  Talking face generation has historically struggled to produce head movements
and natural facial expressions without guidance from additional reference
videos. Recent developments in diffusion-based generative models allow for more
realistic and stable data synthesis and their performance on image and video
generation has surpassed that of other generative models. In this work, we
present an autoregressive diffusion model that requires only one identity image
and audio sequence to generate a video of a realistic talking human head. Our
solution is capable of hallucinating head movements, facial expressions, such
as blinks, and preserving a given background. We evaluate our model on two
different datasets, achieving state-of-the-art results on both of them.
