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    • 39-Issue 7
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    Image-Driven Furniture Style for Interactive 3D Scene Modeling

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    Date
    2020
    Author
    Weiss, Tomer ORCID
    Yildiz, Ilkay ORCID
    Agarwal, Nitin ORCID
    Ataer-Cansizoglu, Esra ORCID
    Choi, Jae-Woo ORCID
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    Abstract
    Creating realistic styled spaces is a complex task, which involves design know-how for what furniture pieces go well together. Interior style follows abstract rules involving color, geometry and other visual elements. Following such rules, users manually select similar-style items from large repositories of 3D furniture models, a process which is both laborious and time-consuming. We propose a method for fast-tracking style-similarity tasks, by learning a furniture's style-compatibility from interior scene images. Such images contain more style information than images depicting single furniture. To understand style, we train a deep learning network on a classification task. Based on image embeddings extracted from our network, we measure stylistic compatibility of furniture. We demonstrate our method with several 3D model style-compatibility results, and with an interactive system for modeling style-consistent scenes.
    BibTeX
    @article {10.1111:cgf.14126,
    journal = {Computer Graphics Forum},
    title = {{Image-Driven Furniture Style for Interactive 3D Scene Modeling}},
    author = {Weiss, Tomer and Yildiz, Ilkay and Agarwal, Nitin and Ataer-Cansizoglu, Esra and Choi, Jae-Woo},
    year = {2020},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14126}
    }
    URI
    https://doi.org/10.1111/cgf.14126
    https://diglib.eg.org:443/handle/10.1111/cgf14126
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA