Lagrangian neural style transfer for fluids
Byungsoo Kim, Vinicius C. Azevedo, Markus Gross, Barbara Solenthaler
Abstract
Fig. 1. Our Lagrangian neural style transfer enables novel artistic manipulations, such as time-coherent stylization of smoke, multiple fluids and liquids.
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.
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Install the CLIlune papers fulltext 40e630d6-ecc4-4cfa-a228-1c5bfb1e7e3eCited by top-tier papers10
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