arXiv 2504.190562025-04-27cs.CVcs.AIcs.CLcs.LGcs.MM

角色动画的生成式AI：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

Computer Engineering Department, Sharif University of Technology（谢里夫理工大学计算机工程系）；Iran University of Science and Technology（伊朗科学技术大学）；Qatar Computing Research Institute（卡塔尔计算研究院）

Mohammad Mahdi Abootorabi, Omid Ghahroodi, Pardis Sadat Zahraei, Hossein Behzadasl, Alireza Mirrokni, Mobina Salimipanah, Arash Rasouli, Bahar Behzadipour, Sara… 展开作者

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

备注：: 50 main pages, 30 pages appendix, 21 figures, 8 tables, GitHub Repository: https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey

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AI总结：

本综述全面回顾了生成式AI在角色动画（含面部、手势、运动等）的技术与应用，介绍基础模型与评估指标，并规划未来研究方向。

摘要：

生成式AI正在重塑艺术、游戏以及最显著的动画领域。基础模型和扩散模型的近期突破降低了制作动画内容的时间和成本。角色是核心动画组件，涉及运动、情感、手势和面部表情。近几个月来发展的速度和广度使得难以保持对该领域的连贯认知，因此需要进行一项综合性综述。与以往将虚拟化身、手势或面部动画孤立对待的概述不同，本综述对角色动画的所有主要生成式AI应用提供了单一且全面的视角。我们首先考察了面部动画、表情渲染、图像合成、虚拟化身创建、手势建模、运动合成、物体生成和纹理合成的最新技术。我们突出了每个领域的领先研究、实际部署、常用数据集和新兴趋势。为了支持新手，我们还提供了一个全面的背景部分，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。我们讨论了开放性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资源。资源可在以下网址获取：https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。