Month: August 2022

• 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 Second Order Cone Programming Approach for Simulating Biphasic Materials
• A Second-Order Explicit Pressure Projection Method for Eulerian Fluid Simulation

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 Simulation of Soil-Structure Destruction with Seepage Flows
• Particle Merging-and-Splitting (TVCG Talk)

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 pressure constraints which are coupled with anti-interpenetration and friction constraints. Furthermore, our method is able to detect multiple air cavities using information from collision detection. We solve the pressure constraints based on the ideal gas law while considering several cavity states. We test our model with a number of scenarios reflecting a variety of uses, for instance, a spring loaded jumping toy, a manipulator performing a pick and place task, and an octopus tentacle grasping a soda can. We also evaluate the ability of our model to reproduce the physics of suction cups of varying shapes, lifting objects of different masses, and sliding on a slippery surface. The results show promise for various applications such as the simulation in soft robotics and computer animation.

Constraint-based Simulation of Passive Suction Cups

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 widely used. In this paper, we propose Unified Many-Worlds Browsing (UMWB), a practical method for sample-level control and exploration of arbitrary physics-based animations. Our approach supports browsing of large simulation ensembles of arbitrary animation phenomena by using a unified volumetric WorldPack representation based on spatiotemporally compressed voxel data associated with geometric occupancy and other low-fidelity animation state. Beyond memory reduction, the WorldPack representation also enables unified query support for interactive browsing: it provides fast evaluation of approximate spatiotemporal queries, such as occupancy tests that find ensemble samples (“worlds”) where material is either IN or NOT IN a user-specified spacetime region. The WorldPack representation also supports real-time hardware-accelerated voxel rendering by exploiting the spatially hierarchical and temporal RLE raster data structure to accelerate GPU ray tracing of compressed animations. Our UMWB implementation supports interactive browsing (and offline refinement) of ensembles containing thousands of simulation samples, and fast spatiotemporal queries and ranking. We show UMWB results using a wide variety of different physics-based animation phenomena—not just Jell-O.

Unified Many Worlds Browsing of Arbitrary Physics-Based Animations

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 term for improved convergence. We also show how one can add bubbles to an already existing fluid simulation using our novel guiding technique, which performs local re-simulation of fluid to achieve more interesting bubble dynamics through coupling. As bubbles reach the surface, they are converted into foam and simulated separately. Our foam is discretized with smoothed particle hydrodynamics (SPH), and we replace forces normal to the fluid surface with a fluid surface manifold advection constraint to achieve more robust and stable results. The SPH forces are derived through proper constitutive modeling of an incompressible viscous liquid, and we explain why this choice is appropriate for “wet” types of foam. This allows us to produce believable dynamics from close-up scenarios to large oceans, with just a few parameters that work intuitively across a variety of scales. Additionally, we present relevant research on air entrainment metrics and bubble distributions that have been used in this work.

Guided Bubbles and Wet Foam for Realistic Whitewater Simulation