| --- |
| license: mit |
| pretty_name: CCORE Three.js 3D Scenes |
| language: |
| - en |
| task_categories: |
| - text-generation |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - 3d |
| - threejs |
| - code |
| - reasoning |
| - conversational |
| - html |
| - javascript |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: |
| - data/train-00000.parquet |
| - data/train-00001.parquet |
| - data/train-00002.parquet |
| - data/train-00003.parquet |
| - split: validation |
| path: |
| - data/validation-00000.parquet |
| default: true |
| - config_name: previews |
| data_files: |
| - split: train |
| path: |
| - previews/train-00000.parquet |
| - previews/train-00001.parquet |
| - previews/train-00002.parquet |
| - previews/train-00003.parquet |
| - previews/train-00004.parquet |
| - previews/train-00005.parquet |
| - previews/train-00006.parquet |
| - previews/train-00007.parquet |
| - previews/train-00008.parquet |
| - previews/train-00009.parquet |
| - previews/train-00010.parquet |
| - previews/train-00011.parquet |
| - previews/train-00012.parquet |
| - previews/train-00013.parquet |
| - previews/train-00014.parquet |
| - previews/train-00015.parquet |
| - split: validation |
| path: |
| - previews/validation-00000.parquet |
| dataset_info: |
| - config_name: default |
| features: |
| - name: id |
| dtype: string |
| - name: title |
| dtype: string |
| - name: level |
| dtype: int32 |
| - name: domain |
| dtype: string |
| - name: voxel |
| dtype: bool |
| - name: messages |
| list: |
| - name: role |
| dtype: string |
| - name: content |
| dtype: string |
| - name: reasoning_content |
| dtype: string |
| splits: |
| - name: train |
| num_examples: 3933 |
| num_bytes: 363885742 |
| - name: validation |
| num_examples: 213 |
| num_bytes: 19822852 |
| download_size: 134444227 |
| dataset_size: 383708594 |
| - config_name: previews |
| features: |
| - name: id |
| dtype: string |
| - name: title |
| dtype: string |
| - name: level |
| dtype: int32 |
| - name: image |
| dtype: image |
| splits: |
| - name: train |
| num_examples: 3933 |
| num_bytes: 1294921572 |
| - name: validation |
| num_examples: 213 |
| num_bytes: 72048979 |
| download_size: 1366958111 |
| dataset_size: 1366970551 |
| --- |
| |
| # CCORE Three.js 3D Scenes |
|
|
| **4,146 high-quality 3D scene examples with prompts, full thinking traces, and complete HTML/JavaScript responses.** |
|
|
| The scene code and thinking traces were generated with [**MiniMax M3.1 Flash**](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash). |
|
|
| This dataset brings together a broad range of Three.js scenes, from individual objects and detailed interiors to architecture, landscapes, animated mechanisms, and voxel environments. It is intended for training and evaluating models that generate 3D scenes from natural-language instructions. |
|
|
| This dataset was used to train [**Qwen3.5-4B-3D-Coder**](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder). |
|
|
| ## Scene examples |
|
|
| The images below are rendered references from examples included in this dataset. A matching reference image is available for every example in the optional `previews` configuration. |
|
|
| <table> |
| <tbody> |
| <tr> |
| <td width="50%" align="center"><a href="https://huggingface.co/datasets/ccore/ThreeJS-3D-Scenes/blob/main/assets/dragonrider-aerie.png"><img src="assets/dragonrider-aerie.png" alt="Dragonrider Aerie" width="768" /></a><br /><strong>Dragonrider Aerie</strong><br /><code>scene-000013</code></td> |
| <td width="50%" align="center"><a href="https://huggingface.co/datasets/ccore/ThreeJS-3D-Scenes/blob/main/assets/temple-garden.png"><img src="assets/temple-garden.png" alt="Temple Garden" width="768" /></a><br /><strong>Temple Garden</strong><br /><code>scene-003268</code></td> |
| </tr> |
| <tr> |
| <td width="50%" align="center"><a href="https://huggingface.co/datasets/ccore/ThreeJS-3D-Scenes/blob/main/assets/bookbinding-bench.png"><img src="assets/bookbinding-bench.png" alt="Bookbinding Bench" width="768" /></a><br /><strong>Bookbinding Bench</strong><br /><code>scene-002556</code></td> |
| <td width="50%" align="center"><a href="https://huggingface.co/datasets/ccore/ThreeJS-3D-Scenes/blob/main/assets/canal-lock.png"><img src="assets/canal-lock.png" alt="Canal Lock and Weir" width="768" /></a><br /><strong>Canal Lock and Weir</strong><br /><code>scene-000597</code></td> |
| </tr> |
| <tr> |
| <td width="50%" align="center"><a href="https://huggingface.co/datasets/ccore/ThreeJS-3D-Scenes/blob/main/assets/medieval-cathedral-voxel.png"><img src="assets/medieval-cathedral-voxel.png" alt="Medieval Cathedral and Builders' Quarter — Voxel Study" width="768" /></a><br /><strong>Medieval Cathedral and Builders' Quarter — Voxel Study</strong><br /><code>scene-003894</code></td> |
| <td width="50%" align="center"><a href="https://huggingface.co/datasets/ccore/ThreeJS-3D-Scenes/blob/main/assets/power-station-decommissioning.png"><img src="assets/power-station-decommissioning.png" alt="Power-Station Decommissioning Exhibit" width="768" /></a><br /><strong>Power-Station Decommissioning Exhibit</strong><br /><code>scene-003933</code></td> |
| </tr> |
| <tr> |
| <td colspan="2" align="center"><a href="https://huggingface.co/datasets/ccore/ThreeJS-3D-Scenes/blob/main/assets/optical-telescope-cutaway.png"><img src="assets/optical-telescope-cutaway.png" alt="Optical Telescope Dome Cutaway" width="50%" /></a><br /><strong>Optical Telescope Dome Cutaway</strong><br /><code>scene-003559</code></td> |
| </tr> |
| </tbody> |
| </table> |
|
|
| ## Dataset contents |
|
|
| | Split | Scene examples | |
| | --- | ---: | |
| | Train | 3,933 | |
| | Validation | 213 | |
| | **Total** | **4,146** | |
|
|
| The original training and validation split is preserved. Each example includes its full prompt, thinking trace, and response, without truncation. Complexity levels range from **1 to 10** and are annotations, rather than model benchmark scores. |
|
|
| ## Format |
|
|
| The default configuration contains conversational data in **Parquet** format, ready to load with Hugging Face Datasets. |
|
|
| | Field | Description | |
| | --- | --- | |
| | `id` | Stable scene identifier, shared with the reference-image configuration. | |
| | `title` | Human-readable scene title. | |
| | `level` | Annotated scene complexity, from 1 to 10. | |
| | `domain` | Scene subject category. | |
| | `voxel` | Whether the scene uses a voxel style. | |
| | `messages` | Conversation with an optional system message, a user prompt, and an assistant response. | |
|
|
| Within the assistant message, **`reasoning_content` contains the thinking trace** and **`content` contains the full response**, including the HTML/JavaScript code. Other messages have a null `reasoning_content`. |
| |
| The optional **`previews`** configuration contains `id`, `title`, `level`, and an `image` feature for each scene. It uses the same splits and row order as the default configuration. Load it separately when you want to inspect or reuse the visual references. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("ccore/ThreeJS-3D-Scenes") |
| sample = dataset["train"][0] |
| |
| messages = sample["messages"] |
| thinking = messages[-1]["reasoning_content"] |
| response = messages[-1]["content"] |
| level = sample["level"] |
| |
| # Optional rendered references, aligned by id and split. |
| previews = load_dataset("ccore/ThreeJS-3D-Scenes", "previews") |
| assert previews["train"][0]["id"] == sample["id"] |
| image = previews["train"][0]["image"] |
| ``` |
|
|
| The reference images show the included target scenes. They are separate from the text conversations. Some scenes contain animation or interactive controls that a single screenshot cannot capture. |
|
|
| ## License |
|
|
| This dataset is released under the [MIT License](LICENSE), to the extent of the rights held by the dataset publisher. |
|
|
| ## What’s next |
|
|
| I’m preparing a companion dataset for [**GLB-3D-Coder**](https://huggingface.co/ccore/Qwen3.5-4B-GLB-3D-Coder), following a similar approach and with **roughly the same number of examples as this release**. Its focus is on generating reusable **3D assets with GLB export**, ready to bring into game editors such as **Godot**. |
|
|
| Beyond that, I’m planning a larger dataset of **100,000 high-quality examples**, with context lengths ranging from **8K to 128K tokens**, targeting a training run roughly **15× larger than the current GLB model’s run**. The first goal is **reliable one-shot asset generation**, followed by a version designed to work directly inside an **agent harness**, generating, testing, and refining assets. |
|
|
| As with my previous releases, I plan to share the datasets and model weights openly. **Community support would help fund the data generation, validation, and GPU compute needed to make these next steps possible.** |
|
|
| ## ❤️ Support Future Open-Source AI Models |
|
|
| I'm independently developing and releasing specialized AI models and datasets for 3D generation, procedural environments, and creative coding, with the goal of making these tools accessible to everyone. |
|
|
| Training new models, generating high-quality datasets, validating thousands of examples, and running experiments require significant GPU resources, time, and effort. |
|
|
| **If you enjoy this project and would like to help me train more models, expand the datasets, and explore new capabilities, any contribution is greatly appreciated!** |
|
|
| My goal is to keep sharing models, weights, datasets, and improvements openly with the community. |
|
|
| ### ☕ Support via Ko-fi |
|
|
| [https://ko-fi.com/ccorecaio](https://ko-fi.com/ccorecaio) |
|
|
| ### Crypto Donations |
|
|
| **Bitcoin (BTC) — Bitcoin Network (SegWit)** |
|
|
| `bc1qr2gnr9g0uzk8rmsq0k6xpjgg4l7r0rfyrq69e6` |
|
|
| **Ethereum (ETH) — Ethereum Mainnet (ERC20)** |
|
|
| `0xa17608FCdeAd9e717Cd42e04127175527b4E75CB` |
|
|
| **Solana (SOL / USDC / USDT) — Solana Network** |
|
|
| `GhNmYmezKi1uWGKZeVxgddjiRW2rYNHZrf84ZaMcnha` |
|
|
|
|
|
|
| Every contribution helps fund more GPU compute, better datasets, new training runs, and future public releases. |
|
|
| **Thank you for supporting independent open-source AI research!** |
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|
