# Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation

Michał Stypułkowski 1   Konstantinos Vougioukas 2   Sen He   Maciej Zięba 3,4   Stavros Petridis 2   Maja Pantic 2

1 University of Wrocław   2 Imperial College London   3 Wrocław University of Science and Technology   4 Tooploox

Diffused Heads is the first method successfully using a diffusion model to generate talking faces. Given a single identity frame and an audio clip containing speech, the model samples consecutive frames in an autoregressive manner, preserving the identity, and modeling lip and head movement to match the audio input. Contrary to other methods, no additional guidance is required.

## Generated videos

### CREMA

Test set examples from CREMA. None of the participants was included in the training set.

### Generalization

Results of video generation given inputs from sources not used during the training.

### Abstract

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.

### Bibtex

@inproceedings{stypulkowski2022diffused,
title     = {{Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation}},
author    = {Stypu{\l}kowski, Micha{\l} and Vougioukas, Konstantinos and He, Sen and Zi\k{e}ba, Maciej and Petridis, Stavros and Pantic, Maja},
booktitle = {https://arxiv.org/abs/2301.03396},
year      = {2023}
} 

### Acknowledgements

We thank Kacper Kania for the website's template.