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

by Yudi Li1, Min Tang1, Yun Yang1, Zi Huang1, Ruofeng Tong1, Shuangcai Yang3, Yao Li3, and Dinesh Manocha2

1 - Zhejiang University, China

2 - University of Maryland at College Park, USA

3 - Tencent

Results: Given the initial template of the cloth mesh and the target obstacle mesh, our network can predicate a plausible target 3D cloth mesh for general scenes. We highlight (a) the final cloth mesh wrapped around a bunny; (b) the draping jacket on a non-SMPL human body; (c) the t-shirt deformation on a SMPL human body; (d) a human dressed in a robe represented by 100K triangles. All predicted meshes are different from the datasets used for training. Our approach runs at 30 − 45fps on an NVIDIA GeForce RTX 3090 GPU.


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.


Paper  (PDF 11.6 MB)

Yudi Li, Min Tang, Yun Yang, Zi Huang, Ruofeng Tong, Shuangcai Yang, Yao Li, Dinesh Manocha, N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks, Computer Graphics Forum, 41(2): 547-558 (Proceedings of Eurographics 2022), 2022.

      author = {Li, Yudi and Tang, Min and Yang, Yun and Huang, Zi and Tong, Ruofeng and Yang, Shuangcai and Li, Yao and Manocha, Dinesh},
      title = {{N-Cloth}: Predicting {3D} Cloth Deformation with Mesh-Based Networks},
      journal = {Computer Graphics Forum (Proceedings of Eurographics)},
      volume = {41},
      number = {2},
      pages = {547--558},
      month = May,
      year = {2022},




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This work is supported in part by the National Natural Science Foundation of China under Grant No.: 61972341, Grant No.: 61972342, Grant No.: 61732015, and the Tencent-Zhejiang University joint laboratory.