Computer Vision: Python Face Swap & Quick Deepfake in Colab

Custom Face Swap using Python and OpenCV & Deepfake Image Animation using 'First Order Motion Model' paper in Colab

4.25 (78 reviews)
Computer Vision: Python Face Swap & Quick Deepfake in Colab
4 hours
Mar 2024
last update
regular price

What you will learn

Python based Custom Face Swap Application with Image, Video and Camera.

Deepfake Videos based on First Order Motion Model Image Animation Paper

Build your own deep fake application using python

Deep Fake using Generative Adversarial Neural Networks

Why take this course?

🚀 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! 🎉✨


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Our review

Overall Course Rating: 4.25/5

Course Review:


  • Comprehensive Content: The course provides a solid introduction to the technology, making it suitable for beginners. It effectively covers the issues and solutions related to installing programs on Windows, which is beneficial for learners who may encounter these challenges.

  • Useful Explanations: The explanation of face swap techniques within the course is reported to be excellent and has been very helpful for understanding the process involved in image processing.

  • Supportive Resources: The course content seems to address common issues, which means that students can find solutions to problems they might encounter during their learning journey.

  • Multilingual Support: Although there are challenges with language, the availability of subtitles allows non-English speaking learners to engage with the material and potentially benefit from the course.


  • Flow of Information: Some users found that the flow of information within the course could be improved. There may be a need for a clearer structure or more step-by-step guidance.

  • Non-Google Cloud Approaches: One reviewer expressed the desire for the course to include non-Google Cloud approaches, as some learners have hardware like CUDA cards that they wish to utilize.

  • Technical Details on Deepfake Neural Network Implementation: There is a mention of deep fake neural network implementation in the title, but the course content lacks the technical details that some learners are expecting. This could be a point of disappointment for those interested in the technical aspects of the technology.

  • Installation Errors: One learner reported encountering frequent error messages during installation, particularly with the wheel file. This indicates potential issues that might be present for other students attempting to set up their environments.

  • Accessibility Concerns: The course may pose challenges to learners who do not speak English, as reliance on subtitles can make it difficult to fully grasp complex concepts without language proficiency.

Course Recommendation:

This course is highly recommended for individuals interested in learning about face swap techniques and gaining a broader understanding of image processing. It offers valuable insights into the technology, with a focus on practical applications and solutions to common issues. However, learners with non-Google Cloud setups and those looking for detailed technical specifications on deepfake neural networks should proceed with a clear understanding of what the course covers, or consider supplementing their learning with additional resources that address these topics in depth. For English speakers, this course appears to be an excellent resource for understanding and implementing face swap technologies within the constraints of image processing challenges. Non-English speakers may need additional support to fully benefit from the content.



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