  Imitation learning has been applied to mimic the operation of a human
cameraman in several autonomous cinematography systems. To imitate different
filming styles, existing methods train multiple models, where each model
handles a particular style and requires a significant number of training
samples. As a result, existing methods can hardly generalize to unseen styles.
In this paper, we propose a framework, which can imitate a filming style by
"seeing" only a single demonstration video of the same style, i.e., one-shot
imitation filming. This is done by two key enabling techniques: 1) feature
extraction of the filming style from the demo video, and 2) filming style
transfer from the demo video to the new situation. We implement the approach
with deep neural network and deploy it to a 6 degrees of freedom (DOF) real
drone cinematography system by first predicting the future camera motions, and
then converting them to the drone's control commands via an odometer. Our
experimental results on extensive datasets and showcases exhibit significant
improvements in our approach over conventional baselines and our approach can
successfully mimic the footage with an unseen style.
