# A Survey for Graphic Design Intelligence

arXiv: https://arxiv.org/abs/2309.01371 · HTML: https://mlsanity.com/paper/2309.01371/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2023.

## Built on by (most cited first, 9 of 9)

- [Alfie: Democratising RGBA Image Generation With No ](https://mlsanity.com/paper/2408.14826/) (2024)
- [Desigen: A Pipeline for Controllable Design Template Generation](https://mlsanity.com/paper/2403.09093/) (2024)
- [LTSim: Layout Transportation-based Similarity Measure for Evaluating Layout Generation](https://mlsanity.com/paper/2407.12356/) (2024)
- [AesthetiQ: Enhancing Graphic Layout Design via Aesthetic-Aware Preference Alignment of Multi-modal Large Language Models](https://mlsanity.com/paper/2503.00591/) (2025)
- [DesignProbe: A Graphic Design Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2404.14801/) (2024)
- [Spot the Error: Non-autoregressive Graphic Layout Generation with Wireframe Locator](https://mlsanity.com/paper/2401.16375/) (2024)
- [From Fragment to One Piece: A Survey on AI-Driven Graphic Design](https://mlsanity.com/paper/2503.18641/) (2025)
- [Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations](https://mlsanity.com/paper/2410.07211/) (2024)
- [Through the PRISM: Principle-Aware, Interpretable, and Multi-Scale Evaluation of Visual Designs](https://mlsanity.com/paper/2606.00592/) (2026)

## Abstract (arXiv)

Graphic design is an effective language for visual communication. Using complex composition of visual elements (e.g., shape, color, font) guided by design principles and aesthetics, design helps produce more visually-appealing content. The creation of a harmonious design requires carefully selecting and combining different visual elements, which can be challenging and time-consuming. To expedite the design process, emerging AI techniques have been proposed to automatize tedious tasks and facilitate human creativity. However, most current works only focus on specific tasks targeting at different scenarios without a high-level abstraction. This paper aims to provide a systematic overview of graphic design intelligence and summarize literature in the taxonomy of representation, understanding and generation. Specifically we consider related works for individual visual elements as well as the overall design composition. Furthermore, we highlight some of the potential directions for future explorations.