---
language:
- ase
license: cc-by-nc-4.0
tags:
- sign-language
- motion
- smplx
- how2sign
pretty_name: How2Sign-MV3D
---

# How2Sign-MV3D

Final reconstructed SMPL-X signing-motion sequences produced by the MV-Sign multi-view curation pipeline. This is a derived motion resource, **not original 3D motion-capture ground truth**.

Project: https://github.com/ronggong000/MV-Sign
Paper: https://arxiv.org/abs/2611.07811
This dataset is associated with the MV-Sign research project. Please cite the MV-Sign paper and the original source dataset when using this resource.

## Contents

There is one final motion NPZ per completed sample. Frontal and side estimates are jointly checked during curation; the released final sequence uses the frontal body coordinate representation. Side-only intermediate sequences, raw videos, OBJ meshes, model weights, and SMPL-X/MANO model assets are not included.

| Split | Sequences |
|---|---:|
| Train | 30,903 |
| Validation | 1,711 |
| Test | 2,325 |
| Total | 34,939 |

These counts describe the regenerated release, not a rerun of the submitted paper's training experiments. From 35,241 paired input clips, 289 were excluded by input screening and 13 failed processing. Another 22 frontal validation clips lacked a corresponding side clip and were not in the paired processing manifest. See `processing_audit.json`.

## Download and Verify

Download the `data/` tar files and the metadata from the Files tab, or use:

```python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="mage0001/How2Sign-MV3D", repo_type="dataset", local_dir="How2Sign-MV3D")
```

```bash
cd How2Sign-MV3D
sha256sum -c SHA256SUMS
mkdir -p extracted
for archive in data/*.tar; do tar -xf "$archive" -C extracted; done
```

The tar files contain `train/<sample_id>.npz`, `val/<sample_id>.npz`, or `test/<sample_id>.npz`. NPZ arrays are already compressed, so the outer tar is intentionally uncompressed. `samples.csv` records the split, archive, member, frame count, FPS, byte size, and per-NPZ SHA-256. `manifest.json` records archive counts and checksums. All released NPZ payloads are preserved byte-for-byte from the final frontal outputs.

## Array Conventions

Load with `numpy.load(path, allow_pickle=False)`. `poses` is a per-frame SMPL-X local axis-angle pose in radians (55 joints, 3 components); shape and field metadata are in `array_schema.json`. `betas` contains fitted shape parameters. Read frame rates from `mocap_framerate` and `samples.csv`; do not assume 30 FPS. Non-sign pruning and synthetic motion blends mean released frame indices are not a direct copy of source-video indices. The arrays `source_frame_index`, `source_padding_mask`, and `synthetic_bridge_mask` preserve the saved temporal bookkeeping.

`trans`, `cam_trans`, and saved joint/projection fields have different meanings: do not treat `trans` alone as a calibrated global camera translation, or assume saved tracking joints were recomputed from the final repaired mesh. This release is not camera-calibrated ground-truth geometry. The companion pipeline documentation is being prepared.

## Source, Changes, and Terms

Based on [How2Sign](https://how2sign.github.io/), released by its authors under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Credit the original authors, retain attribution and license information, and use for noncommercial research. Changes comprise automatic body/hand reconstruction, cross-view synchronization and repair, non-sign pruning, and smoothing. This derived resource is not endorsed by the How2Sign authors. SMPL-X/MANO model files require their own licenses and are not distributed here.

Please cite How2Sign and MV-Sign when using this resource. MV-Sign's final bibliographic entry will be added with the paper release.

```bibtex
@inproceedings{duarte2021how2sign,
  title={How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language},
  author={Duarte, Amanda and Palaskar, Shruti and Ventura, Lucas and Ghadiyaram, Deepti and DeHaan, Kenneth and Metze, Florian and Torres, Jordi and Giro-i-Nieto, Xavier},
  booktitle={CVPR},
  year={2021}
}
```