Zhang, Paul, Dmitriy Smirnov, Justin Solomon Much of computer-generated animation is created by manipulating meshes with rigs. While this approach works well for animating articulated objects like animals, it has limited flexibility for animating less structured free-form objects. We introduce Wassersplines, a novel trajectory inference method for animating unstructured densities based on recent advances in […]

Song Bai, Craig Schroeder In this paper we analyze the stability of the explicit material point method (MPM). We focus on PIC, APIC, and CPIC transfers using quadratic and cubic splines in two and three dimensions. We perform a fully three-dimensional Von Neumann stability analysis to study the behavior within the bulk of a material. […]

Zhongyun He, Jesús Pérez, Miguel A. Otaduy Numerical coarsening methods offer an attractive methodology for fast simulation of objects with high-resolution heterogeneity. However, they rely heavily on preprocessing, and are not suitable when objects undergo dynamic material or topology updates. We present methods that largely accelerate the two main processes of numerical coarsening, namely training […]

Yongxu Jin , Yushan Han , Zhenglin Geng , Joseph Teran , Ronald Fedkiw We present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks. In order to significantly reduce the requirements on data (especially time-dependent data), as well as decrease generalization error, our approach utilizes […]

Amir Hossein Rabbani, Jean-Philippe Guertin , Damien Rioux-Lavoie, Arnaud Schoentgen, Kaitai Tong, Alexandre Sirois-Vigneux, Derek Nowrouzezahrai Poisson equations appear in many graphics settings including, but not limited to, physics-based fluid simulation. Numerical solvers for such problems strike context-specific memory, performance, stability and accuracy trade-offs. We propose a new Poisson filter-based solver that balances between the […]

Lingchen Yang, Byungsoo Kim, Gaspard Zoss, Baran Gözcü, Markus Gross, Barbara Solenthaler 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-static, and physics-based simulation layer to optimize for actuation signals parameterized by neural networks. Our key contribution is […]

Xuwen Chen, Xingyu Ni, Bo Zhu, Bin Wang, Baoquan Chen Magnetoelastic thin shells exhibit great potential in realizing versatile functionalities through a broad range of combination of material stiffness, remnant magnetization intensity, and external magnetic stimuli. In this paper, we propose a novel computational method for forward simulation and inverse design of magnetoelastic thin shells. […]

Learning Physics with a Hierarchical Graph Network Physically Based Shape Matching Fast Numerical Coarsening with Local Factorizations Stability Analysis of Explicit MPM Wassersplines for Neural Vector-Field Controlled Animation Voronoi Filters for Simulation Enrichment Differentiable Simulation for Outcome-Driven Orthognathic Surgery Planning High-Order Elasticity Interpolants for Microstructure Simulation Surface-Only Dynamic Deformables using a Boundary Element Method A […]

Somehow I seem to have missed making a page for SCA 2021, so here it is! Coupling Friction with Visual Appearance Volume Preserving Simulation of Soft Tissue with Skin Fast Corotated Elastic SPH Solids with Implicit Zero-Energy Mode Control Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquids Visual […]

A. Bernardin, E. Coevoet, P.G. Kry, S. Andrews, C. Duriez, and M. Marchal In this paper, we propose a physics-based model of suction phenomenon to achieve simulation of deformable objects like suction cups. Our model uses a constraint-based formulation to simulate the variations of pressure inside suction cups. The respective internal pressures are represented as […]