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README.md

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-license: cc-by-4.0
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+# 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: [spunjwani3@gatech.edu](mailto:spunjwani3@gatech.edu)
+- **Larry Heck**
+  - Email: [larryheck@gatech.edu](mailto:larryheck@gatech.edu)
+
+---
+
+## 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.