  Slow motion videos are becoming increasingly popular, but capturing
high-resolution videos at extremely high frame rates requires professional
high-speed cameras. To mitigate this problem, current techniques increase the
frame rate of standard videos through frame interpolation by assuming linear
object motion which is not valid in challenging cases. In this paper, we
address this problem using two video streams as input; an auxiliary video with
high frame rate and low spatial resolution, providing temporal information, in
addition to the standard main video with low frame rate and high spatial
resolution. We propose a two-stage deep learning system consisting of alignment
and appearance estimation that reconstructs high resolution slow motion video
from the hybrid video input. For alignment, we propose to compute flows between
the missing frame and two existing frames of the main video by utilizing the
content of the auxiliary video frames. For appearance estimation, we propose to
combine the warped and auxiliary frames using a context and occlusion aware
network. We train our model on synthetically generated hybrid videos and show
high-quality results on a variety of test scenes. To demonstrate practicality,
we show the performance of our system on two real dual camera setups with small
baseline.
