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FigGen: Text to Scientific Figure Generation

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arxiv 2306.00800 v3 pith:SQATAQSV submitted 2023-06-01 cs.CV cs.AI

FigGen: Text to Scientific Figure Generation

Juan A Rodriguez , David Vazquez , Issam Laradji , Marco Pedersoli , Pau Rodriguez This is my paper
classification cs.CV cs.AI
keywords creatingfiggengenerationimagesimpressivenaturalrecentscientific
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GENFIG1: Visual Summaries of Scholarly Work as a Challenge for Vision-Language Models

    cs.CV 2026-04 unverdicted novelty 7.0

    GENFIG1 is a new benchmark that tests whether vision-language models can create effective Figure 1 visuals capturing the central scientific idea from paper text.

  2. SciForma: Structure-Faithful Generation of Scientific Diagrams

    cs.CV 2026-07 conditional novelty 6.0

    A 9B open-weights model trained with axis-decomposed conjunctive preference optimization (M-DPO) and a structural inventory beats GPT-Image-1.5 on scientific-diagram structural-fidelity benchmarks.

  3. S1-Omni-Image: A Unified Model for Scientific Image Understanding, Generation, and Editing

    cs.CV 2026-06 unverdicted novelty 4.0

    S1-Omni-Image unifies scientific image understanding, generation and editing via a think-before-generate paradigm on top of S1-VL-32B, trained on a 314K-sample SciGenEdit dataset, and reports SOTA results on multiple ...