  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. 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.
