B\'ezierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters

Understand

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 B\'ezier curve.
• We demonstrate the performance of our method in completing both synthetic and real-world generative tasks.