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

Im2Vec: Synthesizing Vector Graphics without Vector Supervision