  Human motion prediction, which aims to predict future human poses given past
poses, has recently seen increased interest. Many recent approaches are based
on Recurrent Neural Networks (RNN) which model human poses with exponential
maps. These approaches neglect the pose velocity as well as temporal relation
of different poses, and tend to converge to the mean pose or fail to generate
natural-looking poses. We therefore propose a novel Position-Velocity Recurrent
Encoder-Decoder (PVRED) for human motion prediction, which makes full use of
pose velocities and temporal positional information. A temporal position
embedding method is presented and a Position-Velocity RNN (PVRNN) is proposed.
We also emphasize the benefits of quaternion parameterization of poses and
design a novel trainable Quaternion Transformation (QT) layer, which is
combined with a robust loss function during training. We provide quantitative
results for both short-term prediction in the future 0.5 seconds and long-term
prediction in the future 0.5 to 1 seconds. Experiments on several benchmarks
show that our approach considerably outperforms the state-of-the-art methods.
In addition, qualitative visualizations in the future 4 seconds show that our
approach could predict future human-like and meaningful poses in very long time
horizons. Code is publicly available on GitHub:
\textcolor{red}{https://github.com/hongsong-wang/PVRNN}.
