Rethinking Layered Graphic Design Generation with a Top-Down Approach

• URL: http://arxiv.org/abs/2507.05601v1
• Date: Tue, 08 Jul 2025 02:26:08 GMT
• Title: Rethinking Layered Graphic Design Generation with a Top-Down Approach
• Authors: Jingye Chen, Zhaowen Wang, Nanxuan Zhao, Li Zhang, Difan Liu, Jimei Yang, Qifeng Chen,
• Abstract summary: Graphic design is crucial for conveying ideas and messages. Designers usually organize their work into objects, backgrounds, and vectorized text layers to simplify editing.<n>With the rise of GenAI methods, an endless supply of high-quality graphic designs in pixel format has become more accessible.<n>Despite this, non-layered designs still inspire human designers, influencing their choices in layouts and text styles, ultimately guiding the creation of layered designs.<n>Motivated by this observation, we propose Accordion, a graphic design generation framework taking the first attempt to convert AI-generated designs into editable layered designs.
• Score: 76.33538798060326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graphic design is crucial for conveying ideas and messages. Designers usually organize their work into objects, backgrounds, and vectorized text layers to simplify editing. However, this workflow demands considerable expertise. With the rise of GenAI methods, an endless supply of high-quality graphic designs in pixel format has become more accessible, though these designs often lack editability. Despite this, non-layered designs still inspire human designers, influencing their choices in layouts and text styles, ultimately guiding the creation of layered designs. Motivated by this observation, we propose Accordion, a graphic design generation framework taking the first attempt to convert AI-generated designs into editable layered designs, meanwhile refining nonsensical AI-generated text with meaningful alternatives guided by user prompts. It is built around a vision language model (VLM) playing distinct roles in three curated stages. For each stage, we design prompts to guide the VLM in executing different tasks. Distinct from existing bottom-up methods (e.g., COLE and Open-COLE) that gradually generate elements to create layered designs, our approach works in a top-down manner by using the visually harmonious reference image as global guidance to decompose each layer. Additionally, it leverages multiple vision experts such as SAM and element removal models to facilitate the creation of graphic layers. We train our method using the in-house graphic design dataset Design39K, augmented with AI-generated design images coupled with refined ground truth created by a customized inpainting model. Experimental results and user studies by designers show that Accordion generates favorable results on the DesignIntention benchmark, including tasks such as text-to-template, adding text to background, and text de-rendering, and also excels in creating design variations.

Related papers

• IGD: Instructional Graphic Design with Multimodal Layer Generation [83.31320209596991]
  Two-stage methods that rely primarily on layout generation lack creativity and intelligence, making graphic design still labor-intensive.<n>We propose instructional graphic designer (IGD) to swiftly generate multimodal layers with editable flexibility with only natural language instructions.
  arXiv  Detail & Related papers  (2025-07-14T04:31:15Z)
• CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generation [13.354283356097563]
  CreatiPoster is a framework that generates editable, multilayer compositions from optional natural-language instructions or assets.<n>To further research, we release a copyright-free corpus of 100,000 multi-layer designs.
  arXiv  Detail & Related papers  (2025-06-12T16:54:39Z)
• POSTA: A Go-to Framework for Customized Artistic Poster Generation [87.16343612086959]
  POSTA is a modular framework for customized artistic poster generation.<n>Background Diffusion creates a themed background based on user input.<n>Design MLLM then generates layout and typography elements that align with and complement the background style.<n>ArtText Diffusion applies additional stylization to key text elements.
  arXiv  Detail & Related papers  (2025-03-19T05:22:38Z)
• DesignDiffusion: High-Quality Text-to-Design Image Generation with Diffusion Models [115.62816053600085]
  We present DesignDiffusion, a framework for synthesizing design images from textual descriptions.<n>The proposed framework directly synthesizes textual and visual design elements from user prompts.<n>It utilizes a distinctive character embedding derived from the visual text to enhance the input prompt.
  arXiv  Detail & Related papers  (2025-03-03T15:22:57Z)
• From Elements to Design: A Layered Approach for Automatic Graphic Design Composition [16.262338090888342]
  We introduce the layered design principle into Large Multimodal Models (LMMs)<n>LaDeCo performs layer planning for a given element set, dividing the input elements into different semantic layers according to their contents.<n>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.
  arXiv  Detail & Related papers  (2024-12-27T16:13:08Z)
• GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts [53.568057283934714]
  We propose a VLM-based framework that generates content-aware text logo layouts. We introduce two model techniques to reduce the computation for processing multiple glyph images simultaneously. To support instruction-tuning of out model, we construct two extensive text logo datasets, which are 5x more larger than the existing public dataset.
  arXiv  Detail & Related papers  (2024-11-18T10:04:10Z)
• COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Design [39.809852329070466]
  This paper introduces the COLE system - a hierarchical generation framework designed to address these challenges. This COLE system can transform a vague intention prompt into a high-quality multi-layered graphic design, while also supporting flexible editing based on user input.
  arXiv  Detail & Related papers  (2023-11-28T17:22:17Z)
• PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout [62.12447593298437]
  Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements. We propose design sequence formation (DSF) that reorganizes elements in layouts to imitate the design processes of human designers. A novel CNN-LSTM-based conditional generative adversarial network (GAN) is presented to generate proper layouts.
  arXiv  Detail & Related papers  (2023-03-28T12:48:36Z)
• LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer [80.61492265221817]
  Graphic layout designs play an essential role in visual communication. Yet handcrafting layout designs is skill-demanding, time-consuming, and non-scalable to batch production. Generative models emerge to make design automation scalable but it remains non-trivial to produce designs that comply with designers' desires.
  arXiv  Detail & Related papers  (2022-12-19T21:57:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.