Proc-GS: Procedural Building Generation for City Assembly with 3D Gaussians
Not on the map. Why?
Abstract (arXiv)
Buildings are primary components of cities, often featuring repeated elements such as windows and doors. Traditional 3D building asset creation is labor-intensive and requires specialized skills to develop design rules. Recent generative models for building creation often overlook these patterns, leading to low visual fidelity and limited scalability. Drawing inspiration from procedural modeling techniques used in the gaming and visual effects industry, our method, Proc-GS, integrates procedural code into the 3D Gaussian Splatting (3D-GS) framework, leveraging their advantages in high-fidelity rendering and efficient asset management from both worlds. By manipulating procedural code, we can streamline this process and generate an infinite variety of buildings. This integration significantly reduces model size by utilizing shared foundational assets, enabling scalable generation with precise control over building assembly. We showcase the potential for expansive cityscape generation while maintaining high rendering fidelity and precise control on both real and synthetic cases.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 32 of its 53 references that are in the map. First posted on arXiv 10 Dec 2024.
Builds on
32 of 53 references.
References · 32
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
- 2024GPT-4o System Cardcited by 4,467
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
- 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,013
- 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
- 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,204
- 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,061
- 2023DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creationcited by 837
- 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
- 2023Instruct-NeRF2NeRF: Editing 3D Scenes with Instructionscited by 482
- 2022Kubric: A scalable dataset generatorcited by 373
20 more
- 2022ProcTHOR: Large-Scale Embodied AI Using Procedural Generationcited by 369
- 2023PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamicscited by 368
- 2023MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyondcited by 185
- 2023GaussianEditor: Editing 3D Gaussians Delicately with Text Instructionscited by 166
- 2023Infinite Photorealistic Worlds using Procedural Generationcited by 142
- 2023Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinementcited by 106
- 2023CityDreamer: Compositional Generative Model of Unbounded 3D Citiescited by 75
- 2023SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collectionscited by 72
- 2023InfiniCity: Infinite-Scale City Synthesiscited by 68
- 2023Persistent Nature: A Generative Model of Unbounded 3D Worldscited by 44
- 2024Generative Gaussian Splatting for Unbounded 3D City Generationcited by 43
- 2024Streetscapes: Large-scale Consistent Street View Generation Using Autoregressive Video Diffusioncited by 39
- 2024CityCraft: A Real Crafter for 3D City Generationcited by 25
- 2024UrbanWorld: An Urban World Model for 3D City Generationcited by 23
- 2023CityGen: Infinite and Controllable City Layout Generationcited by 22
- 2023Generating Procedural Materials from Text or Image Promptscited by 21
- 2024CityX: Controllable Procedural Content Generation for Unbounded 3D Citiescited by 20
- 2017Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applicationscited by 11
- 2024COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generationcited by 10
- 2024RoBus: A Multimodal Dataset for Controllable Road Networks and Building Layouts Generationcited by 2
Built on by
Citing papers per year: 2025: 2; 2026: 4.
6 papers.
- 2025Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoningcited by 27
- 2025Compositional Generative Model of Unbounded 4D Citiescited by 16
- 2026Pair2Scene: Learning Local Object Relations for Procedural Scene Generationcited by 1
- 2026Adaptive Scaling with Geometric and Visual Continuity of completed 3D objectscited by 0
- 2026SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolutioncited by 0
- 2026ProxyBuild: Text-Guided Structured 3D Building Generation with Mesh-Anchored Procedural Proxiescited by 0
- 2026ProxyBuild: Text-Guided Structured 3D Building Generation with Mesh-Anchored Procedural Proxies20 Sep 2026
- 2026SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution4 Sep 2026
- 2026Pair2Scene: Learning Local Object Relations for Procedural Scene Generation13 Apr 2026
- 2026Adaptive Scaling with Geometric and Visual Continuity of completed 3D objects13 Feb 2026
- 2025Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoning5 Jun 2025
- 2025Compositional Generative Model of Unbounded 4D Cities15 Jan 2025