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arXiv · 2012.11543

Building LEGO Using Deep Generative Models of Graphs

Rylee Thompson·Elahe Ghalebi·Terrance DeVries·Graham W. Taylor
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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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Rylee Thompson, Elahe Ghalebi, Terrance DeVries, Graham W. Taylor. 2020-12-21. Building LEGO Using Deep Generative Models of Graphs. https://arxiv.org/abs/2012.11543

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