TY  - JOUR
AU  - Chen, Lan
AU  - Ye, Juntao
AU  - Zhang, Xiaopeng
PY  - 2021
TI  - Multi-Feature Super-Resolution Network for Cloth Wrinkle Synthesis
JO  - Journal of Computer Science and Technology
SN  - 1000-9000
SP  - 478
EP  - 493
VL  - 36
IS  - 3
AB  - 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.
UR  - https://doi.org/10.1007/s11390-021-1331-y
DO  - 10.1007/s11390-021-1331-y