Head2Head++: deep facial attributes re-targeting

Doukas, Michail Christos, Koujan, Mohammad Rami, Sharmanska, Viktoriia, Roussos, Anastasios and Zafeiriou, Stefanos (2021) Head2Head++: deep facial attributes re-targeting. IEEE Transactions on Biometrics, Behavior, and Identity Science, 3 (1). pp. 31-43. ISSN 2637-6407

[img] PDF - Accepted Version
Download (17MB)
[img] PDF - Published Version
Restricted to SRO admin only

Download (2MB)

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).

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 30 Nov 2021 08:34
Last Modified: 30 Nov 2021 11:00
URI: http://sro.sussex.ac.uk/id/eprint/103145

View download statistics for this item

📧 Request an update