Building LEGO Using Deep Generative Models of Graphs

• URL: http://arxiv.org/abs/2012.11543v1
• Date: Mon, 21 Dec 2020 18:24:40 GMT
• Title: Building LEGO Using Deep Generative Models of Graphs
• Authors: Rylee Thompson, Elahe Ghalebi, Terrance DeVries, Graham W. Taylor
• Abstract summary: We advocate LEGO as a platform for developing generative models of sequential assembly. We develop a generative model based on graph-structured neural networks that can learn from human-built structures and produce visually compelling designs.
• Score: 22.926487008829668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models are now used to create a variety of high-quality digital artifacts. Yet their use in designing physical objects has received far less attention. In this paper, we advocate for the construction toy, LEGO, as a platform for developing generative models of sequential assembly. We develop a generative model based on graph-structured neural networks that can learn from human-built structures and produce visually compelling designs. Our code is released at: https://github.com/uoguelph-mlrg/GenerativeLEGO.

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