video_id stringlengths 9 9 | poses listlengths 20 233 | label stringclasses 184
values |
|---|---|---|
00335.mp4 | [
[
[
-0.003077754518017173,
-0.6313287615776062,
-0.33137211203575134
],
[
0.19361591339111328,
-0.4322998821735382,
-0.196548730134964
],
[
-0.1274491399526596,
-0.45613592863082886,
-0.14087246358394623
],
[
0.19604577124118805... | abdomen |
00336.mp4 | [
[
[
0.025501349940896034,
-0.5943601727485657,
-0.30725935101509094
],
[
0.21437285840511322,
-0.40696626901626587,
-0.1396634429693222
],
[
-0.12767024338245392,
-0.4639507532119751,
-0.11616745591163635
],
[
0.1823197454214096... | abdomen |
00338.mp4 | [
[
[
-0.001840207725763321,
-0.6012172102928162,
-0.33962979912757874
],
[
0.18138396739959717,
-0.44214585423469543,
-0.15166838467121124
],
[
-0.15667285025119781,
-0.4395023584365845,
-0.1769064962863922
],
[
0.198739394545555... | abdomen |
00339.mp4 | [
[
[
-0.0017803329974412918,
-0.6087693572044373,
-0.31914347410202026
],
[
0.17111152410507202,
-0.44290223717689514,
-0.1411091685295105
],
[
-0.16081978380680084,
-0.446715384721756,
-0.16145946085453033
],
[
0.189168870449066... | abdomen |
00341.mp4 | [
[
[
-0.01895957440137863,
-0.6010887622833252,
-0.3353932797908783
],
[
0.1658838540315628,
-0.4285588264465332,
-0.16188675165176392
],
[
-0.1769273281097412,
-0.4440457820892334,
-0.16291575133800507
],
[
0.2599526345729828,
... | abdomen |
00376.mp4 | [
[
[
-0.02518477849662304,
-0.6361703872680664,
-0.3274436891078949
],
[
0.17439734935760498,
-0.4422520399093628,
-0.1735299825668335
],
[
-0.15297196805477142,
-0.44964301586151123,
-0.13444292545318604
],
[
0.19790968298912048... | able |
00377.mp4 | [[[0.0158950537443161,-0.5857148170471191,-0.3647933900356293],[0.17199307680130005,-0.4114569127559(...TRUNCATED) | able |
00381.mp4 | [[[0.002456208225339651,-0.5915936231613159,-0.3154506981372833],[0.20485208928585052,-0.41515362262(...TRUNCATED) | able |
00382.mp4 | [[[0.007219525054097176,-0.5992339849472046,-0.3050854206085205],[0.2119465321302414,-0.430248379707(...TRUNCATED) | able |
00384.mp4 | [[[-0.02273101918399334,-0.583722710609436,-0.36859947443008423],[0.17138533294200897,-0.42583239078(...TRUNCATED) | able |
End of preview. Expand in Data Studio
WLASL Pose Landmarks (MediaPipe Lite)
This dataset contains 3D pose landmarks extracted from the WLASL (World Level American Sign Language) video dataset using Google MediaPipe. It is designed to facilitate lightweight sign language recognition models by providing pre-computed skeletal data instead of raw video pixels.
Supported Tasks
- Sign Language Recognition (SLR)
- Skeleton-based Action Recognition
- Keypoint Analysis
Dataset Structure
The dataset contains the following columns:
| Column Name | Type | Description |
|---|---|---|
video_id |
string |
The original filename of the video (e.g., 001.mp4). |
label |
string |
The English gloss (word) being signed (e.g., "book", "hello"). |
poses |
list |
A 3D array-like structure containing the landmark coordinates per frame. |
The poses Column
The poses column is a jagged array (variable length) because videos have different durations.
Structure: [Frame, Landmark_Index, Coordinate]
- Dimension 1: Frame Index (Variable size $T$)
- Dimension 2: Landmark Index (Fixed size 7)
- Dimension 3: Coordinate (Fixed size 3:
[x, y, z])
Keypoint Mapping
The model tracks 7 specific upper-body keypoints relevant to sign language. The indices correspond to the MediaPipe Pose topology:
| Index in Array | MediaPipe Index | Body Part |
|---|---|---|
| 0 | 0 | Nose |
| 1 | 11 | Left Shoulder |
| 2 | 12 | Right Shoulder |
| 3 | 13 | Left Elbow |
| 4 | 14 | Right Elbow |
| 5 | 15 | Left Wrist |
| 6 | 16 | Right Wrist |
Note: If a person was not detected in a specific frame, the values for that frame are
NaN(Not a Number).
Usage
Loading the Dataset
from datasets import load_dataset
import numpy as np
dataset = load_dataset("Kibalama/wlasl-upper-arm-pose-landmarks")
# Inspect the first sample
sample = dataset['train'][0]
print(f"Gloss: {sample['label']}")
print(f"Video ID: {sample['video_id']}")
# Convert poses to numpy array for processing
# Shape: (Frames, 7, 3)
poses = np.array(sample['poses'])
print(f"Pose sequence shape: {poses.shape}")
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