  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.
