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arXiv 2607.23491cs.CVcs.CL

PlanCraft:用于受建筑师启发的渐进式3D住宅场景生成的草图绘制、细化和布置

PlanCraft: Sketch, Refine, and Furnish for Architect-Inspired Progressive 3D Residential Scene Generation

Pengyu Zeng, Yuqin Dai, Jun Yin, Ng Cheuk Hei, Ziyang Han, Jing Zhong, Chaoyang Shi, ZhanXiang Jin, Maowei Jiang, Shuai Lu

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中文总结 AI 辅助

研究针对自动住宅平面图生成中忽视的设计渐进性及二维平面图重要性问题,提出PlanCraft,通过SketchPlan提供训练信号,PlanCraft-Diff细化草图,PlanCraft-Agent布置场景,实验显示其在FID及空间合理性上优于现有方法。

中文摘要 AI 辅助

在自动住宅平面图生成中,有两个结构见解被忽视。一是设计具有渐进性,建筑师从粗略笔触开始并逐步细化,而现有方法通常在生成前要求条件表示完全指定,与实际设计过程不匹配。二是二维平面图是不可替代的空间契约,绕过它会导致房间重叠等问题。基于此,提出PlanCraft。SketchPlan通过在8万张真实平面图上重放绘图过程提供训练信号,PlanCraft-Diff通过粗到细策略将草图细化为精确平面图,PlanCraft-Agent在确定的房间边界内布置场景。实验表明,PlanCraft的FID比现有最佳二维方法低61.1%,在专家评级的空间合理性上比现有三维系统高15分。

英文摘要

Two structural insights have been overlooked in automated residential floor plan generation. First, design is inherently progressive. Architects begin with rough strokes and refine them over time, whereas existing methods typically require their conditioning representation to be fully specified before generation, a fundamental mismatch with how design actually works. Second, the 2D floor plan is not an optional intermediate but an irreplaceable spatial contract. Once room boundaries, doors, and windows are fixed, furnishing reduces from open-ended spatial reasoning to bounded constraint satisfaction. Bypassing this contract, as existing 3D systems do by delegating layout to language models, yields overlapping rooms and implausible proportions; directly calling general-purpose language models likewise produces geometrically invalid layouts. Guided by these insights, we present PlanCraft. SketchPlan supplies the missing training signal by replaying the architect's drawing process on 80K real floor plans, producing partial sketches at every completeness level. PlanCraft-Diff progressively sharpens an incomplete sketch into a geometrically precise, vectorizable floor plan through a coarse-to-fine strategy. With the spatial contract established, PlanCraft-Agent then furnishes the scene within well-defined room boundaries. Experiments show that PlanCraft achieves a 61.1\% lower FID than the best existing 2D method and surpasses existing 3D systems by 15 points in expert-rated spatial rationality, with a sketch at only 25\% completion already outperforming all fully specified baselines.

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