  Talking head synthesis is a promising approach for the video production
industry. Recently, a lot of effort has been devoted in this research area to
improve the generation quality or enhance the model generalization. However,
there are few works able to address both issues simultaneously, which is
essential for practical applications. To this end, in this paper, we turn
attention to the emerging powerful Latent Diffusion Models, and model the
Talking head generation as an audio-driven temporally coherent denoising
process (DiffTalk). More specifically, instead of employing audio signals as
the single driving factor, we investigate the control mechanism of the talking
face, and incorporate reference face images and landmarks as conditions for
personality-aware generalized synthesis. In this way, the proposed DiffTalk is
capable of producing high-quality talking head videos in synchronization with
the source audio, and more importantly, it can be naturally generalized across
different identities without any further fine-tuning. Additionally, our
DiffTalk can be gracefully tailored for higher-resolution synthesis with
negligible extra computational cost. Extensive experiments show that the
proposed DiffTalk efficiently synthesizes high-fidelity audio-driven talking
head videos for generalized novel identities. For more video results, please
refer to \url{https://sstzal.github.io/DiffTalk/}.
