  Analysis of human sketches in deep learning has advanced immensely through
the use of waypoint-sequences rather than raster-graphic representations. We
further aim to model sketches as a sequence of low-dimensional parametric
curves. To this end, we propose an inverse graphics framework capable of
approximating a raster or waypoint based stroke encoded as a point-cloud with a
variable-degree B\'ezier curve. Building on this module, we present
Cloud2Curve, a generative model for scalable high-resolution vector sketches
that can be trained end-to-end using point-cloud data alone. As a consequence,
our model is also capable of deterministic vectorization which can map novel
raster or waypoint based sketches to their corresponding high-resolution
scalable B\'ezier equivalent. We evaluate the generation and vectorization
capabilities of our model on Quick, Draw! and K-MNIST datasets.
