Hierarchical Cloth Simulation using Deep Neural Networks
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Abstract (arXiv)
Fast and reliable physically-based simulation techniques are essential for providing flexible visual effects for computer graphics content. In this paper, we propose a fast and reliable hierarchical cloth simulation method, which combines conventional physically-based simulation with deep neural networks (DNN). Simulations of the coarsest level of the hierarchical model are calculated using conventional physically-based simulations, and more detailed levels are generated by inference using DNN models. We demonstrate that our method generates reliable and fast cloth simulation results through experiments under various conditions.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 2.
5 papers.
- 2023Physics-guided Shape-from-Template: Monocular Video Perception through Neural Surrogate Modelscited by 8
- 2023ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothescited by 7
- 2024Neural Modes: Self-supervised Learning of Nonlinear Modal Subspacescited by 6
- 2021Deep Deformation Detail Synthesis for Thin Shell Modelscited by 5
- 2024A Neural-Network-Based Approach for Loose-Fitting Clothingcited by 2
- 2024Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces26 Apr 2024
- 2024A Neural-Network-Based Approach for Loose-Fitting Clothing25 Apr 2024
- 2023Physics-guided Shape-from-Template: Monocular Video Perception through Neural Surrogate Models21 Nov 2023
- 2023ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes7 Apr 2023
- 2021Deep Deformation Detail Synthesis for Thin Shell Models23 Feb 2021