26039

N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks

Yudi Li, Min Tang, Yun Yang, Zi Huang, Ruofeng Tong, Shuangcai Yang, Yao Li, Dinesh Manocha
Zhejiang University
arXiv:2112.06397 [cs.GR], (13 Dec 2021)

@misc{li2021ncloth,

   title={N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks},

   author={Yudi Li and Min Tang and Yun Yang and Zi Huang and Ruofeng Tong and Shuangcai Yang and Yao Li and Dinesh Manocha},

   year={2021},

   eprint={2112.06397},

   archivePrefix={arXiv},

   primaryClass={cs.GR}

}

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We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topology. We use graph convolution to transform the cloth and object meshes into a latent space to reduce the non-linearity in the mesh space. Our network can predict the target 3D cloth mesh deformation based on the state of the initial cloth mesh template and the target obstacle mesh. Our approach can handle complex cloth meshes with up to 100K triangles and scenes with various objects corresponding to SMPL humans, Non-SMPL humans, or rigid bodies. In practice, our approach demonstrates good temporal coherence between successive input frames and can be used to generate plausible cloth simulation at 30−45 fps on an NVIDIA GeForce RTX 3090 GPU. We highlight its benefits over prior learning-based methods and physically-based cloth simulators.
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