Igor Santesteban, Miguel A. Otaduy, Dan Casas This paper presents a learning-based clothing animation method for highly efficient virtual try-on simulation. Given a garment, we preprocess a rich database of physically-based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning-based model of cloth drape and wrinkles, as […]

Steffen Wiewel, Moritz Becher, Nils Thuerey We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flows, i.e. Navier-Stokes problems, and we propose a novel LSTM-based approach to predict the changes of pressure fields over time. The central challenge in this context is […]

A Geometrically Consistent Viscous Fluid Solver with Two-Way Fluid-Solid Coupling Deep Fluids: A Generative Network for Parameterized Fluid Simulations Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow Learning-Based Animation of Clothing for Virtual Try-On Procedural Tectonic Plates Latent-space Dynamics for Reduced Deformable Simulation A CNN-based Flow Correction Method for Fast Preview

Ziyin Qu*, Xinxin Zhang* (joint 1st authors), Ming Gao, Chenfanfu Jiang, Baoquan Chen In this paper, we introduce BiMocq2, an unconditionally stable, pure Eulerian-based advection scheme to efficiently preserve the advection accuracy of all physical quantities for long-term fluid simulations. Our approach is built upon the method of characteristic mapping (MCM). Instead of the costly […]

Minchen Li, Ming Gao, Timothy Langlois, Chenfanfu Jiang, Danny M. Kaufman Simulation methods are rapidly advancing the accuracy, consistency and controllability of elastodynamic modeling and animation. Critical to these advances, we require efficient time step solvers that reliably solve all implicit time integration problems for elastica. While available time step solvers succeed admirably in some […]

Kentaro Nagasawa, Takayuki Suzuki, Ryohei Seto, Masato Okada, Yonghao Yue The materials around us usually exist as mixtures of constituents, each constituent with possibly a different elasto-viscoplastic property. How can we describe the material property of such a mixture is the core question of this paper. We propose a nonlinear blending model that can capture […]

L. Huang, T. Hädrich, D. L. Michels We present an approach to the accurate and efficient large-scale simulation of the complex dynamics of ferrofluids based on physical principles. Ferrofluids are liquids containing magnetic particles that react to an external magnetic field without solidifying. In this contribution, we employ smooth magnets to simulate ferrofluids in contrast […]

Yu Fang, Minchen Li, Ming Gao, Chenfanfu Jiang Simulating viscoelastic polymers and polymeric fluids requires a robust andaccurate capture of elasticity and viscosity. The computation is known to become very challenging under large deformations and high viscosity. Drawing inspirations from return mapping based elastoplasticity treatment for granular materials, we present a finite strain integration scheme […]

Marcel Padilla, Albert Chern, Felix Knöppel, Ulrich Pinkall, Peter Schröder We introduce variable thickness, viscous vortex filaments. These can model such varied phenomena as underwater bubble rings or the intricate “chandeliers” formed by ink dropping into fluid. Treating the evolution of such filaments as an instance of Newtonian dynamics on a Riemannian configuration manifold we […]

Joshuah Wolper, Yu Fang, Minchen Li, Jiecong Lu, Ming Gao, Chenfanfu Jiang We present two new approaches for animating dynamic fracture involving large elastoplastic deformation. In contrast to traditional mesh-based tech- niques, where sharp discontinuity is introduced to split the continuum at crack surfaces, our methods are based on Continuum Damage Mechanics (CDM) with a […]