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High‐Fidelity Texture Transfer Using Multi‐Scale Depth‐Aware Diffusion.

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Title: High‐Fidelity Texture Transfer Using Multi‐Scale Depth‐Aware Diffusion.
Authors: Lin, Rongzhen1 (AUTHOR), Chen, Zichong1 (AUTHOR), Hao, Xiaoyong1 (AUTHOR), Zhou, Yang1 (AUTHOR), Huang, Hui1 (AUTHOR)
Source: Computer Graphics Forum. Jul2025, Vol. 44 Issue 4, p1-13. 13p.
Subjects: Texture mapping, Probabilistic generative models, Depth perception, Loss functions (Statistics)
Abstract: Textures are a key component of 3D assets. Transferring textures from one shape to another, without user interaction or additional semantic guidance, is a classical yet challenging problem. It can enhance the diversity of existing shape collections, augmenting their application scope. This paper proposes an innovative 3D texture transfer framework that leverages the generative power of pre‐trained diffusion models. While diffusion models have achieved significant success in 2D image generation, their application to 3D domains faces great challenges in preserving coherence across different viewpoints. Addressing this issue, we designed a multi‐scale generation framework to optimize the UV maps coarse‐to‐fine. To ensure multi‐view consistency, we use depth info as geometric guidance; meanwhile, a novel consistency loss is proposed to further constrain the color coherence and reduce artifacts. Experimental results demonstrate that our multi‐scale framework not only produces high‐quality texture transfer results but also excels in handling complex shapes while preserving correct semantic correspondences. Compared to existing techniques, our method achieves improvements in both consistency and texture clarity, as well as time efficiency. [ABSTRACT FROM AUTHOR]
Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Texture+mapping%22">Texture mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilistic+generative+models%22">Probabilistic generative models</searchLink><br /><searchLink fieldCode="DE" term="%22Depth+perception%22">Depth perception</searchLink><br /><searchLink fieldCode="DE" term="%22Loss+functions+%28Statistics%29%22">Loss functions (Statistics)</searchLink>
– Name: Abstract
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  Data: Textures are a key component of 3D assets. Transferring textures from one shape to another, without user interaction or additional semantic guidance, is a classical yet challenging problem. It can enhance the diversity of existing shape collections, augmenting their application scope. This paper proposes an innovative 3D texture transfer framework that leverages the generative power of pre‐trained diffusion models. While diffusion models have achieved significant success in 2D image generation, their application to 3D domains faces great challenges in preserving coherence across different viewpoints. Addressing this issue, we designed a multi‐scale generation framework to optimize the UV maps coarse‐to‐fine. To ensure multi‐view consistency, we use depth info as geometric guidance; meanwhile, a novel consistency loss is proposed to further constrain the color coherence and reduce artifacts. Experimental results demonstrate that our multi‐scale framework not only produces high‐quality texture transfer results but also excels in handling complex shapes while preserving correct semantic correspondences. Compared to existing techniques, our method achieves improvements in both consistency and texture clarity, as well as time efficiency. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.1111/cgf.70172
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 13
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    Subjects:
      – SubjectFull: Texture mapping
        Type: general
      – SubjectFull: Probabilistic generative models
        Type: general
      – SubjectFull: Depth perception
        Type: general
      – SubjectFull: Loss functions (Statistics)
        Type: general
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      – TitleFull: High‐Fidelity Texture Transfer Using Multi‐Scale Depth‐Aware Diffusion.
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            NameFull: Lin, Rongzhen
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            NameFull: Hao, Xiaoyong
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            NameFull: Zhou, Yang
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            NameFull: Huang, Hui
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            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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              Value: 44
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            – TitleFull: Computer Graphics Forum
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