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Generative AI with Python and TensorFlow 2

You're reading from   Generative AI with Python and TensorFlow 2 Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Length 488 pages
Edition 1st Edition
Languages
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Authors (2):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Joseph Babcock Joseph Babcock
Author Profile Icon Joseph Babcock
Joseph Babcock
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Table of Contents (16) Chapters Close

Preface 1. An Introduction to Generative AI: "Drawing" Data from Models 2. Setting Up a TensorFlow Lab FREE CHAPTER 3. Building Blocks of Deep Neural Networks 4. Teaching Networks to Generate Digits 5. Painting Pictures with Neural Networks Using VAEs 6. Image Generation with GANs 7. Style Transfer with GANs 8. Deepfakes with GANs 9. The Rise of Methods for Text Generation 10. NLP 2.0: Using Transformers to Generate Text 11. Composing Music with Generative Models 12. Play Video Games with Generative AI: GAIL 13. Emerging Applications in Generative AI 14. Other Books You May Enjoy
15. Index

Re-enactment using pix2pix

Re-enactment is another mode of operation for the deepfakes setup. It is supposedly better at generating believable fake content compared to the replacement mode. In earlier sections, we discussed different techniques used to perform re-enactment, i.e. by focusing on gaze, expressions, the mouth, and so on.

We also discussed image-to-image translation architectures in Chapter 7, Style Transfer with GANs. Particularly, we discussed in detail how the pix2pix GAN is a powerful architecture which enables paired translation tasks. In this section, we will leverage the pix2pix GAN to develop a face re-enactment setup from scratch. We will work toward building a network where we can use our own face, mouth, and expressions to control Barack Obama's (former US president) face. We will go through each and every step, starting right from preparing the dataset, to defining the pix2pix architecture, to finally generating the output re-enactment. Let's...

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