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---
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`
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