  Speech-driven gesture synthesis is a field of growing interest in virtual
human creation. However, a critical challenge is the inherent intricate
one-to-many mapping between speech and gestures. Previous studies have explored
and achieved significant progress with generative models. Notwithstanding, most
synthetic gestures are still vastly less natural. This paper presents
DiffMotion, a novel speech-driven gesture synthesis architecture based on
diffusion models. The model comprises an autoregressive temporal encoder and a
denoising diffusion probability Module. The encoder extracts the temporal
context of the speech input and historical gestures. The diffusion module
learns a parameterized Markov chain to gradually convert a simple distribution
into a complex distribution and generates the gestures according to the
accompanied speech. Compared with baselines, objective and subjective
evaluations confirm that our approach can produce natural and diverse
gesticulation and demonstrate the benefits of diffusion-based models on
speech-driven gesture synthesis.
