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Multi-Feature Super-Resolution Network for Cloth wrinkle synthesis

Journal of Computer Science & Technology 2021年第3期36卷 478-493页

作者： Lan Chen Juntao Ye Xiaopeng Zhang National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Zhejiang Lab Hangzhou 311121China

Existing physical cloth simulators suffer from expensive computation and difficulties in tuning mechanical parameters to get desired wrinkling ***-driven methods provide an alternative *** typically synthesize cloth animation at a much lower computational cost,and also create wrinkling effects that are similar to the training *** this paper we propose a deep learning based method for synthesizing cloth animation with high resolution *** do this we first create a dataset for training:a pair of low and high resolution meshes are simulated and their motions are *** a result the two meshes exhibit similar large-scale deformation but different small *** 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 *** these image pairs,we design a multi-feature super-resolution(MFSR)network that jointly trains an upsampling synthesizer for the three *** MFSR architecture consists of shared and task-specific layers to learn multi-level features when super-resolving three descriptors ***-to-frame consistency is well maintained thanks to the proposed kinematics-based loss *** method achieves realistic results at high frame rates:12-14 times faster than traditional physical *** 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

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