发表机构
College of Intelligent Robotics and Advanced Manufacturing, Fudan University; Fysics Intelligence Technologies Co., Ltd. (Fysics AI)（复旦大学智能机器人与先进制造学院; Fysics智能科技有限公司（Fysics AI））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SpatialGuard是一种结构化布局引导框架，通过布局管控器实现可验证的空间推理，提升了复杂3D空间文本到图像生成的空间保真度，达到当前最优性能。
AI 中文摘要
复杂3D空间文本到图像生成要求模型将自然语言转换为稳定的视觉几何，而非仅语义外观。现有提示驱动或布局条件方法提升了可控性，但在视觉采样前常缺乏可优化且可验证的空间中介，导致多轮生成中对象关系、遮挡、可见性及相机约束易衰减。本文提出SpatialGuard，一种面向复杂3D空间文本到图像生成的结构化布局引导框架：其通过空间布局架构（Spatial Layout Architect）将提示解析为面向图像合成的3D布局，经视觉实现器（Visual Realizer）将其转化为视觉条件与候选图像，再通过视觉对齐评判器（Visual Alignment Critic）验证提示、布局与图像间的一致性。为使约束在迭代中保持稳定，SpatialGuard引入布局管控器（Layout Harness），围绕可编辑布局状态组织规则约束、工具调用、共享知识与反馈循环。该设计将复杂空间生成从隐式提示遵循转变为规划、实现、验证与修复的可验证流程。综合实验表明，SpatialGuard在复杂3D空间布局生成中达到了当前最优性能，且相比现有文本到图像及布局控制基线方法，空间保真度得到提升。
英文摘要
Complex 3D spatial text to image generation requires models to convert natural language into stable visual geometry, not merely semantic appearance. Existing prompt-driven or layout-conditioned methods improve controllability, but often lack an optimizable and verifiable spatial intermediary before visual sampling. As a result, object relations, occlusion, visibility, and camera constraints can decay during multi-round generation. This paper presents SpatialGuard, a structured layout-guided framework for complex 3D spatial text-to-image generation. SpatialGuard parses prompts into image synthesis-oriented 3D layouts through a Spatial Layout Architect, realizes them as visual conditions and candidate images through a Visual Realizer, and uses a Visual Alignment Critic to validate consistency among prompt, layout, and image. To keep constraints stable across iterations, SpatialGuard introduces a Layout Harness that organizes rule constraints, tool invocation, shared knowledge, and feedback loops around the editable layout state. This design turns complex spatial generation from implicit prompt following into a verifiable process of planning, realization, validation, and repair. Comprehensive experiments show that SpatialGuard achieves state-of-the-art performance in complex 3D spatial layout generation and improves spatial faithfulness over existing text-to-image and layout control baselines.
