Collascope: Supporting Serendipitous Asset Exploration for Collage-Based Storytelling

August 02, 2026 ยท Grace Period ยท ๐Ÿ› ACM UIST 2026

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Authors Jiayi Zhou, Longji Huang, Lvmin Zhang, Yun Wang, Zeyu Wang, Maneesh Agrawala, Huamin Qu, Anyi Rao arXiv ID 2608.01267 Category cs.HC: Human-Computer Interaction Citations 0 Venue ACM UIST 2026
Abstract
Collage-based storytelling requires visual elements that support emerging narratives and inspire creative reinterpretation. Existing tools, however, rely largely on keyword- and image-based retrieval, offering limited support for serendipitous exploration beyond existing assets. We introduce Collascope, an interactive system that helps creators (1) concretize story intent with interactive element groups, (2) expand the exploration space based on concepts or cutouts towards conceptual and visual dimensions, and (3) develop grounded, traceable ideas in parallel with collage composition. Collascope's attribute-aware visual retrieval method, instantiated with collage-relevant visual dimensions, enables creators to retrieve cutouts through dimension-specific visual projections rather than holistic similarity. In a within-subject study (N=12) against a conventional search baseline, our participants used unexpected results and even gaps in the asset collection to redirect narratives, shift tone, and enrich compositions. Scene Parts helped organize exploration into manageable subtasks, while participants used association in distinct ways depending on whether exploration was guided by a clear goal, an evolving story, or visual intuition.
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