  Autonomous driving is an exciting new industry, posing important research
questions. Within the perception module, 3D human pose estimation is an
emerging technology, which can enable the autonomous vehicle to perceive and
understand the subtle and complex behaviors of pedestrians. While hardware
systems and sensors have dramatically improved over the decades -- with cars
potentially boasting complex LiDAR and vision systems and with a growing
expansion of the available body of dedicated datasets for this newly available
information -- not much work has been done to harness these novel signals for
the core problem of 3D human pose estimation. Our method, which we coin HUM3DIL
(HUMan 3D from Images and LiDAR), efficiently makes use of these complementary
signals, in a semi-supervised fashion and outperforms existing methods with a
large margin. It is a fast and compact model for onboard deployment.
Specifically, we embed LiDAR points into pixel-aligned multi-modal features,
which we pass through a sequence of Transformer refinement stages. Quantitative
experiments on the Waymo Open Dataset support these claims, where we achieve
state-of-the-art results on the task of 3D pose estimation.
