CTSN: Predicting cloth deformation for skeleton-based characters with a two-stream skinning network

作     者：Yudi Li Min Tang Yun Yang Ruofeng Tong Shuangcai Yang Yao Li Bailin An Qilong Kou 

作者机构：College of Computer Science and TechnologyZhejiang UniversityHangzhou 310058China Aurora StudiosTencentShenzhen 518057China 

出 版 物：《Computational Visual Media》 (计算可视媒体（英文版）)

年 卷 期：2024年第10卷第3期

页      面：471-485页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported in part by grants from the National Natural Science Foundation of China(61972341,61972342,61732015) the Tencent–Zhejiang University Joint Laboratory 

主　　题：cloth deformation learning network skinning 

摘      要：We present a novel learning method using a two-stream network to predict cloth deformation for skeleton-based *** characters processed in our approach are not limited to humans,and can be other targets with skeleton-based representations such asfish or *** use a novel network architecture which consists of skeleton-based and mesh-based residual networks to learn the coarse features and wrinkle features forming the overall residual from the template cloth *** network may be used to predict the deformation for loose or tight-fitting *** memory footprint of our network is low,thereby resulting in reduced computational *** practice,a prediction for a single cloth mesh for a skeleton-based character takes about 7 ms on an nVidia GeForce RTX 3090 *** to prior methods,our network can generate finer deformation results with details and wrinkles.