The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Visemenet: audio-driven animator-centric speech animation
Open accessAbstract
We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio. Our three-stage Long Short-Term Memory (LSTM) network architecture is motivated by psycho-linguistic insights: segmenting speech audio into a stream of phonetic-groups is sufficient for viseme construction; speech styles like mumbling or shouting are strongly co-related to the motion of facial landmarks; and animator style is encoded in viseme motion curve profiles. Our contribution is an automatic real-time lip-synchronization from audio solution that integrates seamlessly into existing animation pipelines. We evaluate our results by: cross-validation to ground-truth data; animator critique and edits; visual comparison to recent deep-learning lip-synchronization solutions; and showing our approach to be resilient to diversity in speaker and language.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
Davis, S., & Mermelstein, P. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163420 · Cited by 5,465
Video Rewrite: driving visual speech with audio
Bregler, C., Covell, M., & Slaney, M. (1997) · Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97 · doi:10.1145/258734.258880 · Cited by 669
Too real for comfort? Uncanny responses to computer generated faces
MacDorman, K. F., Green, R. D., Ho, C.-C., & Koch, C. T. (2009) · Computers in Human Behavior · doi:10.1016/j.chb.2008.12.026 · Cited by 533
Realtime performance-based facial animation
Weise, T., Bouaziz, S., Li, H., & Pauly, M. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964972 · Cited by 356
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
EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model
Ji, X., Zhou, H., Wang, K., Wu, Q., Wu, W., Xu, F., & Cao, X. (2022) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings · doi:10.1145/3528233.3530745 · Cited by 155
Metadata
- Authors
- Zhou, Y., Xu, Z., Landreth, C., Kalogerakis, E., Maji, S., & Singh, K.
- Venue
- ACM Transactions on Graphics
- Published
- 2018-07-30
- Volume
- 37
- Issue
- 4
- Pages
- 1-10
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2804619907
- Cited by
- 233
- References
- 7
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Zhou, Y., Xu, Z., Landreth, C., Kalogerakis, E., Maji, S., & Singh, K. (2018). Visemenet: audio-driven animator-centric speech animation. ACM Transactions on Graphics. https://doi.org/10.1145/3197517.3201292
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