Recently, huge strides were made in monocular and multi-view pose estimation with known camera parameters, whereas pose estimation from multiple cameras with unknown positions and orientations received much less attention. In this paper, we show how to train a neural model that can perform accurate 3D pose and camera estimation, takes into account joint location uncertainty due 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 with moving cameras. We provide an extensive ablation study separating the error due to the camera model, number of cameras, initialization, and image-space joint localization from the additional error introduced by our model.
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