Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions
Journal:
arXiv
Published Date:
Apr 27, 2025
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:
https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey.
