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 …

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 …

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 …

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 …

Text-driven image manipulation is developed since the vision-language model (CLIP) has
been proposed. Previous work has adopted CLIP to design a text-image consistency-based …

Semantic editing of images is the fundamental goal of computer vision. Although deep
learning methods, such as generative adversarial networks (GANs), are capable of …

We introduce a new method to efficiently create text-to-image models from a pre-trained
CLIP and StyleGAN. It enables text driven sampling with an existing generative model …

Video-driven neural face reenactment aims to synthesize realistic facial images that
successfully preserve the identity and appearance of a source face, while transferring the …

In this technical demonstration, we present SMILEY, a voice-guided virtual assistant. The
system utilizes a deep neural architecture ContraCLIP to manipulate facial attributes using …