InstaCut

This library contains a collection of scripts for automatic video editing. There are 3 main types of script in this library:

1. Atomizer The Atomizer module focuses on the analysis of videos. It performs the following tasks:

   a. Video Analysis:

   • Downloads the video for processing.
   • Splits the video into frames and samples them based on a specified sampling policy.
   • Generates embeddings for each sampled frame, capturing key visual characteristics such as composition, subject, camera movements, and other relevant aspects.

   b. Audio Analysis:

   • Extracts the audio from the video.
   • Applies Automatic Speech Recognition (ASR) to transcribe the audio into text.
   • Utilizes Language Model (LM) techniques to create sections and separate semantic blocks within the audio content.

   c. User Interface:

   • Develops a user-friendly interface to visualize frame snapshots, image semantics, and audio semantics throughout the video.

   Existing Machine Learning Models and Approaches:

   • Computer Vision models for frame sampling and feature extraction.
   • ASR models for audio transcription.
   • Language Models for semantic analysis and text segmentation.
   • User Interface frameworks for creating interactive visualizations.

2. Librarian The Librarian module focuses on cataloging and organizing existing video footage. It performs the following tasks:

   a. Footage Analysis:

   • Splits each video into frames and samples them using a predefined sampling policy.
   • Generates embeddings for each sampled frame, capturing key visual characteristics.

   b. Audio Analysis:

   • Extracts audio from the video footage.
   • Applies ASR to transcribe the audio into text.

   c. User Interface:

   • Develops a user-friendly interface to visualize frame snapshots, image semantics, and audio semantics for each video in the library.

   Existing Machine Learning Models and Approaches:

   • Similar to Atomizer, the Librarian module utilizes computer vision models, ASR models, and UI frameworks for video analysis, audio transcription, and visualization.

3. Director The Director module focuses on generating intelligent editing recommendations based on user preferences and existing footage. It performs the following tasks:

   a. Script Generation:

   • Given a general overview of the desired outcome, the system generates a suggested script for the video.
   • If a script already exists, the system creates a step-by-step sequence of cuts and required footage to match the script.

   b. Intelligent Recommendations:

   • For each cut in the script, the system generates intelligent recommendations for transitions, effects, color grading, and other editing elements.
   • These recommendations are based on the analysis of the existing footage, including visual characteristics and audio semantics.

   c. Post-processing:

   • Includes video stabilization, noise reduction, and color correction techniques to enhance the quality of the final output.

   d. Voice and Music Enhancement:

   • The system incorporates audio processing capabilities to improve voice clarity and enhance background music.
   • It reduces noise, equalizes audio levels, and provides suggestions for appropriate background music based on the video's mood and style.

   e. Real-Time Collaboration:

   • The project aims to develop a cloud-based infrastructure that enables real-time collaboration among multiple editors.
   • The AI assistant facilitates seamless sharing, reviewing, and merging of edits, reducing the need for manual file transfers.

   Existing Machine Learning Models and Approaches:

   • Script generation can be facilitated using Natural Language Processing (NLP) techniques such as language modeling and text generation.
   • Intelligent recommendations for transitions, effects, and color grading can be generated using computer vision models trained on large video datasets.
   • Post-processing techniques can be implemented using existing algorithms for video stabilization, noise reduction, and color correction.
   • Audio enhancement can be achieved using speech enhancement algorithms and music recommendation systems.
   • Real-time collaboration can be facilitated using cloud-based technologies, version control systems, and collaborative editing frameworks.

Overall, the AI-powered video editing system combines various existing machine learning models, computer vision algorithms, and audio processing techniques to streamline the video editing workflow, provide intelligent recommendations, and foster collaborative editing in real-time.