PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent
  LLMs
Authors: name: Zhilin Zhang, name: Xiang Zhang, name: Jiaqi Wei, name: Yiwei Xu, name: Chenyu You

Multi-agent systems built upon large language models (LLMs) have demonstrated
remarkable capabilities in tackling complex compositional tasks. In this work,
we apply this paradigm to the paper-to-poster generation problem, a practical
yet time-consuming process faced by researchers preparing for conferences.
While recent approaches have attempted to automate this task, most neglect core
design and aesthetic principles, resulting in posters that require substantial
manual refinement. To address these design limitations, we propose PosterGen, a
multi-agent framework that mirrors the workflow of professional poster
designers. It consists of four collaborative specialized agents: (1) Parser and
Curator agents extract content from the paper and organize storyboard; (2)
Layout agent maps the content into a coherent spatial layout; (3) Stylist
agents apply visual design elements such as color and typography; and (4)
Renderer composes the final poster. Together, these agents produce posters that
are both semantically grounded and visually appealing. To evaluate design
quality, we introduce a vision-language model (VLM)-based rubric that measures
layout balance, readability, and aesthetic coherence. Experimental results show
that PosterGen consistently matches in content fidelity, and significantly
outperforms existing methods in visual designs, generating posters that are
presentation-ready with minimal human refinements.