  We propose a new approach to learn to segment multiple image objects without
manual supervision. The method can extract objects form still images, but uses
videos for supervision. While prior works have considered motion for
segmentation, a key insight is that, while motion can be used to identify
objects, not all objects are necessarily in motion: the absence of motion does
not imply the absence of objects. Hence, our model learns to predict image
regions that are likely to contain motion patterns characteristic of objects
moving rigidly. It does not predict specific motion, which cannot be done
unambiguously from a still image, but a distribution of possible motions, which
includes the possibility that an object does not move at all. We demonstrate
the advantage of this approach over its deterministic counterpart and show
state-of-the-art unsupervised object segmentation performance on simulated and
real-world benchmarks, surpassing methods that use motion even at test time. As
our approach is applicable to variety of network architectures that segment the
scenes, we also apply it to existing image reconstruction-based models showing
drastic improvement. Project page and code:
https://www.robots.ox.ac.uk/~vgg/research/ppmp .
