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<p>Generating traditional Chinese landscape paintings from classical poetry is a challenging cross-modal task due to the condensed semantics and esthetic abstraction of poetic language. Existing text-to-image models struggle to interpret classical Chinese syntax and reproduce ink-wash artistic styles. We propose Poe2CLP, a phrase-level attention and cross-modal alignment framework that dynamically captures composite semantic units in poems and adaptively fuses global mood with local imagery. Built upon a LoRA-enhanced diffusion model and trained on Poegraph-a new dataset of 5200 poem-painting pairs-Poe2CLP outperforms state-of-the-art methods in FID (130.95), CLIP-T (40.75), and CLIP Style Score (0.348). Our approach advances the digital interpretation of East Asian poetic-visual traditions. The dataset and code are publicly available.</p>

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Poe2CLP: phrase-level attention and cross-modal semantic alignment for poem generate chinese landscape paintings

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月16日

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  • Xianlin Peng,
  • Tianyu Sun,
  • Qiyao Hu,
  • Zengguo Sun,
  • Nuo Xu,
  • Jinye Peng

摘要

Generating traditional Chinese landscape paintings from classical poetry is a challenging cross-modal task due to the condensed semantics and esthetic abstraction of poetic language. Existing text-to-image models struggle to interpret classical Chinese syntax and reproduce ink-wash artistic styles. We propose Poe2CLP, a phrase-level attention and cross-modal alignment framework that dynamically captures composite semantic units in poems and adaptively fuses global mood with local imagery. Built upon a LoRA-enhanced diffusion model and trained on Poegraph-a new dataset of 5200 poem-painting pairs-Poe2CLP outperforms state-of-the-art methods in FID (130.95), CLIP-T (40.75), and CLIP Style Score (0.348). Our approach advances the digital interpretation of East Asian poetic-visual traditions. The dataset and code are publicly available.