Face Video Generation

Face video generation aims to synthesize realistic and expressive talking head videos from audio input, often leveraging a reference image for identity preservation. Current research emphasizes improving lip synchronization, enhancing visual fidelity, and addressing challenges like motion jitters and expression control through techniques such as diffusion models, 3D Morphable Models (3DMMs), and StyleGANs, often incorporating intermediate representations like facial landmarks. These advancements have implications for various applications, including virtual assistants, video conferencing, and film production, by enabling more natural and engaging human-computer interaction and creative content generation.