The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Audio-driven facial animation by joint end-to-end learning of pose and emotion
Abstract
We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 3--5 minutes of high-quality animation data obtained using traditional, vision-based performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results even when driven with audio from other speakers with different gender, accent, or language, as we demonstrate with a user study. The results are applicable to in-game dialogue, low-cost localization, virtual reality avatars, and telepresence.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911
Separating Style and Content with Bilinear Models
Tenenbaum, J. B., & Freeman, W. T. (2000) · Neural Computation · doi:10.1162/089976600300015349 · Cited by 840
Automatic speech recognition and speech variability: A review
Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., et al. (2007) · Speech Communication · doi:10.1016/j.specom.2007.02.006 · Cited by 529
Virtual character performance from speech
Marsella, S., Xu, Y., Lhommet, M., Feng, A., Scherer, S., & Shapiro, A. (2013) · Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation · doi:10.1145/2485895.2485900 · Cited by 168 · cited 1×
“Several authors have tried alleviating the effect by deducing additional motion, such as head movements, eye saccades or blinks from audio (e.g., [Deng et al. 2004; Marsella et al. 2013]).”
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Cited by
VoxCeleb2: Deep Speaker Recognition
Chung, J. S., Nagrani, A., & Zisserman, A. (2018) · Interspeech 2018 · doi:10.21437/interspeech.2018-1929 · Cited by 2,256
Hierarchical Cross-Modal Talking Face Generation With Dynamic Pixel-Wise Loss
Chen, L., Maddox, R. K., Duan, Z., & Xu, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00802 · Cited by 437
X2Face: A Network for Controlling Face Generation Using Images, Audio, and Pose Codes
Wiles, O., Koepke, A. S., & Zisserman, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01261-8_41 · Cited by 435
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
Metadata
- Authors
- Karras, T., Aila, T., Laine, S., Herva, A., & Lehtinen, J.
- Venue
- ACM Transactions on Graphics
- Published
- 2017-07-20
- Volume
- 36
- Issue
- 4
- Pages
- 1-12
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2739192055
- Cited by
- 434
- References
- 6
- FWCI
- 18.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- Karras, T., Aila, T., Laine, S., Herva, A., & Lehtinen, J. (2017). Audio-driven facial animation by joint end-to-end learning of pose and emotion. ACM Transactions on Graphics. https://doi.org/10.1145/3072959.3073658
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