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.
@article{arxiv.2010.10557,
title = {Image-Driven Furniture Style for Interactive 3D Scene Modeling},
author = {Tomer Weiss and Ilkay Yildiz and Nitin Agarwal and Esra Ataer-Cansizoglu and Jae-Woo Choi},
journal= {arXiv preprint arXiv:2010.10557},
year = {2020}
}