Art-Directed Muscle Simulation for High-End Facial Animation

Matthew Cong, Kiran S. Bhat, Ronald Fedkiw

We propose a new framework for the simulation of facial muscle and flesh that so significantly improves the technique that it allows for immediate mainstream use of anatomically and biomechanically accurate muscle models as a bread and butter technique in a high-end production quality pipeline. The key idea is to create a blendshape system for the muscles that gives the precise directability and controllability required in a high-end production environment. The blendshape muscles are used to drive the underlying anatomically and biomechanically motivated simulation in a way that is unbound by the typical restrictions of a simulation system while still retaining the desirable degree of freedom richness that leads to high quality results. We show that we are able to target production quality facial shapes, whether from scans or an animation system, and illustrate that the resulting nonlinear simulation in-betweens are of higher quality than those obtained from traditional linear blendshapes. We also demonstrate the ability to selectively improve areas on a given blendshape using the results of a simulation, as well as the ability to edit muscle shapes and paths in order to produce directability for animator control. Then, we show how these techniques can be used to transition from one blendshape to another or even track and selectively modify an entire performance. The efficacy of our system is further demonstrated by using it to retarget animation onto new creature models given only a single static rest pose as input.

Art-Directed Muscle Simulation for High-End Facial Animation

Two-way coupling of fluids to reduced deformable bodies

Wenlong Lu, Ning Jin, Ronald Fedkiw

We propose a fully monolithic two-way coupling framework that couples incompressible fluids to reduced deformable bodies. Notably, the resulting linear system matrix is both symmetric and positive-definite. Our method allows for the simulation of interesting free-surface as well as underwater phenomena, enabling the use of reduced deformable bodies as full-fledged simulation primitives alongside rigid bodies and deformable bodies. Momentum conservation is crucial to obtaining physically correct and realistic-looking motion in a fluid environment, and we achieve this by following previous work to describe reduced deformable bodies using both a rigid frame and a reduced space deformation component. Our approach partitions forces and impulses between the reduced space and the rigid frame of the reduced deformable bodies using a projection scheme that cleanly accounts for momentum losses in the reduced space via corrections in the rigid frame, resulting in a new theoretical formulation for the momentum-conserving reduced deformable body. We demonstrate that robust and stable contact, collision, articulation, and two-way coupling with fluids are all attainable in a straightforward way using this new formulation. Compared with fully deformable objects, our framework consumes less memory and scales better in large scenes, while still nicely approximating the deformation effects.

Two-way coupling of fluids to reduced deformable bodies

Accurate Contact Modeling for Multi-rate Single-point Haptic Rendering of Static and Deformable Environments

Thomas Knott, Torsten Kuhlen

Common approaches for the haptic rendering of complex scenarios employ multi-rate simulation schemes. Here, the collision queries or the simulation of a complex deformable object are often performed asynchronously on a lower frequency, while some kind of intermediate contact representation is used to simulate interactions on the haptic rate. However, this can produce artifacts in the haptic rendering when the contact situation quickly changes and the intermediate representation is not able to reflect the changes due to the lower update rate. We address this problem utilizing a novel contact model. It facilitates the creation of contact representations that are accurate for a large range of motions and multiple simulation time-steps.We handle problematic convex contact regions using a local convex decomposition and special constraints for convex areas.We combine our accurate contact model with an implicit temporal integration scheme to create an intermediate mechanical contact representation, which reflects the dynamic behavior of the simulated objects. Moreover, we propose a new iterative solving scheme for the involved constrained dynamics problems.We increase the robustness of our method using techniques from trust region-based optimization. Our approach can be combined with standard methods for the modeling of deformable objects or constraint-based approaches for the modeling of, for instance, friction or joints. We demonstrate its benefits with respect to the simulation accuracy and the quality of the rendered haptic forces in multiple scenarios.

Accurate Contact Modeling for Multi-rate Single-point Haptic Rendering of Static and Deformable Environments

Divergence-Free SPH for Incompressible and Viscous Fluids

Jan Bender, Dan Koschier

In this paper we present a novel Smoothed Particle Hydrodynamics (SPH) method for the efficient and stable simulation of incompressible fluids. The most efficient SPH-based approaches enforce incompressibility either on position or velocity level. However, the continuity equation for incompressible flow demands to maintain a constant density and a divergence-free velocity field. We propose a combination of two novel implicit pressure solvers enforcing both a low volume compression as well as a divergence-free velocity field. While a compression-free fluid is essential for realistic physical behavior, a divergence-free velocity field drastically reduces the number of required solver iterations and increases the stability of the simulation significantly. Thanks to the improved stability, our method can handle larger time steps than previous approaches. This results in a substantial performance gain since the computationally expensive neighborhood search has to be performed less frequently. Moreover, we introduce a third optional implicit solver to simulate highly viscous fluids which seamlessly integrates into our solver framework. Our implicit viscosity solver produces realistic results while introducing almost no numerical damping. We demonstrate the efficiency, robustness and scalability of our method in a variety of complex simulations including scenarios with millions of turbulent particles or highly viscous materials.

Divergence-Free SPH for Incompressible and Viscous Fluids

Reconstructing Personalized Anatomical Models for Physics-based Body Animation

Petr Kadlecek, Alexandru-Eugen Ichim, Tiantian Liu, Ladislav Kavan, Jaroslav Krivanek

We present a method to create personalized anatomical models ready for physics-based animation, using only on a set of surface 3D scans. We start by building a template anatomical model of an average male which supports deformations due to both 1) subject-specific variations: shapes and sizes of bones, muscles, and adipose tissues and 2) skeletal poses. Next, we capture a set of 3D scans of an actor in various poses. Our key contribution is formulating and solving a large-scale optimization problem where we solve for both subject-specific and pose-dependent parameters such that our resulting anatomical model explains the captured 3D scans as closely as possible. Compared to data-driven body modeling techniques that focus only on the surface, our approach has the advantage of creating physics-based models, which provide realistic 3D geometry of the bones and muscles, and naturally supports effects such as inertia, gravity, and collisions according to the Newtonian dynamics.

Reconstructing Personalized Anatomical Models for Physics-based Body Animation

SMASH: Physics-guided Reconstruction of Collisions from Videos

Aron Monszpart, Nils Thuerey, Niloy J. Mitra

Collision sequences are commonly used in games and entertainment to add drama and excitement. Authoring even two body collisions in the real world can be difficult, as one has to get timing and the object trajectories to be correctly synchronized. After tedious trial-and-error iterations, when objects can actually be made to collide, then they are difficult to capture in 3D. In contrast, synthetically generating plausible collisions is difficult as it requires adjusting different collision parameters (e.g., object mass ratio, coefficient of restitution, etc.) and appropriate initial parameters. We present SMASH to directly read off appropriate collision parameters directly from raw input video recordings. Technically we enable this by utilizing laws of rigid body collision to regularize the problem of lifting 2D trajectories to a physically valid 3D reconstruction of the collision. The reconstructed sequences can then be modified and combined to easily author novel and plausible collisions. We evaluate our system on a range of synthetic scenes and demonstrate the effectiveness of our method by accurately reconstructing several complex real world collision events.

SMASH: Physics-guided Reconstruction of Collisions from Videos

Eulerian Solid-Fluid Coupling

Yun Teng, David I.W. Levin, Theodore Kim

We present a new method that achieves a two-way coupling between deformable solids and an incompressible fluid where the underlying geometric representation is entirely Eulerian. Using the recently developed Eulerian Solids approach [Levin et al. 2011], we are able to simulate multiple solids undergoing complex, frictional contact while simultaneously interacting with a fluid. The complexity of the scenarios we are able to simulate surpasses those that we have seen from any previous method. Eulerian Solids have previously been integrated using explicit schemes, but we develop an implicit scheme that allows large time steps to be taken. The incompressibility condition is satisfied in both the solid and the fluid, which has the added benefit of simplifying collision handling.

Eulerian Solid-Fluid Coupling

A scalable Schur-complement fluids solver for heterogeneous compute platforms

Haixiang Liu, Nathan Mitchell, Mridul Aanjaneya, Eftychios Sifakis

We present a scalable parallel solver for the pressure Poisson equation in fluids simulation which can accommodate complex irregular domains in the order of a billion degrees of freedom, using a single server or workstation fitted with GPU or Many-Core accelerators. The design of our numerical technique is attuned to the subtleties of heterogeneous computing, and allows us to benefit from the high memory and compute bandwidth of GPU accelerators even for problems that are too large to fit entirely on GPU memory. This is achieved via algebraic formulations that adequately increase the density of the GPU-hosted computation as to hide the overhead of offloading from the CPU, in exchange for accelerated convergence. Our solver follows the principles of Domain Decomposition techniques, and is based on the Schur complement method for elliptic partial differential equations. A large uniform grid is partitioned in non-overlapping subdomains, and bandwidth-optimized (GPU or Many-Core) accelerator cards are used to efficiently and concurrently solve independent Poisson problems on each resulting subdomain. Our novel contributions are centered on the careful steps necessary to assemble an accurate global solver from these constituent blocks, while avoiding excessive communication or dense linear algebra. We ultimately produce a highly effective Conjugate Gradients preconditioner, and demonstrate scalable and accurate performance on high-resolution simulations of water and smoke flow.

A scalable Schur-complement fluids solver for heterogeneous compute platforms

Vivace: a Practical Gauss-Seidel Method for Stable Soft Body Dynamics

Marco Fratarcangeli, Valentina Tibaldo, Fabio Pellacini

The solution of large sparse systems of linear constraints is at the base of most interactive solvers for physically-based animation of soft body dynamics. We focus on applications with hard and tight per-frame resource budgets, such as video games, where the solution of soft body dynamics needs to be computed in a few milliseconds. Linear iterative methods are preferred in these cases since they provide approximate solutions within a given error tolerance and in a short amount of time. We present a parallel randomized Gauss-Seidel method which can be effectively employed to enable the animation of 3D soft objects discretized as large and irregular triangular or tetrahedral meshes. At the beginning of each frame, we partition the set of equations governing the system using a randomized graph coloring algorithm. The unknowns in the equations belonging to the same partition are independent of each other. Then, all the equations belonging to the same partition are solved at the same time in parallel. Our algorithm runs completely on the GPU and can support changes in the constraints topology. We tested our method as a solver for soft body dynamics within the Projective Dynamics and Position Based Dynamics frameworks. We show how the algorithmic simplicity of this iterative strategy enables great numerical stability and fast convergence speed, which are essential features for physically based animations with fixed and small hard time budgets. Compared to the state of the art, we found our method to be faster and scale better while providing stabler solutions for very small time budgets.

Vivace: a Practical Gauss-Seidel Method for Stable Soft Body Dynamics

Descent Methods for Elastic Body Simulation on the GPU

Huamin Wang, Yin Yang

We show that many existing elastic body simulation approaches can be interpreted as descent methods, under a nonlinear optimization framework derived from implicit time integration. The key question is how to find an effective descent direction with a low computational cost. Based on this concept, we propose a new gradient descent method using Jacobi preconditioning and Chebyshev acceleration. The convergence rate of this method is comparable to that of LBFGS or nonlinear conjugate gradient. But unlike other methods, it requires no dot product operation, making it suitable for GPU implementation. To further improve its convergence and performance, we develop a series of step length adjustment, initialization, and invertible model conversion techniques, all of which are compatible with GPU acceleration. Our experiment shows that the resulting simulator is simple, fast, scalable, memory-efficient, and robust against very large time steps and deformations. It can correctly simulate the deformation behaviors of many elastic materials, as long as their energy functions are second-order differentiable and their Hessian matrices can be quickly evaluated. For additional speedups, the method can also serve as a complement to other techniques, such as multi-grid.

Descent Methods for Elastic Body Simulation on the GPU