RT Back2Life

RT Back2Life is an AI-driven video chatbot that aims to replicate a person’s personality and appearance, offering a personalized and interactive experience. Powered by advanced deep learning technologies, the system uses TensorFlow generative models for image, audio, and text synthesis, integrated seamlessly into a Unity-based environment.

Features

• Realistic Avatar: Generates a 3D avatar that mirrors a person’s appearance.
• Personality Replication: Uses NLP and deep learning models to simulate personality, behavior, and conversational style.
• Real-Time Interaction: Enables live video chat with a virtual representation of the person.
• AI-Generated Voice: Real-time synthesis of voice matching the person’s speech patterns.
• Multi-Modal Inputs: Combines visual, textual, and audio models for an immersive experience.

Table of Contents

• Installation
• Usage
• Prerequisites
• Technologies
• Architecture
• Contributing
• License
• Acknowledgements

Installation

Follow these steps to set up RT Back2Life on your local machine:

1. Clone the Repository: bash git clone https://github.com/Delgado1969/RT-Back2Life.git

2. Install Unity:

   • Download and install Unity Hub from the official site. • Create a new Unity project with the correct version that matches the project requirements (refer to Unity version in the README or the ProjectSettings folder).

3. Install TensorFlow:

To use the AI models, install TensorFlow by running the following: bash pip install tensorflow 4. Install Dependencies:

In the project folder, install the required Python packages: bash pip install -r requirements.txt 5. Setup the Unity Environment:

• Open Unity and load the RT_Back2Life project.

• Ensure all Unity packages and assets are properly imported.

1. Run the Project:

In Unity, click Play to start the simulation of the AI-driven video chatbot.

Usage

1. Running the AI Video Chatbot:

• Open the MainScene in Unity.

• Press Play to start the interaction. The avatar will appear on screen, ready for conversation.

• Type or speak to the chatbot, and the system will respond based on the person’s characteristics.

1. Customizing Your Model:

• To customize the appearance or personality, modify the input models or use custom training datasets.

• You can replace avatar textures, voice synthesis, or personality algorithms by updating the corresponding assets and models in the Unity environment and TensorFlow integration.

Prerequisites

Before you begin, ensure you have the following installed:

• Unity (latest stable version or specific version mentioned in the project).

• Python 3.x with TensorFlow.

• CUDA (for GPU acceleration, optional but recommended for faster processing).

Technologies

• Unity: For real-time 3D rendering and interaction.

• TensorFlow: For AI and machine learning models handling image, audio, and text generation.

• Python: For AI model training, data processing, and integration with Unity.

• C#: Used within Unity to handle interactions and logic.

• OpenCV (optional): For advanced image processing.

Architecture

1. Unity Frontend:

• Manages the interactive user interface (UI).

• Handles real-time rendering of the avatar and interface components.

• Responsible for sending and receiving data from AI models.

1. AI Backend:

• Image Generation: Uses TensorFlow models for generating and animating the avatar based on the user’s inputs.

• Audio Synthesis: The voice is generated based on an individual’s voice model and response data.

• Text Generation: Textual responses are generated using NLP models like GPT-3 or other pre-trained language models.

1. Communication:

• WebSockets: For real-time communication between Unity and Python server running TensorFlow models.

• REST APIs: Can be implemented for more structured data requests if needed.

Contributing

Contributions are welcome! If you’d like to contribute to this project, please fork the repository and submit a pull request.

Steps to Contribute:

1. Fork the repository

2. Clone your fork to your local machine

3. Create a new branch for your changes

4. Implement your changes and test them

5. Submit a pull request with a clear description of the changes

We recommend following the code of conduct and reviewing the contribution guidelines.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgements

• TensorFlow: The deep learning library used for the image, text, and audio generation.

• Unity: The engine that powers real-time rendering and interaction.

• OpenCV: Used for some image processing features.

• Special thanks to the community and contributors who have made this project possible.