Demo of Face2Face: Real-time Face Capture and Reenactment of RGB Videos.
Thies J, Zollhöfer M, Stamminger M, Theobald C, Nießner M (2016)
Publication Type: Conference contribution, Original article
Publication year: 2016
Pages Range: S. 5:1-5:2.
Conference Proceedings Title: ACM Siggraph 2016 Emerging Technologies
Event location: Anaheim, California
ISBN: ISBN: 978-1-4503-4372-5
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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APA:
Thies, J., Zollhöfer, M., Stamminger, M., Theobald, C., & Nießner, M. (2016). Demo of Face2Face: Real-time Face Capture and Reenactment of RGB Videos. In ACM Siggraph 2016 Emerging Technologies (pp. S. 5:1-5:2.). Anaheim, California, US.
MLA:
Thies, Justus, et al. "Demo of Face2Face: Real-time Face Capture and Reenactment of RGB Videos." Proceedings of the ACM Siggraph 2016, Anaheim, California 2016. S. 5:1-5:2.
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