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