  Handling all together large displacements, motion details and occlusions
remains an open issue for reliable computation of optical flow in a video
sequence. We propose a two-step aggregation paradigm to address this problem.
The idea is to supply local motion candidates at every pixel in a first step,
and then to combine them to determine the global optical flow field in a second
step. We exploit local parametric estimations combined with patch
correspondences and we experimentally demonstrate that they are sufficient to
produce highly accurate motion candidates. The aggregation step is designed as
the discrete optimization of a global regularized energy. The occlusion map is
estimated jointly with the flow field throughout the two steps. We propose a
generic exemplar-based approach for occlusion filling with motion vectors. We
achieve state-of-the-art results in computer vision benchmarks, with
particularly significant improvements in the case of large displacements and
occlusions.
