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Publication Detail
Neural re-simulation for generating bounces in single images
  • Publication Type:
  • Authors:
    Innamorati C, Russell B, Kaufman D, Mitra N
  • Publisher:
  • Publication date:
  • Pagination:
    8718, 8727
  • Published proceedings:
    Proceedings of the IEEE International Conference on Computer Vision
  • Volume:
  • ISBN-13:
  • Status:
  • Name of conference:
    2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Conference place:
    Seoul, Korea (South)
  • Conference start date:
  • Conference finish date:
  • Print ISSN:
© 2019 IEEE. 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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