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3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code

NeurIPS 2026 · 3D vision › 3D generation · approach: Text-to-3D scene generation · 0 citations (OpenAlex)

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Introduces 3DCodeBench, a benchmark of 212 procedural 3D object categories (about 26K code samples curated from Infinigen) for evaluating VLM agents that write Blender Python code, plus the 3DCodeArena human-preference arena, and evaluates 12 frontier VLMs with single-turn, multi-turn, and coding-agent settings.

Problem

Existing benchmarks do not measure whether vision-language models can author complex, executable procedural 3D code from scratch with iterative refinement, and lack standardized metrics and human preference evaluation.

Approach

Extract procedural factories from Infinigen, convert them into standalone Blender 5.0 scripts via an agentic curation pipeline with human verification, and evaluate VLMs on executability, perceptual and 3D similarity metrics, multi-turn error-feedback retries, coding-agent harnesses, and a pairwise human-vote arena.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: Infinigen procedural factories

Benchmarks: 3DCodeBench

Metrics: executability SigLIP-2 view similarity DINOv3 view similarity Chamfer distance Uni3D similarity Elo

Baselines: BlenderGym VoxelCodeBench CADBench 3DGen-Bench

Builds on: Infinigen Blender

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