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<p>To address morphological distortion and background interference in digital restoration of costume patterns from ancient Chinese paintings, we propose PT-RFLow, a tiling-guided adaptive targeted image editing method built on the Kontext context-aware Rectified Flow Transformer. It enhances pattern morphological feature and arrangement rule learning via targeted low-rank fine-tuning, with a dual-modal guidance enhancement mechanism to boost tiling restoration accuracy. To map locally distorted pattern regions to their complete planar form, we construct a cross-dynasty triplet dataset of typical patterns for model training and testing. Experiments show PT-RFLow significantly outperforms baselines in core metrics including Structural Similarity, Perceptual Fidelity and Tiling Regularity; expert evaluation verifies its advantages in structural accuracy, style fidelity and aesthetic consistency, providing an effective technical approach for digital restoration of traditional Chinese patterns.</p>

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PT-RFLow: a transformer-based method for structured restoration of distorted costume patterns in ancient chinese paintings

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月9日

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  • Yuxuan Guo,
  • Yuejiao Mei,
  • Chun Zhu,
  • Kaixuan Liu

摘要

To address morphological distortion and background interference in digital restoration of costume patterns from ancient Chinese paintings, we propose PT-RFLow, a tiling-guided adaptive targeted image editing method built on the Kontext context-aware Rectified Flow Transformer. It enhances pattern morphological feature and arrangement rule learning via targeted low-rank fine-tuning, with a dual-modal guidance enhancement mechanism to boost tiling restoration accuracy. To map locally distorted pattern regions to their complete planar form, we construct a cross-dynasty triplet dataset of typical patterns for model training and testing. Experiments show PT-RFLow significantly outperforms baselines in core metrics including Structural Similarity, Perceptual Fidelity and Tiling Regularity; expert evaluation verifies its advantages in structural accuracy, style fidelity and aesthetic consistency, providing an effective technical approach for digital restoration of traditional Chinese patterns.