258 citations · 384 across the 8 of their papers we have counts for
8 papers
Spatially Adaptive Cloth Regression with Implicit Neural Representations
Lei Shu, Vinicius Azevedo, Barbara Solenthaler +1
The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics. The inherently non-uniform structure of cloth wrinkles mandates the e…
Curl-Flow: Boundary-Respecting Pointwise Incompressible Velocity Interpolation for Grid-Based Fluids
Jumyung Chang, Ruben Partono, Vinicius C. Azevedo +1
We propose to augment standard grid-based fluid solvers with pointwise divergence-free velocity interpolation, thereby ensuring exact incompressibility down to the sub-cell level.…
Lagrangian Neural Style Transfer for Fluids
Byungsoo Kim, Vinicius C. Azevedo, Markus Gross +1
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…
Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow
Steffen Wiewel, Byungsoo Kim, Vinicius C. Azevedo +2
We propose an end-to-end trained neural networkarchitecture to robustly predict the complex dynamics of fluid flows with high temporal stability. We focus on single-phase smoke sim…
Frequency-Aware Reconstruction of Fluid Simulations with Generative Networks
Simon Biland, Vinicius C. Azevedo, Byungsoo Kim +1
Convolutional neural networks were recently employed to fully reconstruct fluid simulation data from a set of reduced parameters. However, since (de-)convolutions traditionally tra…
Neural Smoke Stylization with Color Transfer
Fabienne Christen, Byungsoo Kim, Vinicius C. Azevedo +1
Artistically controlling fluid simulations requires a large amount of manual work by an artist. The recently presented transportbased neural style transfer approach simplifies work…
Transport-Based Neural Style Transfer for Smoke Simulations
Byungsoo Kim, Vinicius C. Azevedo, Markus Gross +1
Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configu…
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
Byungsoo Kim, Vinicius C. Azevedo, Nils Thuerey +3
This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete…