| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-to-image |
| - text-to-image |
| tags: |
| - synthetic |
| - blender |
| - urban-planning |
| - controlnet |
| - multimodal |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Synthetic Urban Multimodal Dataset (v0.1) |
|
|
| This dataset contains **8,000 high-quality synthetic images** of procedurally generated city models, designed specifically for training and evaluating AI models such as ControlNet and LoRA. |
|
|
| ## Dataset Description |
| All assets were generated using a custom Blender-Python automated pipeline. The dataset provides 100 unique urban architectures, each rendered from 8 directions with 10 different artistic styles. |
|
|
| - **Total Samples:** 8,000 (Total 40,000 files across modalities) |
| - **Resolution:** 512x512 |
| - **Modalities per sample:** |
| - `rgb_images`: Stylized urban rendering. |
| - `depth_maps`: High-precision depth information. |
| - `normal_maps`: Surface normal vectors. |
| - `albedo_images`: Pure texture color (unlit). |
| - `mask_images`: Binary/Alpha masks for segmentation. |
|
|
| ## Data Structure |
| The dataset follows the standard Hugging Face metadata format. Each entry in `metadata.jsonl` maps the RGB image to its corresponding multimodal maps and captions. |
|
|
| ## Intended Use |
| - Training ControlNet for spatial and structural control. |
| - Fine-tuning LoRA for specific architectural or artistic styles. |
| - Research in Computer Vision and Synthetic Data generation. |
|
|
| ## License |
| This dataset is provided under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. Commercial use is prohibited. |
|
|
| ## Author & Technical Background |
| Developed by [ひのき(jp-cypress) / https://zenn.dev/jp_cypress]. |
| For technical details on how this dataset was procedurally generated using Blender API and Python, please refer to the technical report on Zenn (Japanese). |
| |
