Digital collage is an artistic practice that combines image cutouts to tell stories. However, preparing cutouts from a set of photos remains a tedious and time-consuming task. A formative study identified three main challenges: 1) inefficient search for relevant photos, 2) manual image cutout, and 3) difficulty in organizing large sets of cutouts. To meet these challenges and facilitate asset preparation for collage, we propose Collaposer, a tool that transforms a collection of photos into organized, ready-to-use visual cutouts based on user-provided story descriptions. Collaposer tags, detects, and segments photos, and then uses an LLM to select central and related labels based on the user-provided story description. Collaposer presents the resulting visuals in varying sizes, clustered according to semantic hierarchy. Our evaluation shows that Collaposer effectively automates the preparation process to produce diverse sets of visual cutouts adhering to the storyline, allowing users to focus on collaging these assets for storytelling. Project website: https://jiayzhou.github.io/collaposer-website/

翻译：数字拼贴是一种通过组合图像剪裁来讲述故事的艺术实践。然而，从一组照片中准备剪裁素材仍然是一项繁琐且耗时的任务。一项形成性研究识别出三个主要挑战：1) 搜索相关照片效率低下，2) 需手动进行图像剪裁，3) 难以组织大量剪裁素材。为应对这些挑战并促进拼贴素材的准备，我们提出了Collaposer，该工具能够根据用户提供的故事描述，将照片集转化为组织有序、可直接使用的视觉剪裁素材。Collaposer对照片进行标记、检测与分割，随后利用大型语言模型（LLM）基于用户提供的故事描述选择核心及相关标签。Collaposer以不同尺寸呈现生成的视觉素材，并依据语义层次进行聚类。我们的评估表明，Collaposer能有效自动化素材准备流程，生成符合故事线的多样化视觉剪裁素材集，从而使用户能够专注于利用这些素材进行拼贴叙事。项目网站：https://jiayzhou.github.io/collaposer-website/