Advancements in deep learning techniques and the availability of free, large databases
have made it possible, even for non-technical people, to either manipulate or generate …

Sight and hearing are two senses that play a vital role in human communication and scene
understanding. To mimic human perception ability, audio-visual learning, aimed at …

While accurate lip synchronization has been achieved for arbitrary-subject audio-driven
talking face generation, the problem of how to efficiently drive the head pose remains …

Generating expressive talking heads is essential for creating virtual humans. However,
existing one-or few-shot methods focus on lip-sync and head motion, ignoring the emotional …

Although significant progress has been made to audio-driven talking face generation,
existing methods either neglect facial emotion or cannot be applied to arbitrary subjects. In …

We propose an audio-driven talking-head method to generate photo-realistic talking-head
videos from a single reference image. In this work, we tackle two key challenges:(i) …

With the recent success of deep neural networks, remarkable progress has been achieved
on face recognition. However, collecting large-scale real-world training data for face …

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 …

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