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Extracting-mapping scheme for the dynamic details in fluid re-simulations from videos

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Abstract

Reconstructing a 3D fluid simulation (re-simulation) from video has practical significance. In this study, we address the realistic problem of fluid re-simulation in an inverse project. State-of-the-art studies on the inverse problem of fluid simulation have mainly focused on dimensionality reduction for acquiring better time performance, but the realistic aspect has rarely been investigated. This paper presents an extracting-mapping scheme to tightly couple fluid re-simulation with enhanced physically driven data for realistic high-quality dynamic detail. We make a full use of the details extracted from recovered physically driven data to improve the coarse and unrealistic re-simulation from fluid auto-advection. Two schemes are discussed. The first scheme is the density block method (DBM). In this method, a density block database is constructed from the prepartitioned and sorted density blocks, and then, some selected density blocks with dynamic details are selected from the preconstructed database and coupled coherently into the physically driven data to enhance the detail in every auto-advection cycle. The second method is the density spectrum block method (DSBM) in the frequency domain. Using the DSBM and DBM, realistic effects are achieved by extensive quantitative and qualitative evaluation via re-simulation tests driven by the recovered physical data from ground truth fluid video. Both approaches outperform previous auto-advection re-simulation schemes in terms of the rich detail under several challenging scenarios and low-level hardware conditions.

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Acknowledgements

We thank Dyntex for providing rich fluid videos for our study. We also thank Xinquan Zhou for her help in the preliminary work. Special thanks to the reviewers for their valuable comments and suggestions.

Funding

This study was funded by the NSFC Grant No. 61672237, 61532002, and the National High-tech R&D Program of China (863 Program) under Grant 2015AA016404.

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Authors and Affiliations

  1. The School of Computer Science and Software Engineering, East China Normal University, Shanghai, 200062, China

    Hongyan Quan, Ning Wang, Jimeng Li & Changbo Wang

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  1. Hongyan Quan
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  2. Ning Wang
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  3. Jimeng Li
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  4. Changbo Wang
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Correspondence to Hongyan Quan.

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Communicated by Y. Zhang.

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Quan, H., Wang, N., Li, J. et al. Extracting-mapping scheme for the dynamic details in fluid re-simulations from videos. Multimedia Systems 25, 371–381 (2019). https://doi.org/10.1007/s00530-019-00612-0

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  • DOI: https://doi.org/10.1007/s00530-019-00612-0

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