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DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
Alexandre Carlier · Martin Danelljan · Alexandre Alahi · Radu Timofte

Mon Dec 07 09:00 PM -- 11:00 PM (PST) @ Poster Session 0 #109

Scalable Vector Graphics (SVG) are ubiquitous in modern 2D interfaces due to their ability to scale to different resolutions. However, despite the success of deep learning-based models applied to rasterized images, the problem of vector graphics representation learning and generation remains largely unexplored. In this work, we propose a novel hierarchical generative network, called DeepSVG, for complex SVG icons generation and interpolation. Our architecture effectively disentangles high-level shapes from the low-level commands that encode the shape itself. The network directly predicts a set of shapes in a non-autoregressive fashion. We introduce the task of complex SVG icons generation by releasing a new large-scale dataset along with an open-source library for SVG manipulation. We demonstrate that our network learns to accurately reconstruct diverse vector graphics, and can serve as a powerful animation tool by performing interpolations and other latent space operations. Our code is available at https://github.com/alexandre01/deepsvg.

Author Information

Alexandre Carlier (Litso AI | ETH Zurich)

Founder of Litso AI. Creator of DeepSVG. Previously ML at Amazon, CS from ETH Zurich.

Martin Danelljan (ETH Zurich)
Alexandre Alahi (EPFL)
Radu Timofte (ETH Zurich)

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