Innamorati, C; Russell, B; Kaufman, D; Mitra, N; (2020) Neural re-simulation for generating bounces in single images. In: Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV). (pp. pp. 8718-8727). IEEE: Seoul, South Korea.

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Abstract

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.

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