  Over the last few decades, many aspects of human life have been enhanced with
virtual domains, from the advent of digital assistants such as Amazon's Alexa
and Apple's Siri to the latest metaverse efforts of the rebranded Meta. These
trends underscore the importance of generating photorealistic visual depictions
of humans. This has led to the rapid growth of so-called deepfake and
talking-head generation methods in recent years. Despite their impressive
results and popularity, they usually lack certain qualitative aspects such as
texture quality, lips synchronization, or resolution, and practical aspects
such as the ability to run in real-time. To allow for virtual human avatars to
be used in practical scenarios, we propose an end-to-end framework for
synthesizing high-quality virtual human faces capable of speaking with accurate
lip motion with a special emphasis on performance. We introduce a novel network
utilizing visemes as an intermediate audio representation and a novel data
augmentation strategy employing a hierarchical image synthesis approach that
allows disentanglement of the different modalities used to control the global
head motion. Our method runs in real-time, and is able to deliver superior
results compared to the current state-of-the-art.
