# Color Recommendation for Vector Graphic Documents based on Multi-Palette Representation

arXiv: https://arxiv.org/abs/2209.10820 · HTML: https://mlsanity.com/paper/2209.10820/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [CanvasVAE: Learning to Generate Vector Graphic Documents](https://mlsanity.com/paper/2108.01249/) (2021)
- [Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation](https://mlsanity.com/paper/1804.04128/) (2018)
- [InfoColorizer: Interactive Recommendation of Color Palettes for Infographics](https://mlsanity.com/paper/2102.02041/) (2021)
- [Fast Soft Color Segmentation](https://mlsanity.com/paper/2004.08096/) (2020)
- [Palettailor: Discriminable Colorization for Categorical Data](https://mlsanity.com/paper/2009.02969/) (2020)

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

- [OpenCOLE: Towards Reproducible Automatic Graphic Design Generation](https://mlsanity.com/paper/2406.08232/) (2024)
- [Towards Flexible Multi-modal Document Models](https://mlsanity.com/paper/2303.18248/) (2023)
- [Multimodal Markup Document Models for Graphic Design Completion](https://mlsanity.com/paper/2409.19051/) (2024)
- [DesignProbe: A Graphic Design Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2404.14801/) (2024)
- [Multimodal Color Recommendation in Vector Graphic Documents](https://mlsanity.com/paper/2308.04118/) (2023)
- [Exploring Palette based Color Guidance in Diffusion Models](https://mlsanity.com/paper/2508.08754/) (2025)
- [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)
- [ColorGPT: Leveraging Large Language Models for Multimodal Color Recommendation](https://mlsanity.com/paper/2508.08987/) (2025)

## Abstract (arXiv)

Vector graphic documents present multiple visual elements, such as images, shapes, and texts. Choosing appropriate colors for multiple visual elements is a difficult but crucial task for both amateurs and professional designers. Instead of creating a single color palette for all elements, we extract multiple color palettes from each visual element in a graphic document, and then combine them into a color sequence. We propose a masked color model for color sequence completion and recommend the specified colors based on color context in multi-palette with high probability. We train the model and build a color recommendation system on a large-scale dataset of vector graphic documents. The proposed color recommendation method outperformed other state-of-the-art methods by both quantitative and qualitative evaluations on color prediction and our color recommendation system received positive feedback from professional designers in an interview study.