---
license: cc-by-sa-4.0
task_categories:
- video-classification
- image-to-3d
language:
- en
tags:
- oakink2
- vitra
- mano
- human-object-interaction
---

# OakInk2 → VITRA Stage-1 (complete audited release)

This repository contains all 627 physical OakInk2-TaMF sequences converted to
VITRA Stage-1. Every sequence source pair is pinned to
`kelvin34501/OakInk-v2` revision `21705616140d726607027e70d58b7837f442ffd8`, aligned by exact
frame identity, converted across the four calibrated views, checked by geometry
and every-frame RGB audits, smoke-tested through the VITRA loader, uploaded,
and verified at an immutable commit before local cleanup.

| split | physical sequences | accepted camera episodes | indexed anchors |
|---|---:|---:|---:|
| train | 431 | 1722 | 2368048 |
| val | 39 | 155 | 183925 |
| test | 157 | 627 | 805606 |

## Files and VITRA loader contract

RGB episodes are under `Video/OakInk2_root/`. Labels are NumPy dictionaries at
`Annotation/oakink2_<split>/episodic_annotations/<episode_id>.npy`. Each label
contains the exact `video_name`, `video_decode_frame`, source frame IDs and
timestamps, calibrated `intrinsics`, per-frame `world_to_camera` `extrinsics`,
world/camera 21-joint arrays, MANO beta/rotation-matrix pose/translation fields,
object trajectories, causal task text, and `training_anchor_valid`.

The final `Annotation/oakink2_<split>/episode_frame_index.npz` contains
`index_frame_pair` and `index_to_episode_id`. Column 0 of each pair selects an
episode ID; column 1 selects a label row. Only rows whose
`training_anchor_valid` value is true are indexed. A minimal lookup is:

```python
import numpy as np

with np.load(index_path, allow_pickle=True) as index:
    episode_slot, frame_id = index["index_frame_pair"][sample_id]
    episode_id = index["index_to_episode_id"][episode_slot]
episode = np.load(
    label_dir / (episode_id + ".npy"), allow_pickle=True
).item()
rgb_frame_id = int(episode["video_decode_frame"][frame_id])
assert bool(episode["training_anchor_valid"][frame_id])
```

Before the final commit, the release gate independently re-reads all
2504 accepted camera
reports and calibrates wrist/joint speed distributions over all 627 physical
sequences. Publication is blocked if a maximum exceeds both its conservative
absolute floor and ten robust deviations in log1p space. This release has
0 blocked temporal
sequences; the complete evidence is `Audit/temporal_quality.json`.

The legacy RGB audit helper only distinguished train/test. Before final
publication, all 39 official-val RGB summaries and every per-frame JSONL record
are therefore normalized to `val`, their sequence-manifest hashes are updated,
and every changed remote byte is downloaded and verified. This correction does
not alter videos, MANO/keypoint labels, action chunks, or training indices.

The official OakInk2-TaMF split is pinned at commit
`4cb9c461b92c032715374bd252c7179c70a14bde`. Both keypoint and MANO-angle normalization files
are exact merges of the per-sequence float64 accumulators. Global frame indices
are generated only after all 627 sequences are CLEANED.

Conversion used 337 serial and
290 parallel-orchestrated
physical sequences. The controller recorded
293 atomic claim events with
0 invalid overlapping
claim lifecycles. Its exact source and audit report are preserved under
`Audit/orchestration/`.

`Build/final_manifest.json` hashes every release-level artifact, and
`Build/release_ledger.sqlite3` records the exact source files, sequence
manifests, camera episodes, anchor masks, and partial statistics. The fresh
independent audit is published at
`Audit/independent_release_verification.json`.

OakInk2 is human hand-object motion rather than robot control. Direct robot
policy training still requires an explicit embodiment/retargeting decision.
Source data and this derivative are CC BY-SA 4.0; MANO assets are not included
and retain their separate license.