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Accurate 3D human pose estimation (3D HPE) is crucial for enabling autonomousvehicles (AVs) to make informed decisions and respond proactively in criticalroad scenarios. Promising results of 3D HPE have been gained in several domainssuch 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 limitedresearch attention, due to the challenges posed by obtaining accurate 3D poseannotations and the limited suitability of data from other domains.  We present a simple yet efficient weakly supervised approach for 3D HPE inthe AV context by employing a high-level sensor fusion between camera and LiDARdata. The weakly supervised setting enables training on the target datasetswithout any 2D/3D keypoint labels by using an off-the-shelf 2D joint extractorand pseudo labels generated from LiDAR to image projections. Our approachoutperforms state-of-the-art results by up to $\sim$ 13% on the Waymo OpenDataset in the weakly supervised setting and achieves state-of-the-art resultsin the supervised setting.

Weakly Supervised Multi-Modal 3D Human Body Pose Estimation for Autonomous Driving