西安电子科技大学学报（自然科学版）2025，Vol.52Issue(2)：57-84,28.DOI:10.19665/j.issn1001-2400.20240907
基于深度学习的人脸动画驱动方法综述
Review of deep learning-based methods for driving facial animation
摘要
Abstract
Facial animation technology aims to dynamically drive static facial images using source data such as audio or video to produce realistic animation effects.The development of deep learning technology has greatly promoted the progress of facial animation technology.This deep learning technology can learn and capture facial features and movement patterns,achieving realistic and personalized facial animation through an automated driving process.Currently,there are numerous research achievements in the field of facial animation based on deep learning.However,existing reviews focus mostly on specific technologies or single-modality driving sources.This paper systematically reviews the facial animation driving technology based on deep learning,summarizing the research status according to the process of audio and video driving facial animation.First,it introduces the common process of extracting facial features from input source data.Second,it deeply analyzes the key technologies of feature extraction and animate generation,and compares the advantages and disadvantages of different deep learning network architectures in each step.Finally,it summarizes the animation generation methods under different architectures and compares their similarities and differences.In addition,this paper also lists the commonly used datasets and evaluation metrics for facial animation,summarizes the existing challenges in the field,further elaborates on the development trends of future work,and makes some prospects,aiming to provide researchers with a more comprehensive perspective on the application of deep learning in the field of facial animation.关键词
人脸动画/深度学习/音视频驱动/虚拟人/研究综述Key words
facial animation/deep learning/audio-driven and video-driven/virtual avatars/research review引用本文复制引用
刘龙,李浩生,张梦璇,杜莹,常雅淇,张文博..基于深度学习的人脸动画驱动方法综述[J].西安电子科技大学学报（自然科学版）,2025,52(2):57-84,28.基金项目
陕西省技术创新引导计划(2023KXJ-279) （2023KXJ-279）
