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.

Face2Face 是一种用于实时面部复刻的创新方法，可以通过非刚性基于模型的束缚，跟踪面部表情，实现来自单眼录制的目标视频序列的面部表情的动画化，并以逼真的方式重新渲染操作后的输出视频。