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July 26, 2021 • 2 min read
建筑立面永动机及控制器—原创论文讲解
by Simon的白日梦, mp.weixin.qq.com • See original
我今年在CDRF 2021（同济 digital futures 2021会议）上发表的论文，《建筑立面永动机—在高维隐空间中探索建筑设计》（中译名，随便译了一下，使得看起来好像很厉害的样子）的中文讲解录屏剪辑，探索了一种能够生成高置信度建筑立面的人工智能方法，并实现了在高维隐空间中对图片根据其隐向量坐标进行降维可视化，以及根据主成分分析对图片内容进行语义操作。会涉及到一些高数和机器学习的知识，听不懂的可以问我(可能我也不能解释得再清楚了)。非计算机出身，有错误欢迎指出，请轻拍哦~！
论文成果演示
论文讲解
1 Introduction
With the emerging of Generative Adversarial Network (GAN) based image generation methodin recent years, many attempts havebeen made to apply GAN into architectural images and drawing generation research(Goodfellowet al. 2014). However, for therealistic building façade images generation task, most attempts faced different challenges, such as quality and controllability ofgenerated image, and interpretabilityof model.
These challenges were due to various limitations, such as performance ofthe selected GAN model, the size of training dataset, the understanding oflatent space, etc. In this paper, by training the state-of-the-art GAN basedimage generation model, StyleGAN2 (Karras et al. 2020), with high-resolution building façade image dataset, andexploring its latent space by applying PCA and GANSpace analysis, we couldovercome above challenges in different extend (Härkönen et al. 2020).
In summary,the main functions and contributions of this paper are:
1. A StyleGAN2 model instance which could generate plausible buildingfaçade images without conditional input.
2. Introduce GANSpace and image embedding method to visualize the correlationbetween the generated building façade images and their corresponding latentvectors, which achievedunsupervised classification and high-level propertiescontrol of both generated and novel images.
- 作者:Simon阿蒙
- 链接:https://shengyu.me//article/stylegan-arch
- 声明:本文采用 CC BY-NC-SA 4.0 许可协议，转载请注明出处。
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