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Demo of Face2Face: real-time face capture and reenactment of RGB videos
Abstract
We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. We demonstrate our method in a live setup, where Youtube videos are reen-acted in real time.
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Research works printed but not linked: 1
- Thies , J. , Zollhöfer , M. , Stamminger , M. , Theobalt , C. , and Niessner , M . 2016. Face2Face: Real-time Face Capture and Reenactment of RGB Videos . In Proc. CVPR 2016 . Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., and Niessner, M. 2016. Face2Face: Real-time Face Capture and Reenactment of RGB Videos. In Proc. CVPR 2016.
Cited by
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Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics
Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00327 · Cited by 1,846
Deepfake Video Detection Using Recurrent Neural Networks
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Exposing Deep Fakes Using Inconsistent Head Poses
Yang, X., Li, Y., & Lyu, S. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683164 · Cited by 1,196
Face X-Ray for More General Face Forgery Detection
Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00505 · Cited by 1,211
Metadata
- Authors
- Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., & Nießner, M.
- Venue
- ACM SIGGRAPH 2016 Emerging Technologies
- Publisher
- ACM
- Published
- 2016-07-24
- Pages
- 1-2
- OpenAlex
- W2486034530
- Cited by
- 589
- References
- 0
- FWCI
- 18.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Demo of Face2Face: real-time face capture and reenactment of RGB videos. https://app.cochat.ai/papers/10.1145/2929464.2929475
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- Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., & Nießner, M. (2016). Demo of Face2Face: real-time face capture and reenactment of RGB videos. ACM SIGGRAPH 2016 Emerging Technologies. https://doi.org/10.1145/2929464.2929475
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