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Text-based editing of talking-head video
Open accessAbstract
Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
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
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Binary Codes capable of currecting deletions, insertions, and reversals
Levenshtein, V. I. (1965) · Soviet physics. Doklady · Cited by 10,387
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
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
A morphable model for the synthesis of 3D faces
Blanz, V., & Vetter, T. R. (1999) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/311535.311556 · Cited by 4,695
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461368 · Cited by 2,791
TIMIT Acoustic-Phonetic Continuous Speech Corpus
Garofolo, J. S., Lamel, L. F., Fisher, W. M., Pallett, D. S., Dahlgren, N. L., Zue, V. W., & Fiscus, J. G. (2020) · Americanae (AECID Library) · doi:10.35111/17gk-bn40 · Cited by 2,509
Face2Face: Real-Time Face Capture and Reenactment of RGB Videos
Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., & Niessner, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.262 · Cited by 1,747
Self-citationDARPA TIMIT:: acoustic-phonetic continuous speech corpus CD-ROM, NIST speech disc 1-1.1
Garofolo, J. S., Lamel, L., Fisher, W. M., Fiscus, J. G., Pallett, D., & Dahlgren, N. L. (1993) · doi:10.6028/nist.ir.4930 · Cited by 1,533
Unit selection in a concatenative speech synthesis system using a large speech database
Hunt, A., & Black, A. (1996) · 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings · doi:10.1109/icassp.1996.541110 · Cited by 1,508
Statistical parametric speech synthesis
Zen, H., Tokuda, K., & Black, A. W. (2009) · Speech Communication · doi:10.1016/j.specom.2009.04.004 · Cited by 1,202
Synthesizing Obama: learning lip sync from audio
Suwajanakorn, S., Seitz, S. M., & Kemelmacher-Shlizerman, I. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073640 · Cited by 1,057
Photographic Image Synthesis with Cascaded Refinement Networks
Chen, Q., & Koltun, V. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.168 · Cited by 887
Chan, C., Ginosar, S., Zhou, T., & Efros, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00603 · Cited by 737
Kim, H., Garrido, P., Tewari, A., Xu, W., Thies, J., Nießner, M., Pérez, P., Richardt, C., Zollhöfer, M., & Theobalt, C. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201283 · Cited by 702
Self-citationVideo 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
Speaker identification on the SCOTUS corpus
Yuan, J., & Liberman, M. (2008) · The Journal of the Acoustical Society of America · doi:10.1121/1.2935783 · Cited by 610
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Liu, G., Tao, A., Kautz, J., & Catanzaro, B. (2018) · arXiv · doi:10.48550/arxiv.1808.06601 · Cited by 594
Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., & Nießner, M. (2016) · ACM SIGGRAPH Emerging Technologies · doi:10.1145/2929464.2929475 · Cited by 589
Self-citationFace transfer with multilinear models
Vlasic, D., Brand, M., Pfister, H., & Popović, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073209 · Cited by 565
Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network
Tran, A. T., Hassner, T., Masi, I., & Medioni, G. G. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.163 · Cited by 503
MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
Tewari, A., Zollhöfer, M., Kim, H., Garrido, P., Bernard, F., Pérez, P., & Theobalt, C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.401 · Cited by 398
Self-citationRecycle-GAN: Unsupervised Video Retargeting
Bansal, A., Ma, S., Ramanan, D., & Sheikh, Y. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_8 · Cited by 346
Learning Detailed Face Reconstruction from a Single Image
Richardson, E., Sela, M., Or-El, R., & Kimmel, R. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.589 · Cited by 339
Large Scale 3D Morphable Models
Booth, J. G., Roussos, A., Ponniah, A. J. T., Dunaway, D., & Zafeiriou, S. P. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1009-7 · Cited by 337
Unsupervised Training for 3D Morphable Model Regression
Genova, K., Cole, F. H., Maschinot, A., Sarna, A., Vlasic, D., & Freeman, W. T. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00874 · Cited by 311
Self-citation3D Face Reconstruction by Learning from Synthetic Data
Richardson, E., Sela, M., & Kimmel, R. (2016) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.56 · Cited by 309
State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications
Zollhöfer, M., Thies, J., Garrido, P., Bradley, D., Beeler, T., Pérez, P., Stamminger, M., Nießner, M., & Theobalt, C. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13382 · Cited by 306
Self-citationA deep learning approach for generalized speech animation
Taylor, S. L., Kim, T., Yue, Y., Mahler, M., Krahe, J., Rodriguez, A. G., Hodgins, J. K., & Matthews, I. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073699 · Cited by 305
Trainable videorealistic speech animation
Ezzat, T., Geiger, G., & Poggio, T. (2002) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/566570.566594 · Cited by 304
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
Sela, M., Richardson, E., & Kimmel, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.175 · Cited by 281
End-to-End 3D Face Reconstruction with Deep Neural Networks
Dou, P., Shah, S. K., & Kakadiaris, I. A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.164 · Cited by 271
Self-Supervised Multi-level Face Model Learning for Monocular Reconstruction at Over 250 Hz
Tewari, A., Zollhöfer, M., Garrido, P., Bernard, F., Kim, H., Pérez, P., & Theobalt, C. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00270 · Cited by 270
Self-citationReconstruction of Personalized 3D Face Rigs from Monocular Video
Garrido, P., Zollhöfer, M., Casas, D., Valgaerts, L., Varanasi, K., Pérez, P., & Theobalt, C. (2016) · ACM Transactions on Graphics · doi:10.1145/2890493 · Cited by 260
Self-citationZhou, Y., Xu, Z., Landreth, C., Kalogerakis, E., Maji, S., & Singh, K. S. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201292 · Cited by 256
Dynamic 3D avatar creation from hand-held video input
Ichim, A.-E., Bouaziz, S., & Pauly, M. V. (2015) · ACM Transactions on Graphics · doi:10.1145/2766974 · Cited by 249
Expressive expression mapping with ratio images
Liu, Z., Shan, Y., & Zhang, Z. (2001) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/383259.383289 · Cited by 241
Blanz, V., Scherbaum, K., Vetter, T. R., & Seidel, H. (2004) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2004.00799.x · Cited by 231
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Cao, C., Bradley, D., Zhou, K., & Beeler, T. (2015) · ACM Transactions on Graphics · doi:10.1145/2766943 · Cited by 217
CNN-Based Real-Time Dense Face Reconstruction with Inverse-Rendered Photo-Realistic Face Images
Guo, Y., Zhang, J., Cai, J., Jiang, B., & Zheng, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2837742 · Cited by 211
Edwards, P., Landreth, C., Fiume, E. L., & Singh, K. S. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925984 · Cited by 195
Averbuch‐Elor, H., Cohen‐Or, D., Kopf, J., & Cohen, M. F. (2017) · ACM Transactions on Graphics · doi:10.1145/3130800.3130818 · Cited by 187
Garrido, P., Valgaerts, L., Rehmsen, O., Thormaehlen, T., Pérez, P., & Theobalt, C. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.537 · Cited by 177
Self-citationNagano, K., Seo, J., Xing, J., Wei, L., Li, Z., Saito, S., Agarwal, A., Fursund, J., & Li, H. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275075 · Cited by 156
VDub: Modifying Face Video of Actors for Plausible Visual Alignment to a Dubbed Audio Track
Garrido, P., Valgaerts, L., Sarmadi, H., Steiner, I., Varanasi, K., Pérez, P., & Theobalt, C. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12552 · Cited by 130
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A Hybrid Model for Identity Obfuscation by Face Replacement
Sun, Q., Tewari, A., Xu, W., Fritz, M., Theobalt, C., & Schiele, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_34 · Cited by 121
Self-citationMartin-Brualla, R., Pandey, R., Yang, S., Pidlypenskyi, P., Taylor, J., Valentin, J., Khamis, S., Davidson, P., Tkach, A., Lincoln, P., et al. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275099 · Cited by 120
Self-citationPavel, A., Reed, C., Hartmann, B., & Agrawala, M. (2014) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2642918.2647400 · Cited by 112
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Berthouzoz, F., Li, W., & Agrawala, M. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185563 · Cited by 100
Self-citationDriving High-Resolution Facial Scans with Video Performance Capture
Fyffe, G., Jones, A., Alexander, O., Ichikari, R., & Debevec, P. E. (2014) · ACM Transactions on Graphics · doi:10.1145/2638549 · Cited by 94
Realistic Dynamic Facial Textures from a Single Image Using GANs
Olszewski, K., Li, Z., Yang, C., Zhou, Y., Yu, R., Huang, Z., Xiang, S., Saito, S., Kohli, P., & Li, H. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.580 · Cited by 92
Truong, A., Berthouzoz, F., Li, W., & Agrawala, M. (2016) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2984511.2984569 · Cited by 89
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Kemelmacher-Shlizerman, I. (2013) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.404 · Cited by 70
Jin, Z., Mysore, G. J., DiVerdi, S., Lu, J., & Finkelstein, A. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073702 · Cited by 64
Self-citationTransferable videorealistic speech animation
Chang, Y., & Ezzat, T. (2005) · ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA) · doi:10.1145/1073368.1073388 · Cited by 51
Adaptive 3D Face Reconstruction from Unconstrained Photo Collections
Roth, J., Tong, Y., & Liu, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2636829 · Cited by 49
Pavel, A., Goldman, D. B., Hartmann, B., & Agrawala, M. (2016) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2984511.2984552 · Cited by 43
Self-citationA Data-Driven Approach for Facial Expression Retargeting in Video
Li, K., Dai, Q., Wang, R., Liu, Y., Xu, F., & Wang, J. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2293064 · Cited by 36
Realistic facial expression synthesis for an image-based talking head
Liu, K., & Östermann, J. (2011) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2011.6011835 · Cited by 32
Kemelmacher-Shlizerman, I., Sankar, A., Shechtman, E., & Seitz, S. M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15549-9_25 · Cited by 23
Self-citationDynamic Authoring of Audio with Linked Scripts
Shin, H. V., Li, W., & Durand, F. (2016) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2984511.2984561 · Cited by 21
Everybody Dance NowPreprint
Chan, C., Ginosar, S., Zhou, T., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.07371 · Cited by 21
Neural Animation and Reenactment of Human Actor VideosPreprint
Liu, L., Xu, W., Zollhöfer, M., Kim, H., Bernard, F., Habermann, M., Wang, W., & Theobalt, C. (2018) · arXiv (Cornell University) · Cited by 17
Self-citationNeural Rendering and Reenactment of Human Actor VideosPreprint
Liu, L., Xu, W., Zollhöfer, M., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03658 · Cited by 10
Self-citationOptimized Photorealistic Audiovisual Speech Synthesis Using Active Appearance Modeling
Mattheyses, W., Latacz, L., & Verhelst, W. (2010) · VUBIR (Vrije Universiteit Brussel) · Cited by 9
Photorealistic 2D audiovisual text-to-speech synthesis using active appearance models
Mattheyses, W., & Verhelst, W. (2010) · doi:10.1145/1924035.1924042 · Cited by 5
Research works printed but not linked: 10
- Chan Caroline, 2018, Efros
- Dou Pengfei, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
- Genova Kyle, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
- Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems. Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems.
- Karras Tero, 2018, International Conference on Learning Representations (ICLR).
- 10.1145/3197517.3201283 · DOI
- Levenshtein Vladimir I, Soviet physics doklady
- Ochshorn Robert, 2016, Gentle: A Forced Aligner. https://lowerquality.com/gentle/.
- Ting-Chun Wang Ming-Yu Liu Jun-Yan Zhu Andrew Tao Jan Kautz and Bryan Catanzaro. 2018b. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. In CVPR. Ting-Chun Wang Ming-Yu Liu Jun-Yan Zhu Andrew Tao Jan Kautz and Bryan Catanzaro. 2018b. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. In CVPR.
- Wiles O., European Conference on Computer Vision.
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Prajwal, K. R., Mukhopadhyay, R., Namboodiri, V. P., & Jawahar, C. V. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413532 · Cited by 897
Park, K., Sinha, U., Hedman, P., Barron, J. T., Bouaziz, S., Goldman, D. B., Martin-Brualla, R., & Seitz, S. M. (2021) · ACM Transactions on Graphics · doi:10.1145/3478513.3480487 · Cited by 986
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Metadata
- Authors
- Fried, O., Tewari, A., Zollhöfer, M., Finkelstein, A., Shechtman, E., Goldman, D. B., Genova, K., Jin, Z., Theobalt, C., & Agrawala, M.
- Affiliations
- Stanford University (US)
- Max Planck Institute for Informatics (DE)
- Princeton University (US)
- Adobe Systems (United States) (US)
- ORCID
- Venue
- ACM Transactions on Graphics
- Published
- 2019-07-12
- Volume
- 38
- Issue
- 4
- Pages
- 1-14
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2948284812
- Cited by
- 270
- References
- 73
- Citations by year
- 2026: 14 · 2025: 30 · 2024: 36 · 2023: 51 · 2022: 46 · 2021: 47 · 2020: 39 · 2019: 7
- FWCI
- 15.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Graphics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Adobe Systems
- European Research Council (Consolidator Grant 4DRepLy #770784)
- Princeton University
- Brown Institute for Media Innovation
- National Science Foundation (NSF GRFP #DGE-1656466)
- Office of the Dean for Research, Princeton University
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- speech modification, face geometry, reflectance estimation, facial expression estimation, video generation, speech translation, speech synthesis
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