Recently, emotional talking face generation has received considerable attention. However,
existing methods only adopt one-hot coding, image, or audio as emotion conditions, thus …

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 …

Recent advancements in personalized image generation using diffusion models have been
noteworthy. However existing methods suffer from inefficiencies due to the requirement for …

The great advancements of generative adversarial networks and face recognition models in
computer vision have made it possible to swap identities on images from single sources …

We introduce a novel framework for one-shot audio-driven talking head generation. Unlike
prior works that require additional driving sources for controlled synthesis in a deterministic …

The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are
extensively being harnessed across a diverse range of domains, eg, forensic science …

Deepfake is a technology dedicated to creating highly realistic facial images and videos
under specific conditions, which has significant application potential in fields such as …

Current face reenactment and swapping methods mainly rely on GAN frameworks, but
recent focus has shifted to pre-trained diffusion models for their superior generation …

In human-centric content generation the pre-trained text-to-image models struggle to
produce user-wanted portrait images which retain the identity of individuals while exhibiting …

The SOTA face swap models still suffer the problem of either target identity (ie, shape) being
leaked or the target non-identity attributes (ie, background, hair) failing to be fully preserved …