Sketch-based City Generation Using Procedural Modeling and Generative Model
Junya Kanda, Yi He, Haoran Xie, Kazunori Miyata · 2022
In this study, we propose an efficient city-generation method based on user sketches that combine a deep generative model with the procedural modeling approach. The proposed framework adopts the deep learning-based network of conditional generative adversarial networks. For the data training, we randomly generated three-dimensional cities from perlin noise. The contours of the cities were extracted by the holistically-nested edge detection approach. The proposed method is a deep learning model that uses paired data of cities generated by a procedural model, along with the corresponding hand-drawn style sketches.