用代码生成3D世界，评估模型真懂空间关系吗？
VoxelCodeBench: Benchmarking 3D World Modeling Through Code Generation
- 用Unreal引擎执行代码，真实环境验证生成结果
- 模型能写代码但难造对空间结构，多物体组合最吃力
- 适合研究3D理解与空间推理的开发者和评测者
评估代码生成模型在3D空间推理方面的能力，需要在真实环境中执行生成代码并评估输出是否准确。我们提出VoxelCode平台，用于分析代码生成在3D理解与环境构建中的能力。该平台集成自然语言任务描述、基于API的Unreal Engine代码执行，以及支持自动化指标与人工评估的统一评估流程。为展示其价值，我们构建了VoxelCodeBench，一个涵盖三类推理维度的体素操作任务基准：符号理解、几何构造与艺术构图。评估主流代码生成模型发现，生成可执行代码远比生成空间正确输出容易，几何构造与多物体组合尤为困难。通过开源平台与基准，我们为社区提供可扩展的基础设施，支持未来3D代码生成基准开发与空间推理研究。
原文摘要 · Abstract (English)
Evaluating code generation models for 3D spatial reasoning requires executing generated code in realistic environments and assessing outputs beyond surface-level correctness. We introduce a platform VoxelCode, for analyzing code generation capabilities for 3D understanding and environment creation. Our platform integrates natural language task specification, API-driven code execution in Unreal Engine, and a unified evaluation pipeline supporting both automated metrics and human assessment. To demonstrate its utility, we construct VoxelCodeBench, a benchmark of voxel manipulation tasks spanning three reasoning dimensions: symbolic interpretation, geometric construction, and artistic composition. Evaluating leading code generation models, we find that producing executable code is far easier than producing spatially correct outputs, with geometric construction and multi-object composition proving particularly challenging. By open-sourcing our platform and benchmark, we provide the community with extensible infrastructure for developing new 3D code generation benchmarks and probing spatial reasoning in future models.
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