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arXiv · 2608.13665

FabDreamer: Exploring the Image-to-Physical Workflow Through AI-Assisted Layered Fabrication

Chenfeng Gao·Zeya Chen·Anjie Yang·Karan Ahuja·Danli Luo
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Abstract

Generative AI lets anyone create rich visual content in seconds, yet translating that content into a physically fabricable artifact still demands manual decomposition, occlusion repair, and structural verification that most tools leave entirely to the user. We present FabDreamer, an image-to-physical system that carries an image to fabrication-ready SVGs through three stages with deliberately staged AI initiative: (1) AI leads decomposition into depth-ordered layers, (2) assists on demand during creative editing with realtime 3D preview, and (3) advises on structural integrity before export. We instantiate this workflow for layered laser-cut art and evaluate it through three rounds including a formative analysis, an early prototype user evaluation (N=13), and a cross-domain practitioner study with specialists from 6 fabrication domains (N=6). Our findings show that physical awareness during design opens creative opportunities beyond error prevention, that practitioners appropriate the system's generic geometric operations for their own domains, and that the fabrication agent covers geometry-readable constraints while domain knowledge remains with the maker.

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Chenfeng Gao, Zeya Chen, Anjie Yang, Karan Ahuja, Danli Luo. 2026-08-13. FabDreamer: Exploring the Image-to-Physical Workflow Through AI-Assisted Layered Fabrication. https://doi.org/10.1145/3830398.3830571

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