  Video stabilization is a fundamental and important technique for higher
quality videos. Prior works have extensively explored video stabilization, but
most of them involve cropping of the frame boundaries and introduce moderate
levels of distortion. We present a novel deep approach to video stabilization
which can generate video frames without cropping and low distortion. The
proposed framework utilizes frame interpolation techniques to generate in
between frames, leading to reduced inter-frame jitter. Once applied in an
iterative fashion, the stabilization effect becomes stronger. A major advantage
is that our framework is end-to-end trainable in an unsupervised manner. In
addition, our method is able to run in near real-time (15 fps). To the best of
our knowledge, this is the first work to propose an unsupervised deep approach
to full-frame video stabilization. We show the advantages of our method through
quantitative and qualitative evaluations comparing to the state-of-the-art
methods.
