update: complete dataset card

README.md

@@ -29,45 +29,37 @@ dataset_info:
 # ChingMu 1000-Hour Embodied Motion Dataset
 ### 青瞳1000小时具身智能动作数据集
 
-## 🌟 Why ChingMu?
-
-**The largest and most precise optical motion capture dataset purpose-built for humanoid robots and dexterous manipulation.**
-
-Unlike monocular video-based datasets, ChingMu provides **sub-millimeter accuracy**, **120 fps temporal resolution**, and **co-registered multi-modal signals** — including full-body skeleton, finger articulation, object 6D pose, and synchronized multi-view video. Every take is manually quality-checked and robot-retargeted, eliminating the noise and ambiguity common in internet-sourced data.
-
-**Key differentiators:**
-- ✅ **Optical ground truth** – not estimated, not synthetic
-- ✅ **Dexterous hand data** – 20+ DoF per hand, synchronized with object tracking
-- ✅ **Robot-ready** – pre-retargeted to Unitree G1 and customizable to your platform
-- ✅ **Real-world scenarios** – 15+ environments, 500+ tasks, 1000+ objects
-- ✅ **Scalable** – from single-task samples to full 1000-hour corpus
-
 > High-precision **optical motion capture** data for humanoid robots, dexterous hands, embodied AI, and virtual production.
 
 | | |
 |---|---:|
-| **Motion Capture Duration** | **1000+ hours** @ 120 Hz |
+| **Duration** | **1000+ hours** @ 120 Hz |
 | **Scenarios** | 15+ real-world scenes |
-| **Task categories** | 500+ standardized tasks |
-| **Interactive objects** | 1000+ tracked props (6D pose) |
-| **Modalities** | Full-body skeleton · Finger DoF · Object 6D pose · Multi-view sync video · Task labels |
-| **Output formats** | BVH · Retargeted joint trajectories · NPY · CSV · FBX |
+| **Tasks** | 500+ standardized tasks |
+| **Objects** | 1000+ 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**.
 
 ---
 
-## Dataset Summary
+## Key Features
 
-ChingMu 1000H is a high-precision, multi-modal motion capture dataset built for training and validating embodied AI and humanoid robot controllers.
+- **Optical ground truth** – sub-mm accuracy, 120 fps, no estimation errors.
+- **Dexterous hands** – 20+ DoF per hand, synchronized with object 6D pose.
+- **Robot-ready** – pre-retargeted to Unitree G1; custom retargeting available.
+- **Real-world diversity** – 15+ scenarios, 500+ tasks, 1000+ 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.
 
-Data is captured using **optical mocap systems** (sub-mm precision, 120 fps) and covers:
+---
 
-- **Full-body skeleton motion** (23–67 joints, BVH-compatible hierarchies)
-- **Finger & hand motion** (per-hand 20+ DoF, glove + marker hybrid)
-- **Object 6D pose tracking** (rigid-body markers → position + quaternion @120Hz)
-- **Multi-view video** (2–4 synchronized cameras, co-registered with mocap timeline)
-- **Semantic labels**: task name, scenario tag, skill category, quality flag
+## Dataset Summary
 
-Scenarios span industrial assembly, household service, retail interaction, healthcare assistance, logistics handling, agricultural work, and staged performance.
+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 Highlights
 
@@ -113,9 +105,10 @@ Display is a snapshot from our motion capture studio showing a subject performin
 
 Watch a short demonstration of the motion capture data in action:
 
-<img src="https://github.com/ChingmuData/CMRD/tree/main/assets/logo.png"></img>
+## 🎥 Preview Video
 
-<video src="https://github.com/ChingmuData/CMRD/tree/main/assets/jibengongjia.mp4" controls autoplay muted loop>
+<video width="100%" height="auto" controls preload="none">
+  <source src="assets/videos/jibengongjia.mp4" type="video/mp4">
   Your browser does not support the video tag.
 </video>
 
@@ -130,19 +123,25 @@ Watch a short demonstration of the motion capture data in action:
 
 ---
 
-## Task & Scenario Taxonomy (How to Filter)
+## Task & Scenario Taxonomy
 
-All motion takes are indexed in `metadata/index.csv`. The key **filter columns** you'll use most:
+All takes are indexed in `metadata/index.csv`. Key filter columns:
 
-| Column | Values (examples) | Use |
+| Column | Values | Use |
 |---|---|---|
 | `scenario` | `industrial`, `household`, `retail`, `healthcare`, `logistics`, `agri`, `performance` | Filter by scene |
 | `task_category` | `locomotion`, `manipulation`, `dexterous_hand`, `tool_use`, `interaction` | Broad category |
-| `task_label` | `walk_carrying_box`, `screw_with_driver`, `pinch_grasp_bottle`, `open_fridge_door` … | Specific task |
+| `task_label` | `walk_carrying_box`, `screw_with_driver`, `pinch_grasp_bottle` … | Specific task |
 | `has_finger_data` | `true` / `false` | Needs hand DoF? |
 | `has_object_6d` | `true` / `false` | Needs object tracking? |
 | `quality_flag` | `pass` / `warning` / `fail` | Skip bad takes |
-| `retarget_available` | `g1` / `soma` / `none` | Which robot formats exist |
+| `retarget_available` | `g1` / `none` | Robot format |
+
+Full taxonomy includes locomotion, manipulation, dexterous hand, tool use, object interaction, social contact, and performance.
+
+> 👀 Try the interactive **Dataset Preview** at the top of this page (select `metadata` config) or download [`metadata/index.csv`](https://huggingface.co/datasets/ZIHLING/Chingmu-RobotData/resolve/main/metadata/index.csv) for offline filtering.
+
+---
 
 ### Full Taxonomy (abridged)
 
@@ -160,6 +159,37 @@ All motion takes are indexed in `metadata/index.csv`. The key **filter columns**
 
 ---
 
+## Dataset Structure
+chingmu-1000h/
+├── metadata/ ← Index files (CSV, taxonomy)
+├── retargeted/ ← Robot-ready trajectories (gated)
+│ └── g1_joint_trajectory/
+├── raw_bvh/ ← Original BVH (gated)
+├── annotations/ ← Semantic labels
+├── samples/ ← Public preview (no gate)
+└── LICENSE.md
+
+---
+
+## Quick Start
+
+```bash
+pip install huggingface_hub
+
+```python
+from huggingface_hub import hf_hub_download
+repo_id = "ZIHLING/Chingmu-RobotData"
+file_path = hf_hub_download(
+repo_id=repo_id,
+filename="samples/Move_the_box_001.bvh",
+repo_type="dataset",
+local_dir="./robot_samples")
+print(f"Downloaded: {file_path}")
+
+For full access, request permission via the **Request access** button, then use `snapshot_download`.
+
+---
+
 ## Data Format Specifications
 
 | Property | Value |
@@ -174,21 +204,15 @@ All motion takes are indexed in `metadata/index.csv`. The key **filter columns**
 
 ## Quality & Limitations
 
-### Quality controls applied
-- Marker swap detection & auto-correct (per-take)
-- Rigid-body jitter filter (median + threshold)
-- Foot-skating metric computed (flagged when >2cm/s static-foot drift)
-- Gap-fill ≤ 6 frames via cubic spline; longer gaps tagged `quality_flag=warning`
-
-### Known limitations
-- Performer population: currently skewed toward 18–40yo (planned expansion in v2)
-- Finger data precision depends on glove calibration per session
-- Object 6D pose accuracy: ±2mm translation, ±0.5° orientation
-
-### Ethical & Privacy
-- All performers signed informed-consent & appearance release
-- Faces are **not** included in skeleton/metric data
-- No biometric identifier is retained in the released features
+## 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), finger precision depends on calibration, object accuracy varies with marker cluster size.
+
+**Ethics:** all performers consented; faces excluded from skeleton data; no biometric identifiers retained.
 
 ---