---
license: cc-by-nc-4.0
pretty_name: HairCS
size_categories:
  - 10K<n<100K
tags:
  - hair
  - strands
  - 3d
  - computer-graphics
  - point-cloud
---

# HairCS

HairCS is a strand-based hair dataset: **53,336 hair assets** in 13 versions, each stored as a
point cloud of **60,000 strands x 64 samples** (xyz, float16), plus a rendered preview image of
every asset, per-asset hairstyle labels, the head/scalp geometry the hair is grown on, and a small
Python toolkit (viewer and strand-to-surface conversion).

All versions share the same head and the same coordinate frame (see `meta/`).

## Versions

| Version | Assets | Description |
|---|---|---|
| `v0`  | 1,076 | Base hair |
| `v1`  | 1,076 | `v0` + scale and fuzz modifier |
| `v2`  |   818 | Curly profile 1 |
| `v3`  |   844 | Curly profile 2 |
| `v4`  |   650 | Curly + scale and fuzz modifier |
| `v5`  | 1,539 | Blend: bob (`00000`-`00065`) and shoulder (`00066`-`01538`) |
| `v6`  | 1,539 | `v5` + curly profile 1 |
| `v7`  | 6,086 | Blend: shoulder |
| `v8`  | 6,086 | `v7` + curly profile 1 |
| `v9`  | 8,585 | Blend: short |
| `v10` | 8,585 | `v9` + curly profile 1 |
| `v11` | 8,226 | Blend: long |
| `v12` | 8,226 | `v11` + curly profile 1 |

Terminology:

- **Blend** – assets produced with the hair blending method described in the paper.
- **Curly profile 1** – the helix operator from the paper, with its first parameter profile.
- **Curly profile 2** – the helix operator with a second parameter profile.
- **Scale and fuzz modifier** – per-strand scaling plus a fuzz perturbation applied on top of the base hair.

## Repository layout

```
data/vN/XXXXX.npz        one asset per file, key "P": (60000, 64, 3) float16, samples ordered root -> tip
render/vN.zip            one PNG preview per asset, named XXXX.png (4-digit index, same order as data/vN)
label/vN.csv             hairstyle class per asset (columns: number,class)
meta/                    head and scalp geometry shared by all versions (OBJ)
  full_body_geo.obj
  head_water_tight_boundary.obj
  head_with_uv.obj
  scalp_with_uv.obj
argument/                viewer + strand-to-surface tools (see argument/README.md)
```

Every `data/vN/` folder holds exactly the number of assets listed in the table, numbered
`00000` to `N-1`.

## Loading an asset

```python
import numpy as np

d = np.load("data/v0/00000.npz")
P = d["P"].astype(np.float32)     # (60000, 64, 3): 60000 strands, 64 samples per strand, xyz
roots, tips = P[:, 0], P[:, -1]   # samples are ordered root -> tip
```

The head meshes in `meta/` are in the same coordinate frame as the strands, so an asset can be
placed on `head_with_uv.obj` or `scalp_with_uv.obj` without any transform.

## Labels

`label/vN.csv` gives one hairstyle class per asset:

| Class | Meaning |
|---|---|
| `short` | short hair |
| `bob` | bob cut |
| `shoulder` | shoulder-length hair |
| `long` | long hair |
| `gather` | gathered / tied-back hair |

For `v0`-`v6` the classes were assigned per asset. For `v7`-`v12` every asset of a version
shares the class of the blend target of that version (`v7`/`v8`: shoulder, `v9`/`v10`: short,
`v11`/`v12`: long).

## Downloading

The full dataset is about 963 GB. Fetch only what you need with `--include`:

```bash
pip install -U huggingface_hub
hf download HairCS2027/HairCS --repo-type dataset --local-dir HairCS \
    --include "meta/*" "label/*" "argument/*"            # small files, ~25 MB
hf download HairCS2027/HairCS --repo-type dataset --local-dir HairCS \
    --include "data/v4/*" "render/v4.zip"                 # one version, ~11 GB
```

| Folder | Size |
|---|---|
| `data/` | ~932 GB (13 versions, 10 to 154 GB each) |
| `render/` | ~31 GB (13 zips, 0.3 to 7 GB each) |
| `meta/`, `label/`, `argument/` | < 25 MB |

## Tools

`argument/` contains a strand viewer (`run_viewer.py`) and a tool that wraps an asset in a
watertight mesh (`run_strand2surface.py`, with a batch runner and optional GPU path).
Usage and the expected on-disk layout are described in `argument/README.md`.

## License

CC BY-NC 4.0. Non-commercial use only.

## Citation

The accompanying paper is under review. A citation entry will be added on publication.