This paper presents Roof-GAN, a novel generative adversarial network that
generates structured geometry of residential roof structures as a set of roof
primitives and their relationships. Given the number of primitives, the
generator produces a structured roof model as a graph, which consists of 1)
primitive geometry as raster images at each node, encoding facet segmentation
and angles; 2) inter-primitive colinear/coplanar relationships at each edge;
and 3) primitive geometry in a vector format at each node, generated by a novel
differentiable vectorizer while enforcing the relationships. The discriminator
is trained to assess the primitive raster geometry, the primitive
relationships, and the primitive vector geometry in a fully end-to-end
architecture. Qualitative and quantitative evaluations demonstrate the
effectiveness of our approach in generating diverse and realistic roof models
over the competing methods with a novel metric proposed in this paper for the
task of structured geometry generation. Code and data are available at
this https URL .

本文介绍了 Roof-GAN，一种新颖的生成对抗网络，它能够生成住宅屋顶结构的结构几何体系，作为一组屋顶基元及其关系。生成器将基元的数量转换成图形结构，生成一种有结构的屋顶模型，并在完全端到端的体系结构中使用判别器来评估基元栅格几何、基元关系和基元向量几何。我们提出了一种新的度量标准，证明了我们的方法在生成多种多样和逼真的屋顶模型方面的有效性。

Roof-GAN: 学习为住宅生成屋顶几何形状和关系

Roof-GAN: Learning to Generate Roof Geometry and Relations for  Residential Houses

Sketching is more fundamental to human cognition than speech. Deep Neural
Networks (DNNs) have achieved the state-of-the-art in speech-related tasks but
have not made significant development in generating stroke-based sketches a.k.a
sketches in vector format. Though there are Variational Auto Encoders (VAEs)
for generating sketches in vector format, there is no Generative Adversarial
Network (GAN) architecture for the same. In this paper, we propose a standalone
GAN architecture SkeGAN and a VAE-GAN architecture VASkeGAN, for sketch
generation in vector format. SkeGAN is a stochastic policy in Reinforcement
Learning (RL), capable of generating both multidimensional continuous and
discrete outputs. VASkeGAN hybridizes a VAE and a GAN, in order to couple the
efficient representation of data by VAE with the powerful generating
capabilities of a GAN, to produce visually appealing sketches. We also propose
a new metric called the Ske-score which quantifies the quality of vector
sketches. We have validated that SkeGAN and VASkeGAN generate visually
appealing sketches by using Human Turing Test and Ske-score.

本文提出了两种基于深度学习的神经网络架构，SkeGAN 和 VASkeGAN，用于生成矢量格式的手绘素描，并且引入了 Ske-score 度量来评估其质量。经过人类 图灵测试和 Ske-score 评估，验证了这两个模型的生成结果质量较好。

教授 GAN 将草图转换为矢量格式

Teaching GANs to Sketch in Vector Format

We present sketch-rnn, a recurrent neural network (RNN) able to construct
stroke-based drawings of common objects. The model is trained on thousands of
crude human-drawn images representing hundreds of classes. We outline a
framework for conditional and unconditional sketch generation, and describe new
robust training methods for generating coherent sketch drawings in a vector
format.

该研究介绍了 Sketch-RNN，它是一种基于循环神经网络（RNN）的模型，能够构建常见对象的基于笔画的绘图，并描述了生成矢量格式的连贯笔画绘图的鲁棒性训练方法。