Update README.md

README.md

@@ -27,131 +27,117 @@ dataset_info:
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 # ChingMu 1000-Hour Embodied Motion Dataset
-### 青瞳1000小时具身智能动作数据集
 
 > High-precision **optical motion capture** data for humanoid robots, dexterous hands, embodied AI, and virtual production.  
-> 面向人形机器人、灵巧手、具身 AI 和虚拟制作的高精度多模态动作数据。
 
 | | |
 |---|---:|
-| **Duration / 时长** | **1000+ hours** @ 120 Hz |
-| **Scenarios / 场景** | 15+ real-world scenes |
-| **Tasks / 任务** | 500+ standardized tasks |
-| **Objects / 物体** | 200+ tracked props (6D pose) |
-| **Modalities / 模态** | Skeleton · Finger · Object 6D · Video · Labels |
-| **Formats / 格式** | BVH · Retargeted CSV · NPZ |
+| **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**.  
-🔒 **访问说明：** 元数据和样本公开，完整数据需申请**访问权限**。
+
 ---
 
-## Key Features / 核心特点
+## Key Features 
 
 - **Optical ground truth** – sub-mm accuracy, 120 fps, no estimation errors.  
-  **光学真值** – 亚毫米精度，120 fps，无累计误差。
+
 - **Dexterous hands** – 20+ DoF per hand, synchronized with object 6DOP pose.  
-  **灵巧手数据** – 每只手 20+ 自由度，与物体 6 DOP 同步。
+
 - **Robot-ready** – pre-retargeted to Unitree G1; custom retargeting available.  
-  **机器人就绪** – 预重定向至 Unitree G1，可定制其他机器人。
+
 - **Real-world diversity** – 15+ scenarios, 500+ tasks, 200+ objects.  
-  **真实场景多样性** – 15+ 场景，500+ 任务，200+ 物体。
+
 - **Multi-modal** – full-body skeleton, finger motion, object pose, multi-view video, semantic labels.  
-  **多模态** – 全身骨骼、手指运动、物体位姿、多视角视频、语义标签。
+
 - **Quality assured** – every take passes automated cleaning + manual inspection; quality flags provided.  
-  **质量保障** – 每条数据经自动化清洗和人工审核，提供质量标记。
+
 
 ---
 
-## Dataset Summary / 数据集概述
+## 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.
 
-青瞳 1000 小时数据集是为训练和验证具身 AI 与人形机器人控制器而构建的光学动捕数据集。它覆盖全身骨骼、手指关节、物体 6D 位姿、多视角视频和语义标签，跨越 15+ 真实场景（工业、家庭、零售、医疗、物流、农业、表演）。所有数据均经过清洗、质量评估和机器人重定向。
-
 ---
 
-## Data Format Specifications / 数据格式规范
+## Data Format Specifications 
 
-| Component / 组件 | Format / 格式 | Details / 详情 |
+| Component | Format | Details |
 |---|---|---|
-| Raw motion / 原始动作 | `.bvh` | Y-up, 120 fps, ZYX rotation, cm, 47–67 joints |
-| Retargeted trajectories / 重定向轨迹 | `.csv` | Root position (m), quaternion, joint angles (rad) |
-| Object 6D pose / 物体 6D 位姿 | `.csv` | Position (m) + quaternion, 120 Hz |
-| Multi-view video / 多视角视频 | `.mp4` | 4–8 cameras, co-registered |
-| Semantic labels / 语义标签 | `.jsonl` | Task, scenario, action, object |
+| 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 / 预览视频
+## 🎥 Preview Video 
 
 Watch a short demonstration of the motion capture data in action:  
-观看动捕数据的简短演示：
 
 <video src="https://github.com/ChingmuData/MotionDecode/raw/refs/heads/main/assets/video/robotics.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.*  
-*全身动作捕捉演示，包含实时骨骼叠加和物体追踪。*
 
-### Intended Uses / 预期用途
+### Intended Uses
 - Imitation learning / motion policy training for humanoids
-- 人形机器人的模仿学习 / 运动策略训练
+
 - Dexterous manipulation datasets (hand-object interaction)
-- 灵巧操作数据集（手-物交互）
+
 - Motion generation & retrieval (text/motion cross-modal)
-- 运动生成与检��（文本/运动跨模态）
+
 - Sim-to-real validation (MuJoCo via retargeted trajectories)
-- 仿真到现实的验证（通过重定向轨迹在 MuJoCo 中验证）
-- Virtual production & animation reference
-- 虚拟制作与动画参考
 
+- Virtual production & animation reference
 
 ---
 
-### Full Taxonomy (abridged) / 完整分类（简版）
+### Full Taxonomy (abridged)
 
 - **Locomotion** → walk, jog, crouch-walk...
-- **移动** → 行走、慢跑、蹲走……
+
 - **Manipulation (whole-body)** → shelf-pick-place...
-- **移动** → 行走、慢跑、蹲走……
+
 - **Dexterous Hand** → pinch, precision-grasp...
-- **灵巧手** → 捏取、精密抓握、旋盖、插拔、按键、工具握持……
+
 - **Tool Use** → screwdriver, wrench...
-- **工具使用** → 螺丝刀、扳手、剪刀、镊子、喷瓶……
+
 - **Object Interaction** → door-open/close...
-- **物体交互** → 开门/关门、抽屉、冰箱、橱柜、盖子开合……
+
 - **Social / Contact** → handoff-object...、
-- **社交/接触** → 传递物体、引导运动、双人交接……
+
 - **Performance** → dance, martial-arts...
-- **表演** → 舞蹈、武术风格动作、跑酷动作……
+
 
 👀 **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.  
-👀 **现场体验：** 使用页面顶部的**数据集预览**面板筛选和浏览实际索引表。选择 `metadata` 配置查看可用的数据片段。
 
 > 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.  
-> ℹ️ 完整索引（所有行）建议在本地查看。下载 `metadata/index.csv` 后用 Excel 或 pandas 打开进行完整筛选。
 
 ---
 
-## 🖥️ Interactive Showcase / 交互式展示网站
+## 🖥️ Interactive Showcase
 
 Visit our dedicated showcase website for interactive demos, comparison videos, and detailed visualizations:  
-访问我们的专属展示网站，查看交互式演示、对比视频和详细的可视化内容：
 
 [![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.*  
-*包含：预告片、模态分解、机器人重定向对比等。*
 
 ---
 
 ## 🆕 Open-Source Release: Unitree G1 Retargeted Data   
-**本次开源：宇树 G1 重定向数据**
 
 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.**   
-本次开源 **100 小时** 已重定向至 **宇树 G1** 人形机器人的动作轨迹，存放于 `samples/` 目录，格式为 **CSV**，**使用时请标明数据来源：from Chingmu**。
 
-## Quick Start / 快速开始
+## Quick Start
 
 ```bash
 pip install huggingface_hub
@@ -172,41 +158,33 @@ print(f"Downloaded: {file_path}")
 
 ---
 
-## Quality & Limitations / 质量与局限
+## Quality & Limitations
 
 **Quality controls:** marker swap correction, gap-filling (≤6 frames), foot skating detection, manual review. Flags: `pass`, `warning`, `fail`.  
-**质量控制：标记交换修正、缺帧填充（≤6帧）、足部滑动检测、人工审核。标记：pass、warning、fail。**
 
 **Accuracy:** joint error <1mm, object pose ±2mm / ±0.5°, temporal sync <1 frame.  
-**精度：关节误差 <1mm，物体位姿 ±2mm / ±0.5°，时间同步 <1 帧。**
 
 **Limitations:** performer age skew (20–35), object accuracy varies with marker cluster size.  
-**局限：表演者年龄偏向（20-35岁），物体精度随标记簇大小变化。**
 
 ---
 
-## Get Full Dataset / 获取完整数据集
+## 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:  
-此处仅公开了数据集的子集。如果您需要**完整访问**整个 1000 小时数据集，请通过以下渠道联系我们：
 
 - **For Chinese users:** Scan the QR code below to contact us via WeChat, and please indicate your affiliated organization.   
-**国内用户：** 扫描下方二维码通过微信联系我们，备注所属单位
 
 <img src="https://github.com/ChingmuData/MotionDecode/raw/refs/heads/main/assets/invite_code.jpg" width="30%" alt="alt text">
 
-**For international users:** Join our Discord community   
-**海外用户：** 加入我们的 Discord 社区   
+**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.  
-或者，您可以点击页面右侧的 **“Request access”** 按钮自动获得完整数据集的下载权限。
+
 
 *Note: Approval is automatic, but we kindly ask you to provide your contact information for licensing purposes.*  
-*注意：审批是自动的，但我们恳请您提供联系信息以便授权。*
 
 Or email us at: **MotionDecode@chingmu.com**  
- 或发送邮件至：**MotionDecode@chingmu.com**
 
 We look forward to collaborating with researchers and industry partners!  
-我们期待与研究人员和行业伙伴合作！