Tri1/Multi-VSL-front-skeleton
Multi-VSL (front view) — DWPose skeletons Whole-body 2D pose keypoints extracted with DWPose from the front-camera clips of the Multi-VSL Vietnamese Sign Language corpus. 28,406 clips, one .npz per clip Total size: ~4.6 GB Laid out as data/<signer>/<clip>.npz — 30 signer directories, 628–1,167 clips each (HF rejects directories holding more than 10,000 files, so a flat tree is not possible here) Contents of each .npz key shape dtype description all_xy… See the full description on the dataset page: https://huggingface.co/datasets/Tri1/Multi-VSL-front-skeleton.
Multi-VSL (front view) — DWPose skeletons
Whole-body 2D pose keypoints extracted with DWPose from the front-camera clips of the Multi-VSL Vietnamese Sign Language corpus.
- 28,406 clips, one
.npzper clip - Total size: ~4.6 GB
- Laid out as
data/<signer>/<clip>.npz— 30 signer directories, 628–1,167 clips each (HF rejects directories holding more than 10,000 files, so a flat tree is not possible here)
Contents of each .npz
T is the number of frames in the clip. The 128 keypoints follow the DWPose / COCO-WholeBody layout: 17 body + 6 foot + 68 face + 42 hands (21 per hand).
File naming
<session>___<view>_<device>_<signer>_<view>_<order>_<clip_index>.npze.g. 01_Co-Hien_100-200_1-2-3_0118___center_device10_signer01_center_ord1_100.npz
Usage
import numpy as np
from huggingface_hub import hf_hub_download
path = hf_hub_download(
"Tri1/Multi-VSL-front-skeleton",
"data/signer01/01_Co-Hien_100-200_1-2-3_0118___center_device10_signer01_center_ord1_100.npz",
repo_type="dataset",
)
d = np.load(path)
xy, score = d["all_xy"], d["all_score"] # (T, 128, 2), (T, 128)Download everything, or just one signer:
from huggingface_hub import snapshot_download
snapshot_download("Tri1/Multi-VSL-front-skeleton", repo_type="dataset",
local_dir="skeleton")
snapshot_download("Tri1/Multi-VSL-front-skeleton", repo_type="dataset",
allow_patterns="data/signer01/*", local_dir="skeleton")