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

Neural Re-Simulation for Generating Bounces in Single Images