# Neural Re-Simulation for Generating Bounces in Single Images

arXiv: https://arxiv.org/abs/1908.06217 · HTML: https://mlsanity.com/paper/1908.06217/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [gradSim: Differentiable simulation for system identification and visuomotor control](https://mlsanity.com/paper/2104.02646/) (2021)
- [OPEn: An Open-ended Physics Environment for Learning Without a Task](https://mlsanity.com/paper/2110.06912/) (2021)
- [Where Is The Ball: 3D Ball Trajectory Estimation From 2D Monocular Tracking](https://mlsanity.com/paper/2506.05763/) (2025)

## Abstract (arXiv)

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.