  We propose a novel real-time selfie video stabilization method. Our method is
completely automatic and runs at 26 fps. We use a 1D linear convolutional
network to directly infer the rigid moving least squares warping which
implicitly balances between the global rigidity and local flexibility. Our
network structure is specifically designed to stabilize the background and
foreground at the same time, while providing optional control of stabilization
focus (relative importance of foreground vs. background) to the users. To train
our network, we collect a selfie video dataset with 1005 videos, which is
significantly larger than previous selfie video datasets. We also propose a
grid approximation method to the rigid moving least squares warping that
enables the real-time frame warping. Our method is fully automatic and produces
visually and quantitatively better results than previous real-time general
video stabilization methods. Compared to previous offline selfie video methods,
our approach produces comparable quality with a speed improvement of orders of
magnitude.
