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Efficient Solver for Spacetime Control of Smoke

Published: 25 July 2017 Publication History

Abstract

We present a novel algorithm to control the physically-based animation of smoke. Given a set of keyframe smoke shapes, we compute a dense sequence of control force fields that can drive the smoke shape to match several keyframes at certain time instances. Our approach formulates this control problem as a spacetime optimization constrained by partial differential equations. In order to compute the locally optimal control forces, we alternatively optimize the velocity fields and density fields using an alternating direction method of multiplier (ADMM) optimizer. In order to reduce the high complexity of multiple passes of fluid resimulation during velocity field optimization, we utilize the coherence between consecutive fluid simulation passes. We demonstrate the benefits of our approach by computing accurate solutions on 2D and 3D benchmarks. In practice, we observe up to an order of magnitude improvement over prior optimal control methods.

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    cover image ACM Transactions on Graphics
    ACM Transactions on Graphics  Volume 36, Issue 5
    October 2017
    161 pages
    ISSN:0730-0301
    EISSN:1557-7368
    DOI:10.1145/3127587
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 25 July 2017
    Accepted: 01 April 2017
    Revised: 01 March 2017
    Received: 01 August 2016
    Published in TOG Volume 36, Issue 5

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    1. Fluid simulation
    2. optimal control

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