Multi-Feature Super-Resolution Network for Cloth Wrinkle Synthesis  

作　　者：Lan Chen Juntao Ye Xiaopeng Zhang 

机构地区：[1]National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China [2]School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China [3]Zhejiang Lab,Hangzhou 311121,China

出　　处：《Journal of Computer Science & Technology》2021年第3期478-493,共16页计算机科学技术学报（英文版）

基　　金：supported by the National Key Research and Development Program of China under Grant No.2018YFB2100602;the National Natural Science Foundation of China under Grant Nos.61972459,61971418 and 62071157;Open Research Projects of Zhejiang Lab under Grant No.2021KE0AB07.

摘　　要：Existing physical cloth simulators suffer from expensive computation and difficulties in tuning mechanical parameters to get desired wrinkling behaviors.Data-driven methods provide an alternative solution.They typically synthesize cloth animation at a much lower computational cost,and also create wrinkling effects that are similar to the training data.In this paper we propose a deep learning based method for synthesizing cloth animation with high resolution meshes.To do this we first create a dataset for training:a pair of low and high resolution meshes are simulated and their motions are synchronized.As a result the two meshes exhibit similar large-scale deformation but different small wrinkles.Each simulated mesh pair is then converted into a pair of low-and high-resolution"images"(a 2D array of samples),with each image pixel being interpreted as any of three descriptors:the displacement,the normal and the velocity.With these image pairs,we design a multi-feature super-resolution(MFSR)network that jointly trains an upsampling synthesizer for the three descriptors.The MFSR architecture consists of shared and task-specific layers to learn multi-level features when super-resolving three descriptors simultaneously.Frame-to-frame consistency is well maintained thanks to the proposed kinematics-based loss function.Our method achieves realistic results at high frame rates:12-14 times faster than traditional physical simulation.We demonstrate the performance of our method with various experimental scenes,including a dressed character with sophisticated collisions.

关 键 词：cloth animation deep learning multi-feature SUPER-RESOLUTION wrinkle synthesis 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]