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Code2Games: Enabling Coding Agents for Gaming World Generation

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Briefings

Code2Games is an agentic framework that constructs structured gaming worlds by coordinating scene analysis, gameplay planning, and engine adaptation. It builds upon a base Blender world generated from natural-language game intent, then adapts the result to Unreal Engine 5 using execution-guided reconstruction based on compilation diagnostics and runtime feedback. The system was evaluated using the GameCode4D benchmark, which contains ten fixed game prompts across varying complexity levels, measuring visual quality, interactive fidelity, multimodal artifacts, and playable game quality.

Key points

• Code2Games coordinates scene analysis, gameplay planning, and engine adaptation through a shared scene-gameplay representation with persistent element correspondence.
• The framework consistently improves visual quality and interactive fidelity compared to direct coding agent generation and existing baselines.

Context

This research addresses the challenge of maintaining consistency across multiple components of a gaming world, such as scenes, scripts, and assets, which existing coding agents typically struggle to achieve. The GameCode4D benchmark provides a systematic evaluation framework to assess generated gaming worlds across four distinct dimensions.

Limitations

The evaluation relies on a single benchmark with ten fixed game prompts, limiting generalizability to other prompt types. Results are based on experimental data rather than long-term deployment performance, and the framework's long-term stability under varying user inputs remains untested.

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