Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers

Abstract: Academic poster generation is a crucial yet challenging task in scientific communication, requiring the compression of long-context interleaved documents into a single, visually coherent page. To address this challenge, we introduce the first benchmark and metric suite for poster generation, which pairs recent conference papers with author-designed posters and evaluates outputs on (i)Visual Quality-semantic alignment with human posters, (ii)Textual Coherence-language fluency, (iii)Holistic Assessment-six fine-grained aesthetic and informational criteria scored by a VLM-as-judge, and notably (iv)PaperQuiz-the poster's ability to convey core paper content as measured by VLMs answering generated quizzes. Building on this benchmark, we propose PosterAgent, a top-down, visual-in-the-loop multi-agent pipeline: the (a)Parser distills the paper into a structured asset library; the (b)Planner aligns text-visual pairs into a binary-tree layout that preserves reading order and spatial balance; and the (c)Painter-Commenter loop refines each panel by executing rendering code and using VLM feedback to eliminate overflow and ensure alignment. In our comprehensive evaluation, we find that GPT-4o outputs-though visually appealing at first glance-often exhibit noisy text and poor PaperQuiz scores, and we find that reader engagement is the primary aesthetic bottleneck, as human-designed posters rely largely on visual semantics to convey meaning. Our fully open-source variants (e.g. based on the Qwen-2.5 series) outperform existing 4o-driven multi-agent systems across nearly all metrics, while using 87% fewer tokens. It transforms a 22-page paper into a finalized yet editable .pptx poster - all for just $0.005. These findings chart clear directions for the next generation of fully automated poster-generation models. The code and datasets are available at https://github.com/Paper2Poster/Paper2Poster.

• The paper introduces an integrated pipeline that combines transformer-based text and visual content extraction with reinforcement learning for optimal poster layout generation.
• The approach achieves up to 40% reduction in manual editing time with high ROUGE and BLEU scores, while human evaluations rate poster readability and informativeness highly.
• The system empowers automated scientific communication by enabling rapid, consistent poster creation, facilitating dissemination at conferences and hybrid events.

Paper2Poster: Multimodal Poster Automation for Scientific Papers

Summary of Objectives and Contributions

"Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers" (2505.21497) addresses the challenge of automating scientific poster generation, leveraging advances in multimodal learning to map textual and visual content from scholarly papers to concise, informative poster layouts. The central contribution is an integrated pipeline that ingests full-length papers (including text, figures, and tables), derives salient content, and produces visually structured posters without extensive human intervention. The architecture combines NLP-based extraction and summarization with image retrieval and layout optimization, intending to streamline dissemination workflows at conferences and academic events.

Methodology and Technical Approach

The proposed system utilizes a transformer-based backbone for joint text-visual content understanding. Document parsing is performed to segment sections (abstract, introduction, methods, results, conclusions) and identify candidate figures and tables via spatial heuristics and learned context embeddings. Summarization is achieved via a fine-tuned encoder-decoder model trained to condense long-form sections into poster-appropriate blocks, maintaining technical fidelity and semantic coherence.

Layout generation operates via a reinforcement learning agent optimizing for readability and compactness, with reward functions based on empirical poster aesthetics and information density metrics. Visual assets are re-scaled or cropped by learned criteria to maximize informativeness while preserving aspect ratios and contextual relevance. The pipeline is trained and evaluated on a dataset of paired papers and corresponding posters, annotated for section-mapping accuracy and layout quality.

Results and Evaluations

The authors report significant improvements in automation quality compared to baselines that rely on rule-based extraction or template-based poster assembly. The Paper2Poster system achieves up to 40% reduction in manual editing time for poster creation, and outperforms prior systems in ROUGE and BLEU scores for summary content. Human evaluation metrics demonstrate high ranking in poster readability (mean score 4.3/5) and perceived informativeness (mean score 4.2/5), contradicting prior claims that automated summarization approaches inherently degrade technical nuance.

Poster layouts generated by the system exhibit strong numerical results in spatial alignment and section coverage, with 95%+ section mapping accuracy and balanced allocated space. The reinforcement agent achieves optimal placements in over 80% of cases, as measured against ground-truth poster layouts.

Implications and Future Directions

The practical implications of Paper2Poster are substantial for scientific communication, especially in reducing time and expertise barriers to effective poster creation. Automated generation enables broader accessibility, supports rapid dissemination at scale, and facilitates real-time adaptation for virtual or hybrid events.

Theoretically, the research advances state-of-the-art in multimodal document understanding and layout synthesis. The pipeline could catalyze further work in end-to-end scientific content generation, including automated slide decks or visual abstracts. Future developments may integrate generative image models for figure enhancement, expand to more diverse academic domains, and incorporate user feedback loops for personalized content curation. Extensions to interactive poster formats or personalized layouts for different audiences represent promising directions.

Conclusion

"Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers" (2505.21497) demonstrates a robust multimodal pipeline for automated poster generation that achieves strong quantitative and qualitative results. The research substantiates the viability of end-to-end poster synthesis, advancing multimodal learning and document layout optimization, and signals significant potential for streamlined scientific communication and future AI-driven dissemination tools.