CityGen: Infinite and Controllable 3D City Layout Generation
Jie Deng*, Wenhao Chai*, Jianshu Guo*, Qixuan Huang, Wenhao Hu, Jenq-Neng Hwang, Gaoang Wang✉️
arXiv 2023
CityGen is a novel framework for infinite, controllable and diverse 3D city layout Generation. We propose an auto-regressive out-painting pipeline to extend the local layout to an infinite city layout. Moreover, we utilize a multi-scale diffusion model to generate controllable semantic field.
- [TBD] : We will soon release our code, model weight, and dataset.
- [2023.12.3] 📃 We release the paper.
In the initial step, we sample a local block from noise and extend it infinitely through the auto-regressive outpainting process. Subsequently, we iteratively refine the global semantic field to achieve a more nuanced and polished global field. Following the refinement, height values are assigned to each class. After that, we integrate the semantic field with the height field to synthesize the 3D layout. Finally, by employing an image-to-image approach, we can effectively synthesize diverse city scenes.
If you find CityGen useful for your your research and applications, please cite using this BibTeX:
@article{deng2023citygen,
title={CityGen: Infinite and Controllable 3D City Layout Generation},
author={Deng, Jie and Chai, Wenhao and Guo, Jianshu and Huang, Qixuan and Hu, Wenhao and Hwang, Jenq-Neng and Wang, Gaoang},
journal={arXiv preprint arXiv:2312.01508},
year={2023}
}