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52.34669310693175,
54.927381727412275
],
"brightness": 135.73523726851852,
... | 0 | 45 | 15 | 24 | {
"nose": {
"x": -0.23551317526363247,
"y": 1.0846347676527939,
"z": 0.12977409914218485,
"visibility": 0.9883488416671753
},
"left_eye_inner": {
"x": -0.2629384913223569,
"y": 1.1263047643696558,
"z": 0.18709696431633904,
"visibility": 0.9871662259101868
},
"left_eye": {
"... | {
"method": "hip_center_torso_scale",
"origin": {
"x": 0.38973840419203043,
"y": 0.38894659001380205,
"z": -0.0001198046375066042
},
"scale": 0.22317552869209092,
"scale_axis": "torso_length_then_hip_width_fallback",
"scale_fallback": "torso"
} | {
"dt_sample_sec": 0.125,
"visibility_summary": {
"avg_landmark_visibility": 0.788054,
"lower_body_visibility": 0.668655
},
"hip_velocity_norm_per_sec": 0.00960197,
"joint_angles_deg": {
"spine_vs_vertical_deg": -172.9099,
"left_hip_deg": 164.3333,
"right_hip_deg": 166.7636,
"left_knee... | null |
9 | 2,000 | bulk_190_videos_4e1065b2 | jogging_pexels_3048928.mp4 | {"nose":{"x":0.35093128122389317,"y":0.6987370513379574,"z":-0.0297064664773643,"visibility":0.98915(...TRUNCATED) | 1 | [{"person_id":0,"keypoints":{"nose":{"x":0.35093128122389317,"y":0.6987370513379574,"z":-0.029706466(...TRUNCATED) | Full Locomotion Bundle (Walking + Jogging + Running) | 1 | proprietary_or_user_provided | true | motion_dataset_engine | layer11_pose | {"layer_name":"Layer 1.1 fallback","layer_number":1.1,"layer_type":"human_vision_fallback","mean_rgb(...TRUNCATED) | 0 | 48 | 16 | 24 | {"nose":{"x":-0.14903522522325222,"y":1.1730769712991707,"z":-0.10827305189888735,"visibility":0.989(...TRUNCATED) | {"method":"hip_center_torso_scale","origin":{"x":0.3916835645213723,"y":0.3779701655730605,"z":-0.00(...TRUNCATED) | {"dt_sample_sec":0.125,"visibility_summary":{"avg_landmark_visibility":0.779379,"lower_body_visibili(...TRUNCATED) | null |
End of preview. Expand in Data Studio
QualityVision Locomotion Pose Dataset (Walking + Jogging + Running) — Sample
This is a compact, viewer-friendly sample extracted from a much larger HQ locomotion export generated by the QualityVision Motion Dataset Engine.
Looking for the full commercial export or custom delivery? See pricing & ready-made bundles on qvision.space/dataset-pricing.
What’s inside
data.jsonl: one JSON object per line (one frame per row) with 33 MediaPipe/BlazePose landmarks (x,y,z,visibility).metadata/manifest.json: job metadata (fps, stride, post-processing flags).metadata/global_stats.json: dataset-level rollups.metadata/export_quality_report.json: QA thresholds + acceptance rates.metadata/features.json: sequence-level metrics (per-video aggregates).
Note: Auxiliary JSON files are kept under metadata/ so the Hub viewer builds previews only from data.jsonl.
Sample vs full commercial exports
| This Hub sample | Full exports (paid) | |
|---|---|---|
| Scale | Small subset for evaluation | Large HQ exports (e.g. 25k+ frames, multi-video bundles) |
| Use | Validate schema, parsing, model prototyping | Production datasets + optional custom scopes |
| Delivery | Files on the Hub | Secure download link / delivery within 24–72h |
| License | CC BY-NC 4.0 | Commercial license provided in writing |
Data format (high level)
Each row contains:
timestamp_ms: timestamp of the frame in millisecondsoriginal_filename: source clip name (raw videos are not shipped here)keypoints: dict of 33 landmarks (name →{x,y,z,visibility})
Coordinates:
x,yare normalized image coordinates in ([0..1])zis a relative depth coordinate from the pose model
Quick usage
from datasets import load_dataset
ds = load_dataset(
"Alaaharoun/QualityVision-Locomotion-Pose-Dataset-Walking-Jogging-Running",
data_files="data.jsonl",
split="train",
)
print(ds[0].keys())
Free technical references
- GitHub samples repository:
https://github.com/Alaaharoun/QualityVision-Motion-Dataset-Samples
Contact
Commercial licensing and custom datasets: info@qvision.space
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