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	(T, 128, 2)	float16	keypoint pixel coordinates per frame
all_score	(T, 128)	float16	per-keypoint confidence
detected	(T,)	int8	1 if a person was detected in the frame, else 0
frame_size	(2,)	int32	source video frame size
fps	scalar	float32	source video frame rate

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>.npz

e.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")