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Interpolations of Smoke and Liquid Simulations

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Nils ThuereyAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 36, Issue 1

Article No.: 3, Pages 1 - 16

https://doi.org/10.1145/2956233

Published: 09 September 2016 Publication History

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Abstract

We present a novel method to interpolate smoke and liquid simulations in order to perform data-driven fluid simulations. Our approach calculates a dense space-time deformation using grid-based signed-distance functions of the inputs.

A key advantage of this implicit Eulerian representation is that it allows us to use powerful techniques from the optical flow area. We employ a five-dimensional optical flow solve. In combination with a projection algorithm, and residual iterations, we achieve a robust matching of the inputs. Once the match is computed, arbitrary in-between variants can be created very efficiently. To concatenate multiple long-range deformations, we propose a novel alignment technique.

Our approach has numerous advantages, including automatic matches without user input, volumetric deformations that can be applied to details around the surface, and the inherent handling of topology changes. As a result, we can interpolate swirling smoke clouds, and splashing liquid simulations. We can even match and interpolate phenomena with fundamentally different physics: a drop of liquid, and a blob of heavy smoke.

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ZIP File (repository.zip)

This is the reference implementation of the 2017 ACM Transactions on Graphics paper Interpolations of Smoke and Liquid Simulations (FlOF). This source code is based on mantaflow (http://mantaflow.com/), and it interpolates smoke and liquid simulations in order to perform data-driven fluid simulations. The approach calculates a dense space-time deformation using grid-based signed-distance functions of the inputs. The approach has numerous advantages, including automatic matches without user input, volumetric deformations that can be applied to details around the surface, and the inherent handling of topology changes. As a result, it can interpolate swirling smoke clouds, and splashing liquid simulations.

The code may also be downloaded from GitHub: https://github.com/thunil/ofblend

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References

[1]

Morten Bojsen-Hansen, Hao Li, and Chris Wojtan. 2012. Tracking surfaces with evolving topology. ACM Transactions on Graphics 31, 4, Article 53 (July 2012), 10 pages.

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