  Computer-Aided Design (CAD) applications are used in manufacturing to model
everything from coffee mugs to sports cars. These programs are complex and
require years of training and experience to master. A component of all CAD
models particularly difficult to make are the highly structured 2D sketches
that lie at the heart of every 3D construction. In this work, we propose a
machine learning model capable of automatically generating such sketches.
Through this, we pave the way for developing intelligent tools that would help
engineers create better designs with less effort. Our method is a combination
of a general-purpose language modeling technique alongside an off-the-shelf
data serialization protocol. We show that our approach has enough flexibility
to accommodate the complexity of the domain and performs well for both
unconditional synthesis and image-to-sketch translation.
