NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory
arxiv(2023)
Abstract
Despite existing 3D cloth simulators producing realistic results, they
predominantly operate on discrete surface representations (e.g. points and
meshes) with a fixed spatial resolution, which often leads to large memory
consumption and resolution-dependent simulations. Moreover, back-propagating
gradients through the existing solvers is difficult, and they cannot be easily
integrated into modern neural architectures. In response, this paper re-thinks
physically plausible cloth simulation: We propose NeuralClothSim, i.e., a new
quasistatic cloth simulator using thin shells, in which surface deformation is
encoded in neural network weights in the form of a neural field. Our
memory-efficient solver operates on a new continuous coordinate-based surface
representation called neural deformation fields (NDFs); it supervises NDF
equilibria with the laws of the non-linear Kirchhoff-Love shell theory with a
non-linear anisotropic material model. NDFs are adaptive: They 1) allocate
their capacity to the deformation details and 2) allow surface state queries at
arbitrary spatial resolutions without re-training. We show how to train
NeuralClothSim while imposing hard boundary conditions and demonstrate multiple
applications, such as material interpolation and simulation editing. The
experimental results highlight the effectiveness of our continuous neural
formulation.
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