258 citations · 429 across the 9 of their papers we have counts for
9 papers
Implicit Neural Representation for Physics-driven Actuated Soft Bodies
Lingchen Yang, Byungsoo Kim, Gaspard Zoss +3
Active soft bodies can affect their shape through an internal actuation mechanism that induces a deformation. Similar to recent work, this paper utilizes a differentiable, quasi-st…
Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution
Hyojoon Park, Sangeetha Grama Srinivasan, Matthew Cong +5
We present a neural network-based simulation super-resolution framework that can efficiently and realistically enhance a facial performance produced by a low-cost, realtime physics…
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…
Robust Reference Frame Extraction from Unsteady 2D Vector Fields with Convolutional Neural Networks
Byungsoo Kim, Tobias Günther
Robust feature extraction is an integral part of scientific visualization. In unsteady vector field analysis, researchers recently directed their attention towards the computation…
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…