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"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_61_videos_7efafad2 | 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) | jogging | 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 Jogging Pose Dataset (61 videos, 14,550 frames) — Sample
This Hugging Face dataset is a compact sample extracted from the full QualityVision Jogging Pose export.
- Action label:
jogging - Keypoints: 33 landmarks per person (MediaPipe / BlazePose) with
x,y,z,visibility - Post-processing (as exported): temporal smoothing + body normalization flags are included in metadata
Use this sample to validate the schema and quality before purchasing larger exports.
Pricing & ready-made bundles: https://qvision.space/dataset-pricing
What’s inside
data.jsonl— a small subset of frames (rows) from the full exportmetadata/manifest.json— export metadata (fps, stride, processing flags, etc.)metadata/global_stats.json— summary statisticsmetadata/features.json— derived motion/sequence features (if present in your export)metadata/export_quality_report.json— quality report (if present)
Row schema (high level)
Each line in data.jsonl is a JSON object. Common fields you’ll see:
frame_id/timestamp_ms(or similar) for time indexingkeypoints_2d/keypoints_3d(or equivalent) for landmarksvisibilityfor per-landmark confidence- optional derived fields: normalized keypoints, quality scores, motion metrics
Sample vs. Full export (typical)
| Feature | Sample (this repo) | Full export |
|---|---|---|
| Rows | hundreds | thousands to millions |
| Videos | subset | full set (and more actions if requested) |
| Included files | JSONL + metadata | JSONL + metadata + optional extra formats |
| Commercial license | via purchase | included via purchase |
Get the full dataset (commercial)
If you want the full export (more frames, more clips, custom filters, and optional additional formats), contact:
info@qvision.space
More details: https://qvision.space/dataset-pricing
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