Duplicate from CMRobot/MotionDecode

Co-authored-by: ChingmuData <ZIHLING@users.noreply.huggingface.co>

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LICENSE.md

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+# ChingMu Dataset — Access Terms
+
+This dataset is made available under the following conditions:
+
+1. Permitted uses: academic research, personal study, prototyping (non-commercial).
+2. Prohibited without written permission: commercial distribution, re-sale, hosting as a competing dataset.
+3. Users must retain attribution and may not misrepresent data origin.
+4. Request commercial access via Contact.
+
+By requesting access you acknowledge these terms.

README.md

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+---
+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.  
+
+- **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/MotionDecode"
+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     
+
+[![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.  
+
+Or email us at: **MotionDecode@chingmu.com**  
+
+We look forward to collaborating with researchers and industry partners!  

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