---
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.