Cloth and Skin Deformation with a Triangle Mesh Based Convolutional Neural Network

dc.contributor.authorChentanez, Nuttapongen_US
dc.contributor.authorMacklin, Milesen_US
dc.contributor.authorMüller, Matthiasen_US
dc.contributor.authorJeschke, Stefanen_US
dc.contributor.authorKim, Tae-Yongen_US
dc.contributor.editorBender, Jan and Popa, Tiberiuen_US
dc.description.abstractWe 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.en_US
dc.description.sectionheadersData-Driven Cloth
dc.description.seriesinformationComputer Graphics Forum
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectPhysical simulation
dc.subjectNeural networks
dc.titleCloth and Skin Deformation with a Triangle Mesh Based Convolutional Neural Networken_US