update

README.md

@@ -85,16 +85,19 @@ ChingMu 1000H is an optical motion capture dataset designed for training and val
 ## 📸 Sample Visualization / 样本可视化
 
 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:
+
 以下是我们动捕棚的截图，展示了一名受试者执行搬箱子任务的过程，包含实时骨骼叠加和物体追踪：
 
 <img src="https://github.com/ChingmuData/CMRD/raw/refs/heads/main/assets/logo.png"></img>
 
 *Figure: Optical mocap data visualized with skeleton and tracked object.*
+
 *图：光学动捕数据可视化，显示骨骼和追踪物体。*
 
 ## 🎥 Preview Video / 预览视频
 
 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/CMRD/raw/refs/heads/main/assets/jibengongjia.mp4" controls autoplay muted loop>
@@ -102,6 +105,7 @@ Watch a short demonstration of the motion capture data in action:
 </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
@@ -139,6 +143,7 @@ Full taxonomy includes locomotion, manipulation, dexterous hand, tool use, objec
 
 
 > 👀 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.  
+
 > 👀 尝试页面顶部的交互式**数据集预览**（选择 `metadata` 配置），或下载 `metadata/index.csv` 进行离线筛选。
 
 
@@ -162,10 +167,11 @@ Full taxonomy includes locomotion, manipulation, dexterous hand, tool use, objec
 - **表演** → 舞蹈、武术风格动作、风格化手势……
 
 👀 **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。
+
+ 👀 **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。
+ > 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。
 
 ---
 
@@ -185,12 +191,12 @@ chingmu-1000h/
 ## 🖥️ Interactive Showcase / 交互式展示网站
 
 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：
+ 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/CMRD/)
 
 *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。*
+ *包含：预告片、模态分解、机器人重定向对比等。*
 
 ---
 
@@ -214,42 +220,43 @@ print(f"Downloaded: {file_path}")
 ```
 
 For full access, request permission via the **Request access** button, then use `snapshot_download`.
-如需完整访问，请通过 **Request access** 按钮申请权限，然后使用 `snapshot_download`。
+ 如需完整访问，请通过 **Request access** 按钮申请权限，然后使用 `snapshot_download`。
 
 ---
 
 ## Quality & Limitations / 质量与局限
 
-**Quality controls / 质量控制:** marker swap correction, gap-filling (≤6 frames), foot skating detection, manual review. Flags: `pass`, `warning`, `fail`.  
-**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。**
+**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 / 精度:** joint error <1mm, object pose ±2mm / ±0.5°, temporal sync <1 frame.  
-**关节误差 <1mm，物体位姿 ±2mm / ±0.5°，时间同步 <1 帧。**
+**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 / 局限:** performer age skew (20–35), finger precision depends on calibration, object accuracy varies with marker cluster size.  
-**表演者年龄偏向（20-35岁），手指精度取决于校准，物体精度随标记簇大小变化。**
+**Limitations:** performer age skew (20–35), finger precision depends on calibration, object accuracy varies with marker cluster size.  
+**局限：表演者年龄偏向（20-35岁），手指精度取决于校准，物体精度随标记簇大小变化。**
 
-**Ethics / 伦理:** all performers consented; faces excluded from skeleton data; no biometric identifiers retained.  
-**所有表演者已签署同意书；面部未包含在骨骼数据中；未保留生物特征标识。**
+**Ethics:** all performers consented; faces excluded from skeleton data; no biometric identifiers retained.  
+**伦理：所有表演者已签署同意书；面部未包含在骨骼数据中；未保留生物特征标识。**
 ---
 
 ## Get Full Dataset / 获取完整数据集
 
 Only a subset of the dataset is publicly available here. If you need **full access** to the entire 1000-hour dataset, please scan the QR code below to contact us via WeChat:
-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，请扫描下方二维码通过微信联系我们：
+ 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，请扫描下方二维码通过微信联系我们：
 
 <img src="https://github.com/ChingmuData/CMRD/raw/refs/heads/main/assets/cs.jpg" width="30%" alt="alt text">
 
 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: **dataset@chingmu.ai**
-或发送邮件至：**dataset@chingmu.ai**
+ 或发送邮件至：**dataset@chingmu.ai**
 
 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！
+ 我们期待与研究人员和行业伙伴合作！
 
 ## Contact / 联系方式
 - Issues / Requests: [Discussions](https://huggingface.co/datasets/ZIHLING/Chingmu-RobotData/discussions)