  360$^{\circ}$ panoramas are a rich medium, yet notoriously difficult to
visualize in the 2D image plane. We explore how intelligent rotations of a
spherical image may enable content-aware projection with fewer perceptible
distortions. Whereas existing approaches assume the viewpoint is fixed,
intuitively some viewing angles within the sphere preserve high-level objects
better than others. To discover the relationship between these optimal snap
angles and the spherical panorama's content, we develop a reinforcement
learning approach for the cubemap projection model. Implemented as a deep
recurrent neural network, our method selects a sequence of rotation actions and
receives reward for avoiding cube boundaries that overlap with important
foreground objects. We show our approach creates more visually pleasing
panoramas while using 5x less computation than the baseline.
