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Automated Storyboard Synthesis for Digital Advertising

This repository contains the code and implementation for an innovative machine learning framework developed to automate the process of transforming textual descriptions of advertisements into visually compelling storyboards. The solution leverages advanced techniques in machine learning, natural language processing, and computer vision to bridge the gap between textual concepts and visual storytelling in digital advertising.

• Automatic generation of visual and textual assets based on provided advertisement concepts.
• Intelligent composition of ad frames to convey the narrative flow and user interaction within advertisements.
• Synthesis of cohesive storyboards encapsulating the essence of the proposed ad campaign.
• Integration of deep learning models for image segmentation, text generation, and image composition.
• Seamless pipeline for generating, composing, and visualizing ad content, enhancing creativity and efficiency in advertising production.

• data/: Directory containing datasets and sample concepts with asset descriptions.
• models/: Implementation of machine learning models for image segmentation, text generation, and image composition.
• utils/: Utility functions for data preprocessing, model evaluation, and visualization.
• notebooks/: Jupyter notebooks for exploratory data analysis, model training, and experimentation.
• src/: Scripts for running different components of the pipeline and generating storyboards.
• docs/: Documentation and project resources, including this README file.

1. Clone the repository:

   git clone https://github.com/your-username/automated-storyboard-synthesis.git
   

2. Navigate to the project directory:

   cd automated-storyboard-synthesis
   

3. Create a virtual environment:

   python -m venv venv
   

4. Activate the virtual environment:

   • Windows:

     venv\Scripts\activate
     

   • Unix or MacOS:

     source venv/bin/activate
     

5. Install dependencies:

   pip install -r requirements.txt
   

Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request.

This project is licensed under the MIT License. See the LICENSE file for details.

• Biniyam Ajaw

I would like to thank the Adludio and Ten Academy team for their support and guidance throughout this project. Special thanks to the open-source community for providing these amazing diffusion models and tools.