xuejfcc-by-nc-sa-4.0
EgoStandard
9,000 hours of head-view egocentric video capturing human activities with aligned 3D hand pose, body pose, and semantic annotations across three progressive supervision configs.
Downloads0
Hours9000
Why This Matters for Physical AI
This egocentric human activity dataset with precise 3D pose and semantic annotations enables training of embodied AI systems to understand human manipulation tasks, hand-object interactions, and activity semantics from a first-person perspective.
Technical Profile
- Modalities
- rgbhand_pose_3dbody_pose_3dsemantic_annotation
- Environment
- homeindoor
- Task Types
- manipulationgraspingpick_and_place
- Total Hours
- 9000
- Data Format
- parquet
- Annotation Types
- hand_pose_3dbody_pose_3dsemantic_annotationlanguage_instructions
- License
- cc-by-nc-sa-4.0
Access
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