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  4. A GAN-based Approach toward Architectural Line Drawing Colorization Prototyping
 
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2022
Journal Article
Title

A GAN-based Approach toward Architectural Line Drawing Colorization Prototyping

Abstract
Line drawing with colorization is a popular art format and tool for architectural illustration. The goal of this research is toward generating a high-quality and natural-looking colorization based on an architectural line drawing. This paper presents a new Generative Adversarial Network (GAN)-based method, named ArchGANs, including ArchColGAN and ArchShdGAN. ArchColGAN is a GAN-based line-feature-aware network for stylized colorization generation. ArchShdGAN is a lighting effects generation network, from which the building depiction in 3D can benefit. In particular, ArchColGAN is able to maintain the important line features and the correlation property of building parts as well as reduce the uneven colorization caused by sparse lines. Moreover, we proposed a color enhancement method to further improve ArchColGAN. Besides the single line drawing images, we also extend our method to handle line drawing image sequences and achieve rotation animation. Experiments and studies demonstrate the effectiveness and usefulness of our proposed method for colorization prototyping.
Author(s)
Sun, Qian
Tianjin Univ., China
Chen, Yan
Tianjin Univ., China
Tao, Wenyuan
Tianjin Univ., China
Jiang, Han
Tianjin Univ., China
Zhang, Mu
Tianjin Univ., China
Chen, Kan
Fraunhofer Singapore  
Erdt, Marius  
Fraunhofer Singapore  
Journal
The Visual Computer  
Funder
National Natural Science Foundation of China NSFC  
Open Access
DOI
10.1007/s00371-021-02219-x
Additional full text version
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Language
English
Singapore  
Keyword(s)
  • Generative Adversarial Networks (GAN)

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