3DCodeVerse:用代码生成可验证的3D内容研究生态
3DCodeVerse: Exploring 3D Worlds as Code
这个 3DCodeVerse 让 AI 写代码直接生成 3D 场景,还能自动检查修错,人类评测里 90% 胜过 Claude Design,做 3D 生成的可以看看。
3DCodeVerse 是一个让编程智能体批量生成可编辑、可验证 3D 内容的研究生态,包含三部分。其一是 3DCodeVerse-2M 数据集,收录 200 万条可独立执行的 3D 代码程序,每条附带源文件、资产和验证记录。其二是 3DCodeVerse Harness,一个多智能体 DAG 框架,通过执行检查、空间测量和视觉反馈自动评估并修正生成的程序。在人类成对评估中,该 harness 驱动 Claude Opus 5.5 时对 Claude Design(3D Object)取得 90% 的胜率,微调开源模型后 3D 编程能力也显著提升。
3DCodeVerse: Exploring 3D Worlds as Code
We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises large-scale, high-quality datasets of executable 3D code, a coding harness for spatial reasoning and automated verification, and practical guidance for 3D coding. First, we construct 3DCodeVerse-2M, a large-scale curated dataset of standalone 3D executable code. We build this corpus by collecting open-source data, converting specialized representations, and generating new programs with coding agents. Each program is packaged with the source files and associated assets required for execution in its declared environment, along with available descriptions and verification records. Second, we develop the 3DCodeVerse Harness, a multi-agent directed acyclic graph framework for generating and refining 3D content. It uses execution checks, spatial measurements, and visual feedback to automatically evaluate and revise generated programs without human feedback during generation. Third, we conduct systematic empirical studies of 3D coding, establishing baselines with fine-tuned open models and deriving practical guidance through component-level analyses of our harness. Extensive experiments demonstrate that fine-tuning on our dataset significantly improves the 3D coding capabilities of open-source models. Notably, our 3DCodeVerse harness achieves a 90% win rate against Claude Design (3D Object) in pairwise human evaluations, when both are utilized by Claude Opus 5.5.