用图像分割修复生成的愤怒小鸟关卡，让不稳定的关卡变可玩。
From Unstable to Playable: Stabilizing Angry Birds Levels via Object Segmentation
- 通过物体分割和视觉分析识别关卡结构漏洞
- 修复后关卡稳定性与可玩性显著提升
- 方法通用，适合类似2D结构的横版游戏
程序化内容生成（PCG）技术可自动创建多样且复杂的环境，但保证持续高质量、符合工业标准的内容仍是重大挑战。本文以《愤怒的小鸟》为案例，提出一种识别并修复现有PCG模型生成的不稳定关卡的方法。该方法利用物体分割与关卡图像的视觉分析，检测结构空隙并实施精准修复。我们对比多种物体分割模型，选定最优者构建修复流程。实验表明，该方法显著提升了AI生成关卡的稳定性和可玩性。尽管评估聚焦于《愤怒的小鸟》，但基于图像的方法适用于具有相似结构的多种2D游戏。
原文摘要 · Abstract (English)
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.
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