NNWarp: Neural Network-Based Nonlinear Deformation

NNWarp is a highly re-usable and efficient neural network (NN) based nonlinear deformable simulation framework. Unlike other machine learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g., an array of all the pixels in an image), the input...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 26; no. 4; pp. 1745 - 1759
Main Authors: Luo, Ran, Shao, Tianjia, Wang, Huamin, Xu, Weiwei, Chen, Xiang, Zhou, Kun, Yang, Yin
Format: Journal Article
Language:English
Published: United States IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Animation
Artificial neural networks
Computational modeling
Computer simulation
Constitutive models
data-driven animation
deformable model
Deformable models
Deformation
Displacement
Elasticity
Finite element method
Formability
Machine learning
Mathematical analysis
Mathematical models
Matrix methods
Neural network
Neural networks
nonlinear regression
Object recognition
Parameterization
physics-based simulation
Simulation
Strain
Substructuring
Three dimensional models
Training
ISSN:1077-2626, 1941-0506, 1941-0506
Online Access:Get full text
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Summary:NNWarp is a highly re-usable and efficient neural network (NN) based nonlinear deformable simulation framework. Unlike other machine learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g., an array of all the pixels in an image), the input for deformable simulation is quite variable, high-dimensional, and parametrization-unfriendly. Consequently, even though the neural network is known for its rich expressivity of nonlinear functions, directly using an NN to reconstruct the force-displacement relation for general deformable simulation is nearly impossible. NNWarp obviates this difficulty by partially restoring the force-displacement relation via warping the nodal displacement simulated using a simplistic constitutive model-the linear elasticity. In other words, NNWarp yields an incremental displacement fix per mesh node based on a simplified (therefore incorrect) simulation result other than synthesizing the unknown displacement directly. We introduce a compact yet effective feature vector including geodesic , potential and digression to sort training pairs of per-node linear and nonlinear displacement. NNWarp is robust under different model shapes and tessellations. With the assistance of deformation substructuring, one NN training is able to handle a wide range of 3D models of various geometries. Thanks to the linear elasticity and its constant system matrix, the underlying simulator only needs to perform one pre-factorized matrix solve at each time step, which allows NNWarp to simulate large models in real time.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2018.2881451