Face2Face: Real-time Face Capture and Reenactment of RGB Videos

• URL: http://arxiv.org/abs/2007.14808v1
• Date: Wed, 29 Jul 2020 12:47:16 GMT
• Title: Face2Face: Real-time Face Capture and Reenactment of RGB Videos
• Authors: Justus Thies and Michael Zollh\"ofer and Marc Stamminger and Christian Theobalt and Matthias Nie{\ss}ner
• Abstract summary: Face2Face is a novel approach for real-time facial reenactment of a monocular target video sequence. We track facial expressions of both source and target video using a dense photometric consistency measure. We convincingly re-render the synthesized target face on top of the corresponding video stream.
• Score: 66.38142459175191
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Face2Face, 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 reenacted in real time.

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