Learning-Based Bending Stiffness Parameter Estimation by a Drape Tester

Real-world fabrics often possess complicated nonlinear, anisotropic bending stiffness properties. Measuring the physical parameters of such properties for physics-based simulation is difficult yet unnecessary, due to the persistent existence of numerical errors in simulation technology. In this work, we propose to adopt a simulation-in-the-loop strategy: instead of measuring the physical parameters, we estimate the simulation parameters to minimize the discrepancy between reality and simulation. This strategy offers good flexibility in test setups, but the associated optimization problem is computationally expensive to solve by numerical methods. Our solution is to train a regression-based neural network for inferring bending stiffness parameters, directly from drape features captured in the real world. Specifically, we choose the Cusick drape test method and treat multiple-view depth images as the feature vector. To effectively and efficiently train our network, we develop a highly expressive and physically validated bending stiffness model, and we use the traditional cantilever test to collect the parameters of this model for 618 real-world fabrics. Given the whole parameter data set, we then construct a parameter subspace, generate new samples within the sub-space, and finally simulate and augment synthetic data for training purposes. The experiment shows that our trained system can replace cantilever tests for quick, reliable and effective estimation of simulation-ready parameters. Thanks to the use of the system, our simulator can now faithfully simulate bending effects comparable to those in the real world.