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.

着色线条画是一种流行的艺术格式和建筑插图工具。这项研究的目标是基于建筑线条图生成高质量和自然的着色。本文提出了一种新的基于生成对抗网络 (GAN) 的方法，名为ArchGAN，包括ArchColGAN和ArchShdGAN。ArchColGAN是一种基于 GAN 的线特征感知网络，用于生成风格化着色。ArchShdGAN是一个光照效果生成网络，3D 中的建筑描绘可以从中受益。特别是，ArchColGAN能够保持重要的线条特征和建筑部件的相关性，并减少稀疏线条引起的着色不均匀。此外，我们提出了一种颜色增强方法来进一步改进ArchColGAN。除了单线绘制图像，我们还扩展了我们的方法来处理线绘制图像序列并实现旋转动画。实验和研究证明了我们提出的着色原型方法的有效性和实用性。