README.md

metadata

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.

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.

---

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	107,364	3,123,675	episodes + text + tactile	17.02 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
OakInk2	29,058	1,052,924	episodes + text	1.56 GB	oakink2-annotations-v1
TACO	23,757	736,136	episodes + text	1.34 GB	taco-annotations-v1
HOT3D	18,805	619,680	episodes + text	1.51 GB	hot3d-annotations-v1
ARCTIC	12,610	425,796	episodes + text	0.85 GB	arctic-annotations-v1
H2O	5,845	200,332	episodes + text	0.40 GB	h2o-annotations-v1
Tachin	2,669	79,584	episodes + text + tactile	3.38 GB	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.