PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design
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.
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Jiazhe Wei
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Ken Li
6931566d2d1e5b0a7d84dace
Tianyu Lao
6931566d2d1e5b0a7d84dacf
Haofan Wang
admin_assigned
2025-12-04T09:38:08.089Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
Haofan Wang
wanghaofan
user
wanghaofan
6931566d2d1e5b0a7d84dad0
Liang Wang
6931566d2d1e5b0a7d84dad1
Caifeng Shan
6931566d2d1e5b0a7d84dad2
Chenyang Si
claimed_verified
2025-12-04T13:48:45.416Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/635f8ed47c05eb9f59963d3a/uQf4p9N9pSaFy87Wg9v4k.jpeg
ChenyangSi
ChenyangSi
user
ChenyangSi
PosterCopilot: Toward Layout Reasoning and Controllable Editing for Professional Graphic Design
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.
PosterCopilot enhances professional graphic design through a three-stage training strategy for LMMs, enabling geometrically accurate and aesthetically superior layouts with controllable iterative editing.
Large Multimodal Models LMMs Perturbed Supervised Fine-Tuning Reinforcement Learning Visual-Reality Alignment Reinforcement Learning from Aesthetic Feedback generative models layer-controllable iterative editing