  Fusion is critical for a two-stream network. In this paper, we propose a
novel temporal fusion (TF) module to fuse the two-stream joints' information to
predict human motion, including a temporal concatenation and a reinforcement
trajectory spatial-temporal (TST) block, specifically designed to keep
prediction temporal consistency. In particular, the temporal concatenation
keeps the temporal consistency of preliminary predictions from two streams.
Meanwhile, the TST block improves the spatial-temporal feature coupling.
However, the TF module can increase the temporal continuities between the first
predicted pose and the given poses and between each predicted pose. The fusion
is based on a two-stream network that consists of a dynamic velocity stream
(V-Stream) and a static position stream (P-Stream) because we found that the
joints' velocity information improves the short-term prediction, while the
joints' position information is better at long-term prediction, and they are
complementary in motion prediction. Finally, our approach achieves impressive
results on three benchmark datasets, including H3.6M, CMU-Mocap, and 3DPW in
both short-term and long-term predictions, confirming its effectiveness and
efficiency.

Computer Vision and Pattern Recognition