TextGround4M: A Prompt-Aligned Dataset for Layout-Aware Text Rendering
Despite recent advances in text-to-image generation, models still struggle to accurately render prompt-specified text with correct spatial layout -- especially in multi-span, structured settings. This challenge is driven not only by the lack of datasets that align prompts with the exact text and layout expected in the image, but also by the absence of effective metrics for evaluating layout quality. To address these issues, we introduce TextGround4M, a large-scale dataset of over 4 million prompt-image pairs, each annotated with span-level text grounded in the prompt and corresponding bounding boxes. This enables fine-grained supervision for layout-aware, prompt-grounded text rendering. B...
| Authors | Dongxing Mao, Yilin Wang, Linjie Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
Models and datasets referencing this paper
- CSU-JPG/Textground4MDataset
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