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license: cc-by-nc-4.0
task_categories:
- robotics
- image-to-3d
language:
- en
tags:
- vitra
- mano
- hand-object-interaction
---
# epic30-annotations-v1
**Annotations only — no images, no video.**
VITRA-style hand episodes for **EPIC-KITCHENS-100**, with per-hand instructions and paraphrases.
| | |
|---|---|
| episodes | **149,570** |
| training samples (`index_frame_pair` rows) | **4,019,534** |
| annotation | MANO pose + world/camera joints + per-frame extrinsics |
| text | one instruction per episode + 1.88 paraphrases on average |
| **images / video** | **not included** — see *Getting the frames* below |
### What we did
**Episodes are ours.** The source release ships either raw video or differently-segmented
clips, so we re-cut it with VITRA's method — speed minima of the 3D wrist in world space:
gaussian smooth (sigma=1.0) -> local speed minima in a fixed window (win=15, i.e. 0.5 s
at 30 Hz) -> merge runs shorter than min_seg=16 -> pad 2 frames on each end
sigma and win are quantities in *time*, converted per source frame rate. Left and right hands
are cut independently, with the other hand's motion ignored.
**Instructions are ours.** Two rounds, both with `Qwen3.5-122B-A10B-FP8`:
round 1 captions 8 frames per episode with the palm's future trajectory drawn on them;
round 2 checks the sentence belongs to that hand, strips same-hand references
("Rinse the right hand." -> "Rinse the hand.", because training already prepends
`Left hand: ... Right hand: ...`), and writes 1-3 paraphrases.
**Episodes with no instruction are not included.** Round 1 returns `N/A` when an episode
shows no object interaction. Those episodes are excluded from both the archive and the
index, so every episode here has a usable instruction.
### Splits
`train.txt` / `val.txt` / `test.txt` under `splits/` list **episode ids**, one per line.
`splits/test_videos.txt` lists the videos the test set owns.
| | videos | episodes | frames | share |
|---|---|---|---|---|
| train | 548 | 134,522 | 3,615,354 | 89.94% |
| val | 494 | 7,527 | 200,835 | 5.00% |
| test | 32 | 7,521 | 203,345 | 5.06% |
**test owns its videos outright** — none of its 32 videos appears in train or val.
**train and val share videos**: within each non-test video, episodes are split between the
two. So val measures fit on unseen clips from *seen* videos (use it for early stopping and
hyper-parameter choice); test is the only held-out set.
Grouping is by video, not by participant. EPIC participants each film many videos in their
own kitchen, so a participant whose videos land in test also has videos in train — the test
set contains no unseen kitchen. It measures generalisation **across clips and videos within
known environments**, not across environments.
### Note on P18_09
`P18_09` is the only 90 fps video in EPIC-KITCHENS-100 (GoPro Hero7 1080p90; every other
video is 59.94 / 50 / 29.97 fps). Its annotations are sampled every 3rd frame rather than
every 2nd, which an earlier version of our cutter treated as discontinuous — it produced
0 episodes for that video. Fixed here: 155 episodes.
### Files
```
epic30.tar -> Annotation/epic30/episodic_annotations/*.npy
episode_frame_index.npz index_frame_pair (N,2) uint32 + index_to_episode_id (E,)
```
`index_frame_pair` **row number is the sample id**: row r = (episode ordinal, frame within
that episode). `len(index_frame_pair)` is the size of the training set.
```python
import numpy as np
# tar -xf epic30.tar
z = np.load("episode_frame_index.npz", allow_pickle=True)
ep_slot, frame_id = z["index_frame_pair"][sample_id]
eid = str(z["index_to_episode_id"][ep_slot])
d = np.load(f"Annotation/epic30/episodic_annotations/{eid}.npy", allow_pickle=True).item()
rgb_frame_id = int(d["video_decode_frame"][frame_id])
```
Each `.npy` is a dict with `video_name`, `video_decode_frame`, `intrinsics`,
per-frame `extrinsics` (world->camera), `anno_type` (which hand this episode is for),
`text`, `text_rephrase`, and a `left`/`right` dict holding `beta`, `hand_pose`,
`global_orient_worldspace`, `transl_worldspace`, `joints_worldspace`, `kept_frames`.
`text[hand] = [(sentence, (0, T))]` and `text_rephrase[hand] = [([paraphrases...], (0, T))]`.
### Getting the frames
`video_decode_frame` indexes the **source** video, which we do not redistribute.
Get it from EPIC-KITCHENS-100 — https://epic-kitchens.github.io/, then decode by index (we use `decord`; a self-maintained
sequential counter drifts silently if the decoder ever skips a frame).
### Known limitations
- Paraphrase count averages 1.88, not a fixed number. Past 3 the model starts inventing;
a sentence with no prepositional phrase honestly supports only one or two.
- Verified: the index lists exactly the episodes that have an instruction, every episode's
stored frame count matches its index rows, and no index entry points at a missing episode.
---
<!-- COLLECTION_TABLE_START -->
| dataset | episodes | training samples | our contribution | size | HF |
|---|---:|---:|---|---:|---|
| **EPIC-KITCHENS-100** | 149,570 | 4,019,534 | episodes + text | 8.70 GB | [`epic30-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/epic30-annotations-v1) |
| **EgoTouch** | 107,364 | 3,123,675 | episodes + text + **tactile** | 17.02 GB | [`egotouch-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/egotouch-annotations-v1) |
| **GigaHands** | 70,486 | 2,266,087 | episodes + text | 2.89 GB | [`gigahands-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/gigahands-annotations-v1) |
| **Ego-Exo4D** | 67,051 | 1,757,474 | text only | 4.09 GB | [`egoexo4d-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/egoexo4d-annotations-v1) |
| **OakInk2** | 29,058 | 1,052,924 | episodes + text | 1.56 GB | [`oakink2-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/oakink2-annotations-v1) |
| **TACO** | 23,757 | 736,136 | episodes + text | 1.34 GB | [`taco-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/taco-annotations-v1) |
| **HOT3D** | 18,805 | 619,680 | episodes + text | 1.51 GB | [`hot3d-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/hot3d-annotations-v1) |
| **ARCTIC** | 12,610 | 425,796 | episodes + text | 0.85 GB | [`arctic-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/arctic-annotations-v1) |
| **H2O** | 5,845 | 200,332 | episodes + text | 0.40 GB | [`h2o-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/h2o-annotations-v1) |
| **Tachin** | 2,669 | 79,584 | episodes + text + **tactile** | 3.38 GB | [`tachin-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/tachin-annotations-v1) |
| **total** | **487,215** | **14,281,222** | | **41.7 GB** | |
> **Something-Something V2 was dropped** from the collection (12 fps against
> 30 fps everywhere else, so a 16-step action chunk spans 1.33 s instead of 0.53 s).
> The repository still exists but should not be used.
<!-- COLLECTION_TABLE_END -->
|