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Audio-driven lip sync has recently drawn significant attention due to its widespread application in the multimedia domain.
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
Earlier work this paper cites.
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
1991
Earlier work this paper cites.
V. Blanz and T. Vetter, “A morphable model for the synthesis of 3d faces,” in Proceedings of the 26th annual conference on Computer graphics and interactive techniques , 1999, pp. 187–194
1999
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
2014
Earlier work this paper cites.
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
2015
Earlier work this paper cites.
J. S. Chung and A. Zisserman, “Out of time: automated lip sync in the wild,” in Asian conference on computer vision . Springer, 2016, pp. 251–263
2016
Earlier work this paper cites.
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
2017
Earlier work this paper cites.
A. Bulat and G. Tzimiropoulos, “How far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks),” in International Conference on Computer Vision , 2017
2017
Earlier work this paper cites.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” in INTERSPEECH , 2017
2017
Earlier work this paper cites.
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
2017
Earlier work this paper cites.
L. Chen, Z. Li, R. K. Maddox, Z. Duan, and C. Xu, “Lip movements generation at a glance,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 520–535
2018
Earlier work this paper cites.
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
2018
Earlier work this paper cites.
P. KR, R. Mukhopadhyay, J. Philip, A. Jha, V. Namboodiri, and C. Jawahar, “Towards automatic face-to-face translation,” in Proceedings of the 27th ACM international conference on multimedia , 2019, pp. 1428–1436
2019
Earlier work this paper cites.
Y. Deng, J. Yang, S. Xu, D. Chen, Y. Jia, and X. Tong, “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 , 2019, pp. 0–0
2019
Earlier work this paper cites.
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
Cited alongside, same era.
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
2020
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Cited alongside, same era.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 8110–8119
2020
S. Wang, L. Li, Y. Ding, and X. Yu, “One-shot talking face generation from single-speaker audio-visual correlation learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 3, 2022, pp. 2531–2539
2022
Later among the works it cites.
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
Later among the works it cites.
L. Chen, Z. Wu, R. Li, W. Bao, J. Ling, X. Tan, and S. Zhao, “Vast: Vivify your talking avatar via zero-shot expressive facial style transfer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2977–2987
2023
Later among the works it cites.
D. Wang, Y. Deng, Z. Yin, H.-Y. Shum, and B. Wang, “Progressive disentangled representation learning for fine-grained controllable talking head synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 979–17 989
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Cited alongside, same era.
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.
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.
H. Wu, J. Jia, H. Wang, Y. Dou, C. Duan, and Q. Deng, “Imitating arbitrary talking style for realistic audio-driven talking face synthesis,” in Proceedings of the 29th ACM International Conference on Multimedia , 2021, pp. 1478–1486
2021
Cited alongside, same era.
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.
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.
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.
S. Shen, W. Li, X. Huang, Z. Zhu, J. Zhou, and J. Lu, “Sd-nerf: Towards lifelike talking head animation via spatially-adaptive dual-driven nerfs,” IEEE Transactions on Multimedia , vol. 26, pp. 3221–3234, 2023
2023
Later among the works it cites.
C. Du, Q. Chen, T. He, X. Tan, X. Chen, K. Yu, S. Zhao, and J. Bian, “Dae-talker: High fidelity speech-driven talking face generation with diffusion autoencoder,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 4281–4289
2023
Later among the works it cites.
2023
Later among the works it cites.
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.
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.
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.
H. Fu, Z. Wang, K. Gong, K. Wang, T. Chen, H. Li, H. Zeng, and W. Kang, “Mimic: Speaking style disentanglement for speech-driven 3d facial animation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 2, 2024, pp. 1770–1777
2024
Closest in time.
S. Tan, B. Ji, Y. Ding, and Y. Pan, “Say anything with any style,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 5, 2024, pp. 5088–5096
2024
Closest in time.
D. Li, K. Zhao, W. Wang, B. Peng, Y. Zhang, J. Dong, and T. Tan, “Ae-nerf: Audio enhanced neural radiance field for few shot talking head synthesis,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 4, 2024, pp. 3037–3045
2024
Closest in time.