The problem of determining whether an object is in motion, irrespective of the
camera motion, is far from being solved. We address this challenging task by
learning motion patterns in videos. The core of our approach is a fully
convolutional network, which is learnt entirely from synthetic video sequences,
and their ground-truth optical flow and motion segmentation. This
encoder-decoder style architecture first learns a coarse representation of the
optical flow field features, and then refines it iteratively to produce motion
labels at the original high-resolution. The output label of each pixel denotes
whether it has undergone independent motion, i.e., irrespective of the camera
motion. We demonstrate the benefits of this learning framework on the moving
object segmentation task, where the goal is to segment all the objects in
motion. To this end we integrate an objectness measure into the framework. Our
approach outperforms the top method on the recently released DAVIS benchmark
dataset, comprising real-world sequences, by 5.6%. We also evaluate on the
Berkeley motion segmentation database, achieving state-of-the-art results.