PSewmuthu/How2Sign_Holistic

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