AI RESEARCH
3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code
arXiv CS.AI
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ArXi:2606.01057v1 Announce Type: cross Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neural 3D generators inherently lack. Authoring such procedural content, however, demands deep expertise in 3D software APIs, parametric design, and code-level geometric reasoning. In this paper, we propose 3DCodeBench, a systematic benchmark for evaluating vision-language model (VLM) agents for procedural 3D generation in 3D modeling software.