The Paper2Poster pipeline is designed to overcome the challenges of long-context long-horizon tasks, interleaved multimodal inputs, and layout-aware multimodal outputs. It uses a hierarchical understanding and selective abstraction approach to summarize key insights from the paper while preserving coherence. The pipeline also uses joint reasoning over language, visual content, and layout to prevent overflow, imbalance, and logical misalignment. The resulting posters are visually appealing and effectively convey the core content of the paper.
Paper2Poster has been evaluated on a comprehensive benchmark and metric suite that includes visual quality, textual coherence, holistic assessment, and PaperQuiz. The results show that Paper2Poster outperforms existing solutions, including GPT-4o-based systems, across nearly all metrics while consuming 87% fewer tokens. The tool has also been shown to be efficient and cost-effective, making it a valuable resource for researchers and scientists who need to generate high-quality posters from their papers.
