  We consider the task of visually estimating the pose of a human from images
acquired by a nearby nano-drone; in this context, we propose a data
augmentation approach based on synthetic background substitution to learn a
lightweight CNN model from a small real-world training set. Experimental
results on data from two different labs proves that the approach improves
generalization to unseen environments.
