  Motion prediction is a classic problem in computer vision, which aims at
forecasting future motion given the observed pose sequence. Various deep
learning models have been proposed, achieving state-of-the-art performance on
motion prediction. However, existing methods typically focus on modeling
temporal dynamics in the pose space. Unfortunately, the complicated and high
dimensionality nature of human motion brings inherent challenges for dynamic
context capturing. Therefore, we move away from the conventional pose based
representation and present a novel approach employing a phase space trajectory
representation of individual joints. Moreover, current methods tend to only
consider the dependencies between physically connected joints. In this paper,
we introduce a novel convolutional neural model to effectively leverage
explicit prior knowledge of motion anatomy, and simultaneously capture both
spatial and temporal information of joint trajectory dynamics. We then propose
a global optimization module that learns the implicit relationships between
individual joint features.
  Empirically, our method is evaluated on large-scale 3D human motion benchmark
datasets (i.e., Human3.6M, CMU MoCap). These results demonstrate that our
method sets the new state-of-the-art on the benchmark datasets. Our code will
be available at https://github.com/Pose-Group/TEID.
