---
license: cc-by-nc-4.0
language:
- zh
pretty_name: MVSign
tags:
- sign-language
- chinese-sign-language
- multi-view
- human-avatar
- smpl-x
- 3d-keypoints
---

# MVSign

MVSign is a multi-view Chinese Sign Language dataset for photorealistic and drivable 3D sign avatar modeling. The dataset is introduced in [**PHOSA: Photorealistic 3D Sign Avatar Modeling and Benchmark**](https://naaapi.github.io/PHOSA/) (ECCV 2026).

MVSign was co-designed with Deaf experts and collected under IRB approval. It captures fluent native Chinese Sign Language signers with synchronized multi-view RGB cameras and provides annotations for avatar reconstruction, rendering, and animation, including camera calibration, body-part segmentation, 3D keypoints and SMPL-X parameters.

## Highlights

- 5 native Chinese Sign Language signers: 3 female and 2 male signers.
- 16 synchronized RGB cameras at 2048 x 2448 resolution and 25 FPS.
- About 23K temporal frames per signer, about 115K temporal frames in total.
- Dedicated camera layout for both full-body coverage and fine-grained head/hand capture.
- Sign content covering 109 basic hand shapes and daily-use sign sentences.
- Rich annotations for sign avatar modeling: segmentations, 3D keypoints, SMPL-X parameters and camera calibration.

## Dataset Structure

The top-level structure is:

```text
MVSign/
|-- README.md
|-- female1/
|-- female2/
|-- female3/
|-- male1/
|-- male2/
|-- scripts/
```

Each subject directory follows the same structure:

```text
<subject>/
|-- calibration.csv
|-- keypoints3d.npy
|-- smplx_params.npz
|-- valid_frames.npy
`-- data/
    `-- <sequence_id>/
        |-- <sequence_id>.tar.000.part
        |-- <sequence_id>.tar.001.part
        |-- ...
        `-- <sequence_id>.tar.NNN.part
```

The files under `data/<sequence_id>/` are split tar archives. After concatenating and extracting a sequence archive, the extracted directory contains the image data and segmentation annotations:

```text
<subject>/<sequence_id>/
|-- images/
|   `-- <camera_name>/
|       `-- <frame_id>.jpeg
`-- segmentations/
    `-- <camera_name>/
        `-- <frame_id>.png
```

## Annotation Files

`calibration.csv` stores camera parameters for each subject. The provided script `scripts/read_camera_params.py` converts the CSV fields into camera intrinsics and extrinsics. The rotation vector fields `rx`, `ry`, and `rz` are converted to a rotation matrix with Rodrigues transformation; `tx`, `ty`, and `tz` form the translation vector. The normalized intrinsic fields `fx`, `fy`, `px`, and `py` are scaled by image width and height.

`keypoints3d.npy` stores per-frame 3D skeleton keypoints obtained from multi-view geometric triangulation and optimization.

`smplx_params.npz` stores the fitted SMPL-X parameter sequence for the subject, including body, hand, and facial parameters used for sign avatar modeling.

`valid_frames.npy` stores the usable frame indices selected by the Motion-aware Data Sampling Strategy. This strategy filters motion-blurred frames and balances the distribution of sign gesture types.

`segmentations/` stores body-part segmentation maps. The helper script `scripts/read_mask.py` can extract background, hand, and head masks from the segmentation color labels.

## Download and Extraction

Clone the dataset with Git LFS or download it with the Hugging Face CLI:

```bash
git lfs install
git clone https://huggingface.co/datasets/naaaaapi/MVSign
```

or:

```bash
huggingface-cli download naaaaapi/MVSign --repo-type dataset --local-dir MVSign
```

Each sequence is stored as split tar parts. Concatenate all parts in order and extract the tar archive:

```bash
cd MVSign

sequence_dir=female1/data/21238139
sequence_id=21238139

cat ${sequence_dir}/${sequence_id}.tar.*.part > ${sequence_dir}/${sequence_id}.tar
tar -xf ${sequence_dir}/${sequence_id}.tar -C ${sequence_dir}
```

The part indices are zero-padded, so the shell glob order is the correct archive order.

## Ethics and License

This dataset was collected under IRB approval. All participants provided informed written consent for public release of anonymized data.

MVSign is released under the **CC BY-NC 4.0** license. It is intended for non-commercial research use. Users should follow the license terms and use the data responsibly, especially when working with human subjects and sign language data.

## Citation

If you use MVSign or PHOSA in your research, please cite:

```bibtex
@inproceedings{wang2026phosa,
  title  = {PHOSA: Photorealistic 3D Sign Avatar Modeling and Benchmark},
  author = {Wang, Haodong and Hu, Hezhen and Zhou, Wengang and Li, Houqiang},
  booktitle = {ECCV},
  year   = {2026}
}
```