论文标题
使用图形的深层生成模型构建乐高
Building LEGO Using Deep Generative Models of Graphs
论文作者
论文摘要
现在使用生成模型来创建各种高质量的数字文物。然而，它们在设计物理对象中的使用却减少了关注。在本文中，我们主张建筑玩具乐高，作为开发顺序组装生成模型的平台。我们开发了一种基于图形结构化神经网络的生成模型，该模型可以从人体建造的结构中学习并产生令人信服的设计。我们的代码在以下网址发布：https：//github.com/uoguelph-mlrg/generativelego。
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.
