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.

References

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.

Index Terms

• Computing methodologies

  • Computer graphics

    • Image manipulation