  Vector graphics are widely used to represent fonts, logos, digital artworks,
and graphic designs. But, while a vast body of work has focused on generative
algorithms for raster images, only a handful of options exists for vector
graphics. One can always rasterize the input graphic and resort to image-based
generative approaches, but this negates the advantages of the vector
representation. The current alternative is to use specialized models that
require explicit supervision on the vector graphics representation at training
time. This is not ideal because large-scale high quality vector-graphics
datasets are difficult to obtain. Furthermore, the vector representation for a
given design is not unique, so models that supervise on the vector
representation are unnecessarily constrained. Instead, we propose a new neural
network that can generate complex vector graphics with varying topologies, and
only requires indirect supervision from readily-available raster training
images (i.e., with no vector counterparts). To enable this, we use a
differentiable rasterization pipeline that renders the generated vector shapes
and composites them together onto a raster canvas. We demonstrate our method on
a range of datasets, and provide comparison with state-of-the-art SVG-VAE and
DeepSVG, both of which require explicit vector graphics supervision. Finally,
we also demonstrate our approach on the MNIST dataset, for which no groundtruth
vector representation is available. Source code, datasets, and more results are
available at geometry.cs.ucl.ac.uk/projects/2021/Im2Vec/
