Designing Real-Time, Adaptive Learning Environments for Origami

<p dir="ltr">Origami can be intuitive, yet traditional tutorials often fail to convey spatial and sequential steps clearly—especially in complex patterns. This thesis presents an interactive origami learning system that integrates a real-time camera, hand gesture recognition, and projection-based feedback to support embodied and responsive folding experiences. </p><p dir="ltr">The system operates in three distinct modes: Create Mode allows users to construct custom crease patterns through freeform folding and gesture confirmation; Learner Mode provides step-by-step projection of predefined models with real-time feedback on fold accuracy; and Auto Mode explores data-driven inference by matching user folds to known patterns and suggesting possible next steps. </p><p dir="ltr">Technically, the system tracks paper geometry and hand movement using computer vision, builds a dynamic crease graph, and projects instructions and feedback directly onto the paper surface. Users interact entirely through hand gestures, enabling continuous focus on the material without additional devices. </p><p dir="ltr">A Miura-ori pattern was used as a case study to demonstrate the system’s capabilities in both Create and Learner Modes. The results show that the system successfully supports fold tracking, feedback projection, and gesture-driven interaction, while revealing design challenges related to fold detection resolution, gesture robustness, and projection alignment. This work contributes a novel framework for embodied origami learning and offers insights into the integration of computational interaction with craft-based practices.</p>