CanvasVAE: Learning to Generate Vector Graphic Documents
Kota Yamaguchi

TL;DR
CanvasVAE introduces a generative model for vector graphic documents, capturing their complex structure and attributes, and provides a new dataset for training and evaluation in this domain.
Contribution
We propose CanvasVAE, a variational auto-encoder that models vector graphic documents with multi-modal attributes and structure, along with a new dataset of design templates.
Findings
CanvasVAE effectively generates vector graphic documents.
The model captures complex document structures and occluded elements.
Our dataset enables comprehensive training and evaluation.
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHandwritten Text Recognition Techniques · Advanced Image and Video Retrieval Techniques · Image Processing and 3D Reconstruction
Methodstravel james
