Procedural Content Generation

Overview

This project demonstrates the use of Unreal Engine’s Procedural Content Generation (PCG) system to populate large-scale environments with assets in a scalable, non-destructive, and performance-conscious manner. Given the extensive size of the environment I designed, manual placement of objects was impractical. PCG allowed for automated scattering of meshes—such as rocks, plants, props, and debris—based on fully customizable parameters.

Using Blueprint-based PCG graphs, I defined variables like mesh types, transform ranges, offsets, and density, enabling randomized placement within volume bounds that I could assign to any section of the map. This setup provided complete control and flexibility to build diverse and natural-looking environments while dramatically reducing manual workload.

Challenges

1. Scale Management: Designing for a large environment required a balance between randomized distribution and artist intent, ensuring visual cohesion across PCG volumes.
2. Performance Optimization: Ensuring that dense PCG systems did not impact real-time performance meant implementing careful culling strategies and mesh instancing.
3. Controlled Randomness: While randomness adds realism, it also introduced edge cases like floating meshes or cluttered zones, requiring logic-based filtering and constraint systems.
4. Blueprint Debugging: Fine-tuning parameter graphs involved multiple iterations and visualization passes to ensure correct asset placement behavior.

Learnings

1. Developed a strong understanding of Unreal Engine’s PCG workflow, including dynamic asset generation within defined rulesets.
2. Learned to balance procedural automation with artistic control, creating believable environments while saving time.
3. Enhanced ability to build modular PCG setups, enabling environment-wide asset variation with minimal duplication of effort.
4. Gained experience in using Blueprint-driven logic to refine procedural systems and respond to real-time design feedback.