mahdi Abootorabi, Omid Ghahroodi, Pardis Sadat Zahraei, Hossein Behzadasl, Alireza Mirrokni, Mobina Salimipanah, Arash Rasouli, Bahar Behzadipour, Sara Azarnoush, Benyamin Maleki, Erfan Sadraiye, Kiarash Kiani Feriz, Mahdi Teymouri Nahad, Ali Moghadasi, Abolfazl Eshagh Abianeh, Nizi Nazar, Hamid R. Rabiee, Mahdieh Soleymani Baghshah, Meisam Ahmadi, Ehsaneddin Asgari

cs.AI

摘要

生成式人工智能正在重塑艺术、游戏，尤其是动画领域。近期在基础模型和扩散模型方面的突破，显著降低了动画内容的制作时间和成本。角色作为动画的核心元素，涉及动作、情感、手势及面部表情的呈现。近几个月来，该领域进展的速度与广度使得保持对该领域的整体认知变得困难，这促使我们有必要进行一项整合性综述。与以往分别探讨虚拟形象、手势或面部动画的综述不同，本次调查提供了一个统一的、全面的视角，涵盖了角色动画中所有主要的生成式AI应用。我们首先审视了面部动画、表情渲染、图像合成、虚拟形象创建、手势建模、动作合成、物体生成及纹理合成等领域的最新技术。我们为每个领域重点介绍了领先的研究成果、实际应用、常用数据集以及新兴趋势。为了帮助初学者，我们还提供了一个全面的背景介绍部分，介绍了基础模型和评估指标，为读者提供了进入该领域所需的知识储备。我们探讨了当前面临的开放挑战，并规划了未来的研究方向，为推进AI驱动的角色动画技术提供了路线图。本综述旨在为进入生成式AI动画或相关领域的研究人员和开发者提供参考资源。相关资源可通过以下链接获取：https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey。

English

Generative AI is reshaping art, gaming, and most notably animation. Recent breakthroughs in foundation and diffusion models have reduced the time and cost of producing animated content. Characters are central animation components, involving motion, emotions, gestures, and facial expressions. The pace and breadth of advances in recent months make it difficult to maintain a coherent view of the field, motivating the need for an integrative review. Unlike earlier overviews that treat avatars, gestures, or facial animation in isolation, this survey offers a single, comprehensive perspective on all the main generative AI applications for character animation. We begin by examining the state-of-the-art in facial animation, expression rendering, image synthesis, avatar creation, gesture modeling, motion synthesis, object generation, and texture synthesis. We highlight leading research, practical deployments, commonly used datasets, and emerging trends for each area. To support newcomers, we also provide a comprehensive background section that introduces foundational models and evaluation metrics, equipping readers with the knowledge needed to enter the field. We discuss open challenges and map future research directions, providing a roadmap to advance AI-driven character-animation technologies. This survey is intended as a resource for researchers and developers entering the field of generative AI animation or adjacent fields. Resources are available at: https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey.