Code2Games: Enabling Coding Agents for Gaming World Generation

Wei Wu; Ziyang Xu; Zeyu Zhang; Yang Zhao; Hao Tang

cs.CVOct 4, 2026

Code2Games: Enabling Coding Agents for Gaming World Generation

Authors: Wei Wu, Ziyang Xu, Zeyu Zhang, Yang Zhao, Hao Tang

Organizations: School of Computer Science, Peking University · La Trobe University

Abstract

Generating a high-quality gaming world from a natural-language game intent requires joint reasoning about scene structure, spatial layout, gameplay objectives, interactive entities, and executable gameplay logic. Existing coding agents can generate individual assets, scenes, or scripts, but often struggle to maintain consistency across these components. We propose Code2Games, an agentic framework that builds a structured gaming world upon a base Blender world generated from the same game intent. Code2Games coordinates scene analysis, gameplay planning, constrained gaming-world generation, and gaming-engine customization through a shared scene-gameplay representation with persistent element correspondence. After world generation, Code2Games adapts the generated world to Unreal Engine 5 and employs an execution-guided reconstruction process that uses compilation diagnostics, runtime feedback, and gameplay test results to resolve inconsistencies arising during engine adaptation. To systematically evaluate gaming-world generation, we introduce the GameCode4D benchmark, which comprises ten fixed game prompts spanning different levels of scene and gameplay complexity. We evaluate the generated results across four dimensions: visual quality, interactive fidelity, multimodal artifact quality, and playable-game quality. Experiments demonstrate that, compared with direct gaming-world generation by coding agents and existing baseline methods, Code2Games consistently improves the visual quality and interactive fidelity of generated gaming worlds, as well as the quality of the resulting games after engine adaptation.

Figures & tables

Figure 1: Overview of the gaming world scenarios generated by Code2Games.

Panel of Figure 2 — Figure 2: Complexity of gaming worlds generated by Code2Games. The horizontal axis reports the number of scene entities, whereas the vertical axis reports the number of executable behaviors; both axes use logarithmic scales. Bubble size denotes the number of gameplay elements, and color indicates the game category.

Figure 3: Overview of Code2Games, which transforms a game intent and its paired base world into a gaming world and adapts it to a game engine.

Method	Visual Quality			Interactive Fidelity
Method	IQ $\uparrow$	Dynamic $\uparrow$	Motion $\uparrow$	Setting $\uparrow$	Inter. $\uparrow$	Physics $\uparrow$
OpenGame + Qwen3.8-Max	62.8	80.5	94.1	78.5	65.0	67.0
AutoUE + Qwen3.8-Max	63.1	82.2	93.8	80.3	65.8	68.7
Claude Code + Claude Opus 5	63.6	81.8	95.1	80.0	66.0	68.4
Codex + GPT-5.6 Sol	63.3	83.2	94.9	80.7	67.8	69.2
Qwen Code + Qwen3.8-Max	61.7	77.8	93.5	75.2	60.8	64.8

Table 1: Results on the GameCode4D Benchmark using the same ten prompts. The top panel reports visual quality and interactive fidelity, while the bottom panel reports multimodal artifact quality and playable-game quality. Bold values indicate the best results in each column.

Figure 4: Qualitative results of gaming worlds generated by Code2Games.

Figure 5: Visual quality and interactive fidelity on the GameCode4D benchmark.

Variant	Setting $\uparrow$	Inter. $\uparrow$	Physics $\uparrow$	M $\uparrow$	D $\uparrow$	V $\uparrow$
w/o Scene Representation	78.2	71.5	72.8	60.5	49.8	55.0
w/o Gameplay Specification	82.1	69.8	73.5	62.0	51.5	56.2
w/o Scene Constraints	83.4	74.1	66.2	64.5	53.0	57.8
Full Code2Games	87.5	75.8	73.9	68.8	56.7	61.0

Table 2: Ablation of Scene-grounding.

Appendix figures & tables23 assets

Supplementary material from the paper’s appendix.

Appendix

Variant	M $\uparrow$	D $\uparrow$	V $\uparrow$	A $\uparrow$
Unconstrained Realization	58.0	45.0	52.0	45.0
+ Region Compatibility	62.0	50.0	55.5	50.0
+ Geometric Feasibility Checks	65.0	53.0	58.0	53.0
+ Full Scene-Constrained Selection	68.8	56.7	61.0	57.0

Appendix

Table 4: Fine-grained ablation of scene-constrained game realization.

ID	Prompt Content	Game Scenario	Core Interactions
01	Create a first-person sci-fi assault on a red desert ridgeline. Eliminate three hostile sentries and secure the remote signal relay.	FPS	Shoot; survive; secure relay
02	Create a first-person sci-fi combat sweep on a red-desert ridgeline. Track and eliminate three hostile sentries, then secure the remote signal uplink.	FPS	Track; eliminate; secure uplink
03	Create a third-person monster hunt in a dark forest. Track the Thornback, break its neck armor, defeat it, and claim the trophy.	Monster hunt	Track; break armor; claim trophy
04	Create a storm-weather Thornback hunt through a wet forest. Track the creature in low visibility, defeat it, and retrieve the trophy.	Monster hunt	Track; fight; retrieve trophy
05	Create a high-speed off-road rally through a desert oasis. Drive across uneven terrain, use nitro, pass eight checkpoints, and reach the finish.	Racing	Drive; boost; pass checkpoints
06	Create a desert-storm rally across layered dunes. Control the vehicle over changing slopes, pass eight rally gates, and complete the stage.	Racing	Steer; boost; pass gates

Appendix

Table 5: Game-intent prompts for the qualitative examples. IDs match the qualitative figure sequence.

Dimension	Rating statement
Core mechanics ( $M$ )	The central player action and its consequence are clear and functional.
Content depth ( $D$ )	The world, objectives, and interactions provide meaningful progression.
Functional visuals ( $V$ )	Visual assets, interface, and feedback support readable gameplay.
Art and presentation ( $A$ )	The game appears coherent, complete, and engaging as a demonstration.

Appendix

Table 6: User-study rubric. Each dimension is scored from 0 to 100, using the same playable-game criteria as the automatic evaluation.

Figure 8: Monster-hunt gameplay example 1.

Figure 9: Monster-hunt gameplay example 2.

Figure 14: Temple-run gameplay example 1.

Figure 15: Temple-run gameplay example 2.

Figure 16: Third-person shooter gameplay example 1.

Figure 17: Third-person shooter gameplay example 2.

Figure 18: Underwater exploration gameplay example 1.

Figure 19: Underwater exploration gameplay example 2.

Figure 22: Assassin’s Creed-style gameplay example 1.

Figure 23: Assassin’s Creed-style gameplay example 2.

Figure 24: Minecraft-style gameplay example 1.

Figure 25: Minecraft-style gameplay example 2.

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Open paper AskPDF arXiv

Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional coding tasks, game generation takes place within a game engine, where scripts, scenes, assets, rendering, and runtime interactions must jointly produce coherent gameplay. We formalize end-to-end game generation as the problem of producing a complete game artifact that realizes a specification through observable player-game interaction in a target environment. We argue that evaluating this setting requires three desiderata: Engine Grounding, Artifact Completeness, and Interactive Verification. We propose an interaction-grounded evaluation framework that assesses executable gameplay through replayed demonstrations and rubric-guided multimodal judging. We instantiate this framework as GameCraft-Bench, a benchmark comprising 140 Godot tasks across 15 game families. Evaluations of frontier coding agents show that end-to-end game generation remains highly challenging: the strongest agent achieves only 41.46%, and most agents score below 40%. Further analysis reveals that while agents often implement recognizable mechanics, they struggle to deliver complete games with sufficient content, functional visual feedback, and coherent presentation. See https://tongxuluo.github.io/gamecraft-bench-website for demos, code, and data.

Abstract

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