| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - robotics |
| - image-to-3d |
| language: |
| - en |
| tags: |
| - vitra |
| - mano |
| - hand-object-interaction |
| - egocentric |
| --- |
| |
| # ssv2-annotations-v1 |
|
|
| **Annotations only — no images, no video.** |
|
|
| VITRA-style hand episodes for **Something-Something V2**, with per-hand instructions and paraphrases. |
|
|
| | | | |
| |---|---| |
| | episodes | **52,706** | |
| | training samples (`index_frame_pair` rows) | **1,124,722** | |
| | annotation | MANO pose + world/camera joints + per-frame extrinsics | |
| | text | one instruction per episode + 2.09 paraphrases on average | |
| | **images / video** | **not included** — see *Getting the frames* below | |
|
|
| ### What we did |
|
|
| **Episodes are NOT ours** — they are VITRA-1M's official segmentation, used unchanged. |
|
|
| **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. |
|
|
|
|
| ### Files |
|
|
| ``` |
| ssv2.tar -> Annotation/ssv2/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, tarfile |
| # tar -xf ssv2.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/ssv2/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 Something-Something V2 — https://developer.qualcomm.com/software/ai-datasets/something-something, then decode by index (we use `decord`; a self-maintained |
| sequential counter drifts silently if the decoder ever skips a frame). |
|
|
| ### Known limitations |
|
|
| - Every episode here has an instruction. These episodes come from VITRA-1M's official |
| segmentation, which already drops the ones a captioner marks `N/A`; we measured 0 |
| instruction-less episodes in this release. |
| - Paraphrase count averages 2.09, not a fixed number. Past 3 the model starts inventing; |
| a sentence with no prepositional phrase honestly supports only one or two. |
| - Verified: every episode's stored frame count matches its index rows, and no index entry |
| points at a missing episode. |
|
|
| --- |
|
|
| <!-- COLLECTION_TABLE_START --> |
| ## The collection |
|
|
| Every dataset we have taken through this pipeline, with what is published today. |
| All repos live under [`MIT-Media-Lab`](https://huggingface.co/MIT-Media-Lab) and are |
| **annotations only — no images, no video**. |
|
|
| | 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** | 111,159 | 3,687,389 | episodes + text + **tactile** | 20.12 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) | |
| | **Something-Something V2** | 52,706 | 1,124,722 | text only | 4.63 GB | [`ssv2-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/ssv2-annotations-v1) | |
| | **OakInk2** | 28,264 | 1,371,721 | episodes + text | 1.92 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** | 18,590 | 780,678 | episodes + text | 1.72 GB | [`hot3d-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/hot3d-annotations-v1) | |
| | **Tachin** | 2,422 | 97,638 | episodes + text + **tactile** | 4.13 GB | [`tachin-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/tachin-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** | 5,696 | 196,941 | episodes + text | 0.40 GB | [`h2o-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/h2o-annotations-v1) | |
| | **total** | **542,311** | **16,464,116** | | **50.7 GB** | | |
|
|
| **episodes** = entries in `episode_frame_index.npz`, i.e. what a training run actually sees. |
| **training samples** = rows of `index_frame_pair`; the row number *is* the sample id. |
|
|
| *episodes + text* means we re-cut the source ourselves at wrist-speed minima and then wrote the |
| instructions. *text only* means the episodes are VITRA-1M's official segmentation, used unchanged, |
| and only the instructions are ours. |
|
|
| Episodes whose round-1 caption came back `N/A` (no object interaction) are **not** published — |
| they are excluded from both the archive and the index, so every episode here has a usable |
| instruction. That is why the published counts are below the totals we cut: |
|
|
| | | episodes on disk | published | dropped as `N/A` | |
| |---|---:|---:|---:| |
| | EPIC-KITCHENS-100 | 151,502 | 149,570 | 1,932 (1.3%) | |
| | EgoTouch | 147,386 | 111,159 | 36,227 (24.6%) | |
| | GigaHands | 92,365 | 70,486 | 21,879 (23.7%) | |
| | Ego-Exo4D | 67,051 | 67,051 | 0 | |
| | Something-Something V2 | 52,706 | 52,706 | 0 | |
| | OakInk2 | 37,692 | 28,264 | 9,427 (25.0%) | |
| | TACO | 26,454 | 23,757 | 2,697 (10.2%) | |
| | HOT3D | 22,578 | 18,590 | 3,988 (17.7%) | |
| | Tachin | 3,019 | 2,422 | 597 (19.8%) | |
| | ARCTIC | 14,821 | 12,610 | 2,211 (14.9%) | |
| | H2O | 7,792 | 5,696 | 2,096 (26.9%) | |
|
|
| `ssv2` and `egoexo4d` are 0 because VITRA-1M already dropped `N/A` upstream — their episodes |
| are the official segmentation, so there was nothing left for us to drop. Their on-disk counts are |
| slightly below VITRA-1M's published index (52,718 and 67,053) because round 2 marked a handful of |
| sentences `unusable` and we deleted those episodes: **12** from `ssv2`, **2** from `egoexo4d`. |
|
|
|
|
| ### DexYCB was removed |
|
|
| **DexYCB was removed from this collection on 2026-08-30.** It is captured by 8 fixed RealSense |
| cameras around a table; its own `camera.role` field reads `allocentric` on all 15,878 episodes. |
| Unlike OakInk2, which ships an egocentric view alongside three allocentric ones, DexYCB has no |
| head-mounted camera at all, so there was nothing to filter down to. |
|
|
| ### Not published yet |
|
|
| | dataset | episodes cut | where it stands | |
| |---|---:|---| |
| | HOI4D | — | source converted by a colleague; not re-cut | |
| <!-- COLLECTION_TABLE_END --> |
|
|
