Add README file

README.md

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 ---
-license: mit
+pretty_name: How2Sign Holistic
+language: en
+license:
+  - mit
+tags:
+  - sign-language
+  - asl
+  - mediapipe
+  - holistic
+  - pose-landmarks
+  - hand-landmarks
+  - face-landmarks
+  - gesture-recognition
+  - sequence-modeling
+  - time-series
+  - computer-vision
+  - deep-learning
+source_datasets:
+  - Duarte_CVPR2021/How2Sign
+task_categories:
+  - feature-extraction
+  - translation
+task_ids:
+  - pose-estimation
+  - conversational
+citation:
+  - "@inproceedings{Duarte_CVPR2021,
+    title={{How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language}},
+    author={Duarte, Amanda and Palaskar, Shruti and Ventura, Lucas and Ghadiyaram, Deepti and DeHaan, Kenneth and
+    Metze, Florian and Torres, Jordi and Giro-i-Nieto, Xavier},
+    booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year={2021}
+    }"
+  - "@misc{MediaPipe,
+    title={MediaPipe},
+    author={Google Inc.},
+    year={2020},
+    url={https://mediapipe.dev/}
+    }"
 ---
+
+# How2Sign Holistic
+
+### Mediapipe Holistic Landmark Features Extracted from the How2Sign ASL Dataset
+
+## Overview
+
+**How2Sign Holistic** is a curated dataset providing frame-level Mediapipe Holistic landmarks extracted from the full How2Sign American Sign Language corpus. Each sentence-level video clip has pose, face, and hand landmark sequences stored as `.npy` files.
+
+This dataset is designed to support research in:
+
+- ASL recognition and translation
+- Pose-based sign generation
+- Sequence and time-series modeling
+- Gesture understanding
+- Multiview motion analysis
+
+## Base Directory
+
+**`how2sign_holistic_features/`** is the root folder containing all splits and metadata.
+
+## Sources
+
+The original data comes from the **How2Sign dataset** (Duarte et al., CVPR 2021), a large-scale multimodal American Sign Language dataset sourced from YouTube videos.
+
+## Collection Methodology
+
+- Sentence-level clips were extracted from the original videos according to How2Sign protocol.
+- Frame-level landmarks were extracted using **Google Mediapipe Holistic** (pose, face, hands).
+- Each clip saved as `.npy` with frontal and side views.
+- Metadata CSVs map clips to sentences, start/end timestamps, and video identifiers.
+- CSVs can be opened in pandas: `pd.read_csv('filename.csv', sep='\t')`
+
+## Dataset Structure
+
+```
+how2sign_holistic_features/
+│
+├── metadata/                     # Original How2Sign metadata (CSV files)
+│   ├── how2sign_realigned_train.csv
+│   ├── how2sign_realigned_val.csv
+│   ├── how2sign_realigned_test.csv
+│   ├── how2sign_train.csv
+│   ├── how2sign_val.csv
+│   └── how2sign_test.csv
+│
+├── train/                        # Training split .npy files
+│   ├── frontal/
+│   │   ├── <VIDEO_ID>_front_holistic.npy
+│   │   └── ...
+│   └── side/
+│       ├── <VIDEO_ID>_side_holistic.npy
+│       └── ...
+│
+├── val/                          # Validation split
+│   ├── frontal/
+│   └── side/
+│
+└── test/                         # Test split
+    ├── frontal/
+    └── side/
+```
+
+### Notes
+
+- `.npy` files contain **frame-level Mediapipe Holistic landmarks**.
+- Frontal and side views are synchronized.
+- Filenames follow: `VIDEO_NAME_START-END-rgb_front/side_holistic.npy`
+- Metadata CSVs map clips to video ID, sentence, start/end timestamps, and How2Sign identifiers.
+
+## Citation
+
+If you use this dataset, please cite:
+
+Duarte, A., Palaskar, S., Ventura, L., Ghadiyaram, D., DeHaan, K., Metze, F., Torres, J., & Giro-i-Nieto, X.
+**“How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language.”**
+_Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021._
+
+## Recommended Tags
+
+`ASL`, `Sign Language`, `Mediapipe`, `Holistic`, `Pose Landmarks`, `Hand Landmarks`, `Face Landmarks`, `Keypoints`, `Motion Capture`, `Time Series`, `Gesture Recognition`, `Computer Vision`, `Deep Learning`, `Sequence Modeling`