In this paper, we present our method for neural face reenactment, called HyperReenact, that
aims to generate realistic talking head images of a source identity, driven by a target facial …

This work addresses the problem of anonymizing the identity of faces in a dataset of images,
such that the privacy of those depicted is not violated, while at the same time the dataset is …

Abstract Generative Adversarial Networks (GANs), especially the recent style-based
generators (StyleGANs), have versatile semantics in the structured latent space. Latent …

Semantic image synthesis (SIS) aims to generate realistic images according to semantic
masks given by a user. Although recent methods produce high quality results with fine …

A prominent goal of representation learning research is to achieve representations which
are factorized in a useful manner with respect to the ground truth factors of variation. The …

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. Previous methods focus …

Fairness is crucial when training a deep-learning discriminative model, especially in the
facial domain. Models tend to correlate specific characteristics (such as age and skin color) …

In this paper we address the problem of neural face reenactment, where, given a pair of a
source and a target facial image, we need to transfer the target's pose (defined as the head …

Recent advances in the understanding of Generative Adversarial Networks (GANs) have led
to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich …

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 …