---
dataset_info:
  license: other
  license_name: chingmu-terms
  license_link: LICENSE
  language: ["en", "zh"]
  pretty_name: "ChingMu Robot Motion Dataset"
  tags:
    - motion-capture
    - humanoid-robotics
    - imitation-learning
    - optical-mocap
    - bvh
    - dexterous-hands
    - whole-body-control
  size_categories: 1M<n
  configs:
    - config_name: metadata
      default: true
      data_files:
        - split: train
          path: "metadata/index.csv"
    - config_name: samples
      data_files:
        - split: train
          path: "samples/**/*"
---

# ChingMu 1000-Hour Embodied Motion Dataset

> High-precision **optical motion capture** data for humanoid robots, dexterous hands, embodied AI, and virtual production.  

| | |
|---|---:|
| **Duration** | **1000+ hours** @ 120 Hz |
| **Scenarios ** | 15+ real-world scenes |
| **Tasks ** | 500+ standardized tasks |
| **Objects** | 200+ tracked props (6D pose) |
| **Modalities** | Skeleton · Finger · Object 6D · Video · Labels |
| **Formats ** | BVH · Retargeted CSV · NPZ |

🔒 **Access note:** Metadata and samples are public. Full data requires **Request access**.  

---

## Key Features 

- **Optical ground truth** – sub-mm accuracy, 120 fps, no estimation errors.  

- **Dexterous hands** – 20+ DoF per hand, synchronized with object 6DOP pose.  

- **Robot-ready** – pre-retargeted to Unitree G1; custom retargeting available.  

- **Real-world diversity** – 15+ scenarios, 500+ tasks, 200+ objects.  

- **Multi-modal** – full-body skeleton, finger motion, object pose, multi-view video, semantic labels.  

- **Quality assured** – every take passes automated cleaning + manual inspection; quality flags provided.  


---

## Dataset Summary

ChingMu 1000H is an optical motion capture dataset designed for training and validating embodied AI and humanoid robot controllers. It covers full-body skeleton, finger articulation, object 6D pose, multi-view video, and semantic labels across 15+ real-world scenarios (industrial, household, retail, healthcare, logistics, agriculture, performance). All data is cleaned, quality-assessed, and robot-retargeted.

---

## Data Format Specifications 

| Component | Format | Details |
|---|---|---|
| Raw motion | `.bvh` | Y-up, 120 fps, ZYX rotation, cm, 47–67 joints |
| Retargeted trajectories | `.csv` | Root position (m), quaternion, joint angles (rad) |
| Object 6D pose | `.csv` | Position (m) + quaternion, 120 Hz |
| Multi-view video | `.mp4` | 4–8 cameras, co-registered |
| Semantic labels | `.jsonl` | Task, scenario, action, object |


## 🎥 Preview Video 

Watch a short demonstration of the motion capture data in action:  

<video src="https://github.com/ChingmuData/MotionDecode/raw/refs/heads/main/assets/video/SC01_render_V02_LQ_1.mp4" controls autoplay muted loop>
  Your browser does not support the video tag.
</video>

*Demonstration of full-body motion capture with real-time skeleton overlay and object tracking.*  

### Intended Uses
- Imitation learning / motion policy training for humanoids

- Dexterous manipulation datasets (hand-object interaction)

- Motion generation & retrieval (text/motion cross-modal)

- Sim-to-real validation (MuJoCo via retargeted trajectories)

- Virtual production & animation reference

---

### Full Taxonomy (abridged)

- **Locomotion** → walk, jog, crouch-walk...

- **Manipulation (whole-body)** → shelf-pick-place...

- **Dexterous Hand** → pinch, precision-grasp...

- **Tool Use** → screwdriver, wrench...

- **Object Interaction** → door-open/close...

- **Social / Contact** → handoff-object...、

- **Performance** → dance, martial-arts...


👀 **Try it live:** Use the **Dataset Preview** panel at the top of this page to filter and explore the actual index table. Select the `metadata` config to browse available takes.  

> ℹ️ The full index with all rows is best viewed locally. Download [`metadata/index.csv`](https://huggingface.co/datasets/ZIHLING/Chingmu-RobotData/resolve/main/metadata/index.csv) to open in Excel or pandas for complete filtering.  

---

## 🖥️ Interactive Showcase

Visit our dedicated showcase website for interactive demos, comparison videos, and detailed visualizations:  

[![Visit Showcase](https://img.shields.io/badge/🌐-Visit_Showcase_Website-2ea44f?style=for-the-badge)](https://chingmudata.github.io/MotionDecode/)

*Includes: trailer video, modality breakdowns, robot retargeting comparisons, and more.*  

---

## 🆕 Open-Source Release: Unitree G1 Retargeted Data   

We are releasing **100 hours** of robot-ready motion trajectories retargeted to the **Unitree G1** humanoid. All data is provided in **CSV** format under the `samples/` directory. **Please indicate the source of the data when using it: from Chingmu.**   

## Quick Start

```bash
pip install huggingface_hub
```

```python
from huggingface_hub import hf_hub_download

repo_id = "CMRobot/Chingmu-RobotData"
file_path = hf_hub_download(
    repo_id=repo_id,
    filename="samples/1.1.Basic_Movement_Category/1.1.1.High_Dynamic_Movement/1.1.1.1.Standing_High_Jump/BM_Standing_High_Jump_00001.csv",
    repo_type="dataset",
    local_dir="./robot_samples"
)
print(f"Downloaded: {file_path}")
```

---

## Quality & Limitations

**Quality controls:** marker swap correction, gap-filling (≤6 frames), foot skating detection, manual review. Flags: `pass`, `warning`, `fail`.  

**Accuracy:** joint error <1mm, object pose ±2mm / ±0.5°, temporal sync <1 frame.  

**Limitations:** performer age skew (20–35), object accuracy varies with marker cluster size.  

---

## Get Full Dataset 

Only a subset of the dataset is publicly available here. If you need **full access** to the entire 1000-hour dataset, please contact us through the following channels:  

- **For Chinese users:** Scan the QR code below to contact us via WeChat, and include in the remarks the name of your organization, your name, and the main purpose.   

<img src="https://github.com/ChingmuData/MotionDecode/raw/refs/heads/main/assets/group.jpg" width="30%" alt="alt text">

**For international users:** Join our Discord community     

[![Discord](https://img.shields.io/badge/Discord-Join_Community-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/gAzgFqYDr9)

Alternatively, you can click the **"Request access"** button on the right side of this page to automatically gain download permissions for the complete dataset.  


*Note: Approval is automatic, but we kindly ask you to provide your contact information for licensing purposes.*  

Or email us at: **MotionDecode@chingmu.com**  

We look forward to collaborating with researchers and industry partners!