NNWarp: Neural Network-Based Nonlinear Deformation
Apr 1, 2020·,
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0 min read
Ran Luo
Tianjia Shao

Huamin Wang
Weiwei Xu
Xiang Chen
Kun Zhou
Yin Yang

Abstract
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.
Type
Publication
IEEE Transactions on Visualization and Computer Graphics, 26(4)
Artificial Neural Networks
Deformable Models
Strain
Computational Modeling
Animation
Elasticity
Neural Network
Machine Learning
Data-Driven Animation
Nonlinear Regression
Deformable Model
Physics-Based Simulation
Authors
Chief Scientist
My research interests include computer graphics, computer vision, generative AI, and embodied AI.