生成式AI在角色動畫中的應用：技術、應用與未來方向的全面綜述

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

April 27, 2025

作者: Mohammad Mahdi Abootorabi, Omid Ghahroodi, Pardis Sadat Zahraei, Hossein Behzadasl, Alireza Mirrokni, Mobina Salimipanah, Arash Rasouli, Bahar Behzadipour, Sara Azarnoush, Benyamin Maleki, Erfan Sadraiye, Kiarash Kiani Feriz, Mahdi Teymouri Nahad, Ali Moghadasi, Abolfazl Eshagh Abianeh, Nizi Nazar, Hamid R. Rabiee, Mahdieh Soleymani Baghshah, Meisam Ahmadi, Ehsaneddin Asgari

cs.AI

摘要

生成式人工智慧正在重塑藝術、遊戲，尤其是動畫領域。近期在基礎模型和擴散模型方面的突破，大幅降低了製作動畫內容的時間和成本。角色作為動畫的核心元素，涵蓋了動作、情感、手勢和面部表情等多個方面。近幾個月來，該領域的進展速度和廣度使得保持對這一領域的整體視野變得困難，這促使我們需要進行一次整合性的回顧。與早期分別探討虛擬化身、手勢或面部動畫的概述不同，本次調查提供了角色動畫中所有主要生成式人工智慧應用的單一、全面視角。我們首先審視了面部動畫、表情渲染、圖像合成、虛擬化身創建、手勢建模、動作合成、物體生成和紋理合成等領域的最新技術。我們強調了每個領域的領先研究、實際部署、常用數據集以及新興趨勢。為了支持新入門者，我們還提供了一個全面的背景介紹部分，介紹了基礎模型和評估指標，為讀者提供了進入該領域所需的知識。我們討論了開放的挑戰，並規劃了未來的研究方向，為推動AI驅動的角色動畫技術提供了路線圖。本調查旨在為進入生成式人工智慧動畫或相關領域的研究人員和開發者提供資源。相關資源可訪問：https://github.com/llm-lab-org/Generative-AI-for-Character-Animation-Survey。

English

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.