Mechanics-Aware Modeling of Cloth Appearance
Mechanics-Aware Modeling of Cloth Appearance
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DOI：
10.1109/tvcg.2019.2937301
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发表时间：
2019-04
影响因子：
5.2
通讯作者：
Z. Montazeri;Chang Xiao;Yun Fei;Changxi Zheng;Shuang Zhao
中科院分区：
文献类型：
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作者：
Z. Montazeri;Chang Xiao;Yun Fei;Changxi Zheng;Shuang Zhao
微外观模型为布料渲染带来了前所未有的逼真度和细节。然而，这些模型忽略了织物力学：当一块布与环境相互作用时，其纱线和纤维排列通常会随着外部接触和张力的变化而变化。由于织物微观结构的细微变化会对其宏观外观产生很大影响，因此力学驱动的织物外观变化一直是一个有待捕捉的现象。我们介绍了一个力学感知模型，该模型以基于物理的方式适应布线的微观结构。我们的技术在两个不同的物理尺度上工作：使用基于物理的单个纱线的模拟，我们捕捉到纱线水平结构在外力作用下的重新排列。这些纱线结构进一步丰富，以获得外观驱动的纤维级细节。通过一种新的模拟参数拟合算法，将增广程序纱线模型与定制设计的回归神经网络相结合，使跨尺度浓缩变得可行。我们使用由纱线和纤维水平的联合模拟生成的数据集来训练网络。通过几个例子，我们证明了我们的模型能够以一种机械合理的方式合成出照片级真实感的布料外观。
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.