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Retiming of fluid simulations for VFX: distributed non-linear fluid retiming by sparse bi-directional advection-diffusion

Published:28 July 2019Publication History

ABSTRACT

We present a novel approach to retiming of fluid simulations, which is a common yet challenging practice in visual effects productions. Unlike traditional techniques that are limited to dense simulations and only account for bulk motion by the fluid velocities, our approach also works on sparse simulations and attempts to account for two (vs one) of the fundamental processes governing fluid dynamics, namely the effect of hyperbolic advection and parabolic diffusion, be it physical or numerical. This allows for smoother transitions between the existing and newly generated simulation volumes, thereby preserving the overall look of a retimed fluid animation, while significantly outperforming both forward simulations, which tend to change the look, and guided inverse simulations, which are known to be computationally expensive.

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References

  1. Ken Museth. 2013. VDB: High-resolution Sparse Volumes with Dynamic Topology. ACM Trans. Graph. 32, 3, Article 27 (July 2013), 22 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Retiming of fluid simulations for VFX: distributed non-linear fluid retiming by sparse bi-directional advection-diffusion

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    • Published in

      cover image ACM Conferences
      SIGGRAPH '19: ACM SIGGRAPH 2019 Talks
      July 2019
      143 pages
      ISBN:9781450363174
      DOI:10.1145/3306307

      Copyright © 2019 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 July 2019

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      Overall Acceptance Rate1,822of8,601submissions,21%
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