# From Unstable to Playable: Stabilizing Angry Birds Levels via Object Segmentation

arXiv: https://arxiv.org/abs/2509.23787 · HTML: https://mlsanity.com/paper/2509.23787/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Procedural Content Generation in Games: A Survey with Insights on Emerging LLM Integration](https://mlsanity.com/paper/2410.15644/) (2024)
- [Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution](https://mlsanity.com/paper/2104.06106/) (2021)
- [Deceptive Level Generation for Angry Birds](https://mlsanity.com/paper/2106.01639/) (2021)
- [A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros](https://mlsanity.com/paper/2005.06148/) (2020)

## Abstract (arXiv)

Procedural Content Generation (PCG) techniques enable automatic creation of diverse and complex environments. While PCG facilitates more efficient content creation, ensuring consistently high-quality, industry-standard content remains a significant challenge. In this research, we propose a method to identify and repair unstable levels generated by existing PCG models. We use Angry Birds as a case study, demonstrating our method on game levels produced by established PCG approaches. Our method leverages object segmentation and visual analysis of level images to detect structural gaps and perform targeted repairs. We evaluate multiple object segmentation models and select the most effective one as the basis for our repair pipeline. Experimental results show that our method improves the stability and playability of AI-generated levels. Although our evaluation is specific to Angry Birds, our image-based approach is designed to be applicable to a wide range of 2D games with similar level structures.