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NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory by Kairanda, Navami; Habermann, Marc; Theobalt, Christian; Golyanik, Vladislav is a scholarly article available to read on EtoBox.

What is NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory about?

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 de

Author

Kairanda, Navami; Habermann, Marc; Theobalt, Christian; Golyanik, Vladislav

Published

2023

Language

EN