  Diffusion models have experienced a surge of interest as highly expressive
yet efficiently trainable probabilistic models. We show that these models are
an excellent fit for synthesising human motion that co-occurs with audio, e.g.,
dancing and co-speech gesticulation, since motion is complex and highly
ambiguous given audio, calling for a probabilistic description. Specifically,
we adapt the DiffWave architecture to model 3D pose sequences, putting
Conformers in place of dilated convolutions for improved modelling power. We
also demonstrate control over motion style, using classifier-free guidance to
adjust the strength of the stylistic expression. Experiments on gesture and
dance generation confirm that the proposed method achieves top-of-the-line
motion quality, with distinctive styles whose expression can be made more or
less pronounced. We also synthesise path-driven locomotion using the same model
architecture. Finally, we generalise the guidance procedure to obtain
product-of-expert ensembles of diffusion models and demonstrate how these may
be used for, e.g., style interpolation, a contribution we believe is of
independent interest. See
https://www.speech.kth.se/research/listen-denoise-action/ for video examples,
data, and code.

Machine Learning;Computer Vision and Pattern Recognition;Graphics;Human-Computer Interaction;Sound;Audio and Speech Processing