Face2Face: Real-time Face Capture and Reenactment of RGB Videos (CVPR 2016 Oral)

This CVPR 2016 demo presents Face2Face, a real-time facial reenactment system that transfers expressions from a source actor to a target video.

CVPR 2016 Paper Video (Oral) Project Page: niessnerlab.org/projects/thies2016face.html IMPORTANT NOTE: This demo video is purely research-focused and we would like to clarify the goals and intent of our work. Our aim is to demonstrate the capabilities of modern computer vision and graphics technology, and convey it in an approachable and fun way. We want to emphasize that computer-generated videos have been part in feature-film movies for over 30 years. Virtually every high-end movie production contains a significant percentage of synthetically-generated content (from Lord of the Rings to Benjamin Button). These results are hard to distinguish from reality and it often goes unnoticed that the content is not real. The novelty and contribution of our work is that we can edit pre-recorded videos in real-time on a commodity PC. Please also note that our efforts include the detection of edits in video footage in order to verify a clip’s authenticity. For additional information, we refer to our project website (see above). Hopefully, you enjoyed watching our video, and we hope to provide a positive takeaway :) Paper 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 reenacted in real time.