2022
DOI: 10.1111/cgf.14651
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Detail‐Aware Deep Clothing Animations Infused with Multi‐Source Attributes

Abstract: This paper presents a novel learning-based clothing deformation method to generate rich and reasonable detailed deformations for garments worn by bodies of various shapes in various animations. In contrast to existing learning-based methods, which require numerous trained models for different garment topologies or poses and are unable to easily realize rich details, we use a unified framework to produce high fidelity deformations efficiently and easily. Specifically, we first found that the fit between the gar… Show more

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Cited by 4 publications
(1 citation statement)
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“…Ref. [18] uses the skinning body mesh directly on the target pose as the initial state and learns using a graph-attention-based network to predict the residual between the initial state and the final deformed cloth mesh with wrinkles. These methods use the vertices on the unposed template body mesh or posed target body mesh as the initial state of the deformed cloth mesh and train different networks to fit the residuals with respect to the ground truth.…”
Section: Smpl-based Learning Algorithmmentioning
confidence: 99%
“…Ref. [18] uses the skinning body mesh directly on the target pose as the initial state and learns using a graph-attention-based network to predict the residual between the initial state and the final deformed cloth mesh with wrinkles. These methods use the vertices on the unposed template body mesh or posed target body mesh as the initial state of the deformed cloth mesh and train different networks to fit the residuals with respect to the ground truth.…”
Section: Smpl-based Learning Algorithmmentioning
confidence: 99%