--- license: cc-by-nc-4.0 task_categories: - robotics - image-to-3d language: - en tags: - vitra - mano - hand-object-interaction - bimanual --- # arctic-annotations-v1 **Annotations only — no images, no video.** VITRA-style hand episodes for **ARCTIC**, with per-hand instructions and paraphrases. | | | |---|---| | episodes | **12,610** | | training samples (`index_frame_pair` rows) | **425,796** | | annotation | MANO pose + world/camera joints + per-frame extrinsics | | text | one instruction per episode + 1.86 paraphrases on average | | source frame rate | 30 fps | | **images / video** | **not included** — see *Getting the frames* below | ARCTIC is the one dataset here that was **built for two-handed manipulation of articulated objects** — 11 objects with moving parts, captured while both hands operate them together. ### 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. ### Egocentric view only ARCTIC records each sequence from **8 static cameras plus one head-mounted egocentric camera**. Only the egocentric view is here; we train on head-mounted footage. The public release covers **301 sequences from 9 subjects** (the paper reports 393 from 10; `s03` is withheld for the official test server), which is what these episodes are cut from. One practical note if you fetch the images: ARCTIC's `cropped` image package does **not** crop the egocentric view. `scripts_data/crop_images.py` crops only views 1-8 to 1000x1000 around the object; view 0 is the full frame resized by `EGO_IMAGE_SCALE = 0.3`, giving **840x600** with the geometry intact. Scale the intrinsics by 0.3 and they match — there is no need for the 649 GB full-resolution package. ### Three conventions we converted, and why Values here follow the same conventions as the other repos in this collection, which differ from ARCTIC's raw MANO parameters in three places. All three were measured, not assumed: - **`transl_worldspace` is the wrist joint**, obtained by running MANO forward and taking `joints[0]`. ARCTIC's raw `trans` is **not** the wrist — it sits 9.23 cm (right) / 9.27 cm (left) away from it. - **`hand_pose` has `hands_mean` added back.** Round-tripping with it reproduces ARCTIC's own joints to 0.000000 cm. - **The left hand uses a genuine `MANO_LEFT` model**, not a mirrored right hand. Also checked: 21-joint bone-length CV is 0.00000 (joint order matches ours), median bone length 27.8 mm (TACO 28.2, EPIC 29.0), `extrinsics` deviate from orthonormal by 8.1e-07, and MANO frame counts equal egocentric frame counts on every sequence tested. ### Files ``` arctic.tar -> Annotation/arctic/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 arctic.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/arctic/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 ARCTIC — https://arctic.is.tue.mpg.de/, 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 1.86, not a fixed number. Past 3 the model starts inventing; a sentence with no prepositional phrase honestly supports only one or two. - ARCTIC is a **lab capture**: a mocap studio, 11 objects, 9 subjects. Its pose quality is the highest here, its visual and task diversity the lowest. It is 2.3 hours of source video. - 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.