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

Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, Matthias Niessner; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2387-2395

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.

Related Material


[pdf]
[bibtex]
@InProceedings{Thies_2016_CVPR,
author = {Thies, Justus and Zollhofer, Michael and Stamminger, Marc and Theobalt, Christian and Niessner, Matthias},
title = {Face2Face: Real-Time Face Capture and Reenactment of RGB Videos},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}