首页|One-shot Face Reenactment with Dense Correspondence Estimation

One-shot Face Reenactment with Dense Correspondence Estimation

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One-shot face reenactment is a challenging task due to the identity mismatch between source and driving faces.Most exist-ing methods fail to completely eliminate the interference of driving subjects'identity information,which may lead to face shape distor-tion and undermine the realism of reenactment results.To solve this problem,in this paper,we propose using a 3D morphable model(3DMM)for explicit facial semantic decomposition and identity disentanglement.Instead of using 3D coefficients alone for reenactment control,we take advantage of the generative ability of 3DMM to render textured face proxies.These proxies contain abundant yet com-pact geometric and semantic information of human faces,which enables us to compute the face motion field between source and driving images by estimating the dense correspondence.In this way,we can approximate reenactment results by warping source images accord-ing to the motion field,and a generative adversarial network(GAN)is adopted to further improve the visual quality of warping results.Extensive experiments on various datasets demonstrate the advantages of the proposed method over existing state-of-the-art bench-marks in both identity preservation and reenactment fulfillment.

Generative adversarial networksface image manipulationface image synthesisface reenactment3D morphable model

Yunfan Liu、Qi Li、Zhenan Sun

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School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China

Center for Research on Intelligent Perception and Computing,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

Beijing Municipal Natural Science Foundation,ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaYouth Innovation Promotion Association CAS,China

42220546227626362076240Y2023143

2024

10.1007/s11633-023-1433-9

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(5)