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Previous deep learning-based video stabilizers require a large scale ofpaired unstable and stable videos for training, which are difficult to collect.Traditional trajectory-based stabilizers, on the other hand, divide the taskinto several sub-tasks and tackle them subsequently, which are fragile intextureless and occluded regions regarding the usage of hand-crafted features.In this paper, we attempt to tackle the video stabilization problem in a deepunsupervised learning manner, which borrows the divide-and-conquer idea fromtraditional stabilizers while leveraging the representation power of DNNs tohandle the challenges in real-world scenarios. Technically, DUT is composed ofa trajectory estimation stage and a trajectory smoothing stage. In thetrajectory estimation stage, we first estimate the motion of keypoints,initialize and refine the motion of grids via a novel multi-homographyestimation strategy and a motion refinement network, respectively, and get thegrid-based trajectories via temporal association. In the trajectory smoothingstage, we devise a novel network to predict dynamic smoothing kernels fortrajectory smoothing, which can well adapt to trajectories with differentdynamic patterns. We exploit the spatial and temporal coherence of keypointsand grid vertices to formulate the training objectives, resulting in anunsupervised training scheme. Experiment results on public benchmarks show thatDUT outperforms state-of-the-art methods both qualitatively and quantitatively.The source code is available at https://github.com/Annbless/DUTCode.

DUT: Learning Video Stabilization by Simply Watching Unstable Videos