用大模型把故事变3D游戏关卡，支持自定义复杂度
Word2Minecraft: Generating 3D Game Levels through Large Language Models
- 基于故事要素生成带空间与玩法约束的关卡
- GPT-4-Turbo在叙事连贯性上优于GPT-4o-Mini
- 适合游戏设计与叙事生成研究者使用
我们提出Word2Minecraft，一个利用大语言模型根据结构化故事生成可玩Minecraft关卡的系统。该系统将主角目标、反派挑战和环境设定等叙事元素转化为兼具空间布局与游戏机制的关卡。提出灵活框架，支持故事复杂度定制，实现动态关卡生成。采用缩放算法保持空间一致性并调整核心游戏元素。通过指标评估与人工评测验证效果：GPT-4-Turbo在叙事连贯性和目标体验上优于GPT-4o-Mini，后者则在视觉美感上表现更佳。系统能生成高沉浸感地图，推动叙事生成与游戏设计融合。代码已开源：https://github.com/JMZ-kk/Word2Minecraft/tree/word2mc_v0
原文摘要 · Abstract (English)
We present Word2Minecraft, a system that leverages large language models to generate playable game levels in Minecraft based on structured stories. The system transforms narrative elements-such as protagonist goals, antagonist challenges, and environmental settings-into game levels with both spatial and gameplay constraints. We introduce a flexible framework that allows for the customization of story complexity, enabling dynamic level generation. The system employs a scaling algorithm to maintain spatial consistency while adapting key game elements. We evaluate Word2Minecraft using both metric-based and human-based methods. Our results show that GPT-4-Turbo outperforms GPT-4o-Mini in most areas, including story coherence and objective enjoyment, while the latter excels in aesthetic appeal. We also demonstrate the system' s ability to generate levels with high map enjoyment, offering a promising step forward in the intersection of story generation and game design. We open-source the code at https://github.com/JMZ-kk/Word2Minecraft/tree/word2mc_v0
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