# From Elements to Design: A Layered Approach for Automatic Graphic Design Composition

arXiv: https://arxiv.org/abs/2412.19712 · HTML: https://mlsanity.com/paper/2412.19712/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 38 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 30 of 33)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding](https://mlsanity.com/paper/2307.02499/) (2023)
- [Compositional Chain-of-Thought Prompting for Large Multimodal Models](https://mlsanity.com/paper/2311.17076/) (2023)
- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [CanvasVAE: Learning to Generate Vector Graphic Documents](https://mlsanity.com/paper/2108.01249/) (2021)
- [Neural Design Network: Graphic Layout Generation with Constraints](https://mlsanity.com/paper/1912.09421/) (2019)
- [Constrained Graphic Layout Generation via Latent Optimization](https://mlsanity.com/paper/2108.00871/) (2021)
- [BLT: Bidirectional Layout Transformer for Controllable Layout Generation](https://mlsanity.com/paper/2112.05112/) (2021)
- [Attribute-conditioned Layout GAN for Automatic Graphic Design](https://mlsanity.com/paper/2009.05284/) (2020)
- [PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout](https://mlsanity.com/paper/2303.15937/) (2023)
- [Composition-aware Graphic Layout GAN for Visual-textual Presentation Designs](https://mlsanity.com/paper/2205.00303/) (2022)
- [LayoutPrompter: Awaken the Design Ability of Large Language Models](https://mlsanity.com/paper/2311.06495/) (2023)
- [LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction](https://mlsanity.com/paper/2208.08037/) (2022)
- [LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models](https://mlsanity.com/paper/2303.11589/) (2023)
- [PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM](https://mlsanity.com/paper/2406.02884/) (2024)
- [Graphic Design with Large Multimodal Model](https://mlsanity.com/paper/2404.14368/) (2024)
- [OpenCOLE: Towards Reproducible Automatic Graphic Design Generation](https://mlsanity.com/paper/2406.08232/) (2024)
- [Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation](https://mlsanity.com/paper/2311.13602/) (2023)
- [PosterLlama: Bridging Design Ability of Langauge Model to Contents-Aware Layout Generation](https://mlsanity.com/paper/2404.00995/) (2024)
- [Towards Flexible Multi-modal Document Models](https://mlsanity.com/paper/2303.18248/) (2023)
- [TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design](https://mlsanity.com/paper/2308.04733/) (2023)
- [Multimodal Markup Document Models for Graphic Design Completion](https://mlsanity.com/paper/2409.19051/) (2024)

## Built on by (most cited first, 13 of 13)

- [LayerD: Decomposing Raster Graphic Designs into Layers](https://mlsanity.com/paper/2509.25134/) (2025)
- [Graphic-Design-Bench: A Comprehensive Benchmark for Evaluating AI on Graphic Design Tasks](https://mlsanity.com/paper/2604.04192/) (2026)
- [PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design](https://mlsanity.com/paper/2512.04082/) (2025)
- [LaDe: Unified Multi-Layered Graphic Media Generation and Decomposition](https://mlsanity.com/paper/2603.17965/) (2026)
- [Seeing is Improving: Visual Feedback for Iterative Text Layout Refinement](https://mlsanity.com/paper/2603.22187/) (2026)
- [PSDesigner: Automated Graphic Design with a Human-Like Creative Workflow](https://mlsanity.com/paper/2603.25738/) (2026)
- [AnyDoc: Enhancing Document Generation via Large-Scale HTML/CSS Data Synthesis and Height-Aware Reinforcement Optimization](https://mlsanity.com/paper/2603.25118/) (2026)
- [Designer-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design](https://mlsanity.com/paper/2609.22086/) (2026)
- [ReLayout: Versatile and Structure-Preserving Design Layout Editing via Relation-Aware Design Reconstruction](https://mlsanity.com/paper/2602.01046/) (2026)
- [CPT: Controllable and Editable Design Variations with Language Models](https://mlsanity.com/paper/2604.04380/) (2026)
- [Towards Design Compositing](https://mlsanity.com/paper/2604.14605/) (2026)
- [Learning Interaction between Image and Layout Priors for Joint Image-Layout Generation in Design Templates](https://mlsanity.com/paper/2609.11519/) (2026)
- [PLAYGROUND: Progressive Layout Generation with Render-Grounded Planning](https://mlsanity.com/paper/2610.18865/) (2026)

## Abstract (arXiv)

In this work, we investigate automatic design composition from multimodal graphic elements. Although recent studies have developed various generative models for graphic design, they usually face the following limitations: they only focus on certain subtasks and are far from achieving the design composition task; they do not consider the hierarchical information of graphic designs during the generation process. To tackle these issues, we introduce the layered design principle into Large Multimodal Models (LMMs) and propose a novel approach, called LaDeCo, to accomplish this challenging task. Specifically, LaDeCo first performs layer planning for a given element set, dividing the input elements into different semantic layers according to their contents. Based on the planning results, it subsequently predicts element attributes that control the design composition in a layer-wise manner, and includes the rendered image of previously generated layers into the context. With this insightful design, LaDeCo decomposes the difficult task into smaller manageable steps, making the generation process smoother and clearer. The experimental results demonstrate the effectiveness of LaDeCo in design composition. Furthermore, we show that LaDeCo enables some interesting applications in graphic design, such as resolution adjustment, element filling, design variation, etc. In addition, it even outperforms the specialized models in some design subtasks without any task-specific training.