Stella Bounareli

I am a third year PhD student at School of Computer Science and Mathematics of Kingston University of London (UK), under the supervision of Prof. Vasileios Argyriou and Prof. Georgios Tzimiropoulos. My research focuses on facial analysis and manipulation using Generative Adversarial Networks (GANs).

Before my PhD degree, I was working as a Research Assistant at the Center for Research and Technology Hellas CERTH (Greece). My research was mainly focused on 3D object inspection algorithms, and on object and face detection/tracking on videos.

I obtained my Integrated Masters degree from the ECE Department of Aristotle University of Thessaloniki (Greece), where I did my dissertation, entitled "Recognition of salient locations and creation of a location timeline by using GPS data", under the direction of Prof. Anastasios Delopoulos.

My academic e-mail is k2033759 [ at ] kingston.ac.uk

2023

HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, Georgios Tzimiropoulos IEEE/CVF International Conference on Computer Vision (ICCV), 2023
Paper Project Page Source Code

In this paper, we present our method, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose. Existing state-of-the-art face reenactment methods train controllable generative models that learn to synthesize realistic facial images, yet producing reenacted faces that are prone to significant visual artifacts, especially under the challenging condition of extreme head pose changes, or requiring expensive few-shot fine-tuning. We propose to address these limitations by learning a hypernetwork to perform: (i) refinement of the source identity characteristics and (ii) facial pose re-targeting. Our method operates under the one-shot setting (i.e., using a single source frame) and allows for cross-subject reenactment, without requiring any subject-specific fine-tuning.

StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, Georgios Tzimiropoulos 17th IEEE conference on Automatic Face and Gesture Recognition (FG), 2023
Paper Source Code

In this paper we present a method that using unpaired randomly generated facial images, learns to disentangle the identity characteristics of a human face from its pose by incorporating the recently introduced style space S of StyleGAN2, a latent representation space that exhibits remarkable disentanglement properties. By capitalizing on this, we learn to successfully mix a pair of source and target style codes using supervision from a 3D model. The resulting latent code, that is subsequently used for reenactment, consists of latent units corresponding to the facial pose of the target only and of units corresponding to the identity of the source only, leading to notable improvement in the reenactment performance compared to recent state-of-the-art methods.

2022

Finding Directions in GAN's Latent Space for Neural Face Reenactment Stella Bounareli, Vasileios Argyriou, Georgios Tzimiropoulos

Accepted as oral presentation in:

33rd British Machine Vision Conference (BMVC), 2022
Paper Project Page Source Code

This paper is on face/head reenactment where the goal is to transfer the facial pose (3D head orientation and expression) of a target face to a source face. We introduce a new method for neural face reenactment, by leveraging the high quality image generation of pretrained generative models (GANs) and the disentangled properties of 3D Morphable Models (3DMM). The core of our approach is a method to discover which directions in latent GAN space are responsible for controlling facial pose and expression variations.

Stella Bounareli

Publications

2023

2022

Work Experience