Computer Vision: Python Face Swap & Quick Deepfake in Colab

🚀 Welcome to "Python Face Swap & Quick Deepfake in Colab"! 🎫

Course Overview:

Dive into the fascinating world of Computer Vision and learn how to create custom face swaps using Python and OpenCV, as well as animate images with the 'First Order Motion Model' technique from a Cornell University paper. This course leverages Google Colab for its powerful GPU capabilities without the need for expensive hardware.

What You'll Learn:

Part One: Basic Python Face Swap 👁️✨

• Introduction to Deepfake Techniques: Understand the science and implications behind deepfakes.
• Setup & Dependencies: Get your computer ready with Anaconda, Python, and all necessary libraries.
• Python Programming Basics (Optional): For beginners, a crash course on Python essentials to get started.
• Static Image Face Swap: Learn how to perform face swapping using two static images.
• Realtime Video Face Swap: Extend your skills to realtime video from your webcam.
• Video Face Swap: Apply the technique to pre-saved videos on your computer.

Part Two: Advanced Deepfake with 'First Order Motion Model' 🤖🔥

• Preparing Your Google Drive: Set up your drive and upload necessary files, including a sample driving video.
• Cloning the Repository: Download the 'First Order Motion Model' code and face-alignment repository from Google Drive.
• Animation Setup: Install and set up the required libraries and organize your files into the correct folders.
• Cropping Videos: Use Python to crop the driving video for animation purposes.
• Model Inference: Download the pre-trained model and prepare it for animation.
• Video Animation: Bring your source images to life based on the driving video.
• Audio Mixing: Combine the animated video with its corresponding audio track.

Course Features:

• Hands-On Learning: Get practical experience by writing and running Python code.
• Responsible Use Guidelines: Learn how to ethically apply your new skills.
• Resource Sharing: Access the code, images, and weights used in this course.
• Certification Upon Completion: Add a valuable credential to your portfolio.

What's Included:

• Comprehensive Tutorials: Step-by-step guidance with illustrative examples.
• Code and Resources: All the code and resources required for the course are provided.
• Ethical Considerations: Discussions on the responsible use of deepfake technology.

Bibliographies and Reference Credits:

• NIPS Proceedings & Cornell University: For the "First Order Motion Model for Image Animation" research.
• GitHub & Github Pages: Resources for 'First Order Motion Model' and Face Swapping implementations.
• Learn OpenCV: For Delaunay Triangulation, Voronoi Diagrams, and Python face swapping.

Join Us on This Exciting Journey! 🚀🧠

Embark on a learning adventure that will equip you with the skills to master Python face swaps and deepfakes using cutting-edge computer vision techniques. Remember, this course is for educational and research purposes only. Let's explore the potential of artificial intelligence ethically and responsibly! 🎓🙏

Enroll Now & Start Your Deepfake Journey in Python! 🎉✨