Building LEGO using Deep Generative Models of Graphs

NeurIPS 2020

Details
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. Speakers: Rylee Thompson, Elahe Ghalebi K., Terrance DeVries, Graham W Taylor

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