Towards Brittle Fracture Simulation Based on Deep Learning: Fracture Shape Prediction of Plane Objects Using Conditional GANs

Abstract

Brittle fracture of plane shape objects, such as glass and concrete, is often seen in the real world. Fracture animation of rigid bodies provides impressive effects by using physics-oriented simulation. However, simulation costs become too high when physics-oriented simulation approaches are chosen to generate realistic fracture animation. On the other hand, pre-fractured patterns with Voronoi-based segmentation applied when a collision occurs are usually used in real-time applications such as games. The geometry of such patterns is however monotonous and is hard to represent complicated fracture shapes realistically. There is thus a lack of trade-off methods that can realize realistic features as well as low computational costs. In this paper, we propose an alternative machine learning scheme based on conditional Generative Adversarial Network (cGAN) techniques to replace Voronoi-based segmentation for plane shape objects, which can be applied to rigid body engines such as Bullet Physics. Our learning datasets are generated by Boundary Element Method (BEM) based-fracture simulation. Compared with Voronoi-based segmentation, our method can generate much more complicated fracture shapes realistically at reasonable computational costs.