egoexo4d-annotations-v1

Annotations only — no images, no video.

VITRA-style hand episodes for Ego-Exo4D, with per-hand instructions and paraphrases.

episodes	67,051
training samples (index_frame_pair rows)	1,757,474
annotation	MANO pose + world/camera joints + per-frame extrinsics
text	one instruction per episode + 1.98 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

egoexo4d.tar                 ->  Annotation/egoexo4d/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 egoexo4d.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/egoexo4d/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 Ego-Exo4D — https://ego-exo4d-data.org/, 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 1.98, 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.

---

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.