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Aerial filming is becoming more and more popular thanks to the recent advances in drone technology. It invites many intriguing, unsolved problems at the intersection of aesthetical and scientific challenges. In this work, we propose an intelligent agent which supervises motion planning of a filming drone based on aesthetical values of video shots using deep reinforcement learning. Unlike the current state-of-the-art approaches which mostly require explicit guidance by a human expert, our drone learns how to make favorable shot type selections by experience. We propose a learning scheme which exploits aesthetical features of retrospective shots in order to extract a desirable policy for better prospective shots. We train our agent in realistic AirSim simulations using both hand-crafted and human reward functions. We deploy the same agent on a real DJI M210 drone in order to test generalization capability of our approach to real world conditions. To evaluate the success of our approach in the end, we conduct a comprehensive user study in which participants rate the shots taken using our method and write comments about them.

Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography