annotations_creators:
- expert-generated
language:
- en
license: mit
task_categories:
- text-to-3d
- text-to-video
- other
tags:
- blender
- procedural-generation
- physics-simulation
- 4d-generation
- code-generation
pretty_name: Code4D Benchmark
size_categories:
- n<1K
Dataset Card for Code4D (Code2Worlds)
Dataset Description
- Paper: Code2Worlds: Empowering Coding LLMs for 4D World Generation
- Repository: GitHub
Dataset Summary
The Code4D benchmark is a dataset designed to evaluate the capability of Large Language Models (LLMs) in generating physically grounded 4D environments. It pairs natural language prompts with complex 3D scenes (provided here as .blend files) that exhibit temporal evolution, physical interactions, and atmospheric changes.
Unlike existing text-to-3D datasets that focus solely on static structures, Code4D challenges models on dynamic fidelity, including fluid dynamics, particle systems, rigid-body dynamics, and soft-body simulations.
This dataset supports the Code2Worlds framework, which formulates 4D generation as language-to-simulation code generation using a dual-stream architecture (Object Stream and Scene Stream).
Supported Tasks and Leaderboards
- Text-to-4D Scene Generation: Generating dynamic 3D scenes from text descriptions.
- Procedural Code Generation: Evaluating LLMs on generating Blender/Infinigen API calls.
- Physics Simulation Benchmarking: Assessing the realism of generated physical interactions.
Languages
The prompts and documentation are in English.
Dataset Structure
Data Instances
Each instance in the dataset consists of a text prompt and its corresponding Blender project file (.blend).
Example:
- Prompt: "A breeze stirs through the autumn forest, gently swaying the entire tree as leaves dance in the wind."
- File:
scene_1.blend
Data Fields
prompt(string): The natural language instruction describing the scene and desired dynamics.blend_file(file): The Blender 3D project file containing the scene layout, assets, and simulation settings.
Dataset Creation
Curation Rationale
The dataset was constructed to address the "semantic-physical execution gap" in generative models. It specifically targets scenarios where monolithic generation fails, requiring precise control over both local object structures and global environmental layouts.
Considerations for Using the Data
Software Dependencies
To open and render the .blend files properly, you need:
- Blender 4.3 or higher.
- Infinigen libraries.
Computational Requirements
The benchmark scenes are designed for high-fidelity rendering.
- Nature Scenes: Configured for 1920x1080 resolution, 240 frames, 128 samples.
- Indoor Scenes: Configured for 1920x1080 resolution, 120 frames, 196 samples.
Citation
If you use this dataset in your research, please cite the following paper:
@article{zhang2026code2worlds,
title={Code2Worlds: Empowering Coding LLMs for 4D World Generation},
author={Zhang, Yi and Wang, Yunshuang and Zhang, Zeyu and Tang, Hao},
journal={arXiv preprint arXiv:2602.11757},
year={2026}
}