Weiss, TomerYildiz, IlkayAgarwal, NitinAtaer-Cansizoglu, EsraChoi, Jae-WooEisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue2020-10-292020-10-2920201467-8659https://doi.org/10.1111/cgf.14126https://diglib.eg.org:443/handle/10.1111/cgf14126Creating 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.10.1111/cgf.1412657-68