  Humans have a remarkable ability to use physical commonsense and predict the
effect of collisions. But do they understand the underlying factors? Can they
predict if the underlying factors have changed? Interestingly, in most cases
humans can predict the effects of similar collisions with different conditions
such as changes in mass, friction, etc. It is postulated this is primarily
because we learn to model physics with meaningful latent variables. This does
not imply we can estimate the precise values of these meaningful variables
(estimate exact values of mass or friction). Inspired by this observation, we
propose an interpretable intuitive physics model where specific dimensions in
the bottleneck layers correspond to different physical properties. In order to
demonstrate that our system models these underlying physical properties, we
train our model on collisions of different shapes (cube, cone, cylinder,
spheres etc.) and test on collisions of unseen combinations of shapes.
Furthermore, we demonstrate our model generalizes well even when similar scenes
are simulated with different underlying properties.
