变形
参数统计
计算机科学
人工智能
深度学习
参数化设计
建筑
对象(语法)
学习迁移
机器学习
空格(标点符号)
人工神经网络
生成设计
参数化模型
工程类
数学
艺术
公制(单位)
统计
运营管理
视觉艺术
操作系统
作者
Adam Sebestyen,Urs Leonhard Hirschberg,Shervin Rasoulzadeh
标识
DOI:10.1177/14780771231168232
摘要
We present an early prototype of a design system that uses Deep Learning methodology—Conditional Variational Autoencoders (CVAE)—to arrive at custom design spaces that can be interactively explored using semantic labels. Our work is closely tied to principles of parametric design. We use parametric models to create the dataset needed to train the neural network, thus tackling the problem of lacking 3D datasets needed for deep learning. We propose that the CVAE functions as a parametric tool in itself: The solution space is larger and more diverse than the combined solution spaces of all parametric models used for training. We showcase multiple methods on how this solution space can be navigated and explored, supporting explorations such as object morphing, object addition, and rudimentary 3D style transfer. As a test case, we implemented some examples of the geometric taxonomy of “Operative Design” by Di Mari and Yoo.
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