Collaposer empowers independent creators to prepare visual assets for collage-based storytelling with a streamlined workflow by automatically selecting, cutting out, and visualizing diverse visual assets based on the input photo collection and story description. Given a curated overview of the extracted visual elements, users may interactively compose them into a static collage, or animate them to create expressive visual stories.

Collage is an art form that remixes visual elements to create narratives. However, it remains tedious and time-consuming to select, cut out, and organize visual assets from an unorganized set of images. Informed by a formative study, we propose Collaposer, a tool designed to automate the preparation of visual assets for storytelling with collage. Given a photo collection and a story description, Collaposer selects and cuts out visual elements that are both diverse in content and consistent with the story. Specifically, objects are extracted via image tagging, object detection, and segmentation, and then selected and organized leveraging the reasoning capabilities of LLM. To facilitate asset selection, Collaposer provides a curated overview that groups similar assets while resizing them based on selection criteria. A user study (N=12) demonstrated that Collaposer produced rich story-relevant assets, facilitated overview and navigation, and inspired users to create diverse stories. We further showcase our system's expressiveness in a gallery.

Our pipeline consists of three stages. The inputs include an image collection and a story description. In Stage I, valid visual elements are trimmed out and tagged with an object name. In Stage II, visual elements relevant to the story are selected and clustered into semantic groups. The elements classified as characters undergo part segmentation and pose estimation for later manipulation. In Stage III, the visual assets are visualized in a compact view to facilitate navigation and composition.