  Audio-driven talking head generation is a significant and challenging task
applicable to various fields such as virtual avatars, film production, and
online conferences. However, the existing GAN-based models emphasize generating
well-synchronized lip shapes but overlook the visual quality of generated
frames, while diffusion-based models prioritize generating high-quality frames
but neglect lip shape matching, resulting in jittery mouth movements. To
address the aforementioned problems, we introduce a two-stage diffusion-based
model. The first stage involves generating synchronized facial landmarks based
on the given speech. In the second stage, these generated landmarks serve as a
condition in the denoising process, aiming to optimize mouth jitter issues and
generate high-fidelity, well-synchronized, and temporally coherent talking head
videos. Extensive experiments demonstrate that our model yields the best
performance.
