| --- |
| 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:** This dataset is fully open and publicly accessible. |
|
|
| --- |
|
|
| ## Key Features |
|
|
| - **Optical ground truth** – sub-mm accuracy, 120 fps, no estimation errors. |
|
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| - **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) |
|
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| - Motion generation & retrieval (text/motion cross-modal) |
|
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| - Sim-to-real validation (MuJoCo via retargeted trajectories) |
|
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| - Virtual production & animation reference |
|
|
| --- |
|
|
| ### Full Taxonomy (abridged) |
|
|
| - **Locomotion** → walk, jog, crouch-walk... |
|
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| - **Manipulation (whole-body)** → shelf-pick-place... |
|
|
| - **Dexterous Hand** → pinch, precision-grasp... |
|
|
| - **Tool Use** → screwdriver, wrench... |
|
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| - **Object Interaction** → door-open/close... |
|
|
| - **Social / Contact** → handoff-object...、 |
|
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| - **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: |
|
|
| [](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 |
|
|
| The entire dataset is publicly available here. If you have any questions about the dataset or would like to know more information, 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 |
|
|
| [](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. |
|
|
| Or email us at: **MotionDecode@chingmu.com** |
|
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| We look forward to collaborating with researchers and industry partners! |
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|
