Summary of Svgfusion: Scalable Text-to-svg Generation Via Vector Space Diffusion, by Ximing Xing et al.
SVGFusion: Scalable Text-to-SVG Generation via Vector Space Diffusion
by Ximing Xing, Juncheng Hu, Jing Zhang, Dong Xu, Qian Yu
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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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 The SVGFusion model generates Scalable Vector Graphics (SVG) assets from textual data, addressing the challenge of creating high-quality vector datasets. The Text-to-SVG framework consists of two modules: Vector-Pixel Fusion Variational Autoencoder (VP-VAE) and Vector Space Diffusion Transformer (VS-DiT). VP-VAE learns a continuous latent space for vector graphics, while VS-DiT generates a latent code based on the text prompt. A novel rendering sequence modeling strategy is proposed to embed construction logics in SVGs, enabling human-like design capabilities. The model achieves enhanced quality and generalizability compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces SVGFusion, a Text-to-SVG model that generates high-quality vector graphics from textual data. The model uses two modules: VP-VAE and VS-DiT. It learns a continuous latent space for vector graphics and can generate SVGs based on text prompts. This makes it possible to create human-like designs in vector graphics. The paper also proposes a new way of creating SVGs that is more scalable and prevents occlusion in complex graphic compositions. |
Keywords
» Artificial intelligence » Diffusion » Latent space » Prompt » Transformer » Variational autoencoder » Vector space
