DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
This work addresses the need for scalable vector graphics tools in 2D interfaces, offering a novel method for SVG generation and animation, though it is incremental in applying deep learning to vector graphics.
The paper tackles the problem of vector graphics representation learning and generation, which is largely unexplored, by proposing DeepSVG, a hierarchical generative network for complex SVG icons generation and interpolation, achieving accurate reconstruction and enabling animation through latent space operations.
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.