  We present a deep neural network (DNN) that uses both sensor data (gyroscope)
and image content (optical flow) to stabilize videos through unsupervised
learning. The network fuses optical flow with real/virtual camera pose
histories into a joint motion representation. Next, the LSTM block infers the
new virtual camera pose, and this virtual pose is used to generate a warping
grid that stabilizes the frame. Novel relative motion representation as well as
a multi-stage training process are presented to optimize our model without any
supervision. To the best of our knowledge, this is the first DNN solution that
adopts both sensor data and image for stabilization. We validate the proposed
framework through ablation studies and demonstrated the proposed method
outperforms the state-of-art alternative solutions via quantitative evaluations
and a user study.
