  Inferring universal laws of the environment is an important ability of human
intelligence as well as a symbol of general AI. In this paper, we take a step
toward this goal such that we introduce a new challenging problem of inferring
invariant physical equation from visual scenarios. For instance, teaching a
machine to automatically derive the gravitational acceleration formula by
watching a free-falling object. To tackle this challenge, we present a novel
pipeline comprised of an Observer Engine and a Physicist Engine by respectively
imitating the actions of an observer and a physicist in the real world.
Generally, the Observer Engine watches the visual scenarios and then extracting
the physical properties of objects. The Physicist Engine analyses these data
and then summarizing the inherent laws of object dynamics. Specifically, the
learned laws are expressed by mathematical equations such that they are more
interpretable than the results given by common probabilistic models.
Experiments on synthetic videos have shown that our pipeline is able to
discover physical equations on various physical worlds with different visual
appearances.
