Talking head generation is to generate video based on a given source identity and target
motion. However, current methods face several challenges that limit the quality and …

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 …

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 …

Achieving disentangled control over multiple facial motions and accommodating diverse
input modalities greatly enhances the application and entertainment of the talking head …

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 …

Abstract We propose the Pose Adapted Shape Learning (PASL) for large-pose face
reenactment. The PASL framework consists of three modules namely the Pose-Adapted face …

Deepfake refers to an artificial intelligence-based technique to produce manipulated videos
that look realistic. However, this good aspect of Deepfake sometimes pose serious threats to …

Reconstructing personalized animatable head avatars has significant implications in the
fields of AR/VR. Existing methods for achieving explicit face control of 3D Morphable Models …

Assessing the realism and accuracy of deepfake generators, especially in cross-
reenactment situations, is a major challenge. This challenge is primarily attributed to the …

We propose VLOGGER, a method for audio-driven human video generation from a single
input image of a person, which builds on the success of recent generative diffusion models …