README.md

---
license: cc-by-nc-sa-4.0
language:
  - ms
  - zsm
pretty_name: MySign — Malaysian Sign Language 3D Motion Capture
size_categories:
  - 1K<n<10K
task_categories:
  - other
tags:
  - sign-language
  - bahasa-isyarat-malaysia
  - bim
  - malaysian-sign-language
  - motion-capture
  - 3d
  - fbx
  - skeletal-animation
  - gloss
configs: []
---

# MySign

A 3D motion-capture dataset of Malaysian Sign Language (Bahasa Isyarat Malaysia, BIM), distributed as Filmbox (`.fbx`) skeletal animation files.

> **Note**: this dataset is **not** loadable via `datasets.load_dataset(...)` as a tabular split, because the data itself is binary `.fbx`. `metadata.csv` is an index over the FBX files; consumers must read the file at `file_name` to access the motion data.

## Dataset Summary

- **Modality**: 3D skeletal motion capture (`.fbx`)
- **Language**: Malaysian Sign Language (BIM) — ISO `ms` / `zsm`
- **Signers**: 5 (`Signer001` … `Signer005`)
- **Sample**: one `.fbx` per `(signer, gloss, take)`
- **License**: CC BY-NC-SA 4.0
- **Source repository**: <https://huggingface.co/datasets/mysigner/MySign>

## Repository Structure

```
MySign/
├── Signer001/
│   ├── Above.fbx
│   ├── Apologize.fbx
│   ├── Apologize.001.fbx
│   └── ...
├── Signer002/
├── Signer003/
├── Signer004/
├── Signer005/
├── metadata.csv      # index over all .fbx files
├── croissant.json    # Croissant 1.0 ML-dataset metadata (JSON-LD)
├── README.md
└── .gitattributes
```

## `metadata.csv` Schema

`metadata.csv` is a UTF-8 CSV with a header row. **Each row corresponds to exactly one `.fbx` file in the repository.**

| Column      | Type    | Description |
|-------------|---------|-------------|
| `file_name` | string  | Repository-relative path to the `.fbx`, e.g. `Signer001/Above.fbx`. |
| `gloss`     | string  | Normalized gloss label (UPPERCASE). See [Gloss Normalization](#gloss-normalization). |
| `signer_id` | string  | One of `Signer001` … `Signer005`. |
| `take`      | integer | Take number for the `(signer_id, gloss)` pair. The original recording is `1`; Blender-style duplicate suffixes `.001`, `.002` map to takes `2`, `3`. |

### Example rows

```csv
file_name,gloss,signer_id,take
Signer001/Above.fbx,ABOVE,Signer001,1
Signer001/Apologize.fbx,APOLOGIZE,Signer001,1
Signer001/Apologize.001.fbx,APOLOGIZE,Signer001,2
Signer002/Actor,_Actress.fbx,ACTOR / ACTRESS,Signer002,1
Signer003/1-hr.fbx,1 HOUR,Signer003,1
Signer004/Less (I).fbx,LESS(I),Signer004,1
```

> **Path quoting**: some `file_name` values contain commas (e.g. `Signer002/Actor,_Actress.fbx`). The CSV is written with Python's default `csv.writer`, which quotes such fields automatically. Read it with any standard CSV parser (pandas, `csv.DictReader`).

### Gloss Normalization

The `gloss` column is derived from each filename and normalized so that the same sign performed by different signers gets the same label. The normalization, applied by `generate_metadata_remote.py`, is:

1. Strip Blender suffix `.001` / `.002` (recorded into `take`).
2. Replace `_` and `-` with spaces.
3. Treat `,` and `;` as synonym separators, joined as `WORD_A / WORD_B`.
4. Uppercase.
5. Expand time-unit abbreviations: `HR→HOUR`, `MIN→MINUTE`, `MTH→MONTH`, `WK→WEEK`, `YR→YEAR`, `SEC→SECOND` (and plurals).
6. Normalize whitespace around parentheses: `WORD (X)` → `WORD(X)`.
7. Strip unbalanced trailing `)`.
8. Plural→singular merge: when the corpus contains both forms of a word (e.g. `COURSES` and `COURSE`), the plural is rewritten to the singular. A small block-list keeps semantically distinct plurals (`NEWS`, `SHORTS`, `MATHEMATICS`) as plurals.
9. A small hand-curated override map (currently: `COCHLEAR / IMPLANT` → `COCHLEAR IMPLANT`) fixes cases that no general rule can fix safely.

A full audit of the plural→singular merges is printed by the script every time it runs; check that list before publishing.

## `.fbx` Files

- Format: Autodesk Filmbox (binary `.fbx`).
- One file per take.
- The `file_name` column of `metadata.csv` is the canonical pointer to each file. The same path can be resolved as `https://huggingface.co/datasets/mysigner/MySign/resolve/main/<file_name>`.

## Croissant Metadata

A Croissant 1.0 (JSON-LD) description is provided as `croissant.json`. It declares:

- The repository as a `cr:FileObject` (`encodingFormat: git+https`).
- `metadata.csv` as a `cr:FileObject` of type `text/csv`.
- All `Signer*/*.fbx` files as a `cr:FileSet` named `fbx-files` (`encodingFormat: model/vnd.fbx`).
- A `cr:RecordSet` named `signs` exposing the four columns above. The `file_name` field carries `references: fbx-files`, which tells Croissant consumers the value is a path into the FBX FileSet.

**Limitation**: Croissant 1.0 has no native semantics for FBX. `mlcroissant` will treat every FBX as an opaque binary resource — it will not parse skeletons, animation curves, or any FBX-internal structure. Consumers must use a real FBX library (e.g. Autodesk FBX SDK, Blender, [`pyfbx`](https://pypi.org/project/pyfbx/)) to read the motion data.

## Usage

### List records via `metadata.csv`

```python
import pandas as pd

df = pd.read_csv(
    "https://huggingface.co/datasets/mysigner/MySign/resolve/main/metadata.csv"
)
print(df.head())
print(df["signer_id"].value_counts().sort_index())
print(df["gloss"].nunique(), "unique glosses")
```

### Download a specific FBX

```python
from huggingface_hub import hf_hub_download

local_path = hf_hub_download(
    repo_id="mysigner/MySign",
    filename="Signer001/Above.fbx",
    repo_type="dataset",
)
print(local_path)  # local cache path; open with your FBX library
```

### Inspect via Croissant (mlcroissant)

```python
import mlcroissant as mlc

ds = mlc.Dataset(
    "https://huggingface.co/datasets/mysigner/MySign/resolve/main/croissant.json"
)
print(ds.metadata.name, "—", ds.metadata.description[:80])

for i, rec in enumerate(ds.records(record_set="signs")):
    print(rec)
    if i >= 4:
        break
```

`ds.records("signs")` yields one Python dict per `.fbx`, with `file_name`, `gloss`, `signer_id`, `take`. The FBX bytes are not auto-loaded — read them yourself from `file_name`.

## License

This dataset is released under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). You may share and adapt the work for non-commercial purposes, provided you give appropriate credit and distribute derivative works under the same license.

## Citation

```bibtex
@misc{mysign2026,
  title        = {MySign: A High-Fidelity Motion-Capture Dataset for 3D Sign Generation in Bahasa Isyarat Malaysia},
  author       = {{mysigner}},
  year         = {2026},
  howpublished = {Hugging Face Datasets},
  url          = {https://huggingface.co/datasets/mysigner/MySign},
  note         = {CC BY-NC-SA 4.0}
}
```

(Replace with the canonical citation when a paper or technical report becomes available.)

## Limitations

- **Not auto-loadable**: Hugging Face's Dataset Viewer cannot render `.fbx`. The Viewer will not work for this dataset, and `datasets.load_dataset("mysigner/MySign")` will not produce a usable split. Use the workflows above instead.
- **Gloss is filename-derived**: glosses come from filenames written by signers/annotators with slightly different conventions, then normalized. Some collisions may still exist; run the script's plural-merge audit before relying on a particular gloss inventory.
- **Five signers**: signer-conditioned models trained on MySign will have limited speaker coverage.
- **Take semantics are best-effort**: the `take` column distinguishes Blender-duplicated files (`.001`, `.002`) from the original recording, but it does not encode whether a take was a clean recording or a retry.
- **No segmentation, no transcription**: each FBX is one isolated sign. The dataset does not include continuous-signing video, glossed sentences, or non-manual annotations.