City Research Online

One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space

Bounareli, S., Tzelepis, C. ORCID: 0000-0002-2036-9089, Argyriou, V. , Patras, I. & Tzimiropoulos, G. (2024). One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space. International Journal of Computer Vision,

Abstract

In this paper, we present our framework for neural face/head reenactment whose goal is to transfer the 3D head orientation and expression of a target face to a source face. Previous methods focus on learning embedding networks for identity and head pose/expression disentanglement which proves to be a rather hard task, degrading the quality of the generated images. We take a different approach, bypassing the training of such networks, by using (fine-tuned) pre-trained GANs which have been shown capable of producing high-quality facial images. Because GANs are characterized by weak controllability, the core of our approach is a method to discover which directions in latent GAN space are responsible for controlling head pose and expression variations. We present a simple pipeline to learn such directions with the aid of a 3D shape model which, by construction, inherently captures disentangled directions for head pose, identity, and expression. Moreover, we show that by embedding real images in the GAN latent space, our method can be successfully used for the reenactment of real-world faces. Our method features several favorable properties including using a single source image (one-shot) and enabling cross-person reenactment. Extensive qualitative and quantitative results show that our approach typically produces reenacted faces of notably higher quality than those produced by state-of-the-art methods for the standard benchmarks of VoxCeleb1 & 2.

Publication Type: Article
Additional Information: This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record will be available online at: http://link.springer.com/journal/11263
Publisher Keywords: Neural Face Reenactment, Generative Adversarial Networks (GANs), Image synthesis, Image editing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology
School of Science & Technology > Computer Science
SWORD Depositor:
[thumbnail of ijcv_stella.pdf] Text - Accepted Version
This document is not freely accessible due to copyright restrictions.

To request a copy, please use the button below.

Request a copy

Export

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login