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@@ -1,114 +1,137 @@
-# **EgoHaFL: Egocentric 3D Hand Forecasting Dataset with Language Instruction**
-
-**EgoHaFL** is a dataset designed for **egocentric (first-person) 3D hand forecasting** with accompanying **natural language instructions**.
-It contains short video clips, text descriptions, camera intrinsics, and detailed MANO-based 3D hand annotations.
-The dataset is suitable for tasks such as **3D hand forecasting**, **hand pose estimation**, **hand–object interaction understanding**, and **video–language modeling**.
-
----
-
-## 📦 **Dataset Contents**
-
-### **1. Metadata CSV Files**
-
-* `EgoHaFL_train.csv`
-* `EgoHaFL_test.csv`
-
-Each row corresponds to one sample and contains:
-
-| Field            | Description                                |
-| ---------------- | ------------------------------------------ |
-| `uid`            | Unique sample identifier                   |
-| `video_id`       | Source video identifier                    |
-| `start_second`   | Start time of the clip (seconds)           |
-| `end_second`     | End time of the clip (seconds)             |
-| `caption`        | Natural language instruction / description |
-| `fx`, `fy`       | Camera focal lengths                       |
-| `cx`, `cy`       | Principal point                            |
-| `vid_w`, `vid_h` | Original video resolution                  |
-
----
-
-### **2. 3D Hand Annotations (EgoHaFL_lmdb)**
-
-The folder `EgoHaFL_lmdb` stores all 3D annotations in **LMDB format**.
-
-* **Key**: `uid`
-* **Value**: a **list of length 16**, representing uniformly sampled frames across a **3-second video segment**.
-
-Each of the 16 elements is a dictionary containing:
-
-* `mano_params`
-
-  * `global_orient (n, 1, 3 ,3)` 
-  * `hand_pose (n, 15, 3, 3)`
-  * `betas (n, 10)`
-* `is_right (n,)`
-* `keypoints_3d (n, 21, 3)`
-* `keypoints_2d (n, 21, 2)`
-* `vertices (n, 778, 3)`
-* `box_center (n, 2)`
-* `box_size (n,)`
-* `camera_t (n, 3)`
-* `focal_length (n, 2)`
-
----
-
-## 🌳 **Annotation Structure (Tree View)**
-
-Below is the hierarchical structure for a single annotation entry (`uid → 16-frame list → per-frame dict`):
-
-```
-<uid>
-└── list (length = 16)
-    ├── [0]
-    │   ├── mano_params
-    │   │   ├── global_orient
-    │   │   ├── hand_pose
-    │   │   └── betas
-    │   ├── is_right
-    │   ├── keypoints_3d
-    │   ├── keypoints_2d
-    │   ├── vertices
-    │   ├── box_center
-    │   ├── box_size
-    │   ├── camera_t
-    │   └── focal_length
-    ├── [1]
-    │   └── ...
-    ├── [2]
-    │   └── ...
-    └── ...
-```
-
----
-
-## 🧩 **Example of Use**
-
-For details on how to load and use the EgoHaFL dataset,
-please refer to the **Dataloader** implementation in our GitHub repository:
-
-🔗 **[https://github.com/ut-vision/SFHand](https://github.com/ut-vision/SFHand)**
-
-
-
----
-
-## 🎥 Source of Video Data
-
-The video clips used in **EegoHaFL** originate from the **Ego4D V1** dataset.
-For our experiments, we use the **original-length videos compressed to 224p resolution** to ensure efficient storage and training.
-
-Official Ego4D website:
-🔗 **[https://ego4d-data.org/](https://ego4d-data.org/)**
-
----
-
-## 🧠 **Supported Research Tasks**
-
-* Egocentric 3D hand forecasting
-* Hand motion prediction and trajectory modeling
-* 3D hand pose estimation
-* Hand–object interaction understanding
-* Video–language multimodal modeling
-* Temporal reasoning with 3D human hands
-
+i---
+dataset_name: EgoHaFL
+pretty_name: EgoHaFL: Egocentric 3D Hand Forecasting Dataset with Language Instruction
+tags:
+  - egocentric
+  - 3d-hand
+  - forecasting
+  - hand-pose
+  - video
+  - language
+license: mit
+language:
+  - en
+size_categories:
+  - 200K<n<300K
+task_categories:
+  - vision
+  - natural-language-processing
+  - 3d
+task_ids:
+  - 3d-hand-pose-estimation
+  - video-forecasting
+  - video-language
+---
+
+# **EgoHaFL: Egocentric 3D Hand Forecasting Dataset with Language Instruction**
+
+**EgoHaFL** is a dataset designed for **egocentric (first-person) 3D hand forecasting** with accompanying **natural language instructions**.
+It contains short video clips, text descriptions, camera intrinsics, and detailed MANO-based 3D hand annotations.
+The dataset supports research in **3D hand forecasting**, **hand pose estimation**, **hand–object interaction understanding**, and **video–language modeling**.
+
+---
+
+## 📦 **Dataset Contents**
+
+### **1. Metadata CSV Files**
+
+* `EgoHaFL_train.csv`
+* `EgoHaFL_test.csv`
+
+Each row corresponds to one sample and contains:
+
+| Field            | Description                                |
+| ---------------- | ------------------------------------------ |
+| `uid`            | Unique sample identifier                   |
+| `video_id`       | Source video identifier                    |
+| `start_second`   | Start time of the clip (seconds)           |
+| `end_second`     | End time of the clip (seconds)             |
+| `caption`        | Natural language instruction / description |
+| `fx`, `fy`       | Camera focal lengths                       |
+| `cx`, `cy`       | Principal point                            |
+| `vid_w`, `vid_h` | Original video resolution                  |
+
+---
+
+### **2. 3D Hand Annotations (EgoHaFL_lmdb)**
+
+The folder `EgoHaFL_lmdb` stores all 3D annotations in **LMDB format**.
+
+* **Key**: `uid`
+* **Value**: a **list of length 16**, representing uniformly sampled frames across a **3-second video segment**.
+
+Each of the 16 elements is a dictionary containing:
+
+* `mano_params`
+
+  * `global_orient (n, 1, 3 ,3)`
+  * `hand_pose (n, 15, 3, 3)`
+  * `betas (n, 10)`
+* `is_right (n,)`
+* `keypoints_3d (n, 21, 3)`
+* `keypoints_2d (n, 21, 2)`
+* `vertices (n, 778, 3)`
+* `box_center (n, 2)`
+* `box_size (n,)`
+* `camera_t (n, 3)`
+* `focal_length (n, 2)`
+
+---
+
+## 🌳 **Annotation Structure (Tree View)**
+
+Below is the hierarchical structure for a single annotation entry (`uid → 16-frame list → per-frame dict`):
+
+```
+<uid>
+└── list (length = 16)
+    ├── [0]
+    │   ├── mano_params
+    │   │   ├── global_orient
+    │   │   ├── hand_pose
+    │   │   └── betas
+    │   ├── is_right
+    │   ├── keypoints_3d
+    │   ├── keypoints_2d
+    │   ├── vertices
+    │   ├── box_center
+    │   ├── box_size
+    │   ├── camera_t
+    │   └── focal_length
+    ├── [1]
+    │   └── ...
+    ├── [2]
+    │   └── ...
+    └── ...
+```
+
+---
+
+## 🎥 **Source of Video Data**
+
+The video clips used in **EgoHaFL** originate from the **Ego4D V1** dataset.
+For our experiments, we use the **original-length videos compressed to 224p resolution** to ensure efficient storage and training.
+
+Official Ego4D website:
+🔗 **[https://ego4d-data.org/](https://ego4d-data.org/)**
+
+---
+
+## 🧩 **Example of Use**
+
+For details on how to load and use the EgoHaFL dataset,
+please refer to the **dataloader implementation** in our GitHub repository:
+
+🔗 **[https://github.com/ut-vision/SFHand](https://github.com/ut-vision/SFHand)**
+
+---
+
+## 🧠 **Supported Research Tasks**
+
+* Egocentric 3D hand forecasting
+* Hand motion prediction and trajectory modeling
+* 3D hand pose estimation
+* Hand–object interaction understanding
+* Video–language multimodal modeling
+* Temporal reasoning with 3D human hands
+