Image-Driven Furniture Style for Interactive 3D Scene Modeling

Tomer Weiss, Ilkay Yildiz, Nitin Agarwal, Esra Ataer-Cansizoglu, Jae Woo Choi

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)57-68
Number of pages12
JournalComputer Graphics Forum
Volume39
Issue number7
DOIs
StatePublished - Oct 2020

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design

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