  The accuracy of monocular 3D human pose estimation depends on the viewpoint
from which the image is captured. While freely moving cameras, such as on
drones, provide control over this viewpoint, automatically positioning them at
the location which will yield the highest accuracy remains an open problem.
This is the problem that we address in this paper. Specifically, given a short
video sequence, we introduce an algorithm that predicts which viewpoints should
be chosen to capture future frames so as to maximize 3D human pose estimation
accuracy. The key idea underlying our approach is a method to estimate the
uncertainty of the 3D body pose estimates. We integrate several sources of
uncertainty, originating from deep learning based regressors and temporal
smoothness. Our motion planner yields improved 3D body pose estimates and
outperforms or matches existing ones that are based on person following and
orbiting.
