  In applications such as autonomous driving, it is important to understand,
infer, and anticipate the intention and future behavior of pedestrians. This
ability allows vehicles to avoid collisions and improve ride safety and
quality. This paper proposes a biomechanically inspired recurrent neural
network (Bio-LSTM) that can predict the location and 3D articulated body pose
of pedestrians in a global coordinate frame, given 3D poses and locations
estimated in prior frames with inaccuracy. The proposed network is able to
predict poses and global locations for multiple pedestrians simultaneously, for
pedestrians up to 45 meters from the cameras (urban intersection scale). The
outputs of the proposed network are full-body 3D meshes represented in Skinned
Multi-Person Linear (SMPL) model parameters. The proposed approach relies on a
novel objective function that incorporates the periodicity of human walking
(gait), the mirror symmetry of the human body, and the change of ground
reaction forces in a human gait cycle. This paper presents prediction results
on the PedX dataset, a large-scale, in-the-wild data set collected at real
urban intersections with heavy pedestrian traffic. Results show that the
proposed network can successfully learn the characteristics of pedestrian gait
and produce accurate and consistent 3D pose predictions.
