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Magic decorator: automatic material suggestion for indoor digital scenes

Published: 02 November 2015 Publication History

Abstract

Assigning textures and materials within 3D scenes is a tedious and labor-intensive task. In this paper, we present Magic Decorator, a system that automatically generates material suggestions for 3D indoor scenes. To achieve this goal, we introduce local material rules, which describe typical material patterns for a small group of objects or parts, and global aesthetic rules, which account for the harmony among the entire set of colors in a specific scene. Both rules are obtained from collections of indoor scene images. We cast the problem of material suggestion as a combinatorial optimization considering both local material and global aesthetic rules. We have tested our system on various complex indoor scenes. A user study indicates that our system can automatically and efficiently produce a series of visually plausible material suggestions which are comparable to those produced by artists.

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Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 34, Issue 6
November 2015
944 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/2816795
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 02 November 2015
Published in TOG Volume 34, Issue 6

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Author Tags

  1. computer-aided aesthetic design
  2. data-driven content creation
  3. indoor scene
  4. material suggestion

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Cited By

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  • (2024)Adapting Indoor Scene Design to User-Selected Mood2024 2nd International Conference on Computer Graphics and Image Processing (CGIP)10.1109/CGIP62525.2024.00023(86-90)Online publication date: 12-Jan-2024
  • (2023)Data-driven Digital Lighting Design for Residential Indoor SpacesACM Transactions on Graphics10.1145/358200142:3(1-18)Online publication date: 17-Mar-2023
  • (2023)As-Continuous-As-Possible Extrusion-Based Fabrication of Surface ModelsACM Transactions on Graphics10.1145/357585942:3(1-16)Online publication date: 17-Mar-2023
  • (2023)Creative and Progressive Interior Color Design with Eye-tracked User PreferenceACM Transactions on Computer-Human Interaction10.1145/354292230:1(1-31)Online publication date: 7-Mar-2023
  • (2023)Advances in Data‐Driven Analysis and Synthesis of 3D Indoor ScenesComputer Graphics Forum10.1111/cgf.1492743:1Online publication date: 11-Sep-2023
  • (2023)Text2Scene: Text-driven Indoor Scene Stylization with Part-Aware Details2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.00188(1890-1899)Online publication date: Jun-2023
  • (2023)Color-Correlated Texture Synthesis for Hybrid Indoor ScenesComputer-Aided Design and Computer Graphics10.1007/978-981-99-9666-7_14(200-214)Online publication date: 19-Aug-2023
  • (2022)3D-VR Based Color Design Method for Interior Space in Iot ApplicationsWireless Communications & Mobile Computing10.1155/2022/25802222022Online publication date: 1-Jan-2022
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