Graphic Design with Large Multimodal Model
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Abstract (arXiv)
In the field of graphic design, automating the integration of design elements into a cohesive multi-layered artwork not only boosts productivity but also paves the way for the democratization of graphic design. One existing practice is Graphic Layout Generation (GLG), which aims to layout sequential design elements. It has been constrained by the necessity for a predefined correct sequence of layers, thus limiting creative potential and increasing user workload. In this paper, we present Hierarchical Layout Generation (HLG) as a more flexible and pragmatic setup, which creates graphic composition from unordered sets of design elements. To tackle the HLG task, we introduce Graphist, the first layout generation model based on large multimodal models. Graphist efficiently reframes the HLG as a sequence generation problem, utilizing RGB-A images as input, outputs a JSON draft protocol, indicating the coordinates, size, and order of each element. We develop new evaluation metrics for HLG. Graphist outperforms prior arts and establishes a strong baseline for this field. Project homepage: https://github.com/graphic-design-ai/graphist
Key facts. Cited by 40 papers in the map (data through 14 Oct 2026). Builds on 81 of its 104 references that are in the map. First posted on arXiv 22 Apr 2024.
Builds on
81 of 104 references.
References · 81
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50 most cited of 81 All 81 (JSON)
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Citing papers per year: 2024: 5; 2025: 19; 2026: 16.
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- 2026Seeing is Improving: Visual Feedback for Iterative Text Layout Refinement23 Mar 2026
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- 2026MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale26 May 2026
- 2025OmniPSD: Layered PSD Generation with Diffusion Transformer10 Dec 2025
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- 2024Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models25 Nov 2024
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- 2025BizGen: Advancing Article-level Visual Text Rendering for Infographics Generationcited by 20
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- 2024Multimodal Markup Document Models for Graphic Design Completioncited by 16
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- 2024Can GPTs Evaluate Graphic Design Based on Design Principles?cited by 14
- 2024From Elements to Design: A Layered Approach for Automatic Graphic Design Compositioncited by 13
- 2025BannerAgency: Advertising Banner Design with Multimodal LLM Agentscited by 11
- 2025Rethinking Layered Graphic Design Generation with a Top-Down Approachcited by 10
- 2025LayerPeeler: Autoregressive Peeling for Layer-wise Image Vectorizationcited by 8
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12 most cited of 29 All 29 (JSON)
- 2026PLAYGROUND: Progressive Layout Generation with Render-Grounded Planning13 Oct 2026
- 2026Learning Interaction between Image and Layout Priors for Joint Image-Layout Generation in Design Templates10 Sep 2026
- 2026Editable Visual Design3 Sep 2026
- 2026CommerceVibe: Learning to Design E-Commerce Creatives as Executable Visual Code via Dual-Feedback Reinforcement Learning28 Aug 2026
- 2026Towards Design Compositing16 Apr 2026
- 2026CPT: Controllable and Editable Design Variations with Language Models6 Apr 2026
- 2026PSDesigner: Automated Graphic Design with a Human-Like Creative Workflow26 Mar 2026
- 2026DesignAsCode: Bridging Structural Editability and Visual Fidelity in Graphic Design Generation6 Feb 2026
- 2026ReLayout: Versatile and Structure-Preserving Design Layout Editing via Relation-Aware Design Reconstruction1 Feb 2026
- 2026PaperX: A Unified Framework for Multimodal Academic Presentation Generation with Scholar DAG30 Jan 2026
- 2026PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement16 Jan 2026
- 2025PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design3 Dec 2025
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