tachin-annotations-v1

Annotations only — no images, no video.

VITRA-style hand episodes for the Tachin tactile glove dataset, with per-hand instructions, paraphrases, and dense per-frame tactile.

episodes	2,669
training samples (index_frame_pair rows)	79,584
annotation	MANO pose + world/camera joints + per-frame extrinsics
tactile	880 taxels per hand, plus x/y shear, contact state, and 5 fingertip channels
text	one instruction per episode + 1.92 paraphrases on average
source frame rate	30 fps
recordings	102 task segments
images / video	not included — see Getting the frames below

This is the densest tactile in the collection: 880 taxels per hand against EgoTouch's 16×16 = 256 per palm, and it additionally ships shear (tactile_tf_x / tactile_tf_y), a per-taxel contact state, and 5 fingertip pressure and dynamics channels.

What we did, and what we took from upstream

The pose is not ours. The upstream release MIT-Media-Lab/tachin-vitra-precut-mano-v1 did the hard part: the source ships 21-joint hand tracks and no MANO, so MANO was fitted there (per-recording/hand beta, per-frame pose, wrist J0 translation, with a per-frame fit_mpjpe_m), the ~55.6 Hz glove streams were interpolated onto the 30 Hz RGB timestamps without extrapolating across gaps wider than 100 ms, and a 12.4-second disagreement between two published RGB clock origins was resolved with the per-frame NTP table rather than a blanket offset. We adopted all of that unchanged.

The episodes and the text are ours. We re-cut the continuous recordings with VITRA's method:

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

Left and right hands are cut independently. The validity mask is post_mano_cut_valid_frames, the strictest of the four the source ships (median 0.955; the others are source_valid_frames 0.968, mano_fit_valid_frames 0.967, projection_valid_frames 0.967 — none is constant, so all four encode real validity).

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, and writes 1-3 paraphrases.

Episodes with no instruction are not included — round 1 returned N/A on 597 of 3,019 segments (19.8%); those are excluded from both the archive and the index.

Hands are balanced: 1,019 left / 1,403 right, i.e. left is 42% of episodes.

Coordinates — no SLAM or anchor search needed

Per-frame extrinsics (World2Cam, OpenCV, metres) ship with every episode and one stable world frame per recording. Measured: the camera really moves (max displacement median 0.196 m, trajectory length 2–12 m), rotations are legal (orthogonality deviation ≤ 1.3e-07), and joints_camspace == extrinsics · joints_worldspace to 8e-08 m. Anchor-camera normalisation is not baked in; the training dataloader does chunk-level anchor synchronisation, the same as every other domain here.

Train / val / test split

test owns whole videos — its 5 videos appear in neither train nor val. train and val share the remaining 97 videos and are separated at the episode level. Balanced on frames.

split	videos	episodes	frames	share
train	97	2,159	86,872	88.97%
val	58	120	4,824	4.94%
test	5 (exclusive)	143	5,942	6.09%

The test share overshoots 5% because there are only 102 videos and test must take whole ones; the granularity is limited by video size, not by a bug.

Files

tachin.tar                  ->  Annotation/tachin/episodic_annotations/*.npy
episode_frame_index.npz        index_frame_pair (N,2) uint32 + index_to_episode_id (E,)
splits/{train,val,test}.txt    episode ids
splits/test_videos.txt         the 5 videos test owns
splits/meta.json               parameters + achieved shares + self-checks

import numpy as np
# tar -xf tachin.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/tachin/episodic_annotations/{eid}.npy", allow_pickle=True).item()
rgb_frame_id = int(d["video_decode_frame"][frame_id])
hand = str(d["anno_type"])
tactile = d["tactile"][hand]          # (T, 880) float
valid   = d["tactile_valid"][hand]    # (T, 880) — NaNs are masked here, not filled

Each .npy also carries tactile_tf_x / tactile_tf_y (shear), tactile_contact_state, tactile_fingertip_pressure and tactile_fingertip_dynamics (T,5), the static tactile_sensor_keys (15) and tactile_sensor_offsets (15,2), a quality dict of per-frame masks, and the usual left/right pose dicts. text[hand] = [(sentence, (0, T))], text_rephrase[hand] = [([paraphrases...], (0, T))].

Getting the frames

video_decode_frame indexes the source video, which we do not redistribute. The segment videos are in MIT-Media-Lab/tachin-vitra-precut-mano-v1 under Video/Tachin_root/<recording>/task-*.mp4, and video_name in each episode is already the full relative path from that repo root — join it directly, do not match by basename. The original dataset is Tachintech/TachinTactileGlove-Demo01 (revision 2eb0e45e6d2103f33a7329c657a161c067d516c9).

Known limitations

• Paraphrase count averages 1.92, not a fixed number.
• Small. 2,422 episodes / 54 minutes of source video — the smallest domain in this collection. Its value is tactile density, not scale.
• MANO here is a fit to 21-joint tracks, not a multi-view solve; fit_mpjpe_m is stored per frame so the fit quality can be filtered on.
• Intrinsics are non-square (fx 626.9 vs fy 578.1) and imply (2cx, 2cy) = 1926.7 × 1069.3 while the video is 1920 × 1080. Use the video's native size.
• 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.

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Revision — 2026-09-08 (re-cut)

This release replaces the previous one. The previous episodes contained invalid frames and should not be used.

A defect in our episode-cutting step let frames with kept_frames == False (invalid hand pose — all-zero or NaN wrist coordinates) stay inside published episodes. The validity mask was only used to keep a cut point from landing on an invalid frame; it did not constrain what a segment contained. Worse, a run of invalid frames could suppress cutting altogether, so the gap was swallowed into one long segment instead of being excluded.

Every episode here is now built from a run of consecutive valid frames, so kept_frames is all-True by construction — verified over the whole collection: 568,369 episodes / 16.5 M frames, zero kept_frames == False. Both instruction rounds were regenerated for the new segmentation.

Segment counts and episode ids therefore changed, and the index and splits were rebuilt:

	previous	this release
episodes on disk	3,019	3,420
episodes published (with an instruction)	2,422	2,669
training samples	97,638	79,584

Splits are video_test (test owns whole videos disjoint from train/val; train and val share the remaining videos and are split at the episode level), balanced on frames at 90/5/5, seed 1.

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.