README.md

metadata

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.

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.

---

The collection

Every dataset we have taken through this pipeline, with what is published today. All repos live under 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
EgoTouch	111,159	3,687,389	episodes + text + tactile	20.12 GB	egotouch-annotations-v1
GigaHands	70,486	2,266,087	episodes + text	2.89 GB	gigahands-annotations-v1
Ego-Exo4D	67,051	1,757,474	text only	4.09 GB	egoexo4d-annotations-v1
Something-Something V2	52,706	1,124,722	text only	4.63 GB	ssv2-annotations-v1
OakInk2	28,264	1,371,721	episodes + text	1.92 GB	oakink2-annotations-v1
TACO	23,757	736,136	episodes + text	1.34 GB	taco-annotations-v1
HOT3D	18,590	780,678	episodes + text	1.72 GB	hot3d-annotations-v1
Tachin	2,422	97,638	episodes + text + tactile	4.13 GB	tachin-annotations-v1
ARCTIC	12,610	425,796	episodes + text	0.85 GB	arctic-annotations-v1
H2O	5,696	196,941	episodes + text	0.40 GB	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