| --- |
| 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](https://arxiv.org/abs/2602.11757) |
| - **Repository:** [GitHub](https://github.com/AIGeeksGroup/Code2Worlds) |
|
|
| ### 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: |
| |
| ```bibtex |
| @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} |
| } |
