Gen-HumanWholeBody

# Gen-HumanWholeBody Dataset Card  ### Dataset Summary **Gen-HumanWholeBody** is a high-precision whole-body manipulation dataset captured from DAS-Ego, released by [GenRobot.AI](https://www.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. - X: [@GenrobotAI](https://x.com/GenrobotAI) - LinkedIn: [GenRobot](https://www.linkedin.com/company/108767412/) - Email: [[e-mail]](mailto:[e-mail]) - Discord: [https://discord.gg/rSSb5thgu](https://discord.gg/rSSb5thgu)