arxiv:2504.19056

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

Published on Apr 27

· Submitted by

aboots on May 1

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Authors:

Mohammad Mahdi Abootorabi ,

Mahdi Teymouri Nahad ,

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.

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aboots

Paper author Paper submitter 1 day ago

We are excited to share our new comprehensive survey on Generative AI for Character Animation! As generative AI rapidly transforms animation, art, and gaming, keeping track of the swift advancements across various character components like motion, emotions, and facial expressions has become challenging. This survey offers a unified perspective on this dynamic field.

Our survey provides:

In-Depth Component Analysis: We systematically examine the state-of-the-art in generative AI for core character animation aspects, including facial animation, expression rendering, image synthesis, avatar creation, gesture modeling, motion synthesis, object generation, and texture synthesis. We cover leading research, practical uses, datasets, and trends for each area.
Foundational Knowledge for Newcomers: A dedicated background section introduces essential concepts, foundational models (like diffusion models, GANs, VAEs, transformers), and evaluation metrics to help new researchers and developers get started.
Systematic Taxonomy: We introduce a well-defined taxonomy categorizing current models by their main contributions, highlighting key methodologies and emerging trends in AI-driven animation.
Future Research Roadmap: We identify open challenges, research gaps, and map out future directions to guide advancements in AI-powered character animation technologies.
Open Resources: To foster research and accessibility, we've compiled and shared key resources including datasets, benchmarks, models, and evaluation tools. Check out the GitHub repository for this survey, which we plan to keep updated.

Dive into our work to understand the current landscape and future potential of generative AI in character animation. We encourage you to read, share, and discuss as we explore the future of AI-driven creativity together!