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Datasets:
mysigner
/
MySign

Tasks:
Other
Modalities:
3D
Text
Formats:
json
Languages:
Malay
Standard Malay
Size:
< 1K
Tags:
sign-language
bahasa-isyarat-malaysia
bim
malaysian-sign-language
motion-capture
3d
Libraries:
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pandas
Polars
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metadata 
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.
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 

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) to read the motion data.

Usage

List records via metadata.csv 

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 

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) 

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). 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 

@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.