  In cinema, large camera lenses create beautiful shallow depth of field (DOF),
but make focusing difficult and expensive. Accurate cinema focus usually relies
on a script and a person to control focus in realtime. Casual videographers
often crave cinematic focus, but fail to achieve it. We either sacrifice
shallow DOF, as in smartphone videos; or we struggle to deliver accurate focus,
as in videos from larger cameras. This paper is about a new approach in the
pursuit of cinematic focus for casual videography. We present a system that
synthetically renders refocusable video from a deep DOF video shot with a
smartphone, and analyzes future video frames to deliver context-aware autofocus
for the current frame. To create refocusable video, we extend recent machine
learning methods designed for still photography, contributing a new dataset for
machine training, a rendering model better suited to cinema focus, and a
filtering solution for temporal coherence. To choose focus accurately for each
frame, we demonstrate autofocus that looks at upcoming video frames and applies
AI-assist modules such as motion, face, audio and saliency detection. We also
show that autofocus benefits from machine learning and a large-scale video
dataset with focus annotation, where we use our RVR-LAAF GUI to create this
sizable dataset efficiently. We deliver, for example, a shallow DOF video where
the autofocus transitions onto each person before she begins to speak. This is
impossible for conventional camera autofocus because it would require seeing
into the future.
