  We introduce a method to generate videos of dynamic virtual objects plausibly
interacting via collisions with a still image's environment. Given a starting
trajectory, physically simulated with the estimated geometry of a single,
static input image, we learn to 'correct' this trajectory to a visually
plausible one via a neural network. The neural network can then be seen as
learning to 'correct' traditional simulation output, generated with incomplete
and imprecise world information, to obtain context-specific, visually plausible
re-simulated output, a process we call neural re-simulation. We train our
system on a set of 50k synthetic scenes where a virtual moving object (ball)
has been physically simulated. We demonstrate our approach on both our
synthetic dataset and a collection of real-life images depicting everyday
scenes, obtaining consistent improvement over baseline alternatives throughout.
