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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, How2Sign and PHOENIX-2014T, ready to train/sample with the SignSparK code.
- ๐ Paper: https://arxiv.org/abs/2603.10446
- ๐ Project page: https://cogvis-cvssp.github.io/papers/signspark/
Contents
train/ CSL-Daily_reopt_train.lmdb How2Sign_reopt_train.lmdb PHOENIX-2014T_reopt_train.lmdb
dev/ CSL-Daily_reopt_dev.lmdb How2Sign_reopt_dev.lmdb PHOENIX-2014T_reopt_dev.lmdb
test/ CSL-Daily_reopt_test.lmdb How2Sign_reopt_test.lmdb PHOENIX-2014T_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.
Usage
python tools/download_data.py --datasets CSL-Daily How2Sign PHOENIX-2014T --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, How2Sign and PHOENIX-2014T 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/
- PHOENIX-2014T: https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX-2014-T/
Citation
@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, Ozge Mercanoglu and Bowden, Richard},
booktitle={European Conference on Computer Vision},
pages={648--670},
year={2026},
organization={Springer}
}
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