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With the recent advent of methods that allow for real-time computation, dense3D flows have become a viable basis for fast camera motion estimation. Mostimportantly, dense flows are more robust than the sparse feature matchingtechniques used by existing 3D stabilization methods, able to better handlelarge camera displacements and occlusions similar to those often found inconsumer videos. Here we introduce a framework for 3D video stabilization thatrelies on dense scene flow alone. The foundation of this approach is a novelcamera motion model that allows for real-world camera poses to be recovereddirectly from 3D motion fields. Moreover, this model can be extended todescribe certain types of non-rigid artifacts that are commonly found invideos, such as those resulting from zooms. This framework gives rise toseveral robust regimes that produce high-quality stabilization of the kindachieved by prior full 3D methods while avoiding the fragility typicallypresent in feature-based approaches. As an added benefit, our framework isfast: the simplicity of our motion model and efficient flow calculationscombine to enable stabilization at a high frame rate.

Quotienting Impertinent Camera Kinematics for 3D Video Stabilization