The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
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
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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Nerfies: Deformable Neural Radiance Fields
Park, K., Sinha, U., Barron, J. T., Bouaziz, S., Goldman, D. B., Seitz, S. M., & Martin-Brualla, R. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00581 · Cited by 1,221
Self-citationMedia Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979
A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild
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
Self-citation
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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