Building LEGO Using Deep Generative Models of Graphs

Autor:	Thompson, Rylee, Ghalebi, Elahe, DeVries, Terrance, Taylor, Graham W.
Rok vydání:	2020
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: NeurIPS 2020 ML4eng workshop paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.11543 Zobrazit plný text záznamu View this record from Arxiv