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 .
Peng et al. (Wed,) studied this question.