Head2Head++: Deep Facial Attributes Re-Targeting

Michail Christos Doukas, Mohammad Rami Koujan, Viktoriia Sharmanska, Anastasios Roussos, Stefanos Zafeiriou

Abstract:

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).