CanvasVAE: Learning to Generate Vector Graphic Documents

• URL: http://arxiv.org/abs/2108.01249v1
• Date: Tue, 3 Aug 2021 02:14:25 GMT
• Title: CanvasVAE: Learning to Generate Vector Graphic Documents
• Authors: Kota Yamaguchi
• Abstract summary: We learn a generative model of vector graphic documents using a dataset of design templates from an online service. In experiments, we show that our model, named CanvasVAE, constitutes a strong baseline for generative modeling of vector graphic documents.
• Score: 1.8478165393315746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vector graphic documents present visual elements in a resolution free, compact format and are often seen in creative applications. In this work, we attempt to learn a generative model of vector graphic documents. We define vector graphic documents by a multi-modal set of attributes associated to a canvas and a sequence of visual elements such as shapes, images, or texts, and train variational auto-encoders to learn the representation of the documents. We collect a new dataset of design templates from an online service that features complete document structure including occluded elements. In experiments, we show that our model, named CanvasVAE, constitutes a strong baseline for generative modeling of vector graphic documents.

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