Papers › Building LEGO Using Deep Generative Models of Graphs
Building LEGO Using Deep Generative Models of Graphs
Rylee Thompson, Elahe Ghalebi, Terrance DeVries, Graham W. Taylor
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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