  Human motion generation has paramount importance in computer animation. It is
a challenging generative temporal modelling task due to the vast possibilities
of human motion, high human sensitivity to motion coherence and the difficulty
of accurately generating fine-grained motions. Recently, diffusion methods have
been proposed for human motion generation due to their high sample quality and
expressiveness. However, generated sequences still suffer from motion
incoherence, and are limited to short duration, and simpler motion and take
considerable time during inference. To address these limitations, we propose
\textit{RecMoDiffuse: Recurrent Flow Diffusion}, a new recurrent diffusion
formulation for temporal modelling. Unlike previous work, which applies
diffusion to the whole sequence without any temporal dependency, an approach
that inherently makes temporal consistency hard to achieve. Our method
explicitly enforces temporal constraints with the means of normalizing flow
models in the diffusion process and thereby extends diffusion to the temporal
dimension. We demonstrate the effectiveness of RecMoDiffuse in the temporal
modelling of human motion. Our experiments show that RecMoDiffuse achieves
comparable results with state-of-the-art methods while generating coherent
motion sequences and reducing the computational overhead in the inference
stage.
