JSONL is boring—in a good way
Line-delimited JSON is the default interchange format for large pose exports because it streams well, diffs reasonably in tooling, and avoids loading multi-gigabyte files into memory just to sample a few frames. For ML teams, that means faster iteration and simpler CI checks.
What to inspect in the first 50 lines
- One JSON object per line: confirms streaming compatibility.
- Stable keys: landmark dictionaries should be consistent across rows.
- Time indexing: timestamps or frame IDs should be monotonic per clip where applicable.
- Metadata sidecars: manifests and global stats should explain stride, smoothing, and augmentation policy.
Why metadata matters as much as keypoints
Pose coordinates without context are hard to debug. Exports that include export quality reports, global stats, and optional per-video splits help you reproduce baselines and compare runs honestly across teams.
Try a public sample before a purchase
If you want to validate parsing and evaluation scripts without a budget commitment, start with the public samples in QualityVision-Motion-Dataset-Samples. They mirror the same export layout used in larger bundles.
When you are ready to buy
Ready-made commercial exports are listed with prices and specifications on Quality Vision dataset pricing. You will get JSONL + metadata aligned with the same engineering conventions as the samples—so your integration work carries forward instead of resetting on every purchase.
Dataset pricing (direct link)