Lagrangian neural style transfer for fluids
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Date
2020-07
Publication Type
Journal Article
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yes
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Abstract
Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer approach from images to 3D fluids formulated in a Lagrangian viewpoint. Using particles for style transfer has unique benefits compared to grid-based techniques. Attributes are stored on the particles and hence are trivially transported by the particle motion. This intrinsically ensures temporal consistency of the optimized stylized structure and notably improves the resulting quality. Simultaneously, the expensive, recursive alignment of stylization velocity fields of grid approaches is unnecessary, reducing the computation time to less than an hour and rendering neural flow stylization practical in production settings. Moreover, the Lagrangian representation improves artistic control as it allows for multi-fluid stylization and consistent color transfer from images, and the generality of the method enables stylization of smoke and liquids likewise.© 2020 Association for Computing Machinery.
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published
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Journal / series
Volume
39 (4)
Pages / Article No.
52
Publisher
Association for Computing Machinery
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Subject
Physically-based animation; Fluid simulation; Deep learning; Neural style transfer
Organisational unit
03420 - Gross, Markus (emeritus) / Gross, Markus (emeritus)
Notes
Funding
168997 - Data-driven Methods for Artist-directed Physically-based Simulations (SNF)