  Human motion prediction, i.e., forecasting future body poses given observed
pose sequence, has typically been tackled with recurrent neural networks
(RNNs). However, as evidenced by prior work, the resulted RNN models suffer
from prediction errors accumulation, leading to undesired discontinuities in
motion prediction. In this paper, we propose a simple feed-forward deep network
for motion prediction, which takes into account both temporal smoothness and
spatial dependencies among human body joints. In this context, we then propose
to encode temporal information by working in trajectory space, instead of the
traditionally-used pose space. This alleviates us from manually defining the
range of temporal dependencies (or temporal convolutional filter size, as done
in previous work). Moreover, spatial dependency of human pose is encoded by
treating a human pose as a generic graph (rather than a human skeletal
kinematic tree) formed by links between every pair of body joints. Instead of
using a pre-defined graph structure, we design a new graph convolutional
network to learn graph connectivity automatically. This allows the network to
capture long range dependencies beyond that of human kinematic tree. We
evaluate our approach on several standard benchmark datasets for motion
prediction, including Human3.6M, the CMU motion capture dataset and 3DPW. Our
experiments clearly demonstrate that the proposed approach achieves state of
the art performance, and is applicable to both angle-based and position-based
pose representations. The code is available at
https://github.com/wei-mao-2019/LearnTrajDep
