  Aligning video sequences is a fundamental yet still unsolved component for a
broad range of applications in computer graphics and vision. Most classical
image processing methods cannot be directly applied to related video problems
due to the high amount of underlying data and their limit to small changes in
appearance. We present a scalable and robust method for computing a non-linear
temporal video alignment. The approach autonomously manages its training data
for learning a meaningful representation in an iterative procedure each time
increasing its own knowledge. It leverages on the nature of the videos
themselves to remove the need for manually created labels. While previous
alignment methods similarly consider weather conditions, season and
illumination, our approach is able to align videos from data recorded months
apart.
