| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - robotics |
| - image-to-3d |
| language: |
| - en |
| tags: |
| - vitra |
| - mano |
| - hand-object-interaction |
| - egocentric |
| --- |
| |
| # hot3d-annotations-v1 |
|
|
| **Annotations only — no images, no video.** |
|
|
| VITRA-style hand episodes for **HOT3D**, with per-hand instructions and paraphrases. |
|
|
| | | | |
| |---|---| |
| | episodes | **18,805** | |
| | training samples (`index_frame_pair` rows) | **619,680** | |
| | annotation | MANO pose + world/camera joints + per-frame extrinsics | |
| | text | one instruction per episode + 1.96 paraphrases on average | |
| | source frame rate | 30 fps | |
| | recordings | 126 (Aria only) | |
| | **images / video** | **not included** — see *Getting the frames* below | |
|
|
| ### Aria (colour) only — the Quest3 half is not here |
|
|
| HOT3D is captured with two devices. **Project Aria** has an RGB camera; **Quest 3** uses the |
| headset's own tracking cameras, which are **monochrome**. Measured colourfulness |
| (mean `|B−G| + |G−R|`) is 22.5–47.8 on Aria frames and 0.64–1.11 on Quest3 frames, i.e. Quest3 |
| is exactly `R = G = B`. |
|
|
| Every other domain in this collection is colour, so **only the Aria half is published here**: |
| 126 of the 250 recordings. The Quest3 half is complete in the source release and can be added |
| by anyone who wants it. |
|
|
| ### What we did |
|
|
| **Episodes are ours.** The source release ships whole recordings (median 3,853 frames), 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. The validity mask is the source's |
| own per-frame `kept_frames`, which on HOT3D is a genuine mask: dtype `bool`, values only |
| `{False, True}`, true on 74.1% of frames, per-sequence mean median 0.785 (range 0.171–0.992), |
| and **no sequence is constant** (0 of 252 all-true, 0 all-false). |
|
|
| **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. |
|
|
| **Hands are balanced**: 7,684 left / 10,906 right, i.e. the left hand is 41% of episodes. |
|
|
| ### Train / val / test split |
|
|
| `splits/` ships the split we train with. **`test` owns whole videos**: its 6 videos appear in |
| neither `train` nor `val`. `train` and `val` share the remaining 120 videos and are separated at |
| the episode level, so `val` measures held-out clips from seen videos, not held-out environments. |
| Balanced on **frames**, not episode count. |
|
|
| | split | videos | episodes | frames | share | |
| |---|---:|---:|---:|---:| |
| | train | 120 | 16,745 | 702,685 | 90.01% | |
| | val | 119 | 941 | 39,053 | 5.00% | |
| | test | 6 (exclusive) | 904 | 38,940 | 4.99% | |
|
|
| `train.txt` / `val.txt` / `test.txt` are **episode-id lists** (train and val share videos, so a |
| video-name list cannot express the split). `test_videos.txt` lists the 6 held-out videos so the |
| isolation can be checked by hand. |
|
|
| ### Files |
|
|
| ``` |
| hot3d.tar -> Annotation/hot3d/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 6 videos test owns |
| splits/meta.json parameters + achieved shares + self-checks |
| ``` |
|
|
| `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 hot3d.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/hot3d/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`, a `quality` dict of per-frame masks carried over from the source, |
| 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 HOT3D — https://facebookresearch.github.io/hot3d/ (licence agreement required), |
| then decode by index (we use `decord`; a self-maintained sequential counter drifts silently if |
| the decoder ever skips a frame). |
|
|
| **Resolve the video by its full `video_name` path, never by basename.** The 250 source videos |
| carry only **3 distinct basenames** — `214-1.mp4` (×126), `1201-1.mp4` (×77), `1201-2.mp4` (×47). |
| Any basename, sorted-order or fuzzy match collapses 126 different recordings onto one file. We |
| made exactly this mistake in an earlier render: 12 sample images collapsed pixel-for-pixel into |
| 3 groups matching the 3 basenames, while the source videos have different sizes and checksums. |
| |
| ### 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. |
| - HOT3D subjects wear **markers on the hands**, so the RGB hands are not bare hands |
| (the same is true of ARCTIC in this collection). Not fixable downstream. |
| - Intrinsics imply 1407×1407 while the Aria video is 1408×1408 — a 1 px difference. Use the |
| video's native size, not `(2cx, 2cy)`. |
| - 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. |
| |
| --- |
| ## 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 | 22,578 | 22,964 | |
| | episodes published (with an instruction) | 18,590 | 18,805 | |
| | training samples | 780,678 | 619,680 | |
|
|
| 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. |
|
|
| <!-- COLLECTION_TABLE_START --> |
| | 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. |
| <!-- COLLECTION_TABLE_END --> |
