meta_version string | name string | version string | app_version string | debug bool | build_type string | debug_version string | collection_date timestamp[s] | device_type string | device_sn string | video dict | tracking dict | command_args dict | recording_sync dict | video_info dict | tracking_info dict | movement_summary dict | valid_data list | quality dict | delivery_processing dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1.0 | 2026-07-06-15-29-09-PA9410MGL4110500G | 1.4.1 | 1.4.1 | false | release | 2026-07-06T15:29:09 | PICO 4 Ultra | PA9410MGL4110500G | {
"file_name": "CameraRecord_2026-07-06-15-29-09.mp4",
"width": 4096,
"height": 1536,
"fps": 24.999116,
"recording_api": "pxrcapture_rawfisheye_sbs_hevc",
"duration_sec": 59.682111
} | {
"file_name": "trackingData_2026-07-06-15-29-09.txt",
"timebase": "timeStampNs_wall_ns_and_row_predictTime_CLOCK_MONOTONIC_us",
"timestamp_semantics": {
"timeStampNs": "App-side row write wall-clock timestamp in Unix nanoseconds.",
"predictTime": "Row-level PXR_Enterprise.GetPredictedDisplayTime timestam... | {
"task_id": "283",
"task_name": "A033 居家数采",
"task_description": "A033 居家数采"
} | {
"recording_api": "pxrcapture_rawfisheye_sbs_hevc",
"mvhevc": false,
"output_width": 4096,
"output_height": 1536,
"fps": 30,
"video_start_boot_ns": 873239874249,
"video_start_mono_ns": 873239885811,
"video_start_wall_ns": 1783322951865000000,
"tracking_start_predict_time_us": 873280517.004,
"tracki... | {
"fps": 24.999116,
"frame_count": 1492,
"width": 4096,
"height": 1536,
"duration_sec": 59.682111
} | {
"profile_fps": 73.8,
"profile_total_frames": 5364,
"profile_duration_sec": 60.4
} | {
"moving_frame_ratio": 0,
"cumulative_travel_distance_m": 0.901,
"max_movement_range_m": 0.676,
"net_displacement_m": 0.48,
"cumulative_waist_rotation_deg": 311.53
} | [
{
"start_timestamp": "00:00:04.787",
"end_timestamp": "00:00:22.606",
"start_sec": 4.787,
"end_sec": 22.606,
"duration_sec": 17.819,
"reason": "双手在去畸变视野内连续可见"
},
{
"start_timestamp": "00:00:28.272",
"end_timestamp": "00:00:43.939",
"start_sec": 28.272,
"end_sec": 43.939,
... | {
"valid_duration_sec": 48.647,
"valid_ratio": 0.8151,
"best_clip_count": 3,
"level": "usable",
"notes": "基于hand_visible基础属性自动生成，客户交付前建议结合annotation复核动作语义。"
} | {
"generated_at": "2026-07-06T14:06:03.196164+00:00",
"source_profile": "all_profiles.json",
"source_tracking_raw_preserved": true,
"best_clips_source": "best_clips.json"
} |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
10000-Hour-Egocentric-Video-Dataset
Description
This dataset contains 10,000 hours of egocentric multimodal data collected from diverse real-world environments, including residential, retail, and office scenarios. It covers a wide range of human activities and manipulation tasks, such as meal preparation, cleaning, storage, garment care, merchandising, and object picking. Each sample includes synchronized 4K stereo video, camera calibration parameters, 76-point full-body pose annotations, and fine-grained step-by-step action sequence labels. The dataset is suitable for robot learning, manipulation policy development, and Vision-Language-Action (VLA) models.
For more details, please refer to the link: https://www.nexdata.ai/datasets/embodied-ai/2145=Huggingface
Data size
10,000-Hour Egocentric Full-Body Multimodal Dataset
Data Distribution
Covers residential, retail & office scenarios (kitchen, bedroom, living room, supermarket, office) with diverse real-life tasks: meal prep, cleaning, storage, garment care, merchandising & picking
Data Content
Each sample includes spatiotemporally aligned 4K stereo video, camera calibration params, 76-point full-body pose & step-by-step annotations
Capture Solution
Adopts PICO 4 Ultra head-mounted stereo camera + wrist & ankle IMU motion capture solution
Data Annotation
Supports dense semantic & action-level annotations; all data passes multi-stage quality control reviews
Data Quality
Supports 4096×1536 / 30fps HD video output, tracks 24 torso joints and 52 hand joints, with frame-wise dense annotations and full-process quality control
Full Dataset Access
The complete dataset is available upon request. Reach out to us to learn more and submit an access request.
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