  Scientific posters are used to present the contributions of scientific papers
effectively in a graphical format. However, creating a well-designed poster
that efficiently summarizes the core of a paper is both labor-intensive and
time-consuming. A system that can automatically generate well-designed posters
from scientific papers would reduce the workload of authors and help readers
understand the outline of the paper visually. Despite the demand for poster
generation systems, only a limited research has been conduced due to the lack
of publicly available datasets. Thus, in this study, we built the SciPostLayout
dataset, which consists of 7,855 scientific posters and manual layout
annotations for layout analysis and generation. SciPostLayout also contains 100
scientific papers paired with the posters. All of the posters and papers in our
dataset are under the CC-BY license and are publicly available. As benchmark
tests for the collected dataset, we conducted experiments for layout analysis
and generation utilizing existing computer vision models and found that both
layout analysis and generation of posters using SciPostLayout are more
challenging than with scientific papers. We also conducted experiments on
generating layouts from scientific papers to demonstrate the potential of
utilizing LLM as a scientific poster generation system. The dataset is publicly
available at https://huggingface.co/datasets/omron-sinicx/scipostlayout_v2. The
code is also publicly available at
https://github.com/omron-sinicx/scipostlayout.
