The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A deep learning approach for generalized speech animation
Open accessAbstract
We introduce a simple and effective deep learning approach to automatically generate natural looking speech animation that synchronizes to input speech. Our approach uses a sliding window predictor that learns arbitrary nonlinear mappings from phoneme label input sequences to mouth movements in a way that accurately captures natural motion and visual coarticulation effects. Our deep learning approach enjoys several attractive properties: it runs in real-time, requires minimal parameter tuning, generalizes well to novel input speech sequences, is easily edited to create stylized and emotional speech, and is compatible with existing animation retargeting approaches. One important focus of our work is to develop an effective approach for speech animation that can be easily integrated into existing production pipelines. We provide a detailed description of our end-to-end approach, including machine learning design decisions. Generalized speech animation results are demonstrated over a wide range of animation clips on a variety of characters and voices, including singing and foreign language input. Our approach can also generate on-demand speech animation in real-time from user speech input.
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Cites
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Cited by
AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis
Guo, Y., Chen, K., Liang, S., Liu, Y.-J., Bao, H., & Zhang, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00573 · Cited by 384
Capture, Learning, and Synthesis of 3D Speaking Styles
Cudeiro, D., Bolkart, T., Laidlaw, C., Ranjan, A., & Black, M. J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01034 · Cited by 365
Realistic Speech-Driven Facial Animation with GANs
Vougioukas, K., Petridis, S., & Pantic, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01251-8 · Cited by 298
Text-based Editing of Talking-head Video
Fried, O., Tewari, A., Zollhöfer, M., Finkelstein, A., Shechtman, E., Goldman, D. B., Genova, K., Jin, Z., Theobalt, C., & Agrawala, M. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3323028 · Cited by 270
You Said That?: Synthesising Talking Faces from Audio
Jamaludin, A., Chung, J. S., & Zisserman, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01150-y · Cited by 189
Metadata
- Authors
- Taylor, S., Kim, T., Yue, Y., Mahler, M., Krahe, J., Rodriguez, A. G., Hodgins, J., & Matthews, I.
- Venue
- ACM Transactions on Graphics
- Published
- 2017-07-20
- Volume
- 36
- Issue
- 4
- Pages
- 1-11
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2737658251
- Cited by
- 303
- References
- 7
- FWCI
- 19.46
- Type
- journal article
- Language
- en
- Open access
- green
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- Taylor, S., Kim, T., Yue, Y., Mahler, M., Krahe, J., Rodriguez, A. G., Hodgins, J., & Matthews, I. (2017). A deep learning approach for generalized speech animation. ACM Transactions on Graphics. https://doi.org/10.1145/3072959.3073699
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