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。

Face2Face: 实时 RGB 视频人脸捕获和再现

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

This paper presents a method to automatically and efficiently detect face
tampering in videos, and particularly focuses on two recent techniques used to
generate hyper-realistic forged videos: Deepfake and Face2Face. Traditional
image forensics techniques are usually not well suited to videos due to the
compression that strongly degrades the data. Thus, this paper follows a deep
learning approach and presents two networks, both with a low number of layers
to focus on the mesoscopic properties of images. We evaluate those fast
networks on both an existing dataset and a dataset we have constituted from
online videos. The tests demonstrate a very successful detection rate with more
than 98% for Deepfake and 95% for Face2Face.

本文提出了一种利用深度学习自动有效地检测视频中人脸篡改的方法，并集中研究了最近使用的两种生成超逼真伪造视频的技术：Deepfake 和 Face2Face。通过提出的两个网络，在现有数据集和网络视频数据集上测试，检测率分别达到 98% 和 95% 以及相当成功。