| | --- |
| | 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} |
| | } |
