--- license: cc-by-nc-4.0 task_categories: - robotics - image-to-3d language: - en tags: - vitra - mano - hand-object-interaction --- # taco-annotations-v1 **Annotations only — no images, no video.** VITRA-style hand episodes for **TACO**, with per-hand instructions and paraphrases. | | | |---|---| | episodes | **23,757** | | training samples (`index_frame_pair` rows) | **736,136** | | annotation | MANO pose + world/camera joints + per-frame extrinsics | | text | one instruction per episode + 1.96 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. ### Files ``` taco.tar -> Annotation/taco/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 # tar -xf taco.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/taco/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 TACO — https://taco2024.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 1.96, 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`](https://huggingface.co/datasets/MIT-Media-Lab/epic30-annotations-v1) | | **EgoTouch** | 107,364 | 3,123,675 | episodes + text + **tactile** | 17.02 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) | | **OakInk2** | 29,058 | 1,052,924 | episodes + text | 1.56 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** | 18,805 | 619,680 | episodes + text | 1.51 GB | [`hot3d-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/hot3d-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** | 5,845 | 200,332 | episodes + text | 0.40 GB | [`h2o-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/h2o-annotations-v1) | | **Tachin** | 2,669 | 79,584 | episodes + text + **tactile** | 3.38 GB | [`tachin-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/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.