PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design

• URL: http://arxiv.org/abs/2512.04082v1
• Date: Wed, 03 Dec 2025 18:59:37 GMT
• Title: PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design
• Authors: Jiazhe Wei, Ken Li, Tianyu Lao, Haofan Wang, Liang Wang, Caifeng Shan, Chenyang Si,
• Abstract summary: PosterCopilot is a framework that advances layout reasoning and controllable editing for professional graphic design.<n>We introduce a progressive three-stage training strategy that equips LMMs with geometric understanding and aesthetic reasoning for layout design.<n>We develop a complete workflow that couples the trained LMM-based design model with generative models, enabling layer-controllable, iterative editing for precise element refinement.
• Score: 29.264277919540955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graphic design forms the cornerstone of modern visual communication, serving as a vital medium for promoting cultural and commercial events. Recent advances have explored automating this process using Large Multimodal Models (LMMs), yet existing methods often produce geometrically inaccurate layouts and lack the iterative, layer-specific editing required in professional workflows. To address these limitations, we present PosterCopilot, a framework that advances layout reasoning and controllable editing for professional graphic design. Specifically, we introduce a progressive three-stage training strategy that equips LMMs with geometric understanding and aesthetic reasoning for layout design, consisting of Perturbed Supervised Fine-Tuning, Reinforcement Learning for Visual-Reality Alignment, and Reinforcement Learning from Aesthetic Feedback. Furthermore, we develop a complete workflow that couples the trained LMM-based design model with generative models, enabling layer-controllable, iterative editing for precise element refinement while maintaining global visual consistency. Extensive experiments demonstrate that PosterCopilot achieves geometrically accurate and aesthetically superior layouts, offering unprecedented controllability for professional iterative design.

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)
• Rethinking Layered Graphic Design Generation with a Top-Down Approach [76.33538798060326]
  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.
  arXiv  Detail & Related papers  (2025-07-08T02:26:08Z)
• CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design [69.83433430133302]
  CreatiDesign is a systematic solution for automated graphic design covering both model architecture and dataset construction.<n>First, we design a unified multi-condition driven architecture that enables flexible and precise integration of heterogeneous design elements.<n> Furthermore, to ensure that each condition precisely controls its designated image region, we propose a multimodal attention mask mechanism.
  arXiv  Detail & Related papers  (2025-05-25T12:14:23Z)
• PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM [58.67882997399021]
  Our research introduces a unified framework for automated graphic layout generation.<n>Our data-driven method employs structured text (JSON format) and visual instruction tuning to generate layouts.<n>We develop an automated text-to-poster system that generates editable posters based on users' design intentions.
  arXiv  Detail & Related papers  (2024-06-05T03:05:52Z)
• Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models [81.6240188672294]
  In graphic design, non-professional users often struggle to create visually appealing layouts due to limited skills and resources. We introduce a novel multimodal instruction-following framework for layout planning, allowing users to easily arrange visual elements into tailored layouts. Our method not only simplifies the design process for non-professionals but also surpasses the performance of few-shot GPT-4V models, with mIoU higher by 12% on Crello.
  arXiv  Detail & Related papers  (2024-04-23T17:58:33Z)
• 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)
• 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)
• The Layout Generation Algorithm of Graphic Design Based on Transformer-CVAE [8.052709336750823]
  This paper implemented the Transformer model and conditional variational autoencoder (CVAE) to the graphic design layout generation task. It proposed an end-to-end graphic design layout generation model named LayoutT-CVAE. Compared with the existing state-of-art models, the layout generated by ours performs better on many metrics.
  arXiv  Detail & Related papers  (2021-10-08T13:36:02Z)

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.