TL;DR
This paper introduces SciPostGen, a large dataset linking scientific papers to poster layouts, and proposes a retrieval-augmented framework for generating poster layouts that reflect paper structure and constraints.
Contribution
The paper presents SciPostGen, a novel dataset for paper-to-poster layout understanding, and a retrieval-augmented method for generating layouts aligned with paper content.
Findings
Paper structures correlate with the number of poster layout elements.
The retrieval-augmented framework produces layouts consistent with paper structures.
Layouts generated satisfy constraints specified by poster creators.
Abstract
As the number of scientific papers continues to grow, there is a demand for approaches that can effectively convey research findings, with posters serving as a key medium for presenting paper contents. Poster layouts determine how effectively research is communicated and understood, highlighting their growing importance. In particular, a gap remains in understanding how papers correspond to the layouts that present them, which calls for datasets with paired annotations at scale. To bridge this gap, we introduce SciPostGen, a large-scale dataset for understanding and generating poster layouts from scientific papers. Our analyses based on SciPostGen show that paper structures are associated with the number of layout elements in posters. Based on this insight, we explore a framework, Retrieval-Augmented Poster Layout Generation, which retrieves layouts consistent with a given paper and…
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