Summary of Vector Grimoire: Codebook-based Shape Generation Under Raster Image Supervision, by Moritz Feuerpfeil et al.
Vector Grimoire: Codebook-based Shape Generation under Raster Image Supervision
by Moritz Feuerpfeil, Marco Cipriano, Gerard de Melo
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel generative model, GRIMOIRE, is introduced to tackle the challenge of scalable vector graphics (SVG) generation from natural language. The model consists of two modules: Visual Shape Quantizer (VSQ), which maps raster images to a discrete codebook, and Auto-Regressive Transformer (ART), which models the joint probability distribution over shape tokens, positions, and textual descriptions. Unlike existing approaches that rely on direct SVG supervision, GRIMOIRE learns shape image patches using only raster image supervision, enabling vector generative modeling with significantly more data. The model is demonstrated to surpass previous methods in terms of generative quality and flexibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GRIMOIRE is a new way to create pictures from words. It uses two parts: one that takes a picture and breaks it down into simple shapes, and another that uses those shapes to make a new picture based on what someone says. This means we can make pictures using only text, without needing to start with an existing SVG picture. The model is tested on creating filled shapes from handwritten numbers and outline strokes for icons and fonts, showing better results than other methods. |
Keywords
» Artificial intelligence » Generative model » Probability » Transformer
