  Existing Graph Convolutional Networks to achieve human motion prediction
largely adopt a one-step scheme, which output the prediction straight from
history input, failing to exploit human motion patterns. We observe that human
motions have transitional patterns and can be split into snippets
representative of each transition. Each snippet can be reconstructed from its
starting and ending poses referred to as the transitional poses. We propose a
snippet-to-motion multi-stage framework that breaks motion prediction into
sub-tasks easier to accomplish. Each sub-task integrates three modules:
transitional pose prediction, snippet reconstruction, and snippet-to-motion
prediction. Specifically, we propose to first predict only the transitional
poses. Then we use them to reconstruct the corresponding snippets, obtaining a
close approximation to the true motion sequence. Finally we refine them to
produce the final prediction output. To implement the network, we propose a
novel unified graph modeling, which allows for direct and effective feature
propagation compared to existing approaches which rely on separate space-time
modeling. Extensive experiments on Human 3.6M, CMU Mocap and 3DPW datasets
verify the effectiveness of our method which achieves state-of-the-art
performance.
