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

TL;DR

Face2Face enables real-time facial reenactment by transferring expressions from a live source video to a target video, producing realistic and seamless face animations in real time.

Contribution

This paper introduces a novel real-time facial reenactment method using monocular videos, combining non-rigid model-based tracking and deformation transfer for realistic results.

Findings

Achieves real-time performance in facial reenactment.

Produces photo-realistic and seamless face animations.

Successfully reenacts YouTube videos in live settings.

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.