Chentanez, NuttapongMacklin, MilesMüller, MatthiasJeschke, StefanKim, Tae-YongBender, Jan and Popa, Tiberiu2020-10-162020-10-1620201467-8659https://doi.org/10.1111/cgf.14107https://diglib.eg.org:443/handle/10.1111/cgf14107We introduce a triangle mesh based convolutional neural network. The proposed network structure can be used for problems where input and/or output are defined on a manifold triangle mesh with or without boundary. We demonstrate its applications in cloth upsampling, adding back details to Principal Component Analysis (PCA) compressed cloth, regressing clothing deformation from character poses, and regressing hand skin deformation from bones' joint angles. The data used for training in this work are generated from high resolution extended position based dynamics (XPBD) physics simulations with small time steps and high iteration counts and from an offline FEM simulator, but it can come from other sources. The inference time of our prototype implementation, depending on the mesh resolution and the network size, can provide between 4 to 134 times faster than a GPU based simulator. The inference also only needs to be done for meshes currently visible by the camera.Computing methodologiesPhysical simulationNeural networks10.1111/cgf.14107123-134