COrigami: An AI Pipeline for Co-Designing Flat-Foldable Visually Recognisable Origami

2026-06-24

AI Summary

This study addresses the challenge of generating origami art that simultaneously satisfies strict geometric constraints and subjective visual aesthetics by proposing an end-to-end AI co-design framework. The approach integrates natural language understanding, semantic skeleton generation, base crease pattern computation, flat-foldability verification, and shape refinement, and introduces— for the first time—a reinforcement learning–based autonomous aesthetic evaluation mechanism to establish a closed-loop synergy between algorithmic optimization and aesthetic feedback. This work pioneers a new paradigm for AI-assisted origami design that unifies mathematical rigor with artistic creativity, enabling the efficient generation of foldable structures with high visual recognizability and significantly expanding both the efficiency and creative potential available to human artists.

Abstract

While generative AI has achieved remarkable success in solving problems with verifiable solutions, generating physical art that satisfies both strict geometric constraints and subjective visual aesthetics remains a challenge. This paper presents an approach to tackle these difficulties in the domain of computational origami, a mathematically rigid environment that grounds artistic design within the equations of flat foldability. We present COrigami, an end-to-end AI-driven pipeline that assists the design cycle by generating crease patterns from natural language. Our pipeline involves generating a semantic stick figure, computing a base packing, solving for a flat-foldable crease pattern, shaping the flat-folded crease pattern, and refining the generated model using reinforcement learning driven by an autonomous aesthetic evaluation loop. Our system acts as a highly effective collaborative assistant, generating structural starting points that human artists can further expand and shape. By integrating algorithmic optimisation with autonomous aesthetic critique, this work demonstrates how AI systems can satisfy multi-objective physical constraints to enable reliable, mathematically grounded co-creativity.

Problem

• computational origami
• flat-foldable
• visual aesthetics
• geometric constraints
• AI co-creativity

Innovation

• computational origami
• flat-foldable crease pattern
• AI co-creativity
• aesthetic evaluation loop
• reinforcement learning