Neural Allocentric Intuitive Physics Prediction from Real Videos

Understand

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.