AI 中文总结
针对古画褪色问题，提出PoemColor框架，通过诗歌绘画投影仪和结构感知语义注意力，将诗歌文化语义与绘画恢复对齐，并构建混合数据集。实验证明该框架显著优于现有方法，能实现可控色彩化，恢复古画历史真实性与诗歌语义。
AI 中文摘要
古画不可逆转的褪色破坏了‘诗画合一’这一核心美学原则，即视觉意象与文学题字相协调。尽管扩散模型提供了强大的生成先验，但恢复历史上忠实的色彩仍很困难。为此提出PoemColor框架，通过诗歌绘画投影仪将隐式诗歌语境转化为经典色彩感知条件，利用结构感知语义注意力调节诗歌色彩语义注入扩散主干的方式。还构建了混合恢复数据集。实验表明该框架显著优于现有方法，实现可控色彩化，恢复历史真实性和诗歌语义。
英文摘要
The irreversible fading of ancient paintings disrupts the "congruence between poems and paintings", a core aesthetic principle where visual imagery harmonizes with literary inscriptions. Although diffusion models provide strong generative priors, restoring historically faithful colors remains difficult: visual restoration is inherently ambiguous, while direct text guidance often causes modern semantic bias, over-saturation, and cross-boundary color leakage. To address this, we propose PoemColor, a poem-guided ancient painting colorization framework. Our method aligns poetic cultural semantics with painting restoration through two key designs. First, the Poetic Painting Projector (P3) converts implicit poetic context into a classical color-aware condition via poem-to-palette pretraining, reducing the ambiguity of poem-to-color mapping. Second, Structure-Aware Semantic Attention (SASA) regulates how poetic color semantics are injected into the diffusion backbone by jointly controlling their propagation direction and regional injection strength. In addition, we construct a hybrid restoration dataset that integrates synthetic degradation with expert-restored artifacts, providing both scalable supervision and real classical color references. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art methods, delivering controllable colorization that revives both historical authenticity and poetic semantics.
