Everything in Its Place: Benchmarking Spatial Intelligence of Text-to-Image Models

• URL: http://arxiv.org/abs/2601.20354v2
• Date: Thu, 29 Jan 2026 08:38:27 GMT
• Title: Everything in Its Place: Benchmarking Spatial Intelligence of Text-to-Image Models
• Authors: Zengbin Wang, Xuecai Hu, Yong Wang, Feng Xiong, Man Zhang, Xiangxiang Chu,
• Abstract summary: Text-to-image (T2I) models have achieved remarkable success in generating high-fidelity images, but they often fail in handling complex spatial relationships.<n>We introduce SpatialGenEval, a new benchmark designed to evaluate the spatial intelligence of T2I models.
• Score: 23.6849873930169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image (T2I) models have achieved remarkable success in generating high-fidelity images, but they often fail in handling complex spatial relationships, e.g., spatial perception, reasoning, or interaction. These critical aspects are largely overlooked by current benchmarks due to their short or information-sparse prompt design. In this paper, we introduce SpatialGenEval, a new benchmark designed to systematically evaluate the spatial intelligence of T2I models, covering two key aspects: (1) SpatialGenEval involves 1,230 long, information-dense prompts across 25 real-world scenes. Each prompt integrates 10 spatial sub-domains and corresponding 10 multi-choice question-answer pairs, ranging from object position and layout to occlusion and causality. Our extensive evaluation of 21 state-of-the-art models reveals that higher-order spatial reasoning remains a primary bottleneck. (2) To demonstrate that the utility of our information-dense design goes beyond simple evaluation, we also construct the SpatialT2I dataset. It contains 15,400 text-image pairs with rewritten prompts to ensure image consistency while preserving information density. Fine-tuned results on current foundation models (i.e., Stable Diffusion-XL, Uniworld-V1, OmniGen2) yield consistent performance gains (+4.2%, +5.7%, +4.4%) and more realistic effects in spatial relations, highlighting a data-centric paradigm to achieve spatial intelligence in T2I models.

Related papers

• ESPLoRA: Enhanced Spatial Precision with Low-Rank Adaption in Text-to-Image Diffusion Models for High-Definition Synthesis [45.625062335269355]
  Diffusion models have revolutionized text-to-image (T2I) synthesis, producing high-quality, photorealistic images.<n>However, they still struggle to properly render the spatial relationships described in text prompts.<n>Our approach builds upon a curated dataset of spatially explicit prompts, meticulously extracted and synthesized from LAION-400M.<n>We present ESPLoRA, a flexible fine-tuning framework based on Low-Rank Adaptation, to enhance spatial consistency in generative models.
  arXiv  Detail & Related papers  (2025-04-18T15:21:37Z)
• CoMPaSS: Enhancing Spatial Understanding in Text-to-Image Diffusion Models [18.89863162308386]
  CoMPaSS is a versatile framework that enhances spatial understanding in T2I models.<n>It first addresses data ambiguity with the Spatial Constraints-Oriented Pairing (SCOP) data engine.<n>To leverage these priors, CoMPaSS also introduces the Token ENcoding ORdering (TENOR) module.
  arXiv  Detail & Related papers  (2024-12-17T18:59:50Z)
• Evaluating the Generation of Spatial Relations in Text and Image Generative Models [4.281091463408283]
  spatial relations are naturally understood in a visuo-spatial manner. We develop an approach to convert LLM outputs into an image, thereby allowing us to evaluate both T2I models and LLMs. Surprisingly, we found that T2I models only achieve subpar performance despite their impressive general image-generation abilities.
  arXiv  Detail & Related papers  (2024-11-12T09:30:02Z)
• REVISION: Rendering Tools Enable Spatial Fidelity in Vision-Language Models [67.55362046790512]
  Vision-language models lack the ability to correctly reason over spatial relationships. We develop the REVISION framework which improves spatial fidelity in vision-language models. Our results and findings indicate that utilizing rendering-based frameworks is an effective approach for developing spatially-aware models.
  arXiv  Detail & Related papers  (2024-08-05T04:51:46Z)
• Getting it Right: Improving Spatial Consistency in Text-to-Image Models [103.52640413616436]
  One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt. We create SPRIGHT, the first spatially focused, large-scale dataset, by re-captioning 6 million images from 4 widely used vision datasets. We find that training on images containing a larger number of objects leads to substantial improvements in spatial consistency, including state-of-the-art results on T2I-CompBench with a spatial score of 0.2133, by fine-tuning on 500 images.
  arXiv  Detail & Related papers  (2024-04-01T15:55:25Z)
• Benchmarking Spatial Relationships in Text-to-Image Generation [102.62422723894232]
  We investigate the ability of text-to-image models to generate correct spatial relationships among objects. We present VISOR, an evaluation metric that captures how accurately the spatial relationship described in text is generated in the image. Our experiments reveal a surprising finding that, although state-of-the-art T2I models exhibit high image quality, they are severely limited in their ability to generate multiple objects or the specified spatial relations between them.
  arXiv  Detail & Related papers  (2022-12-20T06:03:51Z)
• SIRI: Spatial Relation Induced Network For Spatial Description Resolution [64.38872296406211]
  We propose a novel relationship induced (SIRI) network for language-guided localization. We show that our method is around 24% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown.
  arXiv  Detail & Related papers  (2020-10-27T14:04:05Z)
• Spatially Aware Multimodal Transformers for TextVQA [61.01618988620582]
  We study the TextVQA task, i.e., reasoning about text in images to answer a question. Existing approaches are limited in their use of spatial relations. We propose a novel spatially aware self-attention layer.
  arXiv  Detail & Related papers  (2020-07-23T17:20:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.