Abstract
Generative Adversarial Networks (GANs) have shown promising improvements in face synthesis and image manipulation. However, it remains difficult to swap the faces in videos with a specific target. The most well-known face swapping method, Deepfakes, focuses on reconstructing the face image with auto-encoder while paying less attention to the identity gap between the source and target faces, which causes the swapped face looks like both the source face and the target face. In this work, we propose to incorporate cross-identity adversarial training mechanism for highly photo-realistic face swapping. Specifically, we introduce corresponding discriminator to faithfully try to distinguish the swapped faces, reconstructed faces and real faces in the training process. In addition, attention mechanism is applied to make our network robust to variation of illumination. Comprehensive experiments are conducted to demonstrate the superiority of our method over baseline models in quantitative and qualitative fashion.
Supported by the Shanghai Key Laboratory of Digital Media Processing and Transmissions, 111 Project (B07022 and Sheitc No. 150633).
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References
Blanz, V., Scherbaum, K., Vetter, T., Seidel, H.P.: Exchanging faces in images. In: Computer Graphics Forum. vol. 23, pp. 669–676. Wiley Online Library (2004)
Dale, K., Sunkavalli, K., Johnson, M.K., Vlasic, D., Matusik, W., Pfister, H.: Video face replacement. In: Proceedings of the 2011 SIGGRAPH Asia Conference, pp. 1–10 (2011)
Friesen, E., Ekman, P.: Facial action coding system: a technique for the measurement of facial movement. Palo Alto 3 (1978)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Hukkelås, H., Mester, R., Lindseth, F.: DeepPrivacy: a generative adversarial network for face anonymization. In: Bebis, G., et al. (eds.) ISVC 2019. LNCS, vol. 11844, pp. 565–578. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33720-9_44
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Jiang, L., Li, R., Wu, W., Qian, C., Loy, C.C.: Deeperforensics-1.0: a large-scale dataset for real-world face forgery detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2889–2898 (2020)
Kim, H., et al.: Deep video portraits. ACM Trans. Graph. (TOG) 37(4), 1–14 (2018)
King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Korshunova, I., Shi, W., Dambre, J., Theis, L.: Fast face-swap using convolutional neural networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3677–3685 (2017)
Li, L., Bao, J., Yang, H., Chen, D., Wen, F.: Faceshifter: towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457 (2019)
Li, L., et al.: Face x-ray for more general face forgery detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5001–5010 (2020)
Ling, J., Xue, H., Song, L., Yang, S., Xie, R., Gu, X.: Toward fine-grained facial expression manipulation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 37–53. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58604-1_3
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Nirkin, Y., Keller, Y., Hassner, T.: FSGAN: subject agnostic face swapping and reenactment. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 7184–7193 (2019)
Petrov, I., et al.: DeepFaceLab: a simple, flexible and extensible face swapping framework. arXiv preprint arXiv:2005.05535 (2020)
Pumarola, A., Agudo, A., Martinez, A.M., Sanfeliu, A., Moreno-Noguer, F.: GANimation: anatomically-aware facial animation from a single image. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 835–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_50
Sun, Q., Tewari, A., Xu, W., Fritz, M., Theobalt, C., Schiele, B.: A hybrid model for identity obfuscation by face replacement. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 570–586. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01246-5_34
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., Nießner, M.: Face2face: real-time face capture and reenactment of RGB videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2387–2395 (2016)
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., Ortega-Garcia, J.: Deepfakes and beyond: a survey of face manipulation and fake detection. arXiv preprint arXiv:2001.00179 (2020)
Tordzf, Andenixa, K.: Deepfakes/faceswap. https://github.com/deepfakes/faceswap
Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 4, 376–380 (1991)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Xue, H., Ling, J., Song, L., Xie, R., Zhang, W.: Realistic talking face synthesis with geometry-aware feature transformation. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 1581–1585. IEEE (2020)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016)
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Yang, S., Xue, H., Ling, J., Song, L., Xie, R. (2021). Deep Face Swapping via Cross-Identity Adversarial Training. In: Lokoč, J., et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12573. Springer, Cham. https://doi.org/10.1007/978-3-030-67835-7_7
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