BravePixelNet60/Night-City-EvolutionPublic

AI summary: Night City Evolution: procedural city generation with neural networks.

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Quick answers

What is Night-City-Evolution?
Night City Evolution: procedural city generation with neural networks.
What does Night-City-Evolution do?
This repository generates urban landscapes using procedural techniques combined with neural network-based models. It addresses the challenge of creating realistic, scalable city environments for simulations or games. The distinctive feature is its hybrid approach, blending deterministic algorithms with AI-driven texture and layout generation.
Who is Night-City-Evolution for?
This is for developers, researchers, and artists with basic Python knowledge and familiarity with 3D modeling tools.
How do I get started with Night-City-Evolution?
Clone the repository and run 'python generate_city.py --config=config.json' to create a sample city.
How popular is Night-City-Evolution on GitHub?
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What Night-City-Evolution does

This repository generates urban landscapes using procedural techniques combined with neural network-based models. It addresses the challenge of creating realistic, scalable city environments for simulations or games. The distinctive feature is its hybrid approach, blending deterministic algorithms with AI-driven texture and layout generation.

This is for developers, researchers, and artists with basic Python knowledge and familiarity with 3D modeling tools.

  • Procedural generation: Uses deterministic algorithms for city layout creation.
  • Neural network textures: Applies trained models for realistic building facades and road textures.
  • Customizable parameters: Allows users to tweak city density, road patterns, and building styles.
  • Export formats: Supports output in OBJ and FBX for integration with 3D engines.
  • Batch generation: Automates the creation of multiple city instances for large-scale simulations.
  • Python-based workflow: Entire pipeline implemented in Python for accessibility and extensibility.

Where teams use it

Game environment creation

Game developers can use this to generate diverse urban landscapes for open-world or simulation games.

Urban planning simulations

Researchers can simulate city layouts to study traffic flow or population density impacts.

3D asset generation

Artists can quickly produce city models for use in films or virtual reality projects.

AI training datasets

Data scientists can generate synthetic urban environments for training autonomous vehicle models.

Architectural visualization

Architects can prototype city-scale developments for client presentations.

Getting started: Clone the repository and run 'python generate_city.py --config=config.json' to create a sample city.

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Jul 3, 2026daily#23+9

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