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--- |
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annotations_creators: |
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- expert-generated |
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language: |
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- en |
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license: mit |
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task_categories: |
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- text-to-3d |
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- text-to-video |
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- other |
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tags: |
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- blender |
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- procedural-generation |
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- physics-simulation |
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- 4d-generation |
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- code-generation |
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pretty_name: Code4D Benchmark |
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size_categories: |
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- n<1K |
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--- |
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# Dataset Card for Code4D (Code2Worlds) |
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## Dataset Description |
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- **Paper:** [Code2Worlds: Empowering Coding LLMs for 4D World Generation](https://arxiv.org/abs/2602.11757) |
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- **Repository:** [GitHub](https://github.com/AIGeeksGroup/Code2Worlds) |
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### Dataset Summary |
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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. |
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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. |
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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). |
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### Supported Tasks and Leaderboards |
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- **Text-to-4D Scene Generation:** Generating dynamic 3D scenes from text descriptions. |
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- **Procedural Code Generation:** Evaluating LLMs on generating Blender/Infinigen API calls. |
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- **Physics Simulation Benchmarking:** Assessing the realism of generated physical interactions. |
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### Languages |
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The prompts and documentation are in **English**. |
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--- |
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## Dataset Structure |
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### Data Instances |
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Each instance in the dataset consists of a text prompt and its corresponding Blender project file (`.blend`). |
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**Example:** |
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* **Prompt:** "A breeze stirs through the autumn forest, gently swaying the entire tree as leaves dance in the wind." |
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* **File:** `scene_1.blend` |
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### Data Fields |
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- `prompt` (string): The natural language instruction describing the scene and desired dynamics. |
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- `blend_file` (file): The Blender 3D project file containing the scene layout, assets, and simulation settings. |
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--- |
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## Dataset Creation |
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### Curation Rationale |
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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. |
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--- |
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## Considerations for Using the Data |
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### Software Dependencies |
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To open and render the `.blend` files properly, you need: |
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- **Blender 4.3** or higher. |
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- **Infinigen** libraries. |
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### Computational Requirements |
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The benchmark scenes are designed for high-fidelity rendering. |
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- **Nature Scenes:** Configured for 1920x1080 resolution, 240 frames, 128 samples. |
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- **Indoor Scenes:** Configured for 1920x1080 resolution, 120 frames, 196 samples. |
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--- |
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## Citation |
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If you use this dataset in your research, please cite the following paper: |
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```bibtex |
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@article{zhang2026code2worlds, |
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title={Code2Worlds: Empowering Coding LLMs for 4D World Generation}, |
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author={Zhang, Yi and Wang, Yunshuang and Zhang, Zeyu and Tang, Hao}, |
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journal={arXiv preprint arXiv:2602.11757}, |
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year={2026} |
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} |
