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Building LEGO using Deep Generative Models of Graphs
Rylee Thompson · Graham Taylor · Terrance DeVries · Elahe Ghalebi

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 argue for the building 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.

Author Information

Rylee Thompson (University of Guelph)
Graham Taylor (University of Guelph)
Terrance DeVries (University of Guelph)
Elahe Ghalebi (Vienna University of Technology)

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