Efficient Penetration Depth Approximation using Active Learning

Jia Pan, Xinyu Zhang, Dinesh Manocha We present a new method for efficiently computing the global penetration depth between two rigid objects using machine learning techniques. Our approach consists of two phases: offline learning and performing run-time queries. In the learning phase, we pre-compute an approximation of the contact space of a pair of intersecting […]

A Material Point Method for Snow Simulation

Alexey Stomakhin, Craig Schroeder, Lawrence Chai, Joseph Teran, Andrew Selle Snow is a challenging natural phenomenon to visually simulate. While the graphics community has previously considered accumulation and rendering of snow, animation of snow dynamics has not been fully addressed. Additionally, existing techniques for solids and fluids have difficulty producing convincing snow results. Specifically, wet or dense snow […]

Course: Turbulent Fluids

Tobias Pfaff, Nils Thuerey, Theodore Kim Over the last decade, the special effects industry has embraced physics simulations as a highly useful tool for creating realistic scenes ranging from a small camp fire to the large scale destruction of whole cities. While fluid simulations are now widely used in the industry, it remains inherently difficult […]

SIGGRAPH Asia 2013

Ke-Sen’s full list here. Without further ado: Physics-based Animation of Large-scale Splashing Liquids Fast Simulation of Mass-Spring Systems Inverse Dynamic Hair Modeling with Frictional Contacts Versatile Surface Tension and Adhesion for SPH Fluids Efficient Penetration Depth Approximation using Active Learning Simulation and Control of Skeleton-Driven Soft Body Characters An Efficient Construction of Reduced Deformable Objects […]