Efficient Cloth Simulation using Miniature Cloth and Upscaling Deep Neural Networks
arXiv:1907.03953
Abstract
Cloth simulation requires a fast and stable method for interactively and realistically visualizing fabric materials using computer graphics. We propose an efficient cloth simulation method using miniature cloth simulation and upscaling Deep Neural Networks (DNN). The upscaling DNNs generate the target cloth simulation from the results of physically-based simulations of a miniature cloth that has similar physical properties to those of the target cloth. We have verified the utility of the proposed method through experiments, and the results demonstrate that it is possible to generate fast and stable cloth simulations under various conditions.
arXiv comment11 pages, 15 figures, 8 tables
References in corpus (5)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- Accelerating Eulerian Fluid Simulation With Convolutional Networks
- A Compositional Object-Based Approach to Learning Physical Dynamics
- DeepWarp: DNN-based Nonlinear Deformation