Talking Face

Talking face generation aims to synthesize realistic and expressive video of a person speaking, driven by audio input or text. Current research heavily focuses on improving the realism and emotional expressiveness of these synthetic videos, employing techniques like diffusion models, generative adversarial networks (GANs), and variational autoencoders (VAEs) to achieve accurate lip-sync and nuanced facial expressions, often incorporating disentangled representations of identity, emotion, and motion. This field is significant for its applications in animation, virtual reality, and communication technologies, as well as its potential to advance research in computer vision, machine learning, and human-computer interaction.