StyleTextGen：风格条件化的多语言场景文字生成
StyleTextGen: Style-Conditioned Multilingual Scene Text Generation
双分支风格编码器从复杂背景中解耦文字风格表征，配合掩码引导推理在生成时做字符级风格对齐，这套「参考图风格注入 + 掩码约束采样」的范式可直接迁移到字体风格迁移、艺术字生成与跨文字系统的风格一致生成任务。
原文依据 a mask-guided inference strategy that ensures precise style alignment between generated and reference text
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论风格条件化的场景文字生成面临两项独特挑战：一是从复杂背景中提取精确的文字风格，二是在字符之间保持细粒度的风格一致性，对多语言文字系统而言尤为困难。本文提出 StyleTextGen，一个学习在不同语言与书写系统之间感知并复现视觉文字风格的新框架。该方法包含三项关键贡献：第一，提出专用于风格建模的双分支风格编码器，能够在复杂真实场景中获得鲁棒的多语言文字风格表征；第二，设计文字风格一致性损失，以增强风格连贯性并提升整体视觉质量；第三，提出掩码引导的推理策略，确保生成文字与参考文字之间精确的风格对齐。为便于系统评测，我们构建了 StyleText-CE，一个涵盖单语言与跨语言设定的双语场景文字风格基准。大量实验表明，StyleTextGen 在风格一致性与跨语言泛化方面显著优于现有方法，在多语言风格条件化文字生成上确立了新的最优性能。
英文原文摘要 1228 字符
Style-conditioned scene text generation faces unique challenges in extracting precise text styles from complex backgrounds and maintaining fine-grained style consistency across characters, especially for multilingual scripts. We propose StyleTextGen, a novel framework that learns to perceive and replicate visual text styles across different languages and writing systems. Our approach features three key contributions: First, we introduce a dual-branch style encoder dedicated to style modeling, yielding robust multilingual text style representations in complex real-world scenes. Second, we design a text style consistency loss that enhances style coherence and improves overall visual quality. Third, we develop a mask-guided inference strategy that ensures precise style alignment between generated and reference text. To facilitate systematic evaluation, we construct StyleText-CE, a bilingual scene text style benchmark covering both monolingual and cross-lingual settings. Extensive experiments demonstrate that StyleTextGen significantly outperforms existing methods in style consistency and cross-lingual generalization, establishing new state-of-the-art performance in multilingual style-conditioned text generation.