  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
