Image-Driven Furniture Style for Interactive 3D Scene Modeling

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Date
2020
Journal Title
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Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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.
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@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
} }
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