D-Cloth: Skinning-based Cloth Dynamic Prediction with a Three-stage Network

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dc.contributor.authorLi, Yu Dien_US
dc.contributor.authorTang, Minen_US
dc.contributor.authorChen, Xiao Ruien_US
dc.contributor.authorYang, Yunen_US
dc.contributor.authorTong, Ruo Fengen_US
dc.contributor.authorAn, Bai Linen_US
dc.contributor.authorYang, Shuang Caien_US
dc.contributor.authorLi, Yaoen_US
dc.contributor.authorKou, Qi Longen_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:34:08Z
dc.date.available2023-10-09T07:34:08Z
dc.date.issued2023
dc.description.abstractWe propose a three-stage network that utilizes a skinning-based model to accurately predict dynamic cloth deformation. Our approach decomposes cloth deformation into three distinct components: static, coarse dynamic, and wrinkle dynamic components. To capture these components, we train our three-stage network accordingly. In the first stage, the static component is predicted by constructing a static skinning model that incorporates learned joint increments and skinning weight increments. Then, in the second stage, the coarse dynamic component is added to the static skinning model by incorporating serialized skeleton information. Finally, in the third stage, the mesh sequence stage refines the prediction by incorporating the wrinkle dynamic component using serialized mesh information. We have implemented our network and used it in a Unity game scene, enabling real-time prediction of cloth dynamics. Our implementation achieves impressive prediction speeds of approximately 3.65ms using an NVIDIA GeForce RTX 3090 GPU and 9.66ms on an Intel i7-7700 CPU. Compared to SOTA methods, our network excels in accurately capturing fine dynamic cloth deformations.en_US
dc.description.number7
dc.description.sectionheadersCloth Simulation
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14937
dc.identifier.issn1467-8659
dc.identifier.pages13 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14937
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14937
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies -> Machine learning; Physical simulation
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.subjectPhysical simulation
dc.titleD-Cloth: Skinning-based Cloth Dynamic Prediction with a Three-stage Networken_US
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