  This paper studies the problem of object discovery -- separating objects from
the background without manual labels. Existing approaches utilize appearance
cues, such as color, texture, and location, to group pixels into object-like
regions. However, by relying on appearance alone, these methods fail to
separate objects from the background in cluttered scenes. This is a fundamental
limitation since the definition of an object is inherently ambiguous and
context-dependent. To resolve this ambiguity, we choose to focus on dynamic
objects -- entities that can move independently in the world. We then scale the
recent auto-encoder based frameworks for unsupervised object discovery from toy
synthetic images to complex real-world scenes. To this end, we simplify their
architecture, and augment the resulting model with a weak learning signal from
general motion segmentation algorithms. Our experiments demonstrate that,
despite only capturing a small subset of the objects that move, this signal is
enough to generalize to segment both moving and static instances of dynamic
objects. We show that our model scales to a newly collected, photo-realistic
synthetic dataset with street driving scenarios. Additionally, we leverage
ground truth segmentation and flow annotations in this dataset for thorough
ablation and evaluation. Finally, our experiments on the real-world KITTI
benchmark demonstrate that the proposed approach outperforms both heuristic-
and learning-based methods by capitalizing on motion cues.
