Human motion prediction is a challenging task due to the stochasticity and
aperiodicity of future poses. Recently, graph convolutional network has been
proven to be very effective to learn dynamic relations among pose joints, which
is helpful for pose prediction. On the other hand, one can abstract a human
pose recursively to obtain a set of poses at multiple scales. With the increase
of the abstraction level, the motion of the pose becomes more stable, which
benefits pose prediction too. In this paper, we propose a novel Multi-Scale
Residual Graph Convolution Network (MSR-GCN) for human pose prediction task in
the manner of end-to-end. The GCNs are used to extract features from fine to
coarse scale and then from coarse to fine scale. The extracted features at each
scale are then combined and decoded to obtain the residuals between the input
and target poses. Intermediate supervisions are imposed on all the predicted
poses, which enforces the network to learn more representative features. Our
proposed approach is evaluated on two standard benchmark datasets, i.e., the
Human3.6M dataset and the CMU Mocap dataset. Experimental results demonstrate
that our method outperforms the state-of-the-art approaches. Code and
pre-trained models are available at this https URL

本文提出一种新的多尺度残差图卷积网络（MSR-GCN），以端到端的方式进行人体姿态预测，该方法在细到粗的尺度上提取特征，然后在粗到细的尺度上进行特征的组合和解码，通过在所有预测姿势上施加中间监督来强制网络学习更具代表性的特征，实验证明该方法优于现有的最优方法。