Pre-processing catalog of smartphone egocentric video recordings. Most episodes are exposed as metadata only; a small set of representative sample episodes are shipped with full raw video + derived data so partners can inspect data quality before signing up for the full corpus.

Built: 2026-05-12T19:05Z
Total episodes: 5962
Total recorded time: ~693.9 hours
Format: Apache Parquet (catalog) + raw media for sample episodes
Maintained by: ZenO Labs — https://zen-o.xyz

Data exposure policy

There are two tiers of access in this repo:

1. Sample episodes (samples/<domain>/<upload_id>/ — full data)

For a curated subset, the actual files are shipped inside this HF repo:

File	Source	Purpose
`video.mp4` (or `.mov`)	ZenO Core / R2 original	Raw egocentric capture
`pose_keypoints.json`	MediaPipe Hands (Zeno pipeline)	Per-frame 2D hand keypoints (21 points × 2 hands)
`depth_overlay.mp4`	Zeno depth pipeline	Colorized relative depth visualization
`meta.json`	this catalog	Episode metadata snapshot

These are the only files in this repo that can be downloaded directly. Mission domain (cooking, kitchen-cleanup, …) decides the sub-folder.

2. All other episodes (~5946+ — metadata only)

The remaining episodes are listed in catalog.parquet with full metadata, but the actual media files are not in this repo. Each row's modality flags (hand_pose, depth, hand_world, …) show what can be delivered on request, packaged in LeRobot v2.1 format.

To obtain raw video, derived data, or a full LeRobot bundle for any upload_id outside the samples/ folder, contact support@zen-o.xyz with the upload_id list and the modalities you need.

Why is no derived data available yet?

These recordings are smartphone first-person videos captured by ZenO contributors. They are queued for processing once the storage infrastructure migration completes. After processing, episodes will graduate to the main egostation-catalog-v1 catalog with full LeRobot v2.1 data layout.

Per-episode availability flags

Each row in catalog.parquet carries a status string per modality:

Field	Meaning
`head_trajectory`	`not-supported` — smartphone capture lacks synchronized inertial data, so mono-inertial SLAM cannot recover 6DoF camera pose.
`hand_pose`	`available` — derivable on request (2D keypoints, both hands, 21 points each).
`depth`	`available` — relative depth maps derivable on request.
`hand_world`	`available` — 3D hand keypoints in camera frame derivable on request (depends on depth).
`action_segment`	`available` — manual annotation possible on request.

After processing, the value updates to completed and the episode is moved to the main catalog.

`catalog.parquet` schema

Field	Type	Description
`episode_id`	string	`ep_NNNNNN`, unique within this repo
`upload_id`	string	ZenO Core upload identifier — abstract handle (does not change across storage migrations)
`source`	string	`smartphone`
`mission_name`	string	Task category (e.g. Cooking, Washing Dishes, Tidy Up Shoes)
`mission_group`	string	Higher-level grouping (Mission 5, Mission 7, …)
`mission_id`	int	Internal ZenO Core mission identifier
`file_name`	string	Original device filename
`file_size_mb`	int	File size in megabytes
`file_type`	string	"Video"
`duration_s`	float	Episode length in seconds
`width`, `height`	int	Original video resolution
`uploaded_at`	timestamp (UTC, ISO 8601)	When the contributor uploaded
`catalog_tier`	string	`private` (raw catalog — metadata only)
`head_trajectory`, `hand_pose`, `depth`, `hand_world`, `action_segment`	string	Modality availability status
`is_processed`	bool	`false` for all rows in this raw catalog

Mission distribution

Washing Dishes: 1216
Cooking: 1131
Wipe Objects and Furniture: 863
Gripping a Door Handle: 629
Kitchen Cleanup: 531
Laundry Folding Challenge: 459
Tidy Up Challenge: 260
Home Cleaning Task: 219
Dishwashing Task: 217
Tidy Up Your Room: 188
Laundry Task: 182
Tidy Up Shoes: 67

Sample episodes (real data)

16 representative episodes with raw video + 2D hand keypoints, organized by mission domain.

egostation-smartphone-raw-v1/
└── samples/
    ├── cooking/
    │   ├── 89c5487e…/
    │   │       ├── video.mp4    (843 MB, 731s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── e6966629…/
    │   │       ├── video.mp4    (718 MB, 625s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 630c7405…/
    │   │       ├── video.mp4    (690 MB, 1046s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   └── 72700d9c…/
    │           ├── video.mp4    (981 MB, 1003s)
    │           ├── pose_keypoints.json
    │           ├── depth_overlay.mp4
    │           └── meta.json
    ├── kitchen-cleanup/
    │   └── e0797265…/
    │           ├── video.mp4    (353 MB, 445s)
    │           ├── pose_keypoints.json
    │           ├── depth_overlay.mp4
    │           └── meta.json
    ├── laundry-folding-challenge/
    │   ├── 8e3cfad8…/
    │   │       ├── video.mp4    (567 MB, 587s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 38c6b391…/
    │   │       ├── video.mp4    (592 MB, 414s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 00c5a81c…/
    │   │       ├── video.mp4    (925 MB, 946s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   └── dc36f9b0…/
    │           ├── video.mp4    (653 MB, 372s)
    │           ├── pose_keypoints.json
    │           ├── depth_overlay.mp4
    │           └── meta.json
    ├── tidy-up-shoes/
    │   └── d311a32e…/
    │           ├── video.mp4    (902 MB, 923s)
    │           ├── pose_keypoints.json
    │           ├── depth_overlay.mp4
    │           └── meta.json
    ├── washing-dishes/
    │   ├── 7c98ebfb…/
    │   │       ├── video.mp4    (638 MB, 802s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 50b9fc4b…/
    │   │       ├── video.mp4    (943 MB, 964s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 61e65f9c…/
    │   │       ├── video.mp4    (704 MB, 619s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 71fcad78…/
    │   │       ├── video.mp4    (618 MB, 718s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   └── dfea9e2f…/
    │           ├── video.mp4    (587 MB, 695s)
    │           ├── pose_keypoints.json
    │           ├── depth_overlay.mp4
    │           └── meta.json
    └── wipe-objects-and-furniture/
        └── 7de39f3a…/
                ├── video.mp4    (910 MB, 930s)
                ├── pose_keypoints.json
                ├── depth_overlay.mp4
                └── meta.json

Sample list

Mission	Episode	Duration	Video	Hand pose	Depth
Laundry Folding Challenge	`8e3cfad8…`	9:46	567 MB	✓	✓
Cooking	`89c5487e…`	12:10	843 MB	✓	✓
Laundry Folding Challenge	`38c6b391…`	6:54	592 MB	✓	✓
Washing Dishes	`7c98ebfb…`	13:22	638 MB	✓	✓
Kitchen Cleanup	`e0797265…`	7:24	353 MB	✓	✓
Laundry Folding Challenge	`00c5a81c…`	15:45	925 MB	✓	✓
Washing Dishes	`50b9fc4b…`	16:03	943 MB	✓	✓
Cooking	`e6966629…`	10:24	718 MB	✓	✓
Washing Dishes	`61e65f9c…`	10:18	704 MB	✓	✓
Cooking	`630c7405…`	17:25	690 MB	✓	✓
Washing Dishes	`71fcad78…`	11:58	618 MB	✓	✓
Wipe Objects and Furniture	`7de39f3a…`	15:30	910 MB	✓	✓
Washing Dishes	`dfea9e2f…`	11:35	587 MB	✓	✓
Tidy Up Shoes	`d311a32e…`	15:22	902 MB	✓	✓
Cooking	`72700d9c…`	16:42	981 MB	✓	✓
Laundry Folding Challenge	`dc36f9b0…`	6:12	653 MB	✓	✓

Sample episodes are full quality — for the rest of the catalog, raw videos are available via R2 pre-signed URL under signed agreement.

Usage

from datasets import load_dataset

catalog = load_dataset("zeno-labs/egostation-smartphone-raw-v1", "catalog", split="train")
df = catalog.to_pandas()
print(df.head())

# Filter to a domain
cooking = df[df["mission_name"] == "Cooking"]
print(f"Cooking episodes: {len(cooking)}, total {cooking['duration_s'].sum()/3600:.1f}h")

Future structure (after processing)

Each episode, once processed, will be packaged in the LeRobot v2.1 format under the main catalog:

egostation-catalog-v1/
└── smartphone/
    └── <mission_name>/
        ├── data/chunk-NNN/episode_NNNNNN.parquet
        ├── videos/chunk-NNN/
        │   ├── observation.images.head/episode_NNNNNN.mp4
        │   └── observation.images.depth/episode_NNNNNN.mp4
        └── meta/
            ├── info.json
            ├── episodes.jsonl
            └── tasks.jsonl

Access to raw videos

Metadata in this catalog is freely browsable. For raw video access (needed for downstream processing or your own pipeline), contact support@zen-o.xyz with your upload_id list.

Licensing

Catalog metadata: CC-BY-NC 4.0. Raw videos: commercial license required.

Contact

Data access & partnerships: support@zen-o.xyz
Technical issues: support@zen-o.xyz
Website: https://zen-o.xyz
Pipeline (ZenO Studio): https://studio.zen-o.xyz

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Total file size:

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EgoStation Smartphone Raw Catalog v1