One-shot Face Reenactment with Dense Correspondence Estimation
文献类型：期刊论文
| 作者 | Yunfan Liu1,2 ; Qi Li2; Zhenan Sun1,2
|
| 刊名 | Machine Intelligence Research
 |
| 出版日期 | 2024 |
| 卷号 | 21期号:5页码:941-953 |
| 关键词 | Generative adversarial networks face image manipulation face image synthesis face reenactment 3D morphable model |
| ISSN号 | 2731-538X |
| DOI | 10.1007/s11633-023-1433-9 |
| 英文摘要 | One-shot face reenactment is a challenging task due to the identity mismatch between source and driving faces. Most existing methods fail to completely eliminate the interference of driving subjects′ identity information, which may lead to face shape distortion 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 compact 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 according 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 benchmarks in both identity preservation and reenactment fulfillment. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59423]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
| 推荐引用方式 GB/T 7714 | Yunfan Liu,Qi Li,Zhenan Sun. One-shot Face Reenactment with Dense Correspondence Estimation[J]. Machine Intelligence Research,2024,21(5):941-953. |
| APA | Yunfan Liu,Qi Li,&Zhenan Sun.(2024).One-shot Face Reenactment with Dense Correspondence Estimation.Machine Intelligence Research,21(5),941-953. |
| MLA | Yunfan Liu,et al."One-shot Face Reenactment with Dense Correspondence Estimation".Machine Intelligence Research 21.5(2024):941-953. |
入库方式： OAI收割
来源：自动化研究所
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