BézierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters

Abstract

Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize smooth curves. The model maps a low-dimensional latent representation to a sequence of discrete points sampled from a rational Bézier curve. We demonstrate the performance of our method in completing both synthetic and real-world generative tasks. Results show that our method can generate diverse and realistic curves, while preserving consistent shape variation in the latent space, which is favorable for latent space design optimization or design space exploration.

Cited by in corpus (5)

• MO-PaDGAN: Reparameterizing Engineering Designs for Augmented Multi-objective Optimization
• 3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders
• Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks
• Generating various airfoil shapes with required lift coefficient using conditional variational autoencoders
• MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement