Efficient Neural Style Transfer for Volumetric Simulations

Artistically controlling fluids has always been a challenging task. Recently, volumetric Neural Style Transfer (NST) techniques have been used to artistically manipulate smoke simulation data with 2D images. In this work, we revisit previous volumetric NST techniques for smoke, proposing a suite of...

Full description

Saved in:
Bibliographic Details
Published in:ACM transactions on graphics 2022-12, Vol.41 (6), p.1-10, Article 257
Main Authors: Aurand, Joshua, Ortiz, Raphael, Nauer, Silvia, Azevedo, Vinicius C.
Format: Article
Language:English
Subjects:
Animation
Computer graphics
Computing methodologies
Physical simulation
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Artistically controlling fluids has always been a challenging task. Recently, volumetric Neural Style Transfer (NST) techniques have been used to artistically manipulate smoke simulation data with 2D images. In this work, we revisit previous volumetric NST techniques for smoke, proposing a suite of upgrades that enable stylizations that are significantly faster, simpler, more controllable and less prone to artifacts. Moreover, the energy minimization solved by previous methods is camera dependent. To avoid that, a computationally expensive iterative optimization performed for multiple views sampled around the original simulation is needed, which can take up to several minutes per frame. We propose a simple feed-forward neural network architecture that is able to infer view-independent stylizations that are three orders of the magnitude faster than its optimization-based counterpart.
ISSN:0730-0301
1557-7368
DOI:10.1145/3550454.3555517