Stochastic human motion prediction aims to predict multiple possible upcoming
pose sequences based on past human motion trajectories. Prior works focused
heavily on generating diverse motion samples, leading to inconsistent, abnormal
predictions from the immediate past observations. To address this issue, in
this work, we propose DiffMotion, a diffusion-based stochastic human motion
prediction framework that considers both the kinematic structure of the human
body and the globally temporally consistent nature of motion. Specifically,
DiffMotion consists of two modules: 1) a transformer-based network for
generating an initial motion reconstruction from corrupted motion, and 2) a
multi-stage graph convolutional network to iteratively refine the generated
motion based on past observations. Facilitated by the proposed direct target
prediction objective and the variance scheduler, our method is capable of
predicting accurate, realistic and consistent motion with an appropriate level
of diversity. Our results on benchmark datasets demonstrate that DiffMotion
outperforms previous methods by large margins in terms of accuracy and fidelity
while demonstrating superior robustness.

该论文提出了一种基于扩散的随机人体运动预测框架 DiffMotion，旨在预测未来的姿势序列，并通过使用多阶段图卷积网络和方差调度器，使预测结果准确、逼真、一致，同时在准确度和保真度方面明显优于先前的方法，并通过基准数据集展现了强大的鲁棒性。