DesignAsCode: Bridging Structural Editability and Visual Fidelity in Graphic Design Generation
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Abstract (arXiv)
Graphic design generation demands a delicate balance between high visual fidelity and fine-grained structural editability. However, existing approaches typically bifurcate into either non-editable raster image synthesis or abstract layout generation devoid of visual content. Recent combinations of these two approaches attempt to bridge this gap but often suffer from rigid composition schemas and unresolvable visual dissonances (e.g., text-background conflicts) due to their inexpressive representation and open-loop nature. To address these challenges, we propose DesignAsCode, a novel framework that reimagines graphic design as a programmatic synthesis task using HTML/CSS. Specifically, we introduce a Plan-Implement-Reflect pipeline, incorporating a Semantic Planner to construct dynamic, variable-depth element hierarchies and a Visual-Aware Reflection mechanism that optimizes the code to rectify rendering artifacts. Extensive experiments demonstrate that DesignAsCode significantly outperforms baselines in both structural validity and aesthetic quality. Furthermore, our code-native representation unlocks advanced capabilities, including automatic layout retargeting, complex document generation (e.g., resumes), and CSS-based animation. Our project page is available at https://liuziyuan1109.github.io/design-as-code/.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 48 of its 60 references that are in the map. First posted on arXiv 6 Feb 2026.
Builds on
48 of 60 references.
References · 48
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36 more
- 2019LayoutGAN: Generating Graphic Layouts with Wireframe Discriminatorscited by 130
- 2024Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Datasetcited by 126
- 2020LayoutTransformer: Layout Generation and Completion with Self-attentioncited by 122
- 2019LayoutVAE: Stochastic Scene Layout Generation From a Label Setcited by 119
- 2021CanvasVAE: Learning to Generate Vector Graphic Documentscited by 93
- 2021Variational Transformer Networks for Layout Generationcited by 88
- 2023SVGDreamer: Text Guided SVG Generation with Diffusion Modelcited by 75
- 2020Attribute-conditioned Layout GAN for Automatic Graphic Designcited by 67
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- 2023COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Designcited by 39
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- 2024Graphic Design with Large Multimodal Modelcited by 38
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- 2025PSD2Code: Automated Front-End Code Generation from Design Files via Multimodal Large Language Modelscited by 2
Built on by
Citing papers per year: 2026: 3.
3 papers.
- 2026AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Designcited by 5
- 2026TransAnyText: Translating Arbitrary Text in E-commerce Images via Structured Visual Generationcited by 0
- 2026CommerceVibe: Learning to Design E-Commerce Creatives as Executable Visual Code via Dual-Feedback Reinforcement Learningcited by 0
- 2026CommerceVibe: Learning to Design E-Commerce Creatives as Executable Visual Code via Dual-Feedback Reinforcement Learning28 Aug 2026
- 2026TransAnyText: Translating Arbitrary Text in E-commerce Images via Structured Visual Generation17 Aug 2026
- 2026AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design13 Aug 2026