NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory
- URL: http://arxiv.org/abs/2308.12970v2
- Date: Fri, 14 Jun 2024 16:21:39 GMT
- Title: NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory
- Authors: Navami Kairanda, Marc Habermann, Christian Theobalt, Vladislav Golyanik,
- Abstract summary: We propose NeuralClothSim, a new quasistatic cloth simulator using thin shells.
Our memory-efficient solver operates on a new continuous coordinate-based surface representation called neural deformation fields.
NDFs are adaptive: They allocate their capacity to the deformation details and 2) allow surface state queries at arbitrary spatial resolutions without re-training.
- Score: 70.10550467873499
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- PhyRecon: Physically Plausible Neural Scene Reconstruction [81.73129450090684]
We introduce PhyRecon, the first approach to leverage both differentiable rendering and differentiable physics simulation to learn implicit surface representations.
PhyRecon features a novel differentiable particle-based physical simulator built on neural implicit representations.
Our results also exhibit superior physical stability in physical simulators, with at least a 40% improvement across all datasets.
arXiv Detail & Related papers (2024-04-25T15:06:58Z) - Physics-Informed Neural Networks with Hard Linear Equality Constraints [9.101849365688905]
This work proposes a novel physics-informed neural network, KKT-hPINN, which rigorously guarantees hard linear equality constraints.
Experiments on Aspen models of a stirred-tank reactor unit, an extractive distillation subsystem, and a chemical plant demonstrate that this model can further enhance the prediction accuracy.
arXiv Detail & Related papers (2024-02-11T17:40:26Z) - Neural Stress Fields for Reduced-order Elastoplasticity and Fracture [43.538728312264524]
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture.
Key innovation is training a low-dimensional manifold for the Kirchhoff stress field via an implicit neural representation.
We demonstrate dimension reduction by up to 100,000X and time savings by up to 10X.
arXiv Detail & Related papers (2023-10-26T21:37:32Z) - NSF: Neural Surface Fields for Human Modeling from Monocular Depth [46.928496022657185]
It is challenging to model dynamic and fine-grained clothing deformations from sparse data.
Existing methods for modeling 3D humans from depth data have limitations in terms of computational efficiency, mesh coherency, and flexibility in resolution and topology.
We propose a novel method Neural Surface Fields for modeling 3D clothed humans from monocular depth.
arXiv Detail & Related papers (2023-08-28T19:08:17Z) - Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves
Propagation [0.0]
We use a high-fidelity simulation code, SEM3D, to build an extensive database of ground motions generated by 30,000 different geologies.
We show that the Fourier Neural Operator can produce accurate ground motion even when the underlying geology exhibits large heterogeneities.
Thanks to the generalizability of our database, we believe that our model can be used to assess the influence of geological features on ground motion.
arXiv Detail & Related papers (2023-04-20T12:01:58Z) - NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with
Spatial-temporal Decomposition [67.46012350241969]
This paper proposes a general acceleration methodology called NeuralStagger.
It decomposing the original learning tasks into several coarser-resolution subtasks.
We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations.
arXiv Detail & Related papers (2023-02-20T19:36:52Z) - Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs.
We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling.
We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
arXiv Detail & Related papers (2022-06-10T04:54:13Z) - Mapping and Validating a Point Neuron Model on Intel's Neuromorphic
Hardware Loihi [77.34726150561087]
We investigate the potential of Intel's fifth generation neuromorphic chip - Loihi'
Loihi is based on the novel idea of Spiking Neural Networks (SNNs) emulating the neurons in the brain.
We find that Loihi replicates classical simulations very efficiently and scales notably well in terms of both time and energy performance as the networks get larger.
arXiv Detail & Related papers (2021-09-22T16:52:51Z) - NeuS: Learning Neural Implicit Surfaces by Volume Rendering for
Multi-view Reconstruction [88.02850205432763]
We present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with high fidelity from 2D image inputs.
Existing neural surface reconstruction approaches, such as DVR and IDR, require foreground mask as supervision.
We observe that the conventional volume rendering method causes inherent geometric errors for surface reconstruction.
We propose a new formulation that is free of bias in the first order of approximation, thus leading to more accurate surface reconstruction even without the mask supervision.
arXiv Detail & Related papers (2021-06-20T12:59:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.