  We introduce latent intuitive physics, a transfer learning framework for
physics simulation that can infer hidden properties of fluids from a single 3D
video and simulate the observed fluid in novel scenes. Our key insight is to
use latent features drawn from a learnable prior distribution conditioned on
the underlying particle states to capture the invisible and complex physical
properties. To achieve this, we train a parametrized prior learner given visual
observations to approximate the visual posterior of inverse graphics, and both
the particle states and the visual posterior are obtained from a learned neural
renderer. The converged prior learner is embedded in our probabilistic physics
engine, allowing us to perform novel simulations on unseen geometries,
boundaries, and dynamics without knowledge of the true physical parameters. We
validate our model in three ways: (i) novel scene simulation with the learned
visual-world physics, (ii) future prediction of the observed fluid dynamics,
and (iii) supervised particle simulation. Our model demonstrates strong
performance in all three tasks.
