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16 | 2,880 | qv_walking_sample_v1 | walking_uhd_sample.mp4 | {
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"x": 0.5387287139892578,
"y": 0.1054171621799469,
"z": 0.2953777015209198,
"visibility": 0.9988070726394653
},
"left_eye_inner": {
"x": 0.5291249752044678,
"y": 0.0944070816040039,
"z": 0.2227853387594223,
"visibility": 0.9987300038337708
},
"left_eye": {
"x": 0... | 1 | [
{
"person_id": 0,
"keypoints": {
"nose": {
"x": 0.5387287139892578,
"y": 0.1054171621799469,
"z": 0.2953777015209198,
"visibility": 0.9988070726394653
},
"left_eye_inner": {
"x": 0.5291249752044678,
"y": 0.0944070816040039,
"z": 0.222... | walking | 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": [
123.99751844618055,
123.1526953125,
114.38207275390624
],
"std_rgb": [
46.38343897037533,
44.35359196768561,
44.85999450443572
],
"brightness": 122.38810709635416,
"c... | 1 | 66 | 25 |
17 | 3,000 | qv_walking_sample_v1 | walking_uhd_sample.mp4 | {
"nose": {
"x": 0.5221389532089233,
"y": 0.12257230281829834,
"z": 0.37679949402809143,
"visibility": 0.9982311129570007
},
"left_eye_inner": {
"x": 0.511939525604248,
"y": 0.11096814274787903,
"z": 0.29168492555618286,
"visibility": 0.9989191293716431
},
"left_eye": {
"x"... | 1 | [
{
"person_id": 0,
"keypoints": {
"nose": {
"x": 0.5221389532089233,
"y": 0.12257230281829834,
"z": 0.37679949402809143,
"visibility": 0.9982311129570007
},
"left_eye_inner": {
"x": 0.511939525604248,
"y": 0.11096814274787903,
"z": 0.2... | walking | 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": [
123.42637288411458,
122.83877739800347,
114.04245551215278
],
"std_rgb": [
47.098957907812945,
45.04576630632046,
45.46651748338677
],
"brightness": 121.98976888020833,... | 1 | 67 | 25 |
18 | 3,360 | qv_walking_sample_v1 | walking_uhd_sample.mp4 | {
"nose": {
"x": 0.5326377153396606,
"y": 0.15758919715881348,
"z": 0.30269482731819153,
"visibility": 0.9992814660072327
},
"left_eye_inner": {
"x": 0.5225018262863159,
"y": 0.1463000476360321,
"z": 0.23029471933841705,
"visibility": 0.9993730187416077
},
"left_eye": {
"x"... | 1 | [
{
"person_id": 0,
"keypoints": {
"nose": {
"x": 0.5326377153396606,
"y": 0.15758919715881348,
"z": 0.30269482731819153,
"visibility": 0.9992814660072327
},
"left_eye_inner": {
"x": 0.5225018262863159,
"y": 0.1463000476360321,
"z": 0.2... | walking | 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": [
126.4831654188368,
125.59800916883681,
116.24291965060763
],
"std_rgb": [
44.896347436707316,
43.868850802356974,
45.19547466380605
],
"brightness": 124.77286376953126,... | 1 | 70 | 25 |
QualityVision Walking Sample (compact)
Need production-scale pose data? Browse ready-made JSONL bundles (thousands of HQ frames, full manifests, schema docs) and Dataset Lab plans on qvision.space — Dataset pricing. This Hub repo is a small non-commercial sample so you can validate parsing and quality before you buy.
Small high-quality 2D pose clip for walking, exported from the Quality Vision Motion Dataset Engine pipeline: HQ frame filtering, optional temporal smoothing on accepted frames, and sequence diagnostics in features.json.
This sample vs full commercial exports
| This Hub sample | Paid exports & Lab (pricing) | |
|---|---|---|
| Scale | Compact clip (~80 rows in data.jsonl on the Hub) |
Thousands of HQ frames across curated bundles; higher caps via annual frame quotas |
| Use case | Parse the format, prototype models, academic / NC research under the license | Production datasets, larger actions (e.g. running, dancing bundles), repeatable exports |
| Support | Community / docs on GitHub | Checkout via Gumroad; commercial licensing and custom scope quoted separately |
| Deliverables | JSONL + metadata/* as listed below |
Full dataset/ trees (manifests, diagnostics, SHA256, optional one-pager PDFs) per product page |
Export format: 2D landmarks in normalized image space (see features.json). This sample does not include raw video files.
For the full 24-source bulk export (~967 HQ frames, ~2901 merged rows, mean quality ~0.82), clone the GitHub repo and use bulk_24_videos_6a173caf_dataset/, or mirror README_HF_BULK.md to a separate Hugging Face dataset via scripts/upload_bulk_to_huggingface.ps1.
Files
| File | Description |
|---|---|
data.jsonl |
One JSON object per line: normalized image-space keypoints, timestamps, optional Layer 1.1 metadata. |
metadata/features.json |
Sequence-level aggregates (visibility, hip motion, motion_consistency). |
metadata/global_stats.json |
Job-level roll-up for this clip. |
metadata/manifest.json |
Export metadata (format compatible with the Motion Dataset Engine). |
metadata/export_quality_report.json |
Pilot-oriented quality summary. |
Auxiliary JSON files live under metadata/ so the Hub dataset viewer only builds previews from data.jsonl (avoids schema clashes with report objects).
Usage
from datasets import load_dataset
# If you load as JSON lines:
ds = load_dataset("Alaaharoun/QualityVision-walking-sample", data_files="data.jsonl", split="train")
Or download raw files:
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="Alaaharoun/QualityVision-walking-sample",
filename="data.jsonl",
repo_type="dataset",
)
License
CC BY-NC 4.0 — see repository LICENSE on GitHub. Non-commercial use is allowed under the license terms. Commercial redistribution or product use requires a separate agreement — see Dataset pricing & contact for ready-made bundles and licensing.
Citation
@misc{qualityvision_walking_sample,
title = {QualityVision Walking Sample (2D Pose)},
author = {Alaaharoun},
howpublished = {\url{https://huggingface.co/datasets/Alaaharoun/QualityVision-walking-sample}},
year = {2026},
}
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