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arXiv · 2503.16536

Word2Minecraft: Generating 3D Game Levels through Large Language Models

Shuo Huang·Muhammad Umair Nasir·Steven James·Julian Togelius
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Abstract

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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Shuo Huang, Muhammad Umair Nasir, Steven James, Julian Togelius. 2025-03-18. Word2Minecraft: Generating 3D Game Levels through Large Language Models. https://arxiv.org/abs/2503.16536

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