CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic
  Design Generation
Graphic design plays a crucial role in both commercial and personal contexts,
yet creating high-quality, editable, and aesthetically pleasing graphic
compositions remains a time-consuming and skill-intensive task, especially for
beginners. Current AI tools automate parts of the workflow, but struggle to
accurately incorporate user-supplied assets, maintain editability, and achieve
professional visual appeal. Commercial systems, like Canva Magic Design, rely
on vast template libraries, which are impractical for replicate. In this paper,
we introduce CreatiPoster, a framework that generates editable, multi-layer
compositions from optional natural-language instructions or assets. A protocol
model, an RGBA large multimodal model, first produces a JSON specification
detailing every layer (text or asset) with precise layout, hierarchy, content
and style, plus a concise background prompt. A conditional background model
then synthesizes a coherent background conditioned on this rendered foreground
layers. We construct a benchmark with automated metrics for graphic-design
generation and show that CreatiPoster surpasses leading open-source approaches
and proprietary commercial systems. To catalyze further research, we release a
copyright-free corpus of 100,000 multi-layer designs. CreatiPoster supports
diverse applications such as canvas editing, text overlay, responsive resizing,
multilingual adaptation, and animated posters, advancing the democratization of
AI-assisted graphic design. Project homepage:
https://github.com/graphic-design-ai/creatiposter
684b8b533b733ba333686fe4
Zhao Zhang
claimed_verified
2025-06-13T07:39:45.153Z
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Zhao Zhang
user
zbrl
684b8b533b733ba333686fe5
Yutao Cheng
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Dexiang Hong
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2025-06-13T02:22:14.202Z
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dexiang hong
user
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Maoke Yang
extracted_pending
2025-06-13T02:22:14.202Z
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YangMaoke
user
YangMaoke
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Gonglei Shi
extracted_confirmed
2025-06-13T02:23:52.345Z
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ShiLayne
user
ShiLayne
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Lei Ma
684b8b533b733ba333686fea
Hui Zhang
684b8b533b733ba333686feb
Jie Shao
684b8b533b733ba333686fec
Xinglong Wu
CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic
  Design Generation
Graphic design plays a crucial role in both commercial and personal contexts,
yet creating high-quality, editable, and aesthetically pleasing graphic
compositions remains a time-consuming and skill-intensive task, especially for
beginners. Current AI tools automate parts of the workflow, but struggle to
accurately incorporate user-supplied assets, maintain editability, and achieve
professional visual appeal. Commercial systems, like Canva Magic Design, rely
on vast template libraries, which are impractical for replicate. In this paper,
we introduce CreatiPoster, a framework that generates editable, multi-layer
compositions from optional natural-language instructions or assets. A protocol
model, an RGBA large multimodal model, first produces a JSON specification
detailing every layer (text or asset) with precise layout, hierarchy, content
and style, plus a concise background prompt. A conditional background model
then synthesizes a coherent background conditioned on this rendered foreground
layers. We construct a benchmark with automated metrics for graphic-design
generation and show that CreatiPoster surpasses leading open-source approaches
and proprietary commercial systems. To catalyze further research, we release a
copyright-free corpus of 100,000 multi-layer designs. CreatiPoster supports
diverse applications such as canvas editing, text overlay, responsive resizing,
multilingual adaptation, and animated posters, advancing the democratization of
AI-assisted graphic design. Project homepage:
https://github.com/graphic-design-ai/creatiposter
CreatiPoster generates high-quality, editable, and customizable graphic compositions from text or assets, outperforming existing tools and templates.
RGBA large multimodal model JSON specification conditional background model automated metrics graphic-design generation multi-layer designs AI-assisted graphic design