Differentiable Rasterization for Vector Graphics

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Added to collectionTop 100 Deep Learning and AI Papers 2021

Differentiable Rasterization for Vector Graphics

Im2Vec: Synthesizing Vector Graphics without Vector Supervision

Vector graphics are widely used to represent fonts, logos, digital artworks,and graphic designs.

We propose a new neuralnetwork 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 models that require explicit vector graphics supervision at training time.

Finally, we also demonstrate our approach on the mnist dataset, for which no groundtruth vector representation is available.

Authors

Pradyumna Reddy, Michael Gharbi, Michal Lukac, Niloy J. Mitra

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