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    • 40-Issue 2
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    Two-step Temporal Interpolation Network Using Forward Advection for Efficient Smoke Simulation

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    Date
    2021
    Author
    Oh, Young Jin ORCID
    Lee, In-Kwon ORCID
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    Abstract
    In this paper, we propose a two-step temporal interpolation network using forward advection to generate smoke simulation efficiently. By converting a low frame rate smoke simulation computed with a large time step into a high frame rate smoke simulation through inference of temporal interpolation networks, the proposed method can efficiently generate smoke simulation with a high frame rate and low computational costs. The first step of the proposed method is optical flow-based temporal interpolation using deep neural networks (DNNs) for two given smoke animation frames. In the next step, we compute temporary smoke frames with forward advection, a physical computation with a low computational cost. We then interpolate between the results of the forward advection and those of the first step to generate more accurate and enhanced interpolated results. We performed quantitative analyses of the results generated by the proposed method and previous temporal interpolation methods. Furthermore, we experimentally compared the performance of the proposed method with previous methods using DNNs for smoke simulation. We found that the results generated by the proposed method are more accurate and closer to the ground truth smoke simulation than those generated by the previous temporal interpolation methods. We also confirmed that the proposed method generates smoke simulation results more efficiently with lower computational costs than previous smoke simulation methods using DNNs.
    BibTeX
    @article {10.1111:cgf.142638,
    journal = {Computer Graphics Forum},
    title = {{Two-step Temporal Interpolation Network Using Forward Advection for Efficient Smoke Simulation}},
    author = {Oh, Young Jin and Lee, In-Kwon},
    year = {2021},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.142638}
    }
    URI
    https://doi.org/10.1111/cgf.142638
    https://diglib.eg.org:443/handle/10.1111/cgf142638
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