| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| dataset_info: |
| features: |
| - name: hairstyle_id |
| dtype: string |
| - name: view |
| dtype: string |
| - name: source |
| dtype: string |
| - name: hairstyle_source_id |
| dtype: string |
| - name: hair_image |
| dtype: image |
| - name: bald_image |
| dtype: image |
| - name: hair_render |
| dtype: image |
| - name: background_image |
| dtype: image |
| - name: render_params_json |
| dtype: string |
| - name: background_prompt |
| dtype: string |
| - name: camera_focal_length |
| dtype: float64 |
| - name: camera_location_x |
| dtype: float64 |
| - name: camera_location_y |
| dtype: float64 |
| - name: camera_location_z |
| dtype: float64 |
| - name: camera_rotation_x |
| dtype: float64 |
| - name: camera_rotation_y |
| dtype: float64 |
| - name: camera_rotation_z |
| dtype: float64 |
| - name: lighting_preset |
| dtype: string |
| - name: body_gender |
| dtype: string |
| - name: face_expression |
| dtype: string |
| - name: hair_melanin |
| dtype: float64 |
| - name: hair_roughness |
| dtype: float64 |
| - name: has_garments |
| dtype: bool |
| - name: views_available |
| dtype: string |
| splits: |
| - name: train |
| num_examples: 6400 |
| license: cc-by-4.0 |
| task_categories: |
| - image-to-image |
| tags: |
| - hair |
| - bald |
| - paired-data |
| - synthetic |
| - blender |
| - controlnet |
| - smplx |
| - 3d-rendering |
| pretty_name: Baldy Dataset |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # Baldy Dataset |
|
|
| A synthetic paired dataset of **hair/bald image pairs** for bald reconstruction research, |
| generated from physically modeled hairstyles rendered on SMPL-X bodies in Blender. |
|
|
| ## Dataset Description |
|
|
| The Baldy dataset provides pixel-aligned, identity-consistent pairs of images showing the |
| same person with and without hair. It was created using a multi-stage pipeline: |
|
|
| 1. **3D Hair Modeling**: Hairstyles from DiffLocks, Hair20K, USC-HairSalon, and CT2Hair |
| are aligned to SMPL-X head meshes with varying poses, expressions, and optional |
| BEDLAM clothing. |
| 2. **Blender Rendering**: Hair is rendered under diverse camera views, lighting conditions, |
| and material settings at 1024×1024 resolution. |
| 3. **Photorealistic Generation**: ControlNet++ and SDXL-based pipelines generate |
| photorealistic composites, with Flux Kontext refinement for identity preservation. |
|
|
| ## Dataset Statistics |
|
|
| **Total samples**: 6400 |
|
|
| ### Samples per View |
|
|
| | View | Count | |
| |------|-------| |
| | back | 99 | |
| | front | 6009 | |
| | side | 292 | |
|
|
| ### Samples per Hairstyle Source |
|
|
| | Source | Count | |
| |--------|-------| |
| | ct2hair | 9 | |
| | difflocks | 3197 | |
| | hair20k | 2824 | |
| | usc | 370 | |
|
|
| ## Data Fields |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `hairstyle_id` | `string` | Unique zero-padded sequential ID (e.g., `"000042"`) | |
| | `view` | `string` | Camera view: `"front"`, `"back"`, or `"side"` | |
| | `source` | `string` | Hairstyle source: `"difflocks"`, `"hair20k"`, `"usc"`, or `"ct2hair"` | |
| | `hairstyle_source_id` | `string` | Source-specific hairstyle identifier | |
| | `hair_image` | `Image` | Photorealistic image of the person **with hair** (1024×1024 PNG, auto-loaded) | |
| | `bald_image` | `Image` | Photorealistic image of the same person **without hair** (1024×1024 PNG, auto-loaded) | |
| | `hair_render` | `Image` | Blender-rendered hair on transparent background (1024×1024 PNG, auto-loaded) | |
| | `background_image` | `Image` | Scene background image (1024×1024 PNG, auto-loaded) | |
| | `render_params_json` | `string` | Full Blender render parameters as embedded JSON string | |
| | `background_prompt` | `string` | Text prompt used to generate the background (empty string if unavailable) | |
| | `camera_focal_length` | `float` | Camera focal length (mm) | |
| | `camera_location_x` | `float` | Camera X position in Blender world space | |
| | `camera_location_y` | `float` | Camera Y position in Blender world space | |
| | `camera_location_z` | `float` | Camera Z position in Blender world space | |
| | `camera_rotation_x` | `float` | Camera X rotation in radians | |
| | `camera_rotation_y` | `float` | Camera Y rotation in radians | |
| | `camera_rotation_z` | `float` | Camera Z rotation in radians | |
| | `lighting_preset` | `string` | Lighting preset name (e.g., `"studio"`, `"outdoor"`) | |
| | `body_gender` | `string` | Subject gender from SMPL-X body configuration | |
| | `face_expression` | `string` | Facial expression (e.g., `"angry"`, `"neutral"`; empty string for some DiffLocks samples) | |
| | `hair_melanin` | `float` | Hair melanin value controlling color darkness | |
| | `hair_roughness` | `float` | Hair surface roughness | |
| | `has_garments` | `bool` | Whether BEDLAM clothing is applied to the body | |
| | `views_available` | `string` | All camera views available for this hairstyle (pipe-separated, e.g., `"front\|back\|side"`) | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the full dataset |
| ds = load_dataset("deepmancer/baldy", split="train") |
| |
| # Images are automatically decoded as PIL Images |
| sample = ds[0] |
| sample["hair_image"] # PIL.Image (person with hair, auto-loaded) |
| sample["bald_image"] # PIL.Image (person without hair, auto-loaded) |
| sample["hair_render"] # PIL.Image (Blender hair render, auto-loaded) |
| sample["background_image"] # PIL.Image (scene background, auto-loaded) |
| |
| # Filter by view |
| front = ds.filter(lambda x: x["view"] == "front") |
| back = ds.filter(lambda x: x["view"] == "back") |
| side = ds.filter(lambda x: x["view"] == "side") |
| |
| # Filter by hairstyle source |
| hair20k = ds.filter(lambda x: x["source"] == "hair20k") |
| |
| # Access render parameters (embedded JSON) |
| import json |
| render_params = json.loads(sample["render_params_json"]) |
| |
| # Stream the dataset (no full download needed) |
| ds_stream = load_dataset("deepmancer/baldy", split="train", streaming=True) |
| for sample in ds_stream: |
| print(sample["hairstyle_id"]) |
| break |
| ``` |
|
|
| ## File Structure |
|
|
| The dataset is stored as sharded Parquet files with embedded images (~1GB per shard): |
|
|
| ``` |
| data/ |
| ├── train-00000-of-NNNNN.parquet |
| ├── train-00001-of-NNNNN.parquet |
| ├── ... |
| └── train-NNNNN-of-NNNNN.parquet |
| ``` |
|
|
| Each Parquet file contains all columns including image bytes — no external files are needed. |
| Images are decoded as PIL Images automatically when accessing rows. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @article{baldy2025, |
| title={HairPort: In-context 3D-Aware Hair Import and Transfer for Images}, |
| year={2025} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. |
|
|
