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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation
%A Qian, Ziyun
%A Chen, Zizhi
%A Liu, Yizhou
%A Sun, Mingyang
%A Yang, Dingkang
%A Zhang, Lihua
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F qian-etal-2026-spatialguard
%X 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.
%U https://aclanthology.org/2026.emnlp-main.653/
%P 15022-15032

Markdown (Informal)

[SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation](https://aclanthology.org/2026.emnlp-main.653/) (Qian et al., EMNLP 2026)

• SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation (Qian et al., EMNLP 2026)

ACL

• Ziyun Qian, Zizhi Chen, Yizhou Liu, Mingyang Sun, Dingkang Yang, and Lihua Zhang. 2026. SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15022–15032, Budapest, Hungary. Association for Computational Linguistics.