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Current face reenactment and swapping methods mainly rely on GAN frameworks, but recent focus has shifted to pre-trained diffusion models for their superior generation capabilities.
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Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4690–4699 (2019)
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Deng, Y., Yang, J., Xu, S., Chen, D., Jia, Y., Tong, X.: Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 0–0 (2019)
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Nirkin, Y., Keller, Y., Hassner, T.: Fsgan: Subject agnostic face swapping and reenactment. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 7184–7193 (2019)
2019
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Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: Faceforensics++: Learning to detect manipulated facial images. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 1–11 (2019)
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Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., Sebe, N.: Animating arbitrary objects via deep motion transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2377–2386 (2019)
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Siarohin, A., Lathuilière, S., Tulyakov, S., Ricci, E., Sebe, N.: First order motion model for image animation. Advances in neural information processing systems 32
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Chen, R., Chen, X., Ni, B., Ge, Y.: Simswap: An efficient framework for high fidelity face swapping. In: Proceedings of the 28th ACM International Conference on Multimedia. pp. 2003–2011 (2020)
2020
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Huang, Y., Wang, Y., Tai, Y., Liu, X., Shen, P., Li, S., Li, J., Huang, F.: Curricularface: adaptive curriculum learning loss for deep face recognition. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5901–5910 (2020)
2020
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Zeng, X., Pan, Y., Wang, M., Zhang, J., Liu, Y.: Realistic face reenactment via self-supervised disentangling of identity and pose. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 12757–12764 (2020)
2020
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Zhang, J., Zeng, X., Wang, M., Pan, Y., Liu, L., Liu, Y., Ding, Y., Fan, C.: Freenet: Multi-identity face reenactment. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5326–5335 (2020)
2020
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Gao, G., Huang, H., Fu, C., Li, Z., He, R.: Information bottleneck disentanglement for identity swapping. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 3404–3413 (2021)
2021
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Ren, Y., Li, G., Chen, Y., Li, T.H., Liu, S.: Pirenderer: Controllable portrait image generation via semantic neural rendering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 13759–13768 (2021)
2021
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Wang, T.C., Mallya, A., Liu, M.Y.: One-shot free-view neural talking-head synthesis for video conferencing. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10039–10049 (2021)
2021
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2021
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Zhu, Y., Li, Q., Wang, J., Xu, C.Z., Sun, Z.: One shot face swapping on megapixels. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4834–4844 (2021)
2021
Cited alongside, same era.
Bounareli, S., Tzelepis, C., Argyriou, V., Patras, I., Tzimiropoulos, G.: Hyperreenact: one-shot reenactment via jointly learning to refine and retarget faces. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7149–7159 (2023)
2023
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Bounareli, S., Tzelepis, C., Argyriou, V., Patras, I., Tzimiropoulos, G.: Hyperreenact: one-shot reenactment via jointly learning to refine and retarget faces. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7149–7159 (2023)
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2023
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Hong, F.T., Xu, D.: Implicit identity representation conditioned memory compensation network for talking head video generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 23062–23072 (2023)
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Foundations, M.: Openclip: Open-source implementation of clip. https://github.com/mlfoundations/open_clip (2022), accessed on: yyyy-mm-dd
2022
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2022
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Hong, F.T., Zhang, L., Shen, L., Xu, D.: Depth-aware generative adversarial network for talking head video generation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 3397–3406 (2022)
2022
Cited alongside, same era.
Hsu, G.S., Tsai, C.H., Wu, H.Y.: Dual-generator face reenactment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 642–650 (2022)
2022
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Tao, J., Wang, B., Xu, B., Ge, T., Jiang, Y., Li, W., Duan, L.: Structure-aware motion transfer with deformable anchor model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3637–3646 (2022)
2022
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Xu, C., Zhang, J., Han, Y., Tian, G., Zeng, X., Tai, Y., Wang, Y., Wang, C., Liu, Y.: Designing one unified framework for high-fidelity face reenactment and swapping. In: European Conference on Computer Vision. pp. 54–71. Springer (2022)
2022
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Xu, C., Zhang, J., Hua, M., He, Q., Yi, Z., Liu, Y.: Region-aware face swapping. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7632–7641 (2022)
2022
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Xu, Z., Hong, Z., Ding, C., Zhu, Z., Han, J., Liu, J., Ding, E.: Mobilefaceswap: A lightweight framework for video face swapping. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 2973–2981 (2022)
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2023
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2023
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Liu, Z., Li, M., Zhang, Y., Wang, C., Zhang, Q., Wang, J., Nie, Y.: Fine-grained face swapping via regional gan inversion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8578–8587 (2023)
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Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22500–22510 (2023)
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Shiohara, K., Yang, X., Taketomi, T.: Blendface: Re-designing identity encoders for face-swapping. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7634–7644 (2023)
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2023
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Zeng, B., Liu, X., Gao, S., Liu, B., Li, H., Liu, J., Zhang, B.: Face animation with an attribute-guided diffusion model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 628–637 (2023)
2023
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Zhang, B., Qi, C., Zhang, P., Zhang, B., Wu, H., Chen, D., Chen, Q., Wang, Y., Wen, F.: Metaportrait: Identity-preserving talking head generation with fast personalized adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22096–22105 (2023)
2023
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Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3836–3847 (2023)
2023
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Zhao, W., Rao, Y., Shi, W., Liu, Z., Zhou, J., Lu, J.: Diffswap: High-fidelity and controllable face swapping via 3d-aware masked diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8568–8577 (2023)
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