---
title: Gen-HumanWholeBody
canonical_url: "https://www.modelscope.cn/datasets/GenRobot.AI/Gen-HumanWholeBody"
md_url: "https://www.modelscope.cn/datasets/GenRobot.AI/Gen-HumanWholeBody.md"
repository: GenRobot.AI/Gen-HumanWholeBody
last_updated: 2026-10-27
license: other
storage_size: "8.0 TB"
downloads: 56825
stars: 0
---

# Gen-HumanWholeBody

> Gen-HumanWholeBody - GenRobot.AI 在 ModelScope 开源的数据集。Gen-HumanWholeBody Dataset Card

GenRobot.AI/Gen-HumanWholeBody 是 ModelScope 魔搭社区上的数据集，存储大小 8.0 TB，采用 other 许可。

- **Repository**: GenRobot.AI/Gen-HumanWholeBody
- **License**: other
- **Storage size**: 8.0 TB
- **Downloads**: 56825
- **Stars**: 0
- **Last updated**: 2026-10-27

Source: https://www.modelscope.cn/datasets/GenRobot.AI/Gen-HumanWholeBody

---

# Gen-HumanWholeBody Dataset Card

![Gen-HumanWholeBody Overview](assets/overview.jpg)

### 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: [opendata@genrobot.ai](mailto:opendata@genrobot.ai)
- Discord: [https://discord.gg/rSSb5thgu](https://discord.gg/rSSb5thgu)