Facial video re-targeting is a challenging problem aiming to modify the
facial attributes of a target subject in a seamless manner by a driving
monocular sequence. We leverage the 3D geometry of faces and Generative
Adversarial Networks (GANs) to design a novel deep learning architecture for
the task of facial and head reenactment. Our method is different to purely 3D
model-based approaches, or recent image-based methods that use Deep
Convolutional Neural Networks (DCNNs) to generate individual frames. We manage
to capture the complex non-rigid facial motion from the driving monocular
performances and synthesise temporally consistent videos, with the aid of a
sequential Generator and an ad-hoc Dynamics Discriminator network. We conduct a
comprehensive set of quantitative and qualitative tests and demonstrate
experimentally that our proposed method can successfully transfer facial
expressions, head pose and eye gaze from a source video to a target subject, in
a photo-realistic and faithful fashion, better than other state-of-the-art
methods. Most importantly, our system performs end-to-end reenactment in nearly
real-time speed (18 fps).

利用 3D 几何和生成对抗网络设计了一种新的深度学习架构，用于实现面部和头部再现任务，通过全新的序列生成器和动态判别器网络，可以以几乎实时的速度（18 fps）以逼真和忠实的方式成功转移来自源视频的面部表情、头部姿态和眼神到目标主体中。