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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV,Computer Science, Graphics, cs.GR
Abstract:
Boundary prediction in images and videos has been a very active topic of
research and organizing visual information into boundaries and segments is
believed to be a corner stone of visual perception. While prior work has
focused on predicting boundaries for observed frames, our work aims at
predicting boundaries of future unobserved frames. This requires our model to
learn about the fate of boundaries and extrapolate motion patterns. We
experiment on established real-world video segmentation dataset, which provides
a testbed for this new task. We show for the first time spatio-temporal
boundary extrapolation, that in contrast to prior work on RGB extrapolation
maintains a crisp result. Furthermore, we show long-term prediction of
boundaries in situations where the motion is governed by the laws of physics.
We argue that our model has with minimalistic model assumptions derived a
notion of "intuitive physics".
