  In this work we propose an approach for estimating 3D human poses of multiple
people from a set of calibrated cameras. Estimating 3D human poses from
multiple views has several compelling properties: human poses are estimated
within a global coordinate space and multiple cameras provide an extended field
of view which helps in resolving ambiguities, occlusions and motion blur. Our
approach builds upon a real-time 2D multi-person pose estimation system and
greedily solves the association problem between multiple views. We utilize
bipartite matching to track multiple people over multiple frames. This proofs
to be especially efficient as problems associated with greedy matching such as
occlusion can be easily resolved in 3D. Our approach achieves state-of-the-art
results on popular benchmarks and may serve as a baseline for future work.

Computer Vision and Pattern Recognition