RSFace: subject agnostic face swapping with expression high fidelity
Face swapping has shown remarkable progress with the flourishing development of deep learning. In particular, the emergence of subject agnostic methods has broadened the range of applications of face swapping. Furthermore, high fidelity implementation has improved the naturalness of generated faces....
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| Published in | The Visual computer Vol. 39; no. 11; pp. 5497 - 5511 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0178-2789 1432-2315 |
| DOI | 10.1007/s00371-022-02675-z |
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| Abstract | Face swapping has shown remarkable progress with the flourishing development of deep learning. In particular, the emergence of subject agnostic methods has broadened the range of applications of face swapping. Furthermore, high fidelity implementation has improved the naturalness of generated faces. However, some high fidelity face swapping methods still suffer from expression distortion at this stage. In this work, we propose an extended Adaptive Embedding Integration Network (AEI-Net) to improve the performance of this network in synthesizing swapped faces on faces in the wild. First, we add a face reenactment module to synchronize the expressions of the input faces and reduce the influence of irrelevant attributes on the synthesis results. Second, we train AEI-Net using a new attribute matching loss to improve the consistency of the generated results and the target face expressions. Finally, extensive experiments on wild faces demonstrate that our method can better restore expression and posture while maintaining identity than previous methods. |
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| AbstractList | Face swapping has shown remarkable progress with the flourishing development of deep learning. In particular, the emergence of subject agnostic methods has broadened the range of applications of face swapping. Furthermore, high fidelity implementation has improved the naturalness of generated faces. However, some high fidelity face swapping methods still suffer from expression distortion at this stage. In this work, we propose an extended Adaptive Embedding Integration Network (AEI-Net) to improve the performance of this network in synthesizing swapped faces on faces in the wild. First, we add a face reenactment module to synchronize the expressions of the input faces and reduce the influence of irrelevant attributes on the synthesis results. Second, we train AEI-Net using a new attribute matching loss to improve the consistency of the generated results and the target face expressions. Finally, extensive experiments on wild faces demonstrate that our method can better restore expression and posture while maintaining identity than previous methods. |
| Author | Fang, Xianjin Yang, Gaoming Wang, Tao Zhang, ji |
| Author_xml | – sequence: 1 givenname: Gaoming surname: Yang fullname: Yang, Gaoming organization: School of Computer Science and Engineering, Anhui University of Science and Technology – sequence: 2 givenname: Tao surname: Wang fullname: Wang, Tao email: taowang@aust.edu.cn organization: School of Computer Science and Engineering, Anhui University of Science and Technology – sequence: 3 givenname: Xianjin surname: Fang fullname: Fang, Xianjin organization: School of Computer Science and Engineering, Anhui University of Science and Technology – sequence: 4 givenname: ji surname: Zhang fullname: Zhang, ji organization: School of Mathematics, Physics, and Computing, University of Southern Queensland |
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| Cites_doi | 10.1109/CVPR46437.2021.00480 10.1109/CVPRW.2018.00281 10.1109/TIP.2021.3089909 10.1109/ICCV.2019.00009 10.1109/FG.2018.00024 10.1007/s11263-019-01151-x 10.1109/TPAMI.2020.2983686 10.1109/ICCV.2017.167 10.1007/s00371-021-02347-4 10.1145/3394171.3413630 10.1109/CVPR42600.2020.00582 10.1109/CVPR.2018.00702 10.1109/TPAMI.2021.3087709 10.1109/CVPR.2017.632 10.1109/CVPR42600.2020.00512 10.1109/CVPR42600.2020.00537 10.1609/aaai.v34i07.6970 10.1109/LSP.2016.2603342 10.1109/CVPR.2018.00552 10.1109/CVPR46437.2021.01605 10.1080/02699930903485076 10.24963/ijcai.2021/157 10.1109/CVPR42600.2020.01380 10.1109/TPAMI.2020.2970919 10.1109/CVPRW.2019.00038 10.1145/3230744.3230818 10.1609/aaai.v34i07.6721 10.1109/CVPR.2016.262 10.1109/CVPR46437.2021.01468 10.1007/978-3-030-01261-8_41 10.5244/C.29.41 10.1109/CVPR42600.2020.00813 10.1109/CVPR.2019.00244 10.1145/3072959.3073640 10.1109/ICCV.2019.00728 10.1145/1399504.1360638 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Attribute matching loss Face reenactment Face swapping High fidelity |
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| References_xml | – reference: ZhangKZhangZLiZQiaoYJoint face detection and alignment using multitask cascaded convolutional networksIEEE Signal Process. Lett.201623101499150310.1109/LSP.2016.2603342 – reference: Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), pp. 16317–16326. https://doi.org/10.1109/CVPR46437.2021.01605 – reference: Bao, J., Chen, D., Wen, F., Li, H., Hua, G.: Towards open-set identity preserving face synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern recognition (CVPR), pp. 6713–6722 (2018). https://doi.org/10.1109/CVPR.2018.00702 – reference: Wiles, O., Koepke, A.S., Zisserman, A.: X2face: A network for controlling face generation using images, audio, and pose codes. 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