title: DiffuMural: Restoring Dunhuang Murals with Multi-scale Diffusion
summary: Large-scale pre-trained diffusion models have produced excellent results in
  the field of conditional image generation. However, restoration of ancient
  murals, as an important downstream task in this field, poses significant
  challenges to diffusion model-based restoration methods due to its large
  defective area and scarce training samples. Conditional restoration tasks are
  more concerned with whether the restored part meets the aesthetic standards of
  mural restoration in terms of overall style and seam detail, and such metrics
  for evaluating heuristic image complements are lacking in current research. We
  therefore propose DiffuMural, a combined Multi-scale convergence and
  Collaborative Diffusion mechanism with ControlNet and cyclic consistency loss
  to optimise the matching between the generated images and the conditional
  control. DiffuMural demonstrates outstanding capabilities in mural restoration,
  leveraging training data from 23 large-scale Dunhuang murals that exhibit
  consistent visual aesthetics. The model excels in restoring intricate details,
  achieving a coherent overall appearance, and addressing the unique challenges
  posed by incomplete murals lacking factual grounding. Our evaluation framework
  incorporates four key metrics to quantitatively assess incomplete murals:
  factual accuracy, textural detail, contextual semantics, and holistic visual
  coherence. Furthermore, we integrate humanistic value assessments to ensure the
  restored murals retain their cultural and artistic significance. Extensive
  experiments validate that our method outperforms state-of-the-art (SOTA)
  approaches in both qualitative and quantitative metrics.
paper_authors._id: 67fe515cfd3df6c5de11c9f2
paper_authors.name: Puyu Han
paper_authors._id: 67fe515cfd3df6c5de11c9f3
paper_authors.name: Jiaju Kang
paper_authors._id: 67fe515cfd3df6c5de11c9f4
paper_authors.name: Yuhang Pan
paper_authors._id: 67fe515cfd3df6c5de11c9f5
paper_authors.name: Erting Pan
paper_authors._id: 67fe515cfd3df6c5de11c9f6
paper_authors.name: Zeyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-15T16:46:58.052Z
paper_authors._id: 64ec877bb93654d4ca5c92e9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
paper_authors.fullname: Zeyu Zhang
paper_authors.type: user
paper_authors.user: SteveZeyuZhang
paper_authors._id: 67fe515cfd3df6c5de11c9f7
paper_authors.name: Qunchao Jin
paper_authors._id: 67fe515cfd3df6c5de11c9f8
paper_authors.name: Juntao Jiang
paper_authors._id: 67fe515cfd3df6c5de11c9f9
paper_authors.name: Zhichen Liu
paper_authors._id: 67fe515cfd3df6c5de11c9fa
paper_authors.name: Luqi Gong
paper_title: DiffuMural: Restoring Dunhuang Murals with Multi-scale Diffusion
paper_summary: Large-scale pre-trained diffusion models have produced excellent results in
  the field of conditional image generation. However, restoration of ancient
  murals, as an important downstream task in this field, poses significant
  challenges to diffusion model-based restoration methods due to its large
  defective area and scarce training samples. Conditional restoration tasks are
  more concerned with whether the restored part meets the aesthetic standards of
  mural restoration in terms of overall style and seam detail, and such metrics
  for evaluating heuristic image complements are lacking in current research. We
  therefore propose DiffuMural, a combined Multi-scale convergence and
  Collaborative Diffusion mechanism with ControlNet and cyclic consistency loss
  to optimise the matching between the generated images and the conditional
  control. DiffuMural demonstrates outstanding capabilities in mural restoration,
  leveraging training data from 23 large-scale Dunhuang murals that exhibit
  consistent visual aesthetics. The model excels in restoring intricate details,
  achieving a coherent overall appearance, and addressing the unique challenges
  posed by incomplete murals lacking factual grounding. Our evaluation framework
  incorporates four key metrics to quantitatively assess incomplete murals:
  factual accuracy, textural detail, contextual semantics, and holistic visual
  coherence. Furthermore, we integrate humanistic value assessments to ensure the
  restored murals retain their cultural and artistic significance. Extensive
  experiments validate that our method outperforms state-of-the-art (SOTA)
  approaches in both qualitative and quantitative metrics.
paper_ai_summary: DiffuMural, a multi-scale diffusion model with ControlNet and cyclic consistency loss, effectively restores ancient murals by addressing large defects and scarce training data, outperforming existing methods.
paper_ai_keywords: diffusion models conditional image generation mural restoration Multi-scale convergence Collaborative Diffusion ControlNet cyclic consistency loss factual accuracy textural detail contextual semantics holistic visual coherence