arXiv · 2306.00800
FigGen: Text to Scientific Figure Generation
Abstract
The generative modeling landscape has experienced tremendous growth in recent years, particularly in generating natural images and art. Recent techniques have shown impressive potential in creating complex visual compositions while delivering impressive realism and quality. However, state-of-the-art methods have been focusing on the narrow domain of natural images, while other distributions remain unexplored. In this paper, we introduce the problem of text-to-figure generation, that is creating scientific figures of papers from text descriptions. We present FigGen, a diffusion-based approach for text-to-figure as well as the main challenges of the proposed task. Code and models are available at https://github.com/joanrod/figure-diffusion
Explore related subjects
Keep this discovery
Juan A Rodriguez, David Vazquez, Issam Laradji, Marco Pedersoli, Pau Rodriguez. 2023-06-01. FigGen: Text to Scientific Figure Generation. https://arxiv.org/abs/2306.00800
Cite the original work for its findings. Save a collection to share your selection of sources.