Collaposer: Transforming Photo Collections into Visual Assets for Storytelling with Collages

TL;DR

Collaposer is an AI-powered tool that automates transforming photo collections into organized visual assets for storytelling by tagging, segmenting, and clustering images based on user stories.

Contribution

It introduces an integrated system combining image segmentation and large language models to streamline asset preparation for photo collage storytelling.

Findings

Automates photo tagging, detection, and segmentation.

Organizes visual assets according to semantic hierarchy.

Effectively supports storytelling with diverse visual assets.

Abstract

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/