Syn3DTxt: Embedding 3D Cues for Scene Text Generation

2025-05-24

AI Summary

Existing synthetic scene text datasets largely lack 3D geometric context, hindering models’ ability to capture spatial interactions between text and curved surfaces or perspective backgrounds. To address this, we propose the first geometry-consistent, 3D-aware text rendering data standard. Our method explicitly incorporates surface normals as a primary 3D cue into the synthesis pipeline, integrating normal-guided spatial modeling, diffusion model adaptation, and multi-view geometric constraints. This enables precise text placement and realistic visual alignment on complex non-planar surfaces. Evaluated on multiple 3D-aware text rendering benchmarks, our approach achieves state-of-the-art geometric fidelity—significantly improving localization accuracy and visual consistency under varying viewpoints and surface curvatures. Crucially, the resulting dataset is fully reproducible and geometrically grounded, establishing a rigorous, geometry-credible foundation for 3D scene text generation research.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Insufficient 3D context in synthetic text datasets
• 2D data limits spatial layout and visual effects
• Lack of geometric cues for text embedding

Innovation

• Incorporates surface normals for 3D context
• Enhances spatial relationships representation
• Improves geometric context in datasets