PosterVerse: A Full-Workflow Framework for Commercial-Grade Poster Generation with HTML-Based Scalable Typography
Junle Liu, Peirong Zhang, Yuyi Zhang, Pengyu Yan, Hui Zhou, Xinyue Zhou, Fengjun Guo, Lianwen Jin

TL;DR
PosterVerse is a comprehensive framework that automates commercial-grade poster creation by integrating AI-driven design stages, including blueprinting, background generation, and precise HTML-based text rendering, addressing current limitations in automation and scalability.
Contribution
It introduces a full-workflow poster generation system with a novel HTML-based dataset, PosterDNA, enabling scalable and accurate text rendering for commercial posters.
Findings
Produces visually appealing posters with accurate text alignment
Demonstrates high scalability and customization in poster design
Outperforms existing methods in commercial-grade poster quality
Abstract
Commercial-grade poster design demands the seamless integration of aesthetic appeal with precise, informative content delivery. Current automated poster generation systems face significant limitations, including incomplete design workflows, poor text rendering accuracy, and insufficient flexibility for commercial applications. To address these challenges, we propose PosterVerse, a full-workflow, commercial-grade poster generation method that seamlessly automates the entire design process while delivering high-density and scalable text rendering. PosterVerse replicates professional design through three key stages: (1) blueprint creation using fine-tuned LLMs to extract key design elements from user requirements, (2) graphical background generation via customized diffusion models to create visually appealing imagery, and (3) unified layout-text rendering with an MLLM-powered HTML engine…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsInteractive and Immersive Displays · Data Visualization and Analytics · Handwritten Text Recognition Techniques
