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Many real-world objects are designed by smooth curves, especially in thedomain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) andhydrodynamic shapes (e.g., hulls) are designed. To facilitate the designprocess of those objects, we propose a deep learning based generative modelthat can synthesize smooth curves. The model maps a low-dimensional latentrepresentation to a sequence of discrete points sampled from a rationalB\'ezier curve. We demonstrate the performance of our method in completing bothsynthetic and real-world generative tasks. Results show that our method cangenerate diverse and realistic curves, while preserving consistent shapevariation in the latent space, which is favorable for latent space designoptimization or design space exploration.

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