  In the era of deep learning, human pose estimation from multiple cameras with
unknown calibration has received little attention to date. We show how to train
a neural model to perform this task with high precision and minimal latency
overhead. The proposed model takes into account joint location uncertainty due
to occlusion from multiple views, and requires only 2D keypoint data for
training. Our method outperforms both classical bundle adjustment and
weakly-supervised monocular 3D baselines on the well-established Human3.6M
dataset, as well as the more challenging in-the-wild Ski-Pose PTZ dataset.
