  Human motion prediction is a challenging and important task in many computer
vision application domains. Existing work only implicitly models the spatial
structure of the human skeleton. In this paper, we propose a novel approach
that decomposes the prediction into individual joints by means of a structured
prediction layer that explicitly models the joint dependencies. This is
implemented via a hierarchy of small-sized neural networks connected
analogously to the kinematic chains in the human body as well as a joint-wise
decomposition in the loss function. The proposed layer is agnostic to the
underlying network and can be used with existing architectures for motion
modelling. Prior work typically leverages the H3.6M dataset. We show that some
state-of-the-art techniques do not perform well when trained and tested on
AMASS, a recently released dataset 14 times the size of H3.6M. Our experiments
indicate that the proposed layer increases the performance of motion
forecasting irrespective of the base network, joint-angle representation, and
prediction horizon. We furthermore show that the layer also improves motion
predictions qualitatively. We make code and models publicly available at
https://ait.ethz.ch/projects/2019/spl.
