  In 3D human pose estimation one of the biggest problems is the lack of large,
diverse datasets. This is especially true for multi-person 3D pose estimation,
where, to our knowledge, there are only machine generated annotations available
for training. To mitigate this issue, we introduce a network that can be
trained with additional RGB-D images in a weakly supervised fashion. Due to the
existence of cheap sensors, videos with depth maps are widely available, and
our method can exploit a large, unannotated dataset. Our algorithm is a
monocular, multi-person, absolute pose estimator. We evaluate the algorithm on
several benchmarks, showing a consistent improvement in error rates. Also, our
model achieves state-of-the-art results on the MuPoTS-3D dataset by a
considerable margin.
