Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision

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 availablefor training. To mitigate this issue, we introduce a network that can betrained with additional RGB-D images in a weakly supervised fashion. Due to theexistence of cheap sensors, videos with depth maps are widely available, andour method can exploit a large, unannotated dataset. Our algorithm is amonocular, multi-person, absolute pose estimator. We evaluate the algorithm onseveral benchmarks, showing a consistent improvement in error rates. Also, ourmodel achieves state-of-the-art results on the MuPoTS-3D dataset by aconsiderable margin.