  This work presents CineTransfer, an algorithmic framework that drives a robot
to record a video sequence that mimics the cinematographic style of an input
video. We propose features that abstract the aesthetic style of the input
video, so the robot can transfer this style to a scene with visual details that
are significantly different from the input video. The framework builds upon
CineMPC, a tool that allows users to control cinematographic features, like
subjects' position on the image and the depth of field, by manipulating the
intrinsics and extrinsics of a cinematographic camera. However, CineMPC
requires a human expert to specify the desired style of the shot (composition,
camera motion, zoom, focus, etc). CineTransfer bridges this gap, aiming a fully
autonomous cinematographic platform. The user chooses a single input video as a
style guide. CineTransfer extracts and optimizes two important style features,
the composition of the subject in the image and the scene depth of field, and
provides instructions for CineMPC to control the robot to record an output
sequence that matches these features as closely as possible. In contrast with
other style transfer methods, our approach is a lightweight and portable
framework which does not require deep network training or extensive datasets.
Experiments with real and simulated videos demonstrate the system's ability to
analyze and transfer style between recordings, and are available in the
supplementary video.
