  Aerial vehicles are revolutionizing the way film-makers can capture shots of
actors by composing novel aerial and dynamic viewpoints. However, despite great
advancements in autonomous flight technology, generating expressive camera
behaviors is still a challenge and requires non-technical users to edit a large
number of unintuitive control parameters. In this work, we develop a
data-driven framework that enables editing of these complex camera positioning
parameters in a semantic space (e.g. calm, enjoyable, establishing). First, we
generate a database of video clips with a diverse range of shots in a
photo-realistic simulator, and use hundreds of participants in a crowd-sourcing
framework to obtain scores for a set of semantic descriptors for each clip.
Next, we analyze correlations between descriptors and build a semantic control
space based on cinematography guidelines and human perception studies. Finally,
we learn a generative model that can map a set of desired semantic video
descriptors into low-level camera trajectory parameters. We evaluate our system
by demonstrating that our model successfully generates shots that are rated by
participants as having the expected degrees of expression for each descriptor.
We also show that our models generalize to different scenes in both simulation
and real-world experiments. Data and video found at:
https://sites.google.com/view/robotcam.
