  In this paper, we develop a neural network model to predict future human
motion from an observed human motion history. We propose a non-autoregressive
transformer architecture to leverage its parallel nature for easier training
and fast, accurate predictions at test time. The proposed architecture divides
human motion prediction into two parts: 1) the human trajectory, which is the
hip joint 3D position over time and 2) the human pose which is the all other
joints 3D positions over time with respect to a fixed hip joint. We propose to
make the two predictions simultaneously, as the shared representation can
improve the model performance. Therefore, the model consists of two sets of
encoders and decoders. First, a multi-head attention module applied to encoder
outputs improves human trajectory. Second, another multi-head self-attention
module applied to encoder outputs concatenated with decoder outputs facilitates
learning of temporal dependencies. Our model is well-suited for robotic
applications in terms of test accuracy and speed, and compares favorably with
respect to state-of-the-art methods. We demonstrate the real-world
applicability of our work via the Robot Follow-Ahead task, a challenging yet
practical case study for our proposed model.
