update

README.md

@@ -29,111 +29,147 @@ dataset_info:
 # 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.
+> 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** | **1000+ hours** @ 120 Hz |
-| **Scenarios** | 15+ real-world scenes |
-| **Tasks** | 500+ standardized tasks |
-| **Objects** | 1000+ tracked props (6D pose) |
-| **Modalities** | Skeleton · Finger · Object 6D · Video · Labels |
-| **Formats** | BVH · Retargeted CSV · NPZ |
+| **Duration / 时长** | **1000+ hours** @ 120 Hz |
+| **Scenarios / 场景** | 15+ real-world scenes |
+| **Tasks / 任务** | 500+ standardized tasks |
+| **Objects / 物体** | 1000+ tracked props (6D pose) |
+| **Modalities / 模态** | Skeleton · Finger · Object 6D · Video · Labels |
+| **Formats / 格式** | BVH · Retargeted CSV · NPZ |
 
-🔒 **Access note:** Metadata and samples are public. Full data requires **Request access**.
+🔒 **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
-
-- **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.
+## Key Features / 核心特点
+
+- **Optical ground truth** – sub-mm accuracy, 120 fps, no estimation errors.  
+  **光学真值** – 亚毫米精度，120 fps，无估计误差。
+- **Dexterous hands** – 20+ DoF per hand, synchronized with object 6D pose.  
+  **灵巧手数据** – 每只手 20+ 自由度，与物体 6D 位姿同步。
+- **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, 1000+ objects.  
+  **真实场景多样性** – 15+ 场景，500+ 任务，1000+ 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
+## Dataset Summary / 数据集概述
 
 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
+## Data Format Specifications / 数据格式规范
 
-| Component | Format | Details |
+| Component / 组件 | Format / 格式 | Details / 详情 |
 |---|---|---|
-| 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` | 2–4 synchronized cameras, co-registered timeline |
+| 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 |
+
 
-## 📸 Sample Visualization
+## 📸 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
+## 🎥 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>
   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
+### Intended Uses / 预期用途
 - 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)
+- 运动生成与检索（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
+
 
 ---
 
-## Task & Scenario Taxonomy
+## Task & Scenario Taxonomy / 任务与场景分类
 
-All takes are indexed in `metadata/index.csv`. Key filter columns:
+All takes are indexed in `metadata/index.csv`. Key filter columns:  
+所有数据片段均在 `metadata/index.csv` 中索引。关键筛选列：
 
-| Column | Values | Use |
+| Column / 列名 | Values / 取值 | Use / 用途 |
 |---|---|---|
-| `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` … | 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` / `none` | Robot format |
+| `scenario` | `industrial`, `household`, `retail`, `healthcare`, `logistics`, `agri`, `performance` | Filter by scene / 按场景筛选 |
+| `task_category` | `locomotion`, `manipulation`, `dexterous_hand`, `tool_use`, `interaction` | Broad category / 大类 |
+| `task_label` | `walk_carrying_box`, `screw_with_driver`, `pinch_grasp_bottle` … | Specific task / 具体任务 |
+| `has_finger_data` | `true` / `false` | Needs hand DoF? / 是否需要手指数据 |
+| `has_object_6d` | `true` / `false` | Needs object tracking? / 是否需要物体追踪 |
+| `quality_flag` | `pass` / `warning` / `fail` | Skip bad takes / 跳过低质量数据 |
+| `retarget_available` | `g1` / `none` | Robot format / 机器人格式 |
+
+Full taxonomy includes locomotion, manipulation, dexterous hand, tool use, object interaction, social contact, and performance.  
+完整分类包括：移动、操作、灵巧手、工具使用、物体交互、社交接触和表演。
+
 
-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.  
+> 👀 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。
 
-> 👀 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)
+### Full Taxonomy (abridged) / 完整分类（简版）
 
 - **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...
-- **Social / Contact** → handoff-object...
+- **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��、引导运动、双人交接……
 - **Performance** → dance, martial-arts...
+- **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。
 
 > 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。
 
 ---
 
-## Dataset Structure
+## Dataset Structure / 数据集结构
 
 ```
 chingmu-1000h/
@@ -146,17 +182,19 @@ chingmu-1000h/
 └── LICENSE.md
 ```
 
-## 🖥️ Interactive Showcase
+## 🖥️ 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：
 
 [![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。*
 
 ---
 
-## Quick Start
+## Quick Start / 快速开始
 
 ```bash
 pip install huggingface_hub
@@ -176,46 +214,45 @@ 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`。
 
 ---
 
-## Data Format Specifications
-
-| Property | Value |
-|---|---|
-| Up axis | Y-up |
-| Frame rate | 120 fps |
-| Rotation order | ZYX (BVH standard per-joint) |
-| Units | cm for position, degrees for channels |
-| Joint count | 47–67 depending on finger inclusion |
-
----
-
-## Quality & Limitations
-
-**Quality controls:** marker swap correction, gap-filling (≤6 frames), foot skating detection, manual review. Flags: `pass`, `warning`, `fail`.
+## Quality & Limitations / 质量与局限
 
-**Accuracy:** joint error <1mm, object pose ±2mm / ±0.5°, temporal sync <1 frame.
+**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。**
 
-**Limitations:** performer age skew (20–35), finger precision depends on calibration, object accuracy varies with marker cluster size.
+**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。**
 
-**Ethics:** all performers consented; faces excluded from skeleton data; no biometric identifiers retained.
+**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。**
 
+**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。**
 ---
 
-## Get Full Dataset
+## 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：
 
 <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**
 
 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！
 
-## Contact
+## Contact / 联系方式
 - Issues / Requests: [Discussions](https://huggingface.co/datasets/ZIHLING/Chingmu-RobotData/discussions)
-- Email: dataset@chingmu.ai
+- 问题/请求：[讨论区](https://huggingface.co/datasets/ZIHLING/Chingmu-RobotData/discussions)
+- Email: dataset@chingmu.ai
+- 邮箱：dataset@chingmu.ai