Simulation of complex nonlinear elastic bodies using lattice deformers

Patterson, Taylor; Mitchell, Nathan; Sifakis, Eftychios

doi:10.1145/2366145.2366216

Cited by 30 publications

(39 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although it is possible to replicate the approach of Patterson et al [2012] and adapt a quadrature scheme to capture the localized presence of an active muscle within a lattice cell, we found it adequate to average the effect of the muscle with respect to each lattice cell that it intersects, an approach similar to what Lee et al [2009] employed in their tetrahedral discretization. Specifically, for a given cell of our lattice deformer, we compute the fractional coverage d m ∈ (0, 1] by the volume of muscle m that it contains.…”

Section: A2 Flesh Constitutive Modelmentioning

confidence: 97%

“…Given the contractile muscle forces plus the external forces from the flesh simulation, we simulate the skeleton using the Articulated Body Method [Featherstone 1987] to compute the forward dynamics in conjunction with a backward Euler time-integration scheme as in Lee et al [2009]. For the purpose of simulating the dynamic deformation of the flesh and muscles, we employ a lattice-based discretization of quasi-incompressible elasticity [Patterson et al 2012] augmented with active muscle terms. This approach avoids the need for multiple meshes conforming to individual muscles and its regular structure offers significant opportunities for performance optimizations.…”

Section: Simulation Componentsmentioning

confidence: 99%

“…Despite this, we construct the discrete governing equations so as to compensate for this geometric discrepancy. We discretize the elasticity equations following the methodology of Patterson et al [2012], which captures the fact that lattice elements on the boundary of the flesh volume are only fractionally covered by elastic material. The jagged boundary of the lattice-derived simulation volume also differs from the actual skin surface where fluid forces are to be applied; we compensate for this by embedding a high-resolution skin surface mesh within the cubic lattice and distributing the forces acting on the skin surface into the volumetric lattice by scaling with the appropriate embedding weights, as discussed in Zhu et al [2010].…”

Section: A1 Lattice Representationmentioning

confidence: 99%

See 2 more Smart Citations

Realistic Biomechanical Simulation and Control of Human Swimming

Lee

Sifakis

et al. 2014

ACM Trans. Graph.

Self Cite

View full text Add to dashboard Cite

We address the challenging problem of controlling a complex biomechanical model of the human body to synthesize realistic swimming animation. Our human model includes all of the relevant articular bones and muscles, including 103 bones (163 articular degrees of freedom) plus a total of 823 muscle actuators embedded in a finite element model of the musculotendinous soft tissues of the body that produces realistic deformations. To coordinate the numerous muscle actuators in order to produce natural swimming movements, we develop a biomimetically motivated motor control system based on Central Pattern Generators (CPGs), which learns to produce activation signals that drive the numerous muscle actuators.

show abstract

Section: A2 Flesh Constitutive Modelmentioning

confidence: 97%

Section: Simulation Componentsmentioning

confidence: 99%

Section: A1 Lattice Representationmentioning

confidence: 99%

See 1 more Smart Citation

Realistic Biomechanical Simulation and Control of Human Swimming

Lee

Sifakis

et al. 2014

ACM Trans. Graph.

Self Cite

View full text Add to dashboard Cite

show abstract

“…These methods use fast matrix inversion techniques and other insights about the algebra of the finite element method to increase its performance. Simulators based on the multigrid method [Peraire et al 1992;Zhu et al 2010;McAdams et al 2011] and Krylov subspace techniques [Patterson et al 2012] have yielded impressive performance increases. Other hierarchical numerical approaches, as well as highly parallel techniques, have also been applied to improve the time required to perform complex simulations [Farhat and Roux 1991;Mandel 1993].…”

Section: Related Workmentioning

confidence: 99%

Data-driven finite elements for geometry and material design

Chen¹,

Levin

Sueda

et al. 2015

ACM Trans. Graph.

View full text Add to dashboard Cite

Figure 1: Examples produced by our data-driven finite element method. Left: A bar with heterogeneous material arrangement is simulated 15x faster than its high-resolution counterpart. Left-Center: Our fast coarsening algorithm dramatically accelerates designing this shoe sole (up to 43x). Right-Center: A comparison to 3D printed results. Right: We repair a flimsy bridge by adding a supporting arch (8.1x) speed-up. We show a High-Res Simulation for comparison. AbstractCrafting the behavior of a deformable object is difficult-whether it is a biomechanically accurate character model or a new multimaterial 3D printable design. Getting it right requires constant iteration, performed either manually or driven by an automated system. Unfortunately, previous algorithms for accelerating three-dimensional finite element analysis of elastic objects suffer from expensive precomputation stages that rely on a priori knowledge of the object's geometry and material composition. In this paper we introduce Data-Driven Finite Elements as a solution to this problem. Given a material palette, our method constructs a metamaterial library which is reusable for subsequent simulations, regardless of object geometry and/or material composition. At runtime, we perform fast coarsening of a simulation mesh using a simple table lookup to select the appropriate metamaterial model for the coarsened elements. When the object's material distribution or geometry changes, we do not need to update the metamaterial library-we simply need to update the metamaterial assignments to the coarsened elements. An important advantage of our approach is that it is applicable to non-linear material models. This is important for designing objects that undergo finite deformation (such as those produced by multimaterial 3D printing). Our method yields speed gains of up to two orders of magnitude while maintaining good accuracy. We demonstrate the effectiveness of the method on both virtual and 3D printed examples in order to show its utility as a tool for deformable object design.

show abstract

“…Recent years witnessed huge improvements in anatomically-based simulation, especially in terms of computational efficiency [Patterson et al 2012]. However, the cost of setting up a 3D anatomical model for a given character remains.…”

Section: Introductionmentioning

confidence: 99%

Anatomy transfer

et al. 2013

View full text Add to dashboard Cite

Figure 1: A reference anatomy (left) is automatically transferred to arbitrary humanoid characters. This is achieved by combining interpolated skin correspondences with anatomical rules. AbstractCharacters with precise internal anatomy are important in film and visual effects, as well as in medical applications. We propose the first semi-automatic method for creating anatomical structures, such as bones, muscles, viscera and fat tissues. This is done by transferring a reference anatomical model from an input template to an arbitrary target character, only defined by its boundary representation (skin). The fat distribution of the target character needs to be specified. We can either infer this information from MRI data, or allow the users to express their creative intent through a new editing tool. The rest of our method runs automatically: it first transfers the bones to the target character, while maintaining their structure as much as possible. The bone layer, along with the target skin eroded using the fat thickness information, are then used to define a volume where we map the internal anatomy of the source model using harmonic (Laplacian) deformation. This way, we are able to quickly generate anatomical models for a large range of target characters, while maintaining anatomical constraints.

show abstract

Simulation of complex nonlinear elastic bodies using lattice deformers

Cited by 30 publications

References 33 publications

Realistic Biomechanical Simulation and Control of Human Swimming

Realistic Biomechanical Simulation and Control of Human Swimming

Data-driven finite elements for geometry and material design

Anatomy transfer

Contact Info

Product

Resources

About