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|a 10.1109/TVCG.2019.2937301
|2 doi
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|a eng
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|a Montazeri, Zahra
|e verfasserin
|4 aut
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| 245 |
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|a Mechanics-Aware Modeling of Cloth Appearance
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|c 2021
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|a Text
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|a ƒaComputermedien
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|a Date Revised 25.11.2020
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Micro-appearance models have brought unprecedented fidelity and details to cloth rendering. Yet, these models neglect fabric mechanics: when a piece of cloth interacts with the environment, its yarn and fiber arrangement usually changes in response to external contact and tension forces. Since subtle changes of a fabric's microstructures can greatly affect its macroscopic appearance, mechanics-driven appearance variation of fabrics has been a phenomenon that remains to be captured. We introduce a mechanics-aware model that adapts the microstructures of cloth yarns in a physics-based manner. Our technique works on two distinct physical scales: using physics-based simulations of individual yarns, we capture the rearrangement of yarn-level structures in response to external forces. These yarn structures are further enriched to obtain appearance-driving fiber-level details. The cross-scale enrichment is made practical through a new parameter fitting algorithm for simulation, an augmented procedural yarn model coupled with a custom-design regression neural network. We train the network using a dataset generated by joint simulations at both the yarn and the fiber levels. Through several examples, we demonstrate that our model is capable of synthesizing photorealistic cloth appearance in a mechanically plausible way
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| 650 |
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|a Journal Article
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|a Xiao, Chang
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| 700 |
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|a Fei, Yun
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| 700 |
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|a Zheng, Changxi
|e verfasserin
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| 700 |
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|a Zhao, Shuang
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
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|g 27(2021), 1 vom: 22. Jan., Seite 137-150
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