Physics-Based Animation

Baptiste Angles, Daniel Rebain, Miles Macklin, Brian Wyvill, Loic Barthe, JP Lewis, Javier von der Pahlen, Shahram Izadi, Julien Valentin, Sofien Bouaziz, Andrea Tagliasacchi

We extend the formulation of position-based rods to include elastic volumetric deformations. We achieve this by introducing an additional degree of freedom per vertex — isotropic scale (and its velocity). Including scale enriches the space of possible deformations, allowing the simulation of volumetric effects, such as a reduction in cross-sectional area when a rod is stretched. We rigorously derive the continuous formulation of its elastic energy potentials, and hence its associated position-based dynamics (PBD) updates to realize this model, enabling the simulation of up to 26000 DOFs at 140 Hz in our GPU implementation. We further show how rods can provide a compact alternative to tetrahedral meshes for the representation of complex muscle deformations, as well as providing a convenient representation for collision detection. This is achieved by modeling a muscle as a bundle of rods, for which we also introduce a technique to automatically convert a muscle surface mesh into a rods-bundle. Finally, we show how rods and/or bundles can be skinned to a surface mesh to drive its deformation, resulting in an alternative to cages for real-time volumetric deformation.

VIPER: Volume Invariant Position-based Elastic Rods

Xuchen Han, Theodore Gast, Qi Guo, Stephanie Wang, Chenfanfu Jiang, Joseph Teran

We present a new hybrid Lagrangian Material Point Method for simulating elastic objects like hair, rubber,and soft tissues that utilizes a Lagrangian mesh for internal force computation and an Eulerian mesh for self collision as well as coupling with external materials. While recent Material Point Method (MPM) techniques allow for natural simulation of hyperelastic materials represented with Lagrangian meshes, they utilize an updated Lagrangian discretization where the Eulerian grid degrees of freedom are used to take variations of the potential energy. This often coarsens the degrees of freedom of the Lagrangian mesh and can lead to artifacts.We develop a hybrid approach that retains Lagrangian degrees of freedom while still allowing for natural coupling with other materials simulated with traditional MPM, e.g. sand, snow, etc. Furthermore, while recent MPM advances allow for resolution of frictional contact with codimensional simulation of hyperelasticity, they do not generalize to the case of volumetric materials. We show that our hybrid approach resolves these issues.We demonstrate the efficacy of our technique with examples that involve elastic soft tissues coupled with kinematic skeletons, extreme deformation, and coupling with multiple elastoplastic materials. Our approach also naturally allows for two-way rigid body coupling.

A Hybrid Material Point Method for Frictional Contact with Diverse Materials

Miles Macklin, Kier Storey, Michelle Lu, Pierre Terdiman, Nuttapong Chentanez, Stefan Jeschke, Matthias Müller

In this paper we re-examine the idea that implicit integrators with large time steps offer the best stability/performance trade-off for stiff systems. We make the surprising observation that performing a single large time step with n constraint solver iterations is less effective than computing n smaller time steps, each with a single constraint solver iteration. Based on this observation, our approach is to split every visual time step into n substeps of length ∆t/n and to perform a single iteration of extended position-based dynamics (XPBD) in each such substep. When compared to a traditional implicit integrator with large time steps we find constraint error and damping are significantly reduced. When compared to an explicit integrator we find that our method is more stable and robust for a wider range of stiffness parameters. This result holds even when compared against more sophisticated implicit solvers based on Krylov methods. Our method is straightforward to implement, and is not sensitive to matrix conditioning nor is it to overconstrained problems

Small Steps in Physics Simulation

Rahul Narain, Jonas Zehnder, Bernhard Thomaszewski

Zehnder et al. [2018] recently introduced an advection-reflection method for fluid simulation that dramatically reduces artificial dissipation. We establish a connection between their method and the implicit midpoint time integration scheme, and present a simple modification to obtain an advection-reflection scheme with second-order accuracy in time. We compare with existing alternatives, including a second-order semi-Lagrangian method based on BDF2, and demonstrate the improved energy-preservation properties.

A Second-Order Advection-Reflection Solver

Jing Li, Tiantian Liu, Ladislav Kavan

We propose a fast and robust solver to simulate continuum-based deformable models with constraints, in particular, rigid-body and joint constraints useful for soft articulated characters. Our method embeds degrees of freedom of both articulated rigid bodies and deformable bodies in one unified optimization problem, thus coupling the deformable and rigid bodies. Our method can efficiently simulate character models, with rigid-body parts (bones) being correctly coupled with deformable parts (flesh). Our method is stable because backward Euler time integration is applied to rigid as well as deformable degrees of freedom. Our method is rigorously derived from constrained Newtonian mechanics. In an example simulation with rigid bodies only, we demonstrate that our method converges to the same motion as classical explicitly integrated rigid body simulator

Fast Simulation of Deformable Characters with Articulated Skeletons in Projective Dynamics

Daniel Holden, Bang Chi Duong, Sayantan Datta, Derek Nowrouzezahrai

Data-driven methods for physical simulation are an attractive option for interactive applications due to their ability to trade precomputation and memory footprint in exchange for improved runtime performance. Yet, existing data-driven methods fall short of the extreme memory and performance constraints imposed by modern interactive applications like AAA games and virtual reality. Here, performance budgets for physics simulation range from tens to hundreds of micro-seconds per frame, per object. We present a data-driven physical simulation method that meets these constraints. Our method combines subspace simulation techniques with machine learning which, when coupled, enables a very efficient subspace-only physics simulation that supports interactions with external objects – a longstanding challenge for existing sub-space techniques. We also present an interpretation of our method as a special case of subspace Verlet integration, where we apply machine learning to efficiently approximate the physical forces of the system directly in the subspace. We propose several practical solutions required to make effective use of such a model, including a novel training methodology required for prediction stability, and a GPU-friendly subspace decompression algorithm to accelerate rendering.

Subspace Neural Physics: Fast Data-Driven Interactive Simulation

Peter Kadlecek, Ladislav Kavan

The human face is an anatomical system exhibiting heterogenous and anisotropic mechanical behavior. This leads to complex deformations even in a neutral facial expression due to external forces such as gravity. We start by building a volumetric model from magnetic resonance images of a neutral facial expression. To obtain data on facial deformations we capture and register 3D scans of the face with different gravity directions and with various facial expressions. Our main contribution consists in solving an inverse physics problem where we learn mechanical properties of the face from our training data (3D scans). Specifically, we learn heterogenous stiffness and prestrain (which introduces anisotropy). The generalization capability of our resulting physics-based model is tested on 3D scans. We demonstrate that our model generates predictions of facial deformations more accurately than recent related physics-based techniques.

Building Accurate Physics-Based Face Models From Data

Yu Ju (Edwin) Chen, David I.W. Levin, Danny Kaufman, Uri Ascher, Dinesh K. Pai

Elastodynamic system simulation is a key procedure in computer graphics and robotics applications. To enable these simulations, the governing differential system is discretized in space (employing FEM) and then in time. For many simulation-based applications keeping the spatial resolution of the computational mesh effectively coarse is crucial for securing acceptable computational efficiency.However, this can introduce numerical stiffening effects that impede visual accuracy. We propose and demonstrate, for both linear and nonlinear force models, a new method called EigenFit that improves the consistency and accuracy of the lower energy, primary deformation modes, as the spatial mesh resolution is coarsened. EigenFit applies a partial spectral decomposition, solving a generalized eigenvalue problem in the leading mode subspace and then replacing the first several eigenvalues of the coarse mesh by those of the fine one at rest. EigenFit’s performance relies on a novel subspace model reduction technique which restricts the spectral decomposition to finding just a few of the leading eigenmodes. We demonstrate its efficacy on a number of objects with both homogeneous and heterogeneous material distributions.

EigenFit for Consistent Elastodynamic Simulation Across Mesh Resolution

Jumyung Chang, Fang Da, Eitan Grinspun, Christopher Batty

We present a unified method to simulate deformable elastic bodies consisting of mixed-dimensional components represented with potentially non-manifold simplicial meshes. Building on well-known simplicial rod, shell, and solid models for elastic continua, we categorize and define a comprehensive palette expressing all possible constraints and elastic energies for stiff and flexible connections between the 1D, 2D, and 3D components of a single conforming simplicial mesh. This palette consists of three categories: point connections, in which simplices meet at a single vertex around which they may twist and bend; curve connections in which simplices share an edge around which they may rotate (bend) relative to one another; and surface connections, in which a shell is embedded on or into a solid. To define elastic behaviors across non-manifold point connections, we adapt and apply parallel transport concepts from elastic rods. To address discontinuous forces that would otherwise arise when large accumulated relative rotations wrap around in the space of angles, we develop an incremental angle-update strategy. Our method provides a conceptually simple, flexible, and highly expressive framework for designing complex elastic objects, by modeling the geometry with a single simplicial mesh and decorating its elements with appropriate physical models (rod, shell, solid) and connection types (point, curve, surface). We demonstrate a diverse set of possible interactions achievable with our method, through technical and application examples, including scenes featuring complex aquatic creatures, children’s toys, and umbrellas.

A Unified Simplicial Model for Mixed-Dimensional and Non-Manifold Deformable Elastic Objects

Minjae Lee, David Hyde, Kevin Li, Ronald Fedkiw

We propose a novel volume conserving framework for character-water interaction, using a novel volume-of-fluid solver on a skinned tetrahedral mesh, enabling the high degree of the spatial adaptivity in order to capture thin films and hair-water interactions. For efficiency, the bulk of the fluid volume is simulated with a standard Eulerian solver which is two way coupled to our skinned arbitrary Lagrangian-Eulerian mesh using a fast, robust, and straightforward to implement partitioned approach. This allows for a specialized and efficient treatment of the volume-of-fluid solver, since it is only required in a subset of the domain. The combination of conservation of fluid volume and a kinematically deforming skinned mesh allows us to robustly implement interesting effects such as adhesion, and anisotropic porosity. We illustrate the efficacy of our method by simulating various water effects with solid objects and animated characters.

A Robust Volume Conserving Method for Character-Water Interaction