N‐Cloth: Predicting 3D Cloth Deformation with Mesh‐Based Networks. (24th May 2022)

• Record Type: Journal Article
• Authors: D. Li, Y.; Tang, M.; Yang, Y.; Huang, Z.; F. Tong, R.; Yang, S.C.; Li, Y.; Manocha, D.
• Is Part Of: Computer graphics forum. Volume 41:Number 2(2022)
• Page Start: 547
• Page End: 558
• Publication Date: 2022-05-24
• Subjects: CCS Concepts -- Computing methodologies → Machine learning -- Physical simulation; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.14493
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: 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 topologies. 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 initial state of the cloth mesh template and the target obstacle mesh. Our approach can handle complex cloth meshes with up to 100 K triangles and scenes with various objects corresponding to SMPL humans, non‐SMPL humans or rigid bodies. In practice, our approach 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.