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    • 40-Issue 2
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    Deep Detail Enhancement for Any Garment

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    Date
    2021
    Author
    Zhang, Meng ORCID
    Wang, Tuanfeng ORCID
    Ceylan, Duygu ORCID
    Mitra, Niloy J. ORCID
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    Abstract
    Creating fine garment details requires significant efforts and huge computational resources. In contrast, a coarse shape may be easy to acquire in many scenarios (e.g., via low-resolution physically-based simulation, linear blend skinning driven by skeletal motion, portable scanners). In this paper, we show how to enhance, in a data-driven manner, rich yet plausible details starting from a coarse garment geometry. Once the parameterization of the garment is given, we formulate the task as a style transfer problem over the space of associated normal maps. In order to facilitate generalization across garment types and character motions, we introduce a patch-based formulation, that produces high-resolution details by matching a Gram matrix based style loss, to hallucinate geometric details (i.e., wrinkle density and shape). We extensively evaluate our method on a variety of production scenarios and show that our method is simple, light-weight, efficient, and generalizes across underlying garment types, sewing patterns, and body motion. Project page: http://geometry.cs.ucl.ac.uk/projects/2021/DeepDetailEnhance/
    BibTeX
    @article {10.1111:cgf.142642,
    journal = {Computer Graphics Forum},
    title = {{Deep Detail Enhancement for Any Garment}},
    author = {Zhang, Meng and Wang, Tuanfeng and Ceylan, Duygu and Mitra, Niloy J.},
    year = {2021},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.142642}
    }
    URI
    https://doi.org/10.1111/cgf.142642
    https://diglib.eg.org:443/handle/10.1111/cgf142642
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