DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation

• URL: http://arxiv.org/abs/2007.11301v3
• Date: Thu, 22 Oct 2020 14:31:42 GMT
• Title: DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
• Authors: Alexandre Carlier, Martin Danelljan, Alexandre Alahi, Radu Timofte
• Abstract summary: We propose a novel hierarchical generative network, called DeepSVG, for complex SVG icons generation and manipulation. Our architecture effectively disentangles high-level shapes from the low-level commands that encode the shape itself. We demonstrate that our network learns to accurately reconstruct diverse vector graphics, and can serve as a powerful animation tool.
• Score: 217.86315551526235
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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