  We propose a novel talking head synthesis pipeline called "DiT-Head", which
is based on diffusion transformers and uses audio as a condition to drive the
denoising process of a diffusion model. Our method is scalable and can
generalise to multiple identities while producing high-quality results. We
train and evaluate our proposed approach and compare it against existing
methods of talking head synthesis. We show that our model can compete with
these methods in terms of visual quality and lip-sync accuracy. Our results
highlight the potential of our proposed approach to be used for a wide range of
applications, including virtual assistants, entertainment, and education. For a
video demonstration of the results and our user study, please refer to our
supplementary material.
