update: complete dataset card

README.md

@@ -51,9 +51,7 @@ Unlike monocular video-based datasets, ChingMu provides **sub-millimeter accurac
 | **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 · NPZ · CSV |
-
-🔒 **Access note:** Metadata, samples, and documentation are publicly browsable. Full raw captures and retargeted datasets are released under a **gated license** — click *Request access* to agree to the license terms and receive download instructions.
+| **Output formats** | BVH · Retargeted joint trajectories · NPY · CSV · FBX |
 
 ---
 
@@ -66,7 +64,7 @@ Data is captured using **optical mocap systems** (sub-mm precision, 120 fps) and
 - **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** (4–8 synchronized cameras, co-registered with mocap timeline)
+- **Multi-view video** (2–4 synchronized cameras, co-registered with mocap timeline)
 - **Semantic labels**: task name, scenario tag, skill category, quality flag
 
 Scenarios span industrial assembly, household service, retail interaction, healthcare assistance, logistics handling, agricultural work, and staged performance.
@@ -87,7 +85,7 @@ Scenarios span industrial assembly, household service, retail interaction, healt
 | Raw motion | `.bvh` | Y-up, 120 fps, ZYX rotation, cm units, 47–67 joints |
 | Retargeted trajectories | `.csv` | Root position (m), root quaternion, joint angles (rad) |
 | Object 6D pose | `.csv` | Position (m) + quaternion per object, 120 Hz |
-| Multi-view video | `.mp4` | 4–8 synchronized cameras, co-registered timeline |
+| Multi-view video | `.mp4` | 2–4 synchronized cameras, co-registered timeline |
 
 ## ✅ Quality Assurance
 
@@ -105,11 +103,11 @@ Typical accuracy:
 
 ## 📸 Sample Visualization
 
-Below is a snapshot from our motion capture studio showing a subject performing a box-moving task, with real-time skeleton overlay and object tracking:
+Display is a snapshot from our motion capture studio showing a subject performing a box-moving task, with real-time skeleton overlay and object tracking:
 
 ![Sample](assets/logo.png)
 
-*Figure: Optical mocap data visualized with skeleton (blue) and tracked object (red bounding box).*
+*Figure: Optical mocap data visualized with skeleton and tracked object.*
 
 ## 🎥 Preview Video
 
@@ -126,7 +124,7 @@ Watch a short demonstration of the motion capture data in action:
 - 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 / Isaac Sim via retargeted trajectories)
+- Sim-to-real validation (MuJoCo via retargeted trajectories)
 - Virtual production & animation reference
 
 ---
@@ -182,7 +180,7 @@ All motion takes are indexed in `metadata/index.csv`. The key **filter columns**
 - Gap-fill ≤ 6 frames via cubic spline; longer gaps tagged `quality_flag=warning`
 
 ### Known limitations
-- Performer population: currently skewed toward 20–35yo (planned expansion in v2)
+- 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