PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design
This work addresses the need for professional graphic design automation by enabling precise, iterative editing, though it appears incremental as it builds on existing LMM methods.
The paper tackles the problem of automating graphic design with Large Multimodal Models (LMMs) by addressing geometric inaccuracies and lack of iterative editing, resulting in a framework that achieves geometrically accurate and aesthetically superior layouts with unprecedented controllability.
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.