  An accurate and uncertainty-aware 3D human body pose estimation is key to
enabling truly safe but efficient human-robot interactions. Current
uncertainty-aware methods in 3D human pose estimation are limited to predicting
the uncertainty of the body posture, while effectively neglecting the body
shape and root pose. In this work, we present GloPro, which to the best of our
knowledge the first framework to predict an uncertainty distribution of a 3D
body mesh including its shape, pose, and root pose, by efficiently fusing
visual clues with a learned motion model. We demonstrate that it vastly
outperforms state-of-the-art methods in terms of human trajectory accuracy in a
world coordinate system (even in the presence of severe occlusions), yields
consistent uncertainty distributions, and can run in real-time.

Computer Vision and Pattern Recognition;Robotics