Metraipolis › archive › dc_7b2a6ca356cb

SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation — arXiv:2609.01582

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.01582
License (as archived): CC0-1.0
Archived: 2026-10-17T07:13:59.715Z
SHA-256 of the archived text: c9b80d60483eb4a0db1eabe0c350718a54e52c5eb18aa21766d5aaf13df467d6
Stable URL of this page: https://metraipolis.com/documento/dc_7b2a6ca356cb

How to cite

SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation — arXiv:2609.01582 — arXiv (en), https://arxiv.org/abs/2609.01582 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7b2a6ca356cb (Metraipolis archive dc_7b2a6ca356cb, sha256 c9b80d60483eb4a0db1eabe0c350718a54e52c5eb18aa21766d5aaf13df467d6)

BibTeX

@misc{dc7b2a6ca356cb,
  title = {SpatialGuard: Harness-Guided Verifiable Spatial Reasoning for Text-to-Image Generation — arXiv:2609.01582},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.01582},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7b2a6ca356cb; sha256 c9b80d60483eb4a0db1eabe0c350718a54e52c5eb18aa21766d5aaf13df467d6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.01582. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-02 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.01582 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T07:00:59.001Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).