COrigami: An AI Pipeline for Co-Designing Flat-Foldable Visually Recognisable Origami
COrigami is an end-to-end AI-driven pipeline that generates crease patterns from natural language, satisfying strict flat-foldability constraints and visual aesthetics. It assists human artists by generating structural starting points through steps including semantic stick figure generation, base packing, crease pattern solving, shaping, and reinforcement learning with an autonomous aesthetic evaluation loop.
[2606.26299] COrigami: An AI Pipeline for Co-Designing Flat-Foldable Visually Recognisable Origami
[Submitted on 24 Jun 2026]
Title:COrigami: An AI Pipeline for Co-Designing Flat-Foldable Visually Recognisable Origami
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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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.26299 [cs.AI]
(or arXiv:2606.26299v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.26299
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tom Zahavy [view email] [v1] Wed, 24 Jun 2026 18:43:24 UTC (9,638 KB)
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