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Audio-driven talking face video generation has attracted increasing attention due to its huge industrial potential.
Y. Ma, S. Wang, Z. Hu, C. Fan, T. Lv, Y. Ding, Z. Deng, and X. Yu, “Styletalk: One-shot talking head generation with controllable speaking styles,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 1896–1904
1904
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S. Shen, W. Zhao, Z. Meng, W. Li, Z. Zhu, J. Zhou, and J. Lu, “Difftalk: Crafting diffusion models for generalized audio-driven portraits animation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1982–1991
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 . Springer, 2015, pp. 234–241
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
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A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” in INTERSPEECH , 2017
2017
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M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
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R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 586–595
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J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4690–4699
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J. Thies, M. Elgharib, A. Tewari, C. Theobalt, and M. Nießner, “Neural voice puppetry: Audio-driven facial reenactment,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVI 16 . Springer, 2020, pp. 716–731
2020
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K. Prajwal, R. Mukhopadhyay, V. P. Namboodiri, and C. Jawahar, “A lip sync expert is all you need for speech to lip generation in the wild,” in Proceedings of the 28th ACM international conference on multimedia , 2020, pp. 484–492
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Y. Zhou, X. Han, E. Shechtman, J. Echevarria, E. Kalogerakis, and D. Li, “Makelttalk: speaker-aware talking-head animation,” ACM Transactions on Graphics (TOG) , vol. 39, no. 6, pp. 1–15, 2020
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
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J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
T. Xie, L. Liao, C. Bi, B. Tang, X. Yin, J. Yang, M. Wang, J. Yao, Y. Zhang, and Z. Ma, “Towards realistic visual dubbing with heterogeneous sources,” in Proceedings of the 29th ACM International Conference on Multimedia , 2021, pp. 1739–1747
2021
Cited alongside, same era.
Y. Guo, K. Chen, S. Liang, Y.-J. Liu, H. Bao, and J. Zhang, “Ad-nerf: Audio driven neural radiance fields for talking head synthesis,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 5784–5794
2021
Cited alongside, same era.
W. Zhong, C. Fang, Y. Cai, P. Wei, G. Zhao, L. Lin, and G. Li, “Identity-preserving talking face generation with landmark and appearance priors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9729–9738
2023
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S. Gururani, A. Mallya, T.-C. Wang, R. Valle, and M.-Y. Liu, “Space: Speech-driven portrait animation with controllable expression,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 20 914–20 923
2023
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W. Zhang, X. Cun, X. Wang, Y. Zhang, X. Shen, Y. Guo, Y. Shan, and F. Wang, “Sadtalker: Learning realistic 3d motion coefficients for stylized audio-driven single image talking face animation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 8652–8661
2023
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G. Kim, H. Shim, H. Kim, Y. Choi, J. Kim, and E. Yang, “Diffusion video autoencoders: Toward temporally consistent face video editing via disentangled video encoding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 6091–6100
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Z. Zhang, L. Li, Y. Ding, and C. Fan, “Flow-guided one-shot talking face generation with a high-resolution audio-visual dataset,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 3661–3670
2021
Cited alongside, same era.
C. Zhang, Y. Zhao, Y. Huang, M. Zeng, S. Ni, M. Budagavi, and X. Guo, “Facial: Synthesizing dynamic talking face with implicit attribute learning,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 3867–3876
2021
Cited alongside, same era.
H. Zhou, Y. Sun, W. Wu, C. C. Loy, X. Wang, and Z. Liu, “Pose-controllable talking face generation by implicitly modularized audio-visual representation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 4176–4186
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Cited alongside, same era.
2022
Cited alongside, same era.
Z. Ye, Z. Jiang, Y. Ren, J. Liu, J. He, and Z. Zhao, “Geneface: Generalized and high-fidelity audio-driven 3d talking face synthesis,” in The Eleventh International Conference on Learning Representations , 2022
2022
Cited alongside, same era.
S. Shen, W. Li, Z. Zhu, Y. Duan, J. Zhou, and J. Lu, “Learning dynamic facial radiance fields for few-shot talking head synthesis,” in European Conference on Computer Vision . Springer, 2022, pp. 666–682
2022
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Zhao, T. Hou, Y.-C. Su, X. Jia, Y. Li, and M. Grundmann, “Towards authentic face restoration with iterative diffusion models and beyond,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 7312–7322
2023
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J. Z. Wu, Y. Ge, X. Wang, S. W. Lei, Y. Gu, Y. Shi, W. Hsu, Y. Shan, X. Qie, and M. Z. Shou, “Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 7623–7633
2023
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J. Seo, G. Lee, S. Cho, J. Lee, and S. Kim, “Midms: Matching interleaved diffusion models for exemplar-based image translation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 2191–2199
2023
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L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3836–3847
2023
Later among the works it cites.
2023
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B. Yang, S. Gu, B. Zhang, T. Zhang, X. Chen, X. Sun, D. Chen, and F. Wen, “Paint by example: Exemplar-based image editing with diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 18 381–18 391
2023
Later among the works it cites.
A. K. Bhunia, S. Khan, H. Cholakkal, R. M. Anwer, J. Laaksonen, M. Shah, and F. S. Khan, “Person image synthesis via denoising diffusion model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5968–5976
2023
Later among the works it cites.
2023
Later among the works it cites.
R. Zhen, W. Song, Q. He, J. Cao, L. Shi, and J. Luo, “Human-computer interaction system: A survey of talking-head generation,” Electronics , vol. 12, no. 1, p. 218, 2023
2023
Later among the works it cites.
T. He, J. Guo, R. Yu, Y. Wang, jialiang zhu, K. An, L. Li, X. Tan, C. Wang, H. Hu, H. Wu, sheng zhao, and J. Bian, “GAIA: Data-driven zero-shot talking avatar generation,” in The Twelfth International Conference on Learning Representations , 2024
2024
Closest in time.
M. Stypułkowski, K. Vougioukas, S. He, M. Zięba, S. Petridis, and M. Pantic, “Diffused heads: Diffusion models beat gans on talking-face generation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 5091–5100
2024
Closest in time.
S. Mukhopadhyay, S. Suri, R. T. Gadde, and A. Shrivastava, “Diff2lip: Audio conditioned diffusion models for lip-synchronization,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 5292–5302
2024
Closest in time.
D. Bigioi, S. Basak, M. Stypułkowski, M. Zieba, H. Jordan, R. McDonnell, and P. Corcoran, “Speech driven video editing via an audio-conditioned diffusion model,” Image and Vision Computing , vol. 142, p. 104911, 2024
2024
Closest in time.
S. Wang, Y. Ma, Y. Ding, Z. Hu, C. Fan, T. Lv, Z. Deng, and X. Yu, “Styletalk++: A unified framework for controlling the speaking styles of talking heads,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
Closest in time.