Match Cut AI is an innovative project that aims to revolutionize the creation of movie trailers using machine learning techniques.
Our goal is to automate and enhance the process of match cutting, a crucial technique in creating compelling trailers.
Match cutting, is a time-consuming process that can take days, especially when working with 2000+ frame shots in a single movie.
Our project addresses the challenge of efficient match cutting in video editing, specifically for movie trailers.
We envision a future where multiple trailers of the same movie, each tailored to different audiences, can be created in a fraction of the time it takes today.
Our approach leverages cutting-edge machine learning techniques:
- Object Detection: Using tools like TensorNet and torchvision to train models on movie frames.
- Contrastive Learning: Teaching the model to differentiate between similar and dissimilar inputs, capturing subtle visual similarities that make for compelling transitions.
- Automated identification of potential match cuts in thousands of frames
- Ability to create multiple, audience-tailored trailers efficiently
For our initial training, we plan to use the black and white movie "To Kill a Mockingbird" to train a base model. As we progress, we'll enhance and tune the model, potentially expanding to more diverse datasets.
- Develop a machine learning model capable of identifying potential match cuts
- Significantly reduce the time required for match cutting in trailer creation
- Enable the creation of multiple, diverse trailers for the same movie
- Expand the training dataset to include a wider variety of movies
- Incorporate user feedback to continually improve the model's performance
- Explore additional applications of the technology beyond movie trailers