  This paper presents a high-quality human motion prediction method that
accurately predicts future human poses given observed ones. Our method is based
on the observation that a good initial guess of the future poses is very
helpful in improving the forecasting accuracy. This motivates us to propose a
novel two-stage prediction framework, including an init-prediction network that
just computes the good guess and then a formal-prediction network that predicts
the target future poses based on the guess. More importantly, we extend this
idea further and design a multi-stage prediction framework where each stage
predicts initial guess for the next stage, which brings more performance gain.
To fulfill the prediction task at each stage, we propose a network comprising
Spatial Dense Graph Convolutional Networks (S-DGCN) and Temporal Dense Graph
Convolutional Networks (T-DGCN). Alternatively executing the two networks helps
extract spatiotemporal features over the global receptive field of the whole
pose sequence. All the above design choices cooperating together make our
method outperform previous approaches by large margins: 6%-7% on Human3.6M,
5%-10% on CMU-MoCap, and 13%-16% on 3DPW.
