title: Generative AI for Character Animation: A Comprehensive Survey of
    Techniques, Applications, and Future Directions
summary: 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.
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paper_authors.name: Pardis Sadat Zahraei
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paper_authors.name: Hossein Behzadasl
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paper_authors.name: Alireza Mirrokni
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paper_authors.name: Mobina Salimipanah
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paper_authors.name: Arash Rasouli
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paper_authors.name: Bahar Behzadipour
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paper_authors.name: Sara Azarnoush
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paper_authors.name: Benyamin Maleki
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paper_authors.name: Erfan Sadraiye
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paper_authors.name: Kiarash Kiani Feriz
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paper_authors.name: Mahdi Teymouri Nahad
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paper_authors.fullname: teymouri
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paper_authors.user: mahditn2000
paper_authors._id: 68134b02c65f8ae6486d8842
paper_authors.name: Ali Moghadasi
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paper_authors._id: 68138850d2ea83df1eed8624
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paper_authors.fullname: Ali Moghadasi
paper_authors.type: user
paper_authors.user: ali0083moi
paper_authors._id: 68134b02c65f8ae6486d8843
paper_authors.name: Abolfazl Eshagh Abianeh
paper_authors._id: 68134b02c65f8ae6486d8844
paper_authors.name: Nizi Nazar
paper_authors._id: 68134b02c65f8ae6486d8845
paper_authors.name: Hamid R. Rabiee
paper_authors._id: 68134b02c65f8ae6486d8846
paper_authors.name: Mahdieh Soleymani Baghshah
paper_authors._id: 68134b02c65f8ae6486d8847
paper_authors.name: Meisam Ahmadi
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paper_authors.name: Ehsaneddin Asgari
paper_title: Generative AI for Character Animation: A Comprehensive Survey of
    Techniques, Applications, and Future Directions
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: foundation models diffusion models facial animation expression rendering image synthesis avatar creation gesture modeling motion synthesis object generation texture synthesis evaluation metrics