  Drones have become a common tool, which is utilized in many tasks such as
aerial photography, surveillance, and delivery. However, operating a drone
requires more and more interaction with the user. A natural and safe method for
Human-Drone Interaction (HDI) is using gestures. In this paper, we introduce an
HDI framework building upon skeleton-based pose estimation. Our framework
provides the functionality to control the movement of the drone with simple arm
gestures and to follow the user while keeping a safe distance. We also propose
a monocular distance estimation method, which is entirely based on image
features and does not require any additional depth sensors. To perform
comprehensive experiments and quantitative analysis, we create a customized
testing dataset. The experiments indicate that our HDI framework can achieve an
average of 93.5\% accuracy in the recognition of 11 common gestures. The code
is available at: https://github.com/Zrrr1997/Pose2Drone
