Foreword to the special section on the international conference on computer-aided design and computer graphics (CAD/Graphics) 2019

Tong, Xin; Huang, Jin

doi:10.1016/j.cag.2019.12.002

Cited by 1 publication

(1 citation statement)

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“…The spring coefficients, also known as stiffness constants, are used to model the physical properties of the cloth, such as its elasticity and rigidity. The mass of each point is also considered, as it affects the motion of the cloth under external forces such as gravity and wind [18].…”

Section: Introductionmentioning

confidence: 99%

Coefficient Prediction for Physically-based Cloth Simulation Using Deep learning

Mao,

Va,

Hong

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Physically-based cloth simulation involves modeling cloth as a collection of particles or nodes connected by various types of constraints. These particles interact with each other and the environment, such as gravity or collisions, to accurately simulate the cloth's behavior. One essential component of such simulations is the set of material parameters or coefficients that dictate the cloth's physical properties, such as stiffness and damping. Deep learning-based coefficient prediction in physically-based cloth simulation involves using machine learning techniques, specifically deep neural networks, to predict the material parameters of cloth from its geometric and physical properties. The deep learning model is trained using a dataset of simulated cloth instances, where the material parameters are known. The input to the model is a set of geometric and physical properties of the cloth, such as the dimensions, orientation, and velocity. The output of the model is the set of material parameters that best represent the cloth's behavior under these conditions. This paper proposes a deep learning method for predicting these coefficients using a multi-label video classification approach. The training data is generated from a physics-based simulator, and the method is evaluated on some cloth simulations, such as fabric falling down, fabric with collision, and fabric affected by airflow. The cloth movement dataset is generated from a mass-spring-based simulation. The results show that the transformer model has much higher accuracy than other models. This study provides a promising approach for predicting the coefficients of virtual cloth in physically-based simulations.

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Section: Introductionmentioning

confidence: 99%