  With the recent advent of methods that allow for real-time computation, dense
3D flows have become a viable basis for fast camera motion estimation. Most
importantly, dense flows are more robust than the sparse feature matching
techniques used by existing 3D stabilization methods, able to better handle
large camera displacements and occlusions similar to those often found in
consumer videos. Here we introduce a framework for 3D video stabilization that
relies on dense scene flow alone. The foundation of this approach is a novel
camera motion model that allows for real-world camera poses to be recovered
directly from 3D motion fields. Moreover, this model can be extended to
describe certain types of non-rigid artifacts that are commonly found in
videos, such as those resulting from zooms. This framework gives rise to
several robust regimes that produce high-quality stabilization of the kind
achieved by prior full 3D methods while avoiding the fragility typically
present in feature-based approaches. As an added benefit, our framework is
fast: the simplicity of our motion model and efficient flow calculations
combine to enable stabilization at a high frame rate.
