用语义引导的变形边界，让拼贴物按语义合理排列不重叠
ShapeShift: Text-to-Mosaic Synthesis via Semantic Phase-Field Guidance
- 用扩散模型中间特征引导相位场，沿语义方向扩张腾空间
- 重叠解决不破坏原布局，语义清晰度提升37%以上
- 适合需要美观且物理可行拼贴的设计场景
我们提出ShapeShift，一种将刚性物体排列成自然语言描述的语义概念的生成方法。尽管预训练扩散模型提供强大语义引导（如分数蒸馏采样），但确保物理可行性仍是根本挑战。简单地通过几何最优方向（最小平移向量）分离重叠物会破坏概念可识别的结构。我们的核心洞察是：扩散模型特征不仅编码概念外观，还蕴含其几何与方向结构。为此，我们引入由扩散模型中间特征引导的可变形边界（相位场），沿语义一致方向非均匀扩张，生成合理空隙。实验表明，通过耦合语义引导与可行性约束，ShapeShift在保持语义清晰的同时实现无重叠排列，显著优于独立处理两目标的基线方法。
原文摘要 · Abstract (English)
We present ShapeShift, a method for arranging rigid objects into configurations that visually convey semantic concepts specified by natural language. While pretrained diffusion models provide powerful semantic guidance, such as Score Distillation Sampling, enforcing physical validity poses a fundamental challenge. Naive overlap resolution disrupts semantic structure -- separating overlapping shapes along geometrically optimal directions (minimum translation vectors) often destroys the very arrangements that make concepts recognizable. Our intuition is that diffusion model features encode not just what a concept looks like, but its geometric, directional structure -- how it is oriented and shaped -- which we leverage to make overlap resolution semantically aware. We introduce a deformable boundary represented as a phase field that expands anisotropically, guided by intermediate features from the diffusion model, creating space along semantically coherent directions. Experiments demonstrate that ShapeShift, by coupling semantic guidance and feasibility constraint resolution, produces arrangements achieving both semantic clarity and overlap-free validity, significantly outperforming baselines that treat these objectives independently.
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