# CityGen: Infinite and Controllable City Layout Generation

arXiv: https://arxiv.org/abs/2312.01508 · HTML: https://mlsanity.com/paper/2312.01508/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 70 references that are in the map.
- First posted on arXiv 3 Dec 2023.

## Builds on (most cited first, 30 of 32)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion](https://mlsanity.com/paper/2302.12251/) (2023)
- [Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image](https://mlsanity.com/paper/2012.09855/) (2020)
- [BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering](https://mlsanity.com/paper/2112.05504/) (2021)
- [Scene as Occupancy](https://mlsanity.com/paper/2306.02851/) (2023)
- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [Unconstrained Scene Generation with Locally Conditioned Radiance Fields](https://mlsanity.com/paper/2104.00670/) (2021)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation](https://mlsanity.com/paper/2306.10013/) (2023)
- [Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models](https://mlsanity.com/paper/1811.12463/) (2018)
- [GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds](https://mlsanity.com/paper/2104.07659/) (2021)
- [InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images](https://mlsanity.com/paper/2207.11148/) (2022)
- [Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges](https://mlsanity.com/paper/2009.03137/) (2020)
- [CityDreamer: Compositional Generative Model of Unbounded 3D Cities](https://mlsanity.com/paper/2309.00610/) (2023)
- [SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections](https://mlsanity.com/paper/2302.01330/) (2023)
- [BLT: Bidirectional Layout Transformer for Controllable Layout Generation](https://mlsanity.com/paper/2112.05112/) (2021)
- [InfiniCity: Infinite-Scale City Synthesis](https://mlsanity.com/paper/2301.09637/) (2023)
- [InfinityGAN: Towards Infinite-Pixel Image Synthesis](https://mlsanity.com/paper/2104.03963/) (2021)
- [STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset](https://mlsanity.com/paper/2203.09065/) (2022)
- [Persistent Nature: A Generative Model of Unbounded 3D Worlds](https://mlsanity.com/paper/2303.13515/) (2023)
- [SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry](https://mlsanity.com/paper/2302.10237/) (2023)
- [3D-Aware Indoor Scene Synthesis with Depth Priors](https://mlsanity.com/paper/2202.08553/) (2022)

## Built on by (most cited first, 22 of 22)

- [AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark](https://mlsanity.com/paper/2410.03051/) (2024)
- [BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation](https://mlsanity.com/paper/2401.17053/) (2024)
- [PAD: Personalized Alignment of LLMs at Decoding-Time](https://mlsanity.com/paper/2410.04070/) (2024)
- [CityCraft: A Real Crafter for 3D City Generation](https://mlsanity.com/paper/2406.04983/) (2024)
- [Hierarchical Auto-Organizing System for Open-Ended Multi-Agent Navigation](https://mlsanity.com/paper/2403.08282/) (2024)
- [UrbanWorld: An Urban World Model for 3D City Generation](https://mlsanity.com/paper/2407.11965/) (2024)
- [CityX: Controllable Procedural Content Generation for Unbounded 3D Cities](https://mlsanity.com/paper/2407.17572/) (2024)
- [Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches](https://mlsanity.com/paper/2408.04567/) (2024)
- [Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model](https://mlsanity.com/paper/2404.04619/) (2024)
- [VersaT2I: Improving Text-to-Image Models with Versatile Reward](https://mlsanity.com/paper/2403.18493/) (2024)
- [COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generation](https://mlsanity.com/paper/2407.11294/) (2024)
- [Controllable 3D Outdoor Scene Generation via Scene Graphs](https://mlsanity.com/paper/2503.07152/) (2025)
- [Yo'City: Personalized and Boundless 3D Realistic City Scene Generation via Self-Critic Expansion](https://mlsanity.com/paper/2511.18734/) (2025)
- [Proc-GS: Procedural Building Generation for City Assembly with 3D Gaussians](https://mlsanity.com/paper/2412.07660/) (2024)
- [MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layouts](https://mlsanity.com/paper/2511.20415/) (2025)
- [DiffPO: Diffusion-styled Preference Optimization for Efficient Inference-Time Alignment of Large Language Models](https://mlsanity.com/paper/2503.04240/) (2025)
- [Imagine a City: CityGenAgent for Procedural 3D City Generation](https://mlsanity.com/paper/2602.05362/) (2026)
- [GroundUp: Rapid Sketch-Based 3D City Massing](https://mlsanity.com/paper/2407.12739/) (2024)
- [PrITTI: Primitive-based Generation of Controllable and Editable 3D Semantic Urban Scenes](https://mlsanity.com/paper/2506.19117/) (2025)
- [ExCellGen: Fast, Controllable, Photorealistic 3D Scene Generation from a Single Real-World Exemplar](https://mlsanity.com/paper/2412.16253/) (2024)
- [WonderVerse: Extendable 3D Scene Generation with Video Generative Models](https://mlsanity.com/paper/2503.09160/) (2025)
- [ProxyBuild: Text-Guided Structured 3D Building Generation with Mesh-Anchored Procedural Proxies](https://mlsanity.com/paper/2609.23386/) (2026)

## Abstract (arXiv)

The recent surge in interest in city layout generation underscores its significance in urban planning and smart city development. The task involves procedurally or automatically generating spatial arrangements for urban elements such as roads, buildings, water, and vegetation. Previous methods, whether procedural modeling or deep learning-based approaches like VAEs and GANs, rely on complex priors, expert guidance, or initial layouts, and often lack diversity and interactivity. In this paper, we present CityGen, an end-to-end framework for infinite, diverse, and controllable city layout generation. Our framework introduces an infinite expansion module to extend local layouts to city-scale layouts and a multi-scale refinement module to upsample and refine them. We also designed a user-friendly control scheme, allowing users to guide generation through simple sketching. Additionally, we convert the 2D layout to 3D by synthesizing a height field, facilitating downstream applications. Extensive experiments demonstrate CityGen's state-of-the-art performance across various metrics, making it suitable for a wide range of downstream applications.