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Dynamic Upsampling of Smoke through Dictionary-based Learning

Bai, Kai and Li, Wei and Desbrun, Mathieu and Liu, Xiaopei (2020) Dynamic Upsampling of Smoke through Dictionary-based Learning. ACM Transactions on Graphics, 40 (1). Art. No. 4. ISSN 0730-0301. doi:10.1145/3412360. https://resolver.caltech.edu/CaltechAUTHORS:20210208-105847990

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Abstract

Simulating turbulent smoke flows with fine details is computationally intensive. For iterative editing or simply faster generation, efficiently upsampling a low-resolution numerical simulation is an attractive alternative. We propose a novel learning approach to the dynamic upsampling of smoke flows based on a training set of flows at coarse and fine resolutions. Our multiscale neural network turns an input coarse animation into a sparse linear combination of small velocity patches present in a precomputed over-complete dictionary. These sparse coefficients are then used to generate a high-resolution smoke animation sequence by blending the fine counterparts of the coarse patches. Our network is initially trained from a sequence of example simulations to both construct the dictionary of corresponding coarse and fine patches and allow for the fast evaluation of a sparse patch encoding of any coarse input. The resulting network provides an accurate upsampling when the coarse input simulation is well approximated by patches present in the training set (e.g., for re-simulation), or simply visually plausible upsampling when input and training sets differ significantly. We show a variety of examples to ascertain the strengths and limitations of our approach and offer comparisons to existing approaches to demonstrate its quality and effectiveness.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3412360DOIArticle
https://arxiv.org/abs/1910.09166arXivDiscussion Paper
ORCID:
AuthorORCID
Desbrun, Mathieu0000-0003-3424-6079
Additional Information:© 2020 Association for Computing Machinery. Received October 2019; revised July 2020; accepted July 2020. This work was supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61502305) and a startup funding from ShanghaiTech University.
Funders:
Funding AgencyGrant Number
National Natural Science Foundation of China61502305
ShanghaiTech UniversityUNSPECIFIED
Subject Keywords:Fluid simulation, dictionary learning, neural networks, smoke animation
Issue or Number:1
DOI:10.1145/3412360
Record Number:CaltechAUTHORS:20210208-105847990
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210208-105847990
Official Citation:Kai Bai,Wei Li, Mathieu Desbrun, and Xiaopei Liu. 2020. Dynamic Upsampling of Smoke through Dictionary-based Learning. ACM Trans. Graph. 40, 1, Article 4 (September 2020), 19 pages. https://doi.org/10.1145/3412360
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107948
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Feb 2021 19:08
Last Modified:16 Nov 2021 19:07

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