Computer Vision: Python Face Swap & Quick Deepfake in Colab

Computer Vision Python Face Swap Quick Deepfake in Colab

Description

Computer Vision: Python Face Swap & Quick Deepfake in Colab is a face swap training course using Python and OpenCV and deepfake image animation using First Order Motion Model in Colab. There is an old adage that says seeing is believing, but in the world of deepfake, not everything we see is necessarily true.

In the first half of the course, we will build a face swap program based on Python, and of course, before that, we will have an introduction to deep fake techniques, their advantages and disadvantages. Then we’ll prepare our system requirements and install Anaconda, our Python development environment and platform, and offer optional sessions for introductory Python training. In the following, we will learn more about deepfake by using an animation model. Of course, since working with Deepfake requires expensive GPUs, we will use the alternative and free Google Colab.

What you will learn in the Computer Vision: Python Face Swap & Quick Deepfake in Colab course:

  • Customized face swapping program based on Python with image, video and camera
  • Deepfake videos based on the First Order Motion model

Course details

Publisher: Udemy
Instructors: Abhilash Nelson
English language
Training level: introductory to advanced
Number of courses: 28
Duration: 4 hours and 5 minutes

Course topics Computer Vision: Python Face Swap & Quick Deepfake in Colab 2020-12:

Course prerequisites:

A decent computer configuration (preferably Windows) and an enthusiasm to research with Deepfake Technology

Pictures

Computer Vision Python Face Swap Quick Deepfake in Colab

Sample video

Installation guide

After Extract, view with your favorite Player.

Subtitle: No (based on the course specifications in the picture, this course does not have subtitles.)

Quality: 720

download link

Download part 1 – 1 GB

Download part 2 – 1 GB

Download part 3 – 600 MB

Password file(s): www.downloadly.ir

Size

2.6 GB

4.1/5 – (3931 points)

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