Original title: SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation
Ziyun Qian, Zizhi Chen, Yizhou Liu, Mingyang Sun +2 · September 2026
Image generators struggle to follow tricky 3D instructions. When users ask for specific arrangements, models often mess up object distances, hidden items, and camera angles, especially across multiple steps. To fix this, researchers created SpatialGuard. It works by treating picture creation like a build-and-check loop. First, an architect module translates text into a 3D blueprint. Next, a visual module turns that blueprint into pictures. Then, a critic module checks the text, blueprint, and image together to spot mistakes. A special harness system holds everything together, using rules and tools to edit and repair the 3D blueprint when needed. The researchers found that SpatialGuard reaches state-of-the-art results in 3D layout generation and improves spatial faithfulness over existing systems.
Imagine you are decorating a room with furniture and need to ensure everything fits and is visible from the doorway. You start with a floor plan, place items according to it, then walk through to check if anything blocks the view or looks off. In this paper, SpatialGuard does this for AI-generated images: it creates a 3D plan, builds the scene, checks the visual result, and fixes any layout issues.
When you ask AI to create images with complex scenes, it often places objects incorrectly. SpatialGuard ensures object positions match your text description, making generated images more accurate and reliable for users like artists and designers.
The abstract does not explicitly state limitations of SpatialGuard. It focuses on complex 3D spatial generation and shows improvements over existing methods, but does not discuss computational overhead or performance on simpler scenes.
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SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation — Ziyun Qian, Zizhi Chen, Yizhou Liu et al.
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.