--- license: cc-by-4.0 pretty_name: APAC Egocentric Monocular (Labeled) task_categories: - robotics - video-classification - visual-question-answering tags: - egocentric - first-person - hand-tracking - head-tracking - action-labels - dense-captions - industrial - embodied-ai size_categories: - n<1K configs: - config_name: default data_files: - split: train path: data/train-* --- # APAC Egocentric Monocular (Labeled) Twelve labeled egocentric work sequences from **industrial, hospitality, logistics and retail** settings, released as **hand-tracking and head-tracking renders** with **252 densely captioned 3-second action segments**. *Preview: polishing a car in an automotive garage — hand-tracking render, downscaled to 720p.* --- ## At a glance | | | | --- | --- | | Samples | 12 | | Total duration | 12 min 09 s (~61 s each) | | Resolution | 1920×1080 @ 30 fps | | Action segments | 252 (~21 per clip, 3 s each) | | Unique verbs | 87 | | Audio | none | | Environments | Industrial, Hospitality, Logistics, Retail (3 scenes each) | ### Scenes | Environment | Scenes | | --- | --- | | Industrial | Assembly Line · Automotive Garage · Construction | | Hospitality | Pantry · Housekeeping · Kitchen | | Logistics | Storage Hub · Electronics Factory · Laundromat | | Retail | Clothes Store · Accessories Store · Home Decor Shop | --- ## Important: this release contains tracking renders, not raw camera footage Each sample ships as two rendered views — a **hand-tracking** video and a **head-tracking** video — with tracking overlaid on the scene. **The unmodified egocentric camera feed is not part of this release.** If your method needs pristine RGB input, this dataset will not provide it; see the stereo release, which does include rectified raw video. --- ## Repository layout ``` data/train-*.parquet # 720p previews + all metadata + action segments (powers the viewer) hand_tracking/*.mp4 # full-resolution hand-tracking renders (1920×1080) head_tracking/*.mp4 # full-resolution head-tracking renders annotations/*.jsonl # action labels, one segment per line preview/*.mp4 # 720p proxies used by the viewer and this card metadata.csv # flat table ``` The parquet embeds the **720p previews**, not the originals, so the viewer stays fast and every clip plays in the browser. Full-resolution renders are in `hand_tracking/` and `head_tracking/`. ## Columns | Column | Description | | --- | --- | | `video` | 720p hand-tracking preview — plays in the viewer | | `head_tracking_preview_video` | 720p head-tracking preview | | `sample_id` | UUID shared by every file for this sample | | `environment`, `scene` | Industrial/Hospitality/Logistics/Retail, and the specific setting | | `task_description` | What the operator is doing | | `duration_seconds`, `width`, `height`, `fps` | Probed from the media | | `hand_tracking_path`, `head_tracking_path` | Full-resolution files in this repo | | `action_labels_path` | The `.jsonl` for this sample | | `num_segments` | Labeled segments in this clip | | `verbs`, `objects` | Distinct verbs and object names in the clip | | `segments` | Every labeled segment, inline — see below | | `hand_tracking_sha256`, `hand_tracking_bytes` | Integrity and size of the original | ## Action label format One JSON object per 3-second segment: ```json {"video_id": "0114fd12-c0fb-4115-9cf5-4285e6516f7d", "segment_id": 0, "segment_start_time": "00:00.0", "segment_end_time": "00:03.0", "verb": "polishing", "object_name": "car hood", "object_color": "black", "object_shape": "curved", "object_context": "parked in a garage", "object_description": "black curved car hood parked in a garage", "caption": "The operator is polishing a black car hood with an electric buffer. The left hand guides the tool's head while the right hand controls the handle, moving the spinning pad across the surface."} ``` Captions are two-sentence descriptions that name the acting hand and the tool, which makes them usable for bimanual manipulation grounding rather than just clip-level classification. --- ## Usage ```python from datasets import load_dataset ds = load_dataset("humyn-labs/APAC-Egocentric-Monocular-Labeled", split="train") r = ds[0] print(r["environment"], r["scene"], r["task_description"]) print(r["segments"][0]["caption"]) ``` Every segment across the corpus: ```python segs = [s for r in ds for s in r["segments"]] print(len(segs), "segments") ``` Full-resolution renders: ```python from huggingface_hub import snapshot_download snapshot_download("humyn-labs/APAC-Egocentric-Monocular-Labeled", repo_type="dataset", allow_patterns=["hand_tracking/*", "head_tracking/*", "annotations/*"]) ``` ## Intended uses Fine-grained action recognition · dense video captioning · verb–object grounding · hand–object interaction · industrial and retail workflow understanding · vision-language pretraining for embodied agents. ## Limitations - **Small.** 12 clips, ~12 minutes total — an evaluation and prototyping pack, not pretraining scale. - **Tracking renders only**, as described above. - **One clip per scene**, so there is no within-scene variation to train on and no held-out split is provided. - **Uniform length** (~61 s) means no long-horizon sequences. - Segment boundaries are on a **fixed 3-second grid**, not action-aligned, so a labeled segment can straddle two actions. - **No audio**, no depth, no camera pose. ## Provenance Curated from the HumynLabs egocentric sample collection. All technical fields were probed from the media rather than copied from the source sheet. Every listed file resolved and downloaded; no samples were dropped. ## License [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Recorded in working environments with participant consent for research use. Bystanders and workplace interiors may appear incidentally — please do not attempt to re-identify individuals. ## Citation ```bibtex @misc{humynlabs2026apacmonocular, title = {APAC Egocentric Monocular (Labeled)}, author = {HumynLabs}, year = {2026}, url = {https://huggingface.co/datasets/humyn-labs/APAC-Egocentric-Monocular-Labeled} } ```