Supervised Video Cloth Simulation: Exploring Softness and Stiffness Variations on Fabric Types Using Deep Learning Article Swipe
Makara Mao
,
Hongly Va
,
Ahyoung Lee
,
Min Hong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13179505
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13179505
Physically based cloth simulation requires a model that represents cloth as a collection of nodes connected by different types of constraints. In this paper, we present a coefficient prediction framework using a Deep Learning (DL) technique to enhance video summarization for such simulations. Our proposed model represents virtual cloth as interconnected nodes that are subject to various constraints. To ensure temporal consistency, we train the video coefficient prediction using Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM), and Transformer models. Our lightweight video coefficient network combines Convolutional Neural Networks (CNN) and a Transformer to capture both local and global contexts, thus enabling highly efficient prediction of keyframe importance scores for short-length videos. We evaluated our proposed model and found that it achieved an average accuracy of 99.01%. Specifically, the accuracy for the coefficient prediction of GRU was 20%, while LSTM achieved an accuracy of 59%. Our methodology leverages various cloth simulations that utilize a mass-spring model to generate datasets representing cloth movement, thus allowing for the accurate prediction of the coefficients for virtual cloth within physically based simulations. By taking specific material parameters as input, our model successfully outputs a comprehensive set of geometric and physical properties for each cloth instance. This innovative approach seamlessly integrates DL techniques with physically based simulations, and it therefore has a high potential for use in modeling complex systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13179505
- https://www.mdpi.com/2076-3417/13/17/9505/pdf?version=1692753787
- OA Status
- gold
- Cited By
- 5
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386068498
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386068498Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app13179505Digital Object Identifier
- Title
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Supervised Video Cloth Simulation: Exploring Softness and Stiffness Variations on Fabric Types Using Deep LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-22Full publication date if available
- Authors
-
Makara Mao, Hongly Va, Ahyoung Lee, Min HongList of authors in order
- Landing page
-
https://doi.org/10.3390/app13179505Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/13/17/9505/pdf?version=1692753787Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/13/17/9505/pdf?version=1692753787Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Automatic summarization, Deep learning, Convolutional neural networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Term | 74 |
| abstract_inverted_index.This | 201 |
| abstract_inverted_index.Unit | 71 |
| abstract_inverted_index.both | 95 |
| abstract_inverted_index.each | 198 |
| abstract_inverted_index.high | 217 |
| abstract_inverted_index.such | 41 |
| abstract_inverted_index.that | 7, 52, 119, 151 |
| abstract_inverted_index.this | 22 |
| abstract_inverted_index.thus | 100, 162 |
| abstract_inverted_index.with | 208 |
| abstract_inverted_index.(CNN) | 89 |
| abstract_inverted_index.Gated | 69 |
| abstract_inverted_index.based | 1, 176, 210 |
| abstract_inverted_index.cloth | 2, 9, 48, 149, 160, 173, 199 |
| abstract_inverted_index.found | 118 |
| abstract_inverted_index.local | 96 |
| abstract_inverted_index.model | 6, 45, 116, 155, 186 |
| abstract_inverted_index.nodes | 14, 51 |
| abstract_inverted_index.train | 63 |
| abstract_inverted_index.types | 18 |
| abstract_inverted_index.using | 30, 68 |
| abstract_inverted_index.video | 38, 65, 82 |
| abstract_inverted_index.while | 138 |
| abstract_inverted_index.(GRU), | 72 |
| abstract_inverted_index.Memory | 75 |
| abstract_inverted_index.Neural | 87 |
| abstract_inverted_index.ensure | 59 |
| abstract_inverted_index.global | 98 |
| abstract_inverted_index.highly | 102 |
| abstract_inverted_index.input, | 184 |
| abstract_inverted_index.paper, | 23 |
| abstract_inverted_index.scores | 108 |
| abstract_inverted_index.taking | 179 |
| abstract_inverted_index.within | 174 |
| abstract_inverted_index.(LSTM), | 76 |
| abstract_inverted_index.99.01%. | 126 |
| abstract_inverted_index.average | 123 |
| abstract_inverted_index.capture | 94 |
| abstract_inverted_index.complex | 223 |
| abstract_inverted_index.enhance | 37 |
| abstract_inverted_index.models. | 79 |
| abstract_inverted_index.network | 84 |
| abstract_inverted_index.outputs | 188 |
| abstract_inverted_index.present | 25 |
| abstract_inverted_index.subject | 54 |
| abstract_inverted_index.utilize | 152 |
| abstract_inverted_index.various | 56, 148 |
| abstract_inverted_index.videos. | 111 |
| abstract_inverted_index.virtual | 47, 172 |
| abstract_inverted_index.Learning | 33 |
| abstract_inverted_index.Networks | 88 |
| abstract_inverted_index.accuracy | 124, 129, 142 |
| abstract_inverted_index.accurate | 166 |
| abstract_inverted_index.achieved | 121, 140 |
| abstract_inverted_index.allowing | 163 |
| abstract_inverted_index.approach | 203 |
| abstract_inverted_index.combines | 85 |
| abstract_inverted_index.datasets | 158 |
| abstract_inverted_index.enabling | 101 |
| abstract_inverted_index.generate | 157 |
| abstract_inverted_index.keyframe | 106 |
| abstract_inverted_index.material | 181 |
| abstract_inverted_index.modeling | 222 |
| abstract_inverted_index.physical | 195 |
| abstract_inverted_index.proposed | 44, 115 |
| abstract_inverted_index.requires | 4 |
| abstract_inverted_index.specific | 180 |
| abstract_inverted_index.systems. | 224 |
| abstract_inverted_index.temporal | 60 |
| abstract_inverted_index.Recurrent | 70 |
| abstract_inverted_index.connected | 15 |
| abstract_inverted_index.contexts, | 99 |
| abstract_inverted_index.different | 17 |
| abstract_inverted_index.efficient | 103 |
| abstract_inverted_index.evaluated | 113 |
| abstract_inverted_index.framework | 29 |
| abstract_inverted_index.geometric | 193 |
| abstract_inverted_index.instance. | 200 |
| abstract_inverted_index.leverages | 147 |
| abstract_inverted_index.movement, | 161 |
| abstract_inverted_index.potential | 218 |
| abstract_inverted_index.technique | 35 |
| abstract_inverted_index.therefore | 214 |
| abstract_inverted_index.Long-Short | 73 |
| abstract_inverted_index.Physically | 0 |
| abstract_inverted_index.collection | 12 |
| abstract_inverted_index.importance | 107 |
| abstract_inverted_index.innovative | 202 |
| abstract_inverted_index.integrates | 205 |
| abstract_inverted_index.parameters | 182 |
| abstract_inverted_index.physically | 175, 209 |
| abstract_inverted_index.prediction | 28, 67, 104, 133, 167 |
| abstract_inverted_index.properties | 196 |
| abstract_inverted_index.represents | 8, 46 |
| abstract_inverted_index.seamlessly | 204 |
| abstract_inverted_index.simulation | 3 |
| abstract_inverted_index.techniques | 207 |
| abstract_inverted_index.Transformer | 78, 92 |
| abstract_inverted_index.coefficient | 27, 66, 83, 132 |
| abstract_inverted_index.lightweight | 81 |
| abstract_inverted_index.mass-spring | 154 |
| abstract_inverted_index.methodology | 146 |
| abstract_inverted_index.simulations | 150 |
| abstract_inverted_index.coefficients | 170 |
| abstract_inverted_index.consistency, | 61 |
| abstract_inverted_index.constraints. | 20, 57 |
| abstract_inverted_index.representing | 159 |
| abstract_inverted_index.short-length | 110 |
| abstract_inverted_index.simulations, | 211 |
| abstract_inverted_index.simulations. | 42, 177 |
| abstract_inverted_index.successfully | 187 |
| abstract_inverted_index.Convolutional | 86 |
| abstract_inverted_index.Specifically, | 127 |
| abstract_inverted_index.comprehensive | 190 |
| abstract_inverted_index.summarization | 39 |
| abstract_inverted_index.interconnected | 50 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5006130083 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I24541011 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.7640516 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |