  Vector graphic documents present visual elements in a resolution free,
compact format and are often seen in creative applications. In this work, we
attempt to learn a generative model of vector graphic documents. We define
vector graphic documents by a multi-modal set of attributes associated to a
canvas and a sequence of visual elements such as shapes, images, or texts, and
train variational auto-encoders to learn the representation of the documents.
We collect a new dataset of design templates from an online service that
features complete document structure including occluded elements. In
experiments, we show that our model, named CanvasVAE, constitutes a strong
baseline for generative modeling of vector graphic documents.
