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<p>Digital inpainting of traditional Chinese murals is challenged by the difficulty of disentangling intricate structures from unique artistic styles, often leading to artifacts. To address this, we propose DCADif, a novel diffusion model for high-fidelity mural restoration. DCADif’s core innovation is a Decoupled Conditional Encoder that uses parallel pathways a pre-trained CLIP for structural line art and a new SwinStyle Encoder for stylistic features to achieve independent control. Furthermore, a Time-Adaptive Feature Fusion (TAFF) module dynamically adjusts the influence of these features during denoising, prioritizing structure in early stages and style in later ones, mimicking an expert’s coarse-to-fine workflow. Evaluated on our new large-scale MuralVerse-S dataset, DCADif significantly outperforms state-of-the-art methods across all degradation levels. It establishes a new benchmark for digital cultural heritage preservation by effectively balancing structural accuracy with artistic authenticity. The dataset and code are publicly available.The dataset and code are available at <a href="https://github.com/LPDLG/DCADif">https://github.com/LPDLG/DCADif</a>.</p>

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DCADif: decoupled conditional adaptive time-dynamic fusion diffusion inpainting of traditional Chinese mural paintings

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月28日

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  • Xianlin Peng,
  • Chao Li,
  • Qiyao Hu,
  • Zengguo Sun,
  • Jinye Peng,
  • Manli Sun

摘要

Digital inpainting of traditional Chinese murals is challenged by the difficulty of disentangling intricate structures from unique artistic styles, often leading to artifacts. To address this, we propose DCADif, a novel diffusion model for high-fidelity mural restoration. DCADif’s core innovation is a Decoupled Conditional Encoder that uses parallel pathways a pre-trained CLIP for structural line art and a new SwinStyle Encoder for stylistic features to achieve independent control. Furthermore, a Time-Adaptive Feature Fusion (TAFF) module dynamically adjusts the influence of these features during denoising, prioritizing structure in early stages and style in later ones, mimicking an expert’s coarse-to-fine workflow. Evaluated on our new large-scale MuralVerse-S dataset, DCADif significantly outperforms state-of-the-art methods across all degradation levels. It establishes a new benchmark for digital cultural heritage preservation by effectively balancing structural accuracy with artistic authenticity. The dataset and code are publicly available.The dataset and code are available at https://github.com/LPDLG/DCADif.