[2603.29585] Learn2Fold: Structured Origami Generation with World Model Planning

Computer Science > Graphics arXiv:2603.29585 (cs) [Submitted on 2 Feb 2026] Title:Learn2Fold: Structured Origami Generation with World Model Planning Authors:Yanjia Huang, Yunuo Chen, Ying Jiang, Jinru Han, Zhengzhong Tu, Yin Yang, Chenfanfu Jiang View a PDF of the paper titled Learn2Fold: Structured Origami Generation with World Model Planning, by Yanjia Huang and 6 other authors View PDF HTML (experimental) Abstract:The ability to transform a flat sheet into a complex three-dimensional structure is a fundamental test of physical intelligence. Unlike cloth manipulation, origami is governed by strict geometric axioms and hard kinematic constraints, where a single invalid crease or collision can invalidate the entire folding sequence. As a result, origami demands long-horizon constructive reasoning that jointly satisfies precise physical laws and high-level semantic intent. Existing approaches fall into two disjoint paradigms: optimization-based methods enforce physical validity but require dense, precisely specified inputs, making them unsuitable for sparse natural language descriptions, while generative foundation models excel at semantic and perceptual synthesis yet fail to produce long-horizon, physics-consistent folding processes. Consequently, generating valid origami folding sequences directly from text remains an open challenge. To address this gap, we introduce Learn2Fold, a neuro-symbolic framework that formulates origami folding as conditional program induction o...