  We present a novel approach to estimating physical properties of objects from
video. Our approach consists of a physics engine and a correction estimator.
Starting from the initial observed state, object behavior is simulated forward
in time. Based on the simulated and observed behavior, the correction estimator
then determines refined physical parameters for each object. The method can be
iterated for increased precision. Our approach is generic, as it allows for the
use of an arbitrary - not necessarily differentiable - physics engine and
correction estimator. For the latter, we evaluate both gradient-free
hyperparameter optimization and a deep convolutional neural network. We
demonstrate faster and more robust convergence of the learned method in several
simulated 2D scenarios focusing on bin situations.
