FabDreamer：通过AI辅助分层制造探索图像到物理的工作流
FabDreamer: Exploring the Image-to-Physical Workflow Through AI-Assisted Layered Fabrication
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中文总结 AI 辅助
FabDreamer是一款图像转物理的AI辅助系统，通过三个阶段将图像转化为可制造SVG，经多轮评估验证其可提升制造效率并适配不同领域需求。
中文摘要 AI 辅助
生成式AI能让任何人在数秒内创作出丰富的视觉内容，但将这些内容转化为可实际制造的物品仍需要手动分解、遮挡修复和结构验证，这些工作大多由用户独自完成。我们提出了FabDreamer，这是一个图像到物理的系统，它通过三个阶段将图像转化为可制造的SVG，且AI在各阶段有明确的主导作用：（1）AI主导分解为按深度排序的图层；（2）在创意编辑过程中提供实时3D预览按需协助；（3）在导出前提供结构完整性建议。我们将该工作流应用于分层激光切割艺术，并通过三轮评估：形成性分析、早期原型用户评估（N=13）以及来自6个制造领域专家的跨领域从业者研究（N=6）。我们的发现表明，设计过程中的物理感知除了错误预防外，还能带来超出预期的创意机会；从业者会将系统的通用几何操作适配到自身领域；制造智能体负责处理几何可读的约束，而领域知识仍由制造者掌握。
英文摘要
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.
