  Accurate 3D human pose estimation (3D HPE) is crucial for enabling autonomous
vehicles (AVs) to make informed decisions and respond proactively in critical
road scenarios. Promising results of 3D HPE have been gained in several domains
such as human-computer interaction, robotics, sports and medical analytics,
often based on data collected in well-controlled laboratory environments.
Nevertheless, the transfer of 3D HPE methods to AVs has received limited
research attention, due to the challenges posed by obtaining accurate 3D pose
annotations and the limited suitability of data from other domains.
  We present a simple yet efficient weakly supervised approach for 3D HPE in
the AV context by employing a high-level sensor fusion between camera and LiDAR
data. The weakly supervised setting enables training on the target datasets
without any 2D/3D keypoint labels by using an off-the-shelf 2D joint extractor
and pseudo labels generated from LiDAR to image projections. Our approach
outperforms state-of-the-art results by up to $\sim$ 13% on the Waymo Open
Dataset in the weakly supervised setting and achieves state-of-the-art results
in the supervised setting.
