  Joint forecasting of human trajectory and pose dynamics is a fundamental
building block of various applications ranging from robotics and autonomous
driving to surveillance systems. Predicting body dynamics requires capturing
subtle information embedded in the humans' interactions with each other and
with the objects present in the scene. In this paper, we propose a novel
TRajectory and POse Dynamics (nicknamed TRiPOD) method based on graph
attentional networks to model the human-human and human-object interactions
both in the input space and the output space (decoded future output). The model
is supplemented by a message passing interface over the graphs to fuse these
different levels of interactions efficiently. Furthermore, to incorporate a
real-world challenge, we propound to learn an indicator representing whether an
estimated body joint is visible/invisible at each frame, e.g. due to occlusion
or being outside the sensor field of view. Finally, we introduce a new
benchmark for this joint task based on two challenging datasets (PoseTrack and
3DPW) and propose evaluation metrics to measure the effectiveness of
predictions in the global space, even when there are invisible cases of joints.
Our evaluation shows that TRiPOD outperforms all prior work and
state-of-the-art specifically designed for each of the trajectory and pose
forecasting tasks.
