Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (3): 478-493.doi: 10.1007/s11390-021-1331-y

Special Issue: Computer Graphics and Multimedia

• Special Section of CVM 2021 • Previous Articles     Next Articles

Multi-Feature Super-Resolution Network for Cloth Wrinkle Synthesis

Lan Chen1,2, Member, CCF, Juntao Ye1, Member, CCF, and Xiaopeng Zhang1,3,*, Member, CCF, ACM, IEEE        

  1. 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
  • Received:2021-01-29 Revised:2021-04-27 Online:2021-05-05 Published:2021-05-31
  • Contact: Xiaopeng Zhang
  • About author:Lan Chen received her Bachelor's degree in mathematics from China University of Petroleum, Beijing, in 2016. She is currently a Ph.D. candidate of Institute of Automation, Chinese Academy of Sciences, Beijing. Her research interests include computer graphics, geometry processing and image processing, particularly synthesis of cloth animation.
  • Supported by:
    This work is 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, and Open Research Projects of Zhejiang Lab under Grant No. 2021KE0AB07.

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.

Key words: cloth animation; deep learning; multi-feature; super-resolution; wrinkle synthesis;

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