City Research Online

Abstract

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 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 results and our user study, please refer to our supplementary material.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	This paper will be presented at 16th International Conference on Agents and Artificial Intelligence (ICAART 2024).
Publisher Keywords:	Talking Head Synthesis, Diffusion Transformers
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science

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