论文标题
DeepSVG：用于矢量图形动画的分层生成网络
DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation
论文作者
论文摘要
可扩展的向量图形（SVG）在现代2D接口中无处不在，因为它们可以扩展到不同的分辨率。但是，尽管将基于深度学习的模型成功地应用于栅格化图像，但向量图形表示学习和生成的问题在很大程度上尚未探索。在这项工作中，我们提出了一个新型的层次生成网络，称为DeepSVG，用于复杂的SVG图标生成和插值。我们的体系结构有效地将高级形状从编码形状本身的低级命令中解开。该网络直接以非自动回归方式预测一组形状。我们通过释放新的大型数据集以及用于SVG操纵的开源库来介绍复杂的SVG图标生成的任务。我们证明我们的网络学会了准确地重建各种矢量图形，并可以通过执行插值和其他潜在空间操作来充当强大的动画工具。我们的代码可在https://github.com/alexandre01/deepsvg上找到。
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.
