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Autonomous driving is an exciting new industry, posing important researchquestions. Within the perception module, 3D human pose estimation is anemerging technology, which can enable the autonomous vehicle to perceive andunderstand the subtle and complex behaviors of pedestrians. While hardwaresystems and sensors have dramatically improved over the decades -- with carspotentially boasting complex LiDAR and vision systems and with a growingexpansion of the available body of dedicated datasets for this newly availableinformation -- not much work has been done to harness these novel signals forthe core problem of 3D human pose estimation. Our method, which we coin HUM3DIL(HUMan 3D from Images and LiDAR), efficiently makes use of these complementarysignals, in a semi-supervised fashion and outperforms existing methods with alarge 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. Quantitativeexperiments on the Waymo Open Dataset support these claims, where we achievestate-of-the-art results on the task of 3D pose estimation.

HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving