---
pretty_name: SignSparK Data (CSL-Daily & How2Sign SMPL-X pose features)
license: other
language:
  - zh
  - en
tags:
  - sign-language
  - sign-language-production
  - pose
  - smpl-x
  - flow-matching
size_categories:
  - 1K<n<10K
---

# SignSparK Data

Prebuilt LMDB datasets for **SignSparK: Efficient Multilingual Sign Language
Production via Sparse Keyframe Learning** (ECCV 2026). Contains SMPL-X 6D pose
features, spoken-language translations, glosses, and per-frame segment
annotations for **CSL-Daily** and **How2Sign**, ready to train/sample with the
[SignSparK code](https://github.com/JianHe0628/SignSparK).

- 📄 Paper: https://arxiv.org/abs/2603.10446
- 🌐 Project page: https://cogvis-cvssp.github.io/papers/signspark/

## Contents

```
train/  CSL-Daily_reopt_train.lmdb   How2Sign_reopt_train.lmdb
dev/    CSL-Daily_reopt_dev.lmdb     How2Sign_reopt_dev.lmdb
test/   CSL-Daily_reopt_test.lmdb    How2Sign_reopt_test.lmdb
```

Each `.lmdb` holds one `__meta__` key (clip index) plus one in-memory `.npz`
blob per clip with: `language`, `translation`, `gloss`, `segment` (per-frame
{0,1,2}), and `left/right/body/face_features` (6D rotations). Full schema in the
repo's [`DATA.md`](https://github.com/JianHe0628/SignSparK/blob/main/DATA.md).

## Usage

```bash
python tools/download_data.py --datasets CSL-Daily How2Sign --dest ./data
export DATA_ROOT=$(pwd)/data
python tools/inspect_lmdb.py ${DATA_ROOT}/lmdb/train/CSL-Daily_reopt_train.lmdb
```

## License & terms

These are SMPL-X pose features **derived from** the CSL-Daily and How2Sign
corpora and are released for **non-commercial research use** under the terms of
the original datasets. Obtain and comply with each source dataset's license:

- CSL-Daily: https://ustc-slr.github.io/datasets/2021_csl_daily/
- How2Sign: https://how2sign.github.io/

## Citation

```bibtex
@inproceedings{low2026signspark,
  title     = {SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning},
  author    = {Low, Jianhe and Symeonidis-Herzig, Alexandre and Ivashechkin, Maksym and Sincan, {\"O}zge Mercano{\u{g}}lu and Bowden, Richard},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
```