  Mobile captured images can be aligned using their gyroscope sensors. Optical
image stabilizer (OIS) terminates this possibility by adjusting the images
during the capturing. In this work, we propose a deep network that compensates
the motions caused by the OIS, such that the gyroscopes can be used for image
alignment on the OIS cameras. To achieve this, first, we record both videos and
gyroscopes with an OIS camera as training data. Then, we convert gyroscope
readings into motion fields. Second, we propose a Fundamental Mixtures motion
model for rolling shutter cameras, where an array of rotations within a frame
are extracted as the ground-truth guidance. Third, we train a convolutional
neural network with gyroscope motions as input to compensate for the OIS
motion. Once finished, the compensation network can be applied for other
scenes, where the image alignment is purely based on gyroscopes with no need
for images contents, delivering strong robustness. Experiments show that our
results are comparable with that of non-OIS cameras, and outperform image-based
alignment results with a relatively large margin.
