用论文自动生成匹配的学术海报布局，提升科研传播效率。
SciPostGen: Bridging the Gap between Scientific Papers and Poster Layouts
- 基于论文结构检索相似海报布局并指导生成。
- 实验表明生成布局与论文结构一致，且满足用户约束条件。
- 首个大规模图文配对数据集，适合海报设计与AI生成研究者。
随着科学论文数量持续增长，如何高效传达研究成果成为关键，而海报是重要展示形式。海报布局直接影响信息传递效果，其重要性日益凸显。然而，当前仍缺乏对论文内容与呈现布局之间对应关系的系统理解，亟需大规模带标注的数据集。为此，我们提出SciPostGen，一个用于理解与生成学术海报布局的大规模数据集。基于该数据集的分析显示，论文结构与海报中布局元素数量存在关联。据此，我们构建了检索增强型海报布局生成框架：先检索与论文结构一致的布局，再以此为引导生成新布局。在有无布局约束条件下均进行实验，结果表明检索器能准确匹配论文结构，生成框架所产布局既契合论文内容，又能满足用户指定约束。数据集与代码已公开于https://omron-sinicx.github.io/paper2layout/。
原文摘要 · Abstract (English)
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 uses them as guidance for layout generation. We conducted experiments under two conditions: with and without layout constraints typically specified by poster creators. The results show that the retriever estimates layouts aligned with paper structures, and our framework generates layouts that also satisfy given constraints. The dataset and code are publicly available at https://omron-sinicx.github.io/paper2layout/.
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