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Gen-HumanWholeBody Dataset Card

Dataset Summary

Gen-HumanWholeBody is a high-precision whole-body manipulation dataset captured from DAS-Ego, released by GenRobot.AI. It contains 600 hours of continuous whole-body human manipulation data reconstructed via GenRobot's proprietary Data Foundation Model (DFM), with an average joint position error of approximately 3 cm.

The dataset covers 16,976 real-world scenarios and 1,281 distinct human skills, including tasks that require not only two-handed manipulation but also locomotion, body posture adjustment, and whole-body coordination.

By sharing this dataset, GenRobot aims to provide Physical AI researchers with training-ready whole-body manipulation data and to catalyze a scalable data production pipeline based on lightweight egocentric devices.

Dataset at a Glance

Attribute	Value
Total duration	600 hours
Episodes	15,943
Unique tasks	16,400
Domains	26
Native RGB views	6
Resolution	1600 × 1300
Frame rate	30 FPS
File format	mcap
Modalities	`origin`, `face_mark`, `vio`, `handtracking`, `annotation_v2`, `body_track`, `bold_mark`, `task`, `sub_task`

Supported Tasks and Leaderboards

Gen-HumanWholeBody is designed to support research on:

Whole-Body Motion Learning: Learning continuous and coordinated whole-body motion.
Human-to-Robot Transfer: Exploring skill transfer from human demonstration to robot control.
Embodied Foundation Models: Providing rich real-world interaction data for embodied foundation model pre-training.

The multi-modal signals in the dataset—whole-body motion, two-handed operations, object interactions, contact states, visual observations, and task semantics—are unified in a shared spatiotemporal coordinate system, making the data directly usable for robot policy training.

Precision Metrics

Quantitative evaluation against ground truth:

Body Region	MPJPE
Upper body	~2 cm
Lower body	~3.5 cm
Whole body	~3 cm

The DFM has been validated on challenging conditions including bending, fast motion, long-horizon locomotion, and partial occlusion.

Contact

Questions, suggestions, and requests for additional scenarios or skills are welcome.

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Total file size:

8.79 TB