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Artificial Intelligence Research Centre, Department of Computer Science, City, University of London, Northampton Square, EC1V 0HB, London, U.K.

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1 Papers

DiT-Head: High Resolution Talking Head Synthesis Using Diffusion Transformers

Topics: AI and Creativity; Deep Learning

Authors: Aaron Mir , Eduardo Alonso and Esther Mondragón

Affiliation: Artificial Intelligence Research Centre, Department of Computer Science, City, University of London, Northampton Square, EC1V 0HB, London, U.K.

Digital Object Identifier: 10.5220/0012312200003636

Proceedings of ICAART 2024, ISBN: 978-989-758-680-4

Quick 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.