Physics-Based Animation

Yu Fang, Yuanming Hu, Shi-Min Hu, Chenfanfu Jiang

Spatially and temporally adaptive algorithms can substantially improve the computational efficiency of many numerical schemes in computational mechanics and physics-based animation. Recently, a crucial need for temporal adaptivity in the Material Point Method (MPM) is emerging due to the potentially substantial variation of material stiffness and velocities in multi-material scenes. In this work, we propose a novel temporally adaptive symplectic Euler scheme for MPM with regional time stepping (RTS), where different time steps are used in different regions. We design a time stepping scheduler operating at the granularity of small blocks to maintain a natural consistency with the hybrid particle/grid nature of MPM. Our method utilizes the Sparse Paged Grid (SPGrid) data structure and simultaneously offers high efficiency and notable ease of implementation with a practical multi-threaded particle-grid transfer strategy. We demonstrate the efficacy of our asynchronous MPM method on various examples including elastic objects, granular media, and fluid.

A Temporally Adaptive Material Point Method with Regional Time Stepping

Junbang Liang, Ming C. Lin

Cloth simulations, widely used in computer animation and apparel design, can be computationally expensive for real-time applications. Some parallelization techniques have been proposed for visual simulation of cloth using CPU or GPU clusters and often rely on parallelization using spatial domain decomposition techniques that have a large communication overhead. In this paper, we propose a novel time-domain parallelization technique that makes use of the two-level mesh representation to resolve the time-dependency issue and develop a practical algorithm to smooth the state transition from the corresponding coarse to fine meshes. A load estimation and a load balancing technique used in online partitioning are also proposed to maximize the performance acceleration. Our method achieves a nearly linear performance scaling on manycore clusters and outperforms spatial-domain parallelization on a diverse set of benchmarks.

Time-Domain Parallelization for Accelerating Cloth Simulation

Marie-Lena Eckert, Wolfgang Heidrich, Nils Thuerey

We present a novel method to reconstruct a fluid’s 3D density and motion based on just a single sequence of images. This is rendered possible by using powerful physical priors for this strongly under-determined problem. More specifically, we propose a novel strategy to infer density updates strongly coupled to previous and current estimates of the flow motion. Additionally, we employ an accurate discretization and depth-based regularizers to compute stable solutions. Using only one view for the reconstruction reduces the complexity of the capturing setup drastically and could even allow for online video databases or smart-phone videos as inputs. The reconstructed 3D velocity can then be flexibly utilized, e.g., for re-simulation, domain modification or guiding purposes. We will demonstrate the capacity of our method with a series of synthetic test cases and the reconstruction of real smoke plumes captured with a Raspberry Pi camera.

Coupled Fluid Density and Motion from Single Views

Mridul Aanjaneya

We present an efficient solver for monolithic two-way coupled simulation of rigid bodies with incompressible fluids that is robust to poor conditioning of the coupled system in the presence of large density ratios between the solid and the fluid. Our method leverages ideas from the theory of Domain Decomposition, and uses a hybrid combination of direct and iterative solvers that exploits the low-dimensional nature of the solid equations. We observe that a single Multigrid V-cycle for the fluid equations serves as a very effective preconditioner for solving the Schur-complement system using Conjugate Gradients, which is the main computational bottleneck in our pipeline. We use spectral analysis to give some theoretical insights behind this observation. Our method is simple to implement, is entirely assembly-free besides the solid equations, allows for the use of large time steps because of the monolithic formulation, and remains stable even when the iterative solver is terminated early. We demonstrate the efficacy of our method on several challenging examples of two-way coupled simulation of smoke and water with rigid bodies. To illustrate that our method is applicable to other problems, we also show an example of underwater bubble simulation.

An Efficient Solver for Two-way Coupling Rigid Bodies with Incompressible Flow

Timothy Jeruzalski, John Kanji, Alec Jacobson, David I.W. Levin

Methods to compress simulation data are invaluable as they facilitate efficient transmission along the visual effects pipeline, fast and efficient replay of simulations for visualization and enable storage of scientific data. However, all current approaches to compressing simulation data require access to the entire dynamic simulation, leading to large memory requirements and additional computational burden. In this paper we perform compression of contact-dominated, rigid body simulations in an online, error-bounded fashion. This has the advantage of requiring access to only a narrow window of simulation data at a time while still achieving good agreement with the original simulation. Our approach is simulator agnostic allowing us to compress data from a variety of sources. We demonstrate the efficacy of our algorithm by compressing contact-dominated rigid body simulations from a number of sources, achieving compression rates of up to 360 times over raw data size.

Collision-Aware and Online Compression of Rigid Body Simulations via Integrated Error Minimization

Xiaokai Li, Sheldon Andrews, Ben Jones, Adam Bargteil

Compelling animation of fracture is a vital challenge for computer graphics. Methods based on continuum mechanics are physically accurate, but computationally expensive since they require computing elastic deformation. In many applications, this elastic deformation is imperceptible, so simulation methods based on rigid body dynamic with breakable constraints are popular in practice. Simply deleting constraints when thresholds on force or displacement are reached ignores the elastic energy that is stored just before fracture, which is captured by continuum mechanics based methods. Our approach computes the energy stored in these constraints when they are broken, and reintroduces it to the system as kinetic energy. As a result, our method is able to animate energetic fracture scenarios with results comparable to continuum mechanics approaches, but with the computational efficiency of rigid body simulation.

Energized Rigid Body Fracture

• Liquid Splash Modeling with Neural Networks
• Collision-Aware and Online Compression of Rigid Body Simulations via Integrated Error Minimization
• An Efficient Solver for Two-way Coupling Rigid Bodies with Incompressible Flow
• A Temporally Adaptive Material Point Method with Regional Time Stepping
• Time-Domain Parallelization for Accelerating Cloth Simulation
• Cable Joints
• Distributing and Load Balancing Sparse Fluid Simulations
• Coupled Fluid Density and Motion from Single Views
• Cosserat Rods with Projective Dynamics
• Fast Corotated FEM using Stiffness Matrix Splitting
• Strain Rate Dissipation for Elastic Deformations
• MPM simulation of interactive and reactive fluids and solids
• HairControl: A Tracking Solution for Directable Hair Simulation
• Realtime Performance Driven Physical Simulation for Facial Animation (CGF)
• Real-time High-fidelity Surface Flow Simulation (TVCG)
• A Skinned Tetrahedral Mesh for Hair Animation and Hair-Water Interaction (TVCG)

Kiwon Um, Xiangyu Hu, Nils Thuerey

This paper proposes a new data-driven approach to model detailed splashes for liquid simulations with neural networks. Our model learns to generate small-scale splash detail for the fluid-implicit-particle method using training data acquired from physically parameterized, high resolution simulations. We use neural networks to model the regression of splash formation using a classifier together with a velocity modifier. For the velocity modification, we employ a heteroscedastic model. We evaluate our method for different spatial scales, simulation setups, and solvers. Our simulation results demonstrate that our model significantly improves visual fidelity with a large amount of realistic droplet formation and yields splash detail much more efficiently than finer discretizations.

Liquid Splash Modeling with Neural Networks

Jan Bender, Dan Koschier, Tassilo Kugelstadt, Marcel Weiler

In this paper we introduce a novel micropolar material model for the simulation of turbulent inviscid fluids. The governing equations are solved by using the concept of Smoothed Particle Hydrodynamics (SPH). As already investigated in previous works, SPH fluid simulations suffer from numerical diffusion which leads to a lower vorticity, a loss in turbulent details and finally in less realistic results. To solve this problem we propose a micropolar fluid model. The micropolar fluid model is a generalization of the classical Navier-Stokes equations, which are typically used in computer graphics to simulate fluids. In contrast to the classical Navier-Stokes model, micropolar fluids have a microstructure and therefore consider the rotational motion of fluid particles. In addition to the linear velocity field these fluids also have a field of microrotation which represents existing vortices and provides a source for new ones. However, classical micropolar materials are viscous and the translational and the rotational motion are coupled in a dissipative way. Since our goal is to simulate turbulent fluids, we introduce a novel modified micropolar material for inviscid fluids with a non-dissipative coupling. Our model can generate realistic turbulences, is linear and angular momentum conserving, can be easily integrated in existing SPH simulation methods and its computational overhead is negligible. Another important visual feature of turbulent liquids is foam. Therefore, we present a post-processing method which considers microrotation in the foam particle generation. It works completely automatic and requires only one user-defined parameter to control the amount of foam.

Turbulent Micropolar SPH Fluids with Foam