Generative AI for Character Animation: A Comprehensive Survey of
  Techniques, Applications, and Future Directions
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.
68134b02c65f8ae6486d8835
Mohammad Mahdi Abootorabi
claimed_verified
2025-05-02T06:34:44.751Z
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Mahdi Abootorabi
user
aboots
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Omid Ghahroodi
claimed_verified
2025-05-04T10:05:20.487Z
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Omid Ghahroodi
user
omidgh
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Pardis Sadat Zahraei
68134b02c65f8ae6486d8838
Hossein Behzadasl
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Alireza Mirrokni
68134b02c65f8ae6486d883a
Mobina Salimipanah
68134b02c65f8ae6486d883b
Arash Rasouli
68134b02c65f8ae6486d883c
Bahar Behzadipour
68134b02c65f8ae6486d883d
Sara Azarnoush
68134b02c65f8ae6486d883e
Benyamin Maleki
68134b02c65f8ae6486d883f
Erfan Sadraiye
68134b02c65f8ae6486d8840
Kiarash Kiani Feriz
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Mahdi Teymouri Nahad
claimed_verified
2025-05-02T06:34:42.692Z
680aac133358d246fd875981
/avatars/1758702c1975a432d436a306db76d7c3.svg
teymouri
user
mahditn2000
68134b02c65f8ae6486d8842
Ali Moghadasi
claimed_verified
2025-05-14T07:36:29.364Z
68138850d2ea83df1eed8624
/avatars/9924bd649948cf76347ed78993fb222d.svg
Ali Moghadasi
user
ali0083moi
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Abolfazl Eshagh Abianeh
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Nizi Nazar
68134b02c65f8ae6486d8845
Hamid R. Rabiee
68134b02c65f8ae6486d8846
Mahdieh Soleymani Baghshah
68134b02c65f8ae6486d8847
Meisam Ahmadi
68134b02c65f8ae6486d8848
Ehsaneddin Asgari
Generative AI for Character Animation: A Comprehensive Survey of
  Techniques, Applications, and Future Directions
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.
This survey provides a comprehensive overview of generative AI applications in character animation, covering facial animation, expression rendering, image synthesis, avatar creation, gesture modeling, motion synthesis, object generation, and texture synthesis.
foundation models diffusion models facial animation expression rendering image synthesis avatar creation gesture modeling motion synthesis object generation texture synthesis evaluation metrics