  Observable motion in videos can give rise to the definition of objects moving
with respect to the scene. The task of segmenting such moving objects is
referred to as motion segmentation and is usually tackled either by aggregating
motion information in long, sparse point trajectories, or by directly producing
per frame dense segmentations relying on large amounts of training data. In
this paper, we propose a self supervised method to learn the densification of
sparse motion segmentations from single video frames. While previous approaches
towards motion segmentation build upon pre-training on large surrogate datasets
and use dense motion information as an essential cue for the pixelwise
segmentation, our model does not require pre-training and operates at test time
on single frames. It can be trained in a sequence specific way to produce high
quality dense segmentations from sparse and noisy input. We evaluate our method
on the well-known motion segmentation datasets FBMS59 and DAVIS16.
