  Humans are able to make rich predictions about the future dynamics of
physical objects from a glance. On the other hand, most existing computer
vision approaches require strong assumptions about the underlying system,
ad-hoc modeling, or annotated datasets, to carry out even simple predictions.
To tackle this gap, we propose a new perspective on the problem of learning
intuitive physics that is inspired by the spatial memory representation of
objects and spaces in human brains, in particular the co-existence of
egocentric and allocentric spatial representations. We present a generic
framework that learns a layered representation of the physical world, using a
cascade of invertible modules. In this framework, real images are first
converted to a synthetic domain representation that reduces complexity arising
from lighting and texture. Then, an allocentric viewpoint transformer removes
viewpoint complexity by projecting images to a canonical view. Finally, a novel
Recurrent Latent Variation Network (RLVN) architecture learns the dynamics of
the objects interacting with the environment and predicts future motion,
leveraging the availability of unlimited synthetic simulations. Predicted
frames are then projected back to the original camera view and translated back
to the real world domain. Experimental results show the ability of the
framework to consistently and accurately predict several frames in the future
and the ability to adapt to real images.
