# Infographics Wizard: Flexible Infographics Authoring and Design Exploration

arXiv: https://arxiv.org/abs/2204.09904 · HTML: https://mlsanity.com/paper/2204.09904/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks](https://mlsanity.com/paper/1804.03126/) (2018)
- [VizML: A Machine Learning Approach to Visualization Recommendation](https://mlsanity.com/paper/1808.04819/) (2018)
- [Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements](https://mlsanity.com/paper/1907.09091/) (2019)
- [Predicting Visual Importance Across Graphic Design Types](https://mlsanity.com/paper/2008.02912/) (2020)
- [Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline](https://mlsanity.com/paper/1907.13550/) (2019)
- [InfoColorizer: Interactive Recommendation of Color Palettes for Infographics](https://mlsanity.com/paper/2102.02041/) (2021)
- [Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics](https://mlsanity.com/paper/1807.10441/) (2018)
- [Task Classification Model for Visual Fixation, Exploration, and Search](https://mlsanity.com/paper/1907.12635/) (2019)
- [Understanding Infographics through Textual and Visual Tag Prediction](https://mlsanity.com/paper/1709.09215/) (2017)
- [ICE: An Interactive Configuration Explorer for High Dimensional Categorical Parameter Spaces](https://mlsanity.com/paper/1907.12627/) (2019)
- [User-Centric Semi-Automated Infographics Authoring and Recommendation](https://mlsanity.com/paper/2108.11914/) (2021)

## Built on by (most cited first, 3 of 3)

- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [Show Me the Infographic I Imagine: Intent-Aware Infographic Retrieval for Authoring Support](https://mlsanity.com/paper/2604.07989/) (2026)
- [InfoAgent: Traceable Generation and Repair of Evidence-Grounded Infographics](https://mlsanity.com/paper/2609.39380/) (2026)

## Abstract (arXiv)

Infographics are an aesthetic visual representation of information following specific design principles of human perception. Designing infographics can be a tedious process for non-experts and time-consuming, even for professional designers. With the help of designers, we propose a semi-automated infographic framework for general structured and flow-based infographic design generation. For novice designers, our framework automatically creates and ranks infographic designs for a user-provided text with no requirement for design input. However, expert designers can still provide custom design inputs to customize the infographics. We will also contribute an individual visual group (VG) designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs in this work. Evaluation results confirm that by using our framework, designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining the same quality as hand-designed infographics templates.