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

TL;DR

This survey provides a unified, cross-domain overview of generative AI for character animation, spanning faces, expressions, images, avatars, gestures, motion, objects, and textures. It surveys foundational architectures (GANs, VAEs, Transformers, diffusion), multimodal grounding (CLIP, ControlNet), and 3D representations (SMPL/SMPL-X, NeRF) while detailing datasets, evaluation metrics, and real-world applications. A systematic taxonomy links subfields, highlights open problems (data, real-time performance, controllability, ethics), and outlines future directions and resource sharing to accelerate research. The work emphasizes foundational models and multimodal integration as enablers of realistic, controllable, and scalable AI-driven digital humans across gaming, film, VR/AR, and beyond.

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.

Figures (21)

• Figure 1: Overview of different components in animated character generation. Each aspect, including face, expression, image, avatar, gesture, motion, object, and texture, enhances realism and expressiveness within digital animation environments. Generative AI techniques, such as transformer-based and diffusion-based models, contribute to these components by improving quality, streamlining content creation, and enabling more sophisticated character animation. Generative AI techniques, such as transformer-based and diffusion-based models, contribute to these components, significantly enhancing quality and streamlining content creation.
• Figure 2: Taxonomy of recent advances in generative AI for character animation, organized by key components within the animation environment.
• Figure 3: Overview of the Dual-Generator (DG) Hsu_2022_CVPR network, which consists of two generators: the ID-preserving Shape Generator (IDSG) and the Reenacted Face Generator (RFG). Given a source face $I_s$ and a reference face $I_r$, the IDSG transforms the reference’s actions into landmarks $\hat{l}_t$. Using these landmarks and $I_s$, the RFG produces a reenacted face $\hat{I}_t$ that matches the pose and expression of $I_r$ while preserving the identity of $I_s$. Reprinted from Hsu_2022_CVPR.
• Figure 4: Overview of InViTe 10.24963/ijcai.2024/1008 in three stages: (a) capturing a user’s face to produce a personalized 3D model, (b) generating intermediate outputs for rendering, and (c) performing 3D face manipulation (such as makeup style changes) on a mobile device. Reprinted from 10.24963/ijcai.2024/1008.
• Figure 5: Overview of AdaMesh chen2024adameshpersonalizedfacialexpressions model: (a) The expression adapter integrates MoLoRA zadouri2023pushing parameters (striped patches) into pre-trained encoders and the decoder to enable efficient adaptation for facial expressions. (b) Architecture of the Conformer block 9747691, showcasing its 1D convolution module and multi-head attention. (c) Illustration of MoLoRA applied to the convolution operator, with low-rank decomposition enhancing adaptation efficiency. MoLoRA is also applied to linear layers in the expression adapter. Reprinted from chen2024adameshpersonalizedfacialexpressions.