用AI把游戏设计文档自动转成Unity可运行代码模板。
Automated Unity Game Template Generation from GDDs via NLP and Multi-Modal LLMs
- 结合NLP与多模态大模型解析文档，生成符合规范的C#代码。
- 生成代码在编译成功率、设计符合度等指标上达4.8分（满分5）。
- 适合游戏开发者快速原型制作，降低技术门槛。
本文提出一种新框架，通过自然语言处理和多模态大语言模型，将游戏设计文档（GDD）自动转换为功能完整的Unity游戏原型。系统端到端地解析GDD，提取结构化游戏规格，并合成符合Unity要求的C#代码，实现核心机制、系统与架构。方法融合微调后的LLaMA-3模型与定制Unity集成包，显著优于基线模型，在编译成功率、设计符合度、最佳实践采纳率和代码模块化等指标上获得平均4.8/5.0评分。生成模板在多个游戏类型中均展现出高设计一致性。该系统有效填补了AI辅助游戏开发的关键空白，使大模型成为从设计到实现过渡的重要工具。
原文摘要 · Abstract (English)
This paper presents a novel framework for automated game template generation by transforming Game Design Documents (GDDs) into functional Unity game prototypes using Natural Language Processing (NLP) and multi-modal Large Language Models (LLMs). We introduce an end-to-end system that parses GDDs, extracts structured game specifications, and synthesizes Unity-compatible C# code that implements the core mechanics, systems, and architecture defined in the design documentation. Our approach combines a fine-tuned LLaMA-3 model specialized for Unity code generation with a custom Unity integration package that streamlines the implementation process. Evaluation results demonstrate significant improvements over baseline models, with our fine-tuned model achieving superior performance (4.8/5.0 average score) compared to state-of-the-art LLMs across compilation success, GDD adherence, best practices adoption, and code modularity metrics. The generated templates demonstrate high adherence to GDD specifications across multiple game genres. Our system effectively addresses critical gaps in AI-assisted game development, positioning LLMs as valuable tools in streamlining the transition from game design to implementation.
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