  The problem of predicting human motion given a sequence of past observations
is at the core of many applications in robotics and computer vision. Current
state-of-the-art formulate this problem as a sequence-to-sequence task, in
which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that
predicts future movements, typically in the order of 1 to 2 seconds. However,
one aspect that has been obviated so far, is the fact that human motion is
inherently driven by interactions with objects and/or other humans in the
environment. In this paper, we explore this scenario using a novel
context-aware motion prediction architecture. We use a semantic-graph model
where the nodes parameterize the human and objects in the scene and the edges
their mutual interactions. These interactions are iteratively learned through a
graph attention layer, fed with the past observations, which now include both
object and human body motions. Once this semantic graph is learned, we inject
it to a standard RNN to predict future movements of the human/s and object/s.
We consider two variants of our architecture, either freezing the contextual
interactions in the future of updating them. A thorough evaluation in the
"Whole-Body Human Motion Database" shows that in both cases, our context-aware
networks clearly outperform baselines in which the context information is not
considered.
