DeepAI AI Chat
Log In Sign Up

FigGen: Text to Scientific Figure Generation

by   Juan A. Rodriguez, et al.

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


page 5

page 6


Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models

Text-to-Image diffusion models have made tremendous progress over the pa...

Versatile Diffusion: Text, Images and Variations All in One Diffusion Model

The recent advances in diffusion models have set an impressive milestone...

If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection

Despite their impressive capabilities, diffusion-based text-to-image (T2...

Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion

Diffusion-based generative models' impressive ability to create convinci...

Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps

Despite recent advancements in image generation, diffusion models still ...

Alchemy: Techniques for Rectification Based Irregular Scene Text Recognition

Reading text from natural images is challenging due to the great variety...

OCR-VQGAN: Taming Text-within-Image Generation

Synthetic image generation has recently experienced significant improvem...