  The art of communication beyond speech there are gestures. The automatic
co-speech gesture generation draws much attention in computer animation. It is
a challenging task due to the diversity of gestures and the difficulty of
matching the rhythm and semantics of the gesture to the corresponding speech.
To address these problems, we present DiffuseStyleGesture, a diffusion model
based speech-driven gesture generation approach. It generates high-quality,
speech-matched, stylized, and diverse co-speech gestures based on given
speeches of arbitrary length. Specifically, we introduce cross-local attention
and self-attention to the gesture diffusion pipeline to generate better speech
matched and realistic gestures. We then train our model with classifier-free
guidance to control the gesture style by interpolation or extrapolation.
Additionally, we improve the diversity of generated gestures with different
initial gestures and noise. Extensive experiments show that our method
outperforms recent approaches on speech-driven gesture generation. Our code,
pre-trained models, and demos are available at
https://github.com/YoungSeng/DiffuseStyleGesture.
