  Stochastic Human Motion Prediction (HMP) aims to predict multiple possible
upcoming pose sequences based on past human motion trajectories. Although
previous approaches have shown impressive performance, they face several
issues, including complex training processes and a tendency to generate
predictions that are often inconsistent with the provided history, and
sometimes even becoming entirely unreasonable. To overcome these issues, we
propose DiffMotion, an end-to-end diffusion-based stochastic HMP framework.
DiffMotion's motion predictor is composed of two modules, including (1) a
Transformer-based network for initial motion reconstruction from corrupted
motion, and (2) a Graph Convolutional Network (GCN) to refine the generated
motion considering past observations. Our method, facilitated by this novel
Transformer-GCN module design and a proposed variance scheduler, excels in
predicting accurate, realistic, and consistent motions, while maintaining an
appropriate level of diversity. Our results on benchmark datasets show that
DiffMotion significantly outperforms previous methods in terms of both accuracy
and fidelity, while demonstrating superior robustness.
