research-article

Hierarchical Cloth Simulation using Deep Neural Networks

Authors:

In-Kwon LeeAuthors Info & Claims

CGI 2018: Proceedings of Computer Graphics International 2018

Pages 139 - 146

https://doi.org/10.1145/3208159.3208162

Published: 11 June 2018 Publication History

Abstract

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.

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Cited By

Jin YShi ZYang JLiu YQiao XZhang L(2024)Deep Neural Network-Based Cloth Collision Detection AlgorithmScientific Programming10.1155/2024/78892782024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/7889278
Ju EKim KYoon SShim EKang GChang PChoi M(2024)Estimating Cloth Simulation Parameters From Tag Information and Cusick Drape TestComputer Graphics Forum10.1111/cgf.1502743:2Online publication date: 30-Apr-2024
https://doi.org/10.1111/cgf.15027
Wang JDu YCoros SThomaszewski B(2024)Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02185(23158-23167)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02185
Show More Cited By

Index Terms

Hierarchical Cloth Simulation using Deep Neural Networks
1. Computing methodologies
  1. Computer graphics
    1. Animation
      1. Physical simulation
      2. Procedural animation

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cover image ACM Other conferences

CGI 2018: Proceedings of Computer Graphics International 2018

June 2018

284 pages

ISBN:9781450364010

DOI:10.1145/3208159

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 11 June 2018

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Funding Sources

National Research Foundation of Korea

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CGI 2018

CGI 2018: Computer Graphics International 2018

June 11 - 14, 2018

Island, Bintan, Indonesia

Acceptance Rates

CGI 2018 Paper Acceptance Rate 35 of 159 submissions, 22%;

Overall Acceptance Rate 35 of 159 submissions, 22%

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Cited By

Jin YShi ZYang JLiu YQiao XZhang L(2024)Deep Neural Network-Based Cloth Collision Detection AlgorithmScientific Programming10.1155/2024/78892782024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/7889278
Ju EKim KYoon SShim EKang GChang PChoi M(2024)Estimating Cloth Simulation Parameters From Tag Information and Cusick Drape TestComputer Graphics Forum10.1111/cgf.1502743:2Online publication date: 30-Apr-2024
https://doi.org/10.1111/cgf.15027
Wang JDu YCoros SThomaszewski B(2024)Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02185(23158-23167)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02185
Stotko DWandel NKlein R(2024)Physics-guided Shape-from-Template: Monocular Video Perception through Neural Surrogate Models2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01130(11895-11904)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01130
Ju EShim EKim KYoon SChoi M(2024)Fast constrained optimization for cloth simulation parameters from static drapesComputer Animation and Virtual Worlds10.1002/cav.226535:3Online publication date: 14-Jun-2024
https://doi.org/10.1002/cav.2265
Lee DKang HLee I(2023)ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered ClothesACM Transactions on Graphics10.1145/361837642:6(1-13)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3618376
Chen LGao LYang JXu SYe JZhang XLai Y(2023)Deep Deformation Detail Synthesis for Thin Shell ModelsComputer Graphics Forum10.1111/cgf.1490342:5Online publication date: 10-Aug-2023
https://doi.org/10.1111/cgf.14903
Zhu HGao ZXu L(2023)Cloth simulation based on neural network regression2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)10.1109/ICICML60161.2023.10424751(658-664)Online publication date: 3-Nov-2023
https://doi.org/10.1109/ICICML60161.2023.10424751
Kim JKim SLee J(2022)Geometry image super-resolution with AnisoCBConvNet architecture for efficient cloth modelingPLOS ONE10.1371/journal.pone.027243317:8(e0272433)Online publication date: 24-Aug-2022
https://doi.org/10.1371/journal.pone.0272433
Jin YChen ZLu YYang JLiu YShi Z(2022)Research on Multi-Precision Fabric Modeling Method Based on Machine LearningScientific Programming10.1155/2022/43390952022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4339095
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