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 […]

Purvi Goel, Doug L. James Manually tuning physics-based animation parameters to explore a simulation outcome space or achieve desired motion outcomes can be notoriously tedious. Unfortunately, this problem has motivated many sophisticated and specialized optimization-based methods for fine-grained (keyframe) control, each of which are typically limited to specific animation phenomena, usually complicated, and, unfortunately, not […]

Joel Wretborn, Sean Flynn, Alexey Stomakhin We present a method for enhancing fluid simulations with realistic bubble and foam detail. We treat bubbles as discrete air particles, two-way coupled with a sparse volumetric Euler flow, as first suggested in [Stomakhin et al. 2020]. We elaborate further on their scheme and introduce a bubble inertia correction […]