Descent methods for elastic body simulation on the GPU

Wang, Huamin; Yang, Yin

doi:10.1145/2980179.2980236

Cited by 88 publications

(62 citation statements)

References 29 publications

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“…reduced stiffness matrix (including cubature) causes the Newton based approach to perform approximately 1.3 times slower than the L-BFGS solver. This reflects recent results showing that Quasi-Newton approaches generally provide equal or greater performance in real-time applications for unreduced dynamics [LBK17,WY16]. Other reduced methods have also favored quasi-Newton approaches to avoid expensive hessian construction and inversion [vTSSH13].…”

Section: Full Space: E =10⁵pamentioning

confidence: 58%

Latent‐space Dynamics for Reduced Deformable Simulation

Fulton

Modi

Duvenaud

et al. 2019

Computer Graphics Forum

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We propose the first reduced model simulation framework for deformable solid dynamics using autoencoder neural networks. We provide a data‐driven approach to generating nonlinear reduced spaces for deformation dynamics. In contrast to previous methods using machine learning which accelerate simulation by approximating the time‐stepping function, we solve the true equations of motion in the latent‐space using a variational formulation of implicit integration. Our approach produces drastically smaller reduced spaces than conventional linear model reduction, improving performance and robustness. Furthermore, our method works well with existing force‐approximation cubature methods.

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Section: Full Space: E =10⁵pamentioning

confidence: 58%

Latent‐space Dynamics for Reduced Deformable Simulation

Fulton

Modi

Duvenaud

et al. 2019

Computer Graphics Forum

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“…However, projective dynamics still need to specify filtering thresholds and reflection conventions (Narain et al 2016;Wang and Yang 2016) to avoid numerical singularities. The advantage that our model requires neither carries over directly.…”

Section: ∂P(f)mentioning

confidence: 99%

“…We now examine two specific extensions of our analysis. Fung Hardening: The exponential hardening of Fung-like models (Fung 2013;Pan et al 2015;Wang and Yang 2016) is a secondary feature of many biological tissues (Kautzman et al 2012), so we propose a stabilized Fung model:…”

Section: Extension To Other Energiesmentioning

confidence: 99%

Stable Neo-Hookean Flesh Simulation

Smith¹,

Goes²,

Kim³

2018

ACM Trans. Graph.

135

109

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Nonlinear hyperelastic energies play a key role in capturing the fleshy appearance of virtual characters. Real-world, volume-preserving biological tissues have Poisson's ratios near 1 /2, but numerical simulation within this regime is notoriously challenging. In order to robustly capture these visual characteristics, we present a novel version of Neo-Hookean elasticity. Our model maintains the fleshy appearance of the Neo-Hookean model, exhibits superior volume preservation, and is robust to extreme kinematic rotations and inversions. We obtain closed-form expressions for the eigenvalues and eigenvectors of all of the system's components, which allows us to directly project the Hessian to semipositive definiteness, and also leads to insights into the numerical behavior of the material. These findings also inform the design of more sophisticated hyperelastic models, which we explore by applying our analysis to Fung and Arruda-Boyce elasticity. We provide extensive comparisons against existing material models.

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“…The first obstacle to performance is the continuously changing constraints due to lips contacts that prevents efficient physical simulation techniques such as Projective Dynamics to use a pre‐factorized solver. Recent works handled changing systems by using iterative solvers such as an accelerated Jacobi [Wan15, WY16] or a parallel Gauss–Seidel [FTP16]. However, these methods are designed to take advantage of the computational power of the GPU and are likely to be less efficient on the CPU.…”

Section: Related Workmentioning

confidence: 99%

Realtime Performance‐Driven Physical Simulation for Facial Animation

Barrielle

Stoiber²

2018

Computer Graphics Forum

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We present the first realtime method for generating facial animations enhanced by physical simulation from realtime performance capture data. Unlike purely data‐based techniques, our method is able to produce physical effects on the fly through the simulation of volumetric skin behaviour, lip contacts and sticky lips. It remains however practical as it does not require any physical/medical data which are complex to acquire and process, and instead relies only on the input of a blendshapes model. We achieve realtime performance on the CPU by introducing an efficient progressive Projective Dynamics solver to efficiently solve the physical integration steps even when confronted to constantly changing constraints. Also key to our realtime performance is a new Taylor approximation and memoization scheme for the computation of the Singular Value Decompositions required for the simulation of volumetric skin. We demonstrate the applicability of our method by animating blendshape characters from a simple webcam feed .

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Descent methods for elastic body simulation on the GPU

Cited by 88 publications

References 29 publications

Latent‐space Dynamics for Reduced Deformable Simulation

Latent‐space Dynamics for Reduced Deformable Simulation

Stable Neo-Hookean Flesh Simulation

Realtime Performance‐Driven Physical Simulation for Facial Animation

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