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

2025-04-27 00:09:31
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

Abstract

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: this https URL.

Abstract (translated)

生成式AI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减少了制作动画内容所需的时间和成本。角色是动画中的关键组成部分,涉及到动作、情感、姿态和面部表情等元素。最近几个月进步的速度与广度使得保持对该领域的全面理解变得困难,因此有必要进行一次综合性的回顾。不同于以往将化身(avatar)、手势或面部动画单独处理的综述,本次调查提供了一个对生成式AI在角色动画中的主要应用进行全面审视的独特视角。 我们首先探讨了面部动画、表情渲染、图像合成、化身创建、手势建模、动作合成、物体生成和纹理合成等领域的最新进展。我们将突出领先的研究成果、实际部署案例、常用的数据集以及每个领域内的新兴趋势。为了帮助新入门者,我们也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的知识准备。 此外,我们还讨论了当前面临的挑战,并指出了未来研究的方向,为推进AI驱动的角色动画技术绘制了一条路线图。这篇综述旨在成为正在进入生成式AI动画领域或相关领域的研究人员和技术开发者的一个资源。更多相关信息可在以下网址找到:[此URL](请将“this https URL”替换为实际提供的链接)。

URL

https://arxiv.org/abs/2504.19056

PDF

https://arxiv.org/pdf/2504.19056.pdf


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