  The study of neural generative models of human sketches is a fascinating
contemporary modeling problem due to the links between sketch image generation
and the human drawing process. The landmark SketchRNN provided breakthrough by
sequentially generating sketches as a sequence of waypoints. However this leads
to low-resolution image generation, and failure to model long sketches. In this
paper we present B\'ezierSketch, a novel generative model for fully vector
sketches that are automatically scalable and high-resolution. To this end, we
first introduce a novel inverse graphics approach to stroke embedding that
trains an encoder to embed each stroke to its best fit B\'ezier curve. This
enables us to treat sketches as short sequences of paramaterized strokes and
thus train a recurrent sketch generator with greater capacity for longer
sketches, while producing scalable high-resolution results. We report
qualitative and quantitative results on the Quick, Draw! benchmark.
