SpaceFlow：可局部控制的3D生成

中文摘要 AI 辅助

当前的3D生成方法缺乏显式的局部控制：几何贴合度通常由全局控制强度定义，且外观无法被局部指定。我们提出SpaceFlow，这是一种无需训练的流水线，用于从文本描述和一组几何基元生成可局部控制的3D内容。每个基元作为对象部件的代理，并被分配一个局部控制级别，使用户能够指定区域是应严格遵循输入形状还是允许生成补全。在结构生成过程中，我们在生成流过程中强制执行这些空间约束。对于外观合成，生成的结构被分割并与基元匹配。每个生成的部件仅以其分配的文本或图像线索为条件，从而限制跨部件泄漏。区域几何指标表明，SpaceFlow在高控制区域保留指定几何，并在低控制区域实现合理的形状变化。用户研究进一步表明，几何保真度与生成自由度之间的平衡在整体质量上仍具有竞争力。当在固定几何上评估外观时，基于文本的路由实现了最先进的提示忠实度和颜色/材质准确性。定性结果还显示了图像线索的局部路由。项目页面可在该http URL获取。

英文摘要

Current 3D generation methods lack explicit local control: geometric adherence is often defined by a global control strength, and appearance cannot be specified locally. We present SpaceFlow, a training-free pipeline for locally controllable 3D generation from text descriptions and a collection of geometric primitives. Each primitive serves as a proxy for an object part and is assigned a local control level, enabling users to specify whether regions should strictly follow the input shape or allow generative completion. During structure generation, we enforce these spatial constraints within the generative flow process. For appearance synthesis, the generated structure is segmented and matched to the primitives. Each generated part is conditioned only on its assigned text or image cue, thereby limiting cross-part leakage. Regional geometry metrics demonstrate that SpaceFlow preserves the specified geometry in high-control regions and enables plausible shape variation in low-control areas. A user study further indicates that the resulting balance between geometric fidelity and generative freedom remains competitive in overall quality. When evaluating appearance on fixed geometry, text-conditioned routing achieves state-of-the-art prompt faithfulness and color/material accuracy. Qualitative results additionally show localized routing of image cues. The project page is available at SpaceFlow3D.github.io.

发表机构

• ETH Zürich（苏黎世联邦理工学院）
• Stanford University（斯坦福大学）
• Microsoft（微软公司）

机构由 AI 辅助整理，请以论文原文为准。