Abstract
This study aims to investigate the challenge of insufficient three-dimensional context in synthetic datasets for scene text rendering. Although recent advances in diffusion models and related techniques have improved certain aspects of scene text generation, most existing approaches continue to rely on 2D data, sourcing authentic training examples from movie posters and book covers, which limits their ability to capture the complex interactions among spatial layout and visual effects in real-world scenes. In particular, traditional 2D datasets do not provide the necessary geometric cues for accurately embedding text into diverse backgrounds. To address this limitation, we propose a novel standard for constructing synthetic datasets that incorporates surface normals to enrich three-dimensional scene characteristic. By adding surface normals to conventional 2D data, our approach aims to enhance the representation of spatial relationships and provide a more robust foundation for future scene text rendering methods. Extensive experiments demonstrate that datasets built under this new standard offer improved geometric context, facilitating further advancements in text rendering under complex 3D-spatial conditions.
Abstract (translated)
这项研究旨在探讨合成数据集中三维上下文不足对场景文本渲染的挑战。尽管最近在扩散模型及相关技术方面的进展改善了场景文本生成的一些方面，但大多数现有方法仍然依赖于二维数据，并从电影海报和书籍封面等来源获取真实的训练示例，这限制了它们捕捉现实世界场景中空间布局与视觉效果之间复杂互动的能力。特别是，传统的二维数据集无法提供将文本准确嵌入到各种背景所需的几何线索。 为了解决这一局限性，我们提出了一种新的构建合成数据集的标准，该标准引入表面法线以丰富三维场景特性。通过向常规的二维数据添加表面法线，我们的方法旨在增强空间关系的表现，并为未来的场景文本渲染方法提供更坚实的基石。广泛的实验表明，在这种新标准下建立的数据集提供了改进的几何上下文，有助于在复杂的三维空间条件下进一步推进文本渲染技术的进步。
URL
https://arxiv.org/abs/2505.18479
