Name
Abstract | ||
|---|---|---|
AbstractPredicting the fine and intricate details of a turbulent flow field in both space and time from a coarse input remains a major challenge despite the availability of modern machine learning tools. In this paper, we present a simple and effective dictionary-based approach to spatio-temporal upsampling of fluid simulation. We demonstrate that our neural network approach can reproduce the visual complexity of turbulent flows from spatially and temporally coarse velocity fields even when using a generic training set. Moreover, since our method generates finer spatial and/or temporal details through embarrassingly-parallel upsampling of small local patches, it can efficiently predict high-resolution turbulence details across a variety of grid resolutions. As a consequence, our method offers a whole range of applications varying from fluid flow upsampling to fluid data compression. We demonstrate the efficiency and generalizability of our method for synthesizing turbulent flows on a series of complex examples, highlighting dramatically better results in spatio-temporal upsampling and flow data compression than existing methods as assessed by both qualitative and quantitative comparisons. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3478513.3480492 | ACM Transactions on Graphics |
Keywords | DocType | Volume |
Fluid Simulation, Dictionary Learning, Neural Networks, Smoke Animation | Journal | 40 |
Issue | ISSN | Citations |
6 | 0730-0301 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Bai | 1 | 0 | 0.68 |
| Chunhao Wang | 2 | 0 | 0.34 |
| Mathieu Desbrun | 3 | 0 | 0.34 |
| Xiaopei Liu | 4 | 8 | 4.53 |