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---
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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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}"
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---
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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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This dataset is designed to support research in:
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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
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**`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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```
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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
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If you use this dataset, please cite:
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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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