Datasets:

avalab
/

Allo-AVA

@@ -1,227 +1,9 @@
-# Allo-AVA: A Large-Scale Multimodal Dataset for Allocentric Avatar Animation
-## Overview
-Allo-AVA (Allocentric Audio-Visual Avatar) is a large-scale multimodal dataset designed for research and development in avatar animation. It focuses on generating natural and contextually appropriate gestures from text and audio inputs in an allocentric (third-person) perspective. The dataset addresses the scarcity of high-quality, synchronized multimodal data capturing the intricate synchronization between speech, facial expressions, and body movements, essential for creating lifelike avatar animations in virtual environments.
 ---
-## Dataset Statistics
-- **Total Videos:** 7,500
-- **Total Duration:** 1,250 hours
-- **Average Video Length:** 10 minutes
-- **Unique Speakers:** ~3,500
-- **Total Word Count:** 15 million
-- **Average Words per Minute:** 208
-- **Total Keypoints:** ~135 billion
-- **Dataset Size:** 2.46 TB
----
-## Content Distribution
-- **TED Talks:** 40%
-- **Interviews:** 30%
-- **Panel Discussions:** 20%
-- **Formal Presentations:** 10%
----
-## Directory Structure
-```
-Allo-AVA/
-├── video/
-├── audio/
-├── transcript/
-├── keypoints/
-└── keypoints_video/
-```
-- **`video/`**: Original MP4 video files.
-- **`audio/`**: Extracted WAV audio files.
-- **`transcript/`**: JSON files with word-level transcriptions and timestamps.
-- **`keypoints/`**: JSON files with frame-level keypoint data.
-- **`keypoints_video/`**: MP4 files visualizing the extracted keypoints overlaid on the original video.
----
-## File Formats
-- **Video:** MP4 (1080p, 30 fps)
-- **Audio:** WAV (16-bit PCM, 48 kHz)
-- **Transcripts:** JSON format with word-level timestamps.
-- **Keypoints:** JSON format containing normalized keypoint coordinates.
-- **Keypoints Video:** MP4 format with keypoints overlaid on the original video frames.
----
-## Keypoint Data
-The dataset includes detailed keypoint information extracted using a fusion of **OpenPose** and **MediaPipe** models, capturing comprehensive body pose and movement data.
-### Keypoint Extraction Models
-- **OpenPose**:
-  - Extracts 18 keypoints corresponding to major body joints.
-  - Robust for full-body pose estimation.
-- **MediaPipe**:
-  - Provides 32 additional keypoints with enhanced detail on hands and facial landmarks.
-  - Precise capture of subtle gestures and expressions.
-### Keypoint Structure
-Each keypoint is represented by:
-- **`x`**: Horizontal position, normalized to [0, 1] from left to right of the frame.
-- **`y`**: Vertical position, normalized to [0, 1] from top to bottom of the frame.
-- **`z`**: Depth, normalized to [-1, 1], with 0 at the camera plane.
-- **`visibility`**: Confidence score in [0.0, 1.0], indicating the keypoint's presence and accuracy.
-**Example Keypoint Entry:**
-```json
-{
-    "timestamp": 0.167,
-    "keypoints": [
-        {
-            "x": 0.32285,
-            "y": 0.25760,
-            "z": -0.27907,
-            "visibility": 0.99733
-        },
-        ...
-    ],
-    "transcript": "Today you're going to..."
-}
-```
----
-## Usage
-### Downloading the Dataset
-To obtain access to the Allo-AVA dataset, please [contact us](#contact) for download instructions.
-### Extracting the Dataset
-Once downloaded, extract the dataset to your desired directory:
-```bash
-unzip allo-ava-dataset.zip -d /path/to/destination
-```
-### Accessing the Data
-You can use various programming languages or tools to process the dataset. Below is an example using Python.
-#### Example Usage in Python
-```python
-import json
-import cv2
-import librosa
-# Paths to data
-video_id = "example_video_id"
-video_path = f"Allo-AVA/video/{video_id}.mp4"
-audio_path = f"Allo-AVA/audio/{video_id}.wav"
-transcript_path = f"Allo-AVA/transcript/{video_id}.json"
-keypoints_path = f"Allo-AVA/keypoints/{video_id}.json"
-# Load video
-cap = cv2.VideoCapture(video_path)
-# Load audio
-audio, sr = librosa.load(audio_path, sr=48000)
-# Load transcript
-with open(transcript_path, 'r') as f:
-    transcript = json.load(f)
-# Load keypoints
-with open(keypoints_path, 'r') as f:
-    keypoints = json.load(f)
-# Your processing code here
-# For example, iterate over keypoints and synchronize with video frames
-```
----
-## Ethical Considerations
-- **Data Source**: All videos were collected from publicly available sources such as YouTube, adhering to their terms of service.
-- **Privacy**:
-  - **Face Blurring**: Faces in keypoint visualization videos have been blurred to protect individual identities.
-  - **Voice Anonymization**: Voice pitch modification has been applied to audio files to anonymize speakers.
-  - **Transcript Sanitization**: Personal identifiers (e.g., names, locations) in transcripts have been replaced with placeholders.
-- **Usage Guidelines**:
-  - The dataset is intended for **research and educational purposes** only.
-  - Users must comply with all applicable laws and regulations regarding data privacy and intellectual property.
-  - Any use of the dataset must respect the rights and privacy of individuals represented in the data.
----
-## License
-The Allo-AVA dataset is released under the **Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)**.
-Please refer to the [LICENSE](LICENSE) file for more details.
----
-## Future Work
-Planned enhancements for the Allo-AVA dataset include:
-- **Expanding Linguistic and Cultural Diversity**: Incorporating more languages and cultural contexts to enable cross-cultural studies.
-- **Enhanced Annotations**: Adding fine-grained labels for gestures, emotions, and semantic meanings.
-- **Multiview Recordings**: Including multiview videos to support 3D reconstruction and the study of interactive behaviors.
-- **Improved Synchronization**: Refining multimodal synchronization to capture subtle expressions and micro-movements.
-- **Domain-Specific Subsets**: Creating subsets tailored to specific research domains or applications.
----
-## Citing Allo-AVA
-If you use the Allo-AVA dataset in your research, please cite our paper:
-```bibtex
-@inproceedings{punjwani2024alloava,
-  title={Allo-AVA: A Large-Scale Multimodal Dataset for Allocentric Avatar Animation},
-  author={Punjwani, Saif and Heck, Larry},
-  booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
-  year={2024}
-}
-```
----
-## Contact
-For any questions or issues regarding the Allo-AVA dataset, please contact:
-- **Saif Punjwani**
-  - Email: [[email protected]](mailto:[email protected])
-- **Larry Heck**
-  - Email: [[email protected]](mailto:[email protected])
----
-## Acknowledgments
-We thank all the content creators whose public videos contributed to this dataset. This work was supported by [list any funding sources or supporting organizations].
----
-## Disclaimer
-The authors are not responsible for any misuse of the dataset. Users are expected to comply with all relevant ethical guidelines and legal regulations when using the dataset.
----
-Thank you for your interest in the Allo-AVA dataset! We hope it serves as a valuable resource for advancing research in avatar animation and human-computer interaction.

 ---
+license: cc
+language:
+- en
+tags:
+- code
+size_categories:
+- n>1T
+---