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Hands-On Image Generation with TensorFlow

You're reading from   Hands-On Image Generation with TensorFlow A practical guide to generating images and videos using deep learning

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781838826789
Length 306 pages
Edition 1st Edition
Languages
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Author (1):
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Soon Yau Cheong Soon Yau Cheong
Author Profile Icon Soon Yau Cheong
Soon Yau Cheong
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Fundamentals of Image Generation with TensorFlow
2. Chapter 1: Getting Started with Image Generation Using TensorFlow FREE CHAPTER 3. Chapter 2: Variational Autoencoder 4. Chapter 3: Generative Adversarial Network 5. Section 2: Applications of Deep Generative Models
6. Chapter 4: Image-to-Image Translation 7. Chapter 5: Style Transfer 8. Chapter 6: AI Painter 9. Section 3: Advanced Deep Generative Techniques
10. Chapter 7: High Fidelity Face Generation 11. Chapter 8: Self-Attention for Image Generation 12. Chapter 9: Video Synthesis 13. Chapter 10: Road Ahead 14. Other Books You May Enjoy

Video retargeting

Video synthesis is a broad term used for describing all forms of video generation. This can include generating video from random noise or words, to colorize black-and-white video, and so on, much like image generation.

In this section, we will look at a subgroup of video synthesis known as video retargeting. We will first look at two applications – face reenactment and pose transfer – and then introduce a powerful model that uses motion to generalize video targeting.

Face reenactment

Face reenactment was introduced along with face swapping in Chapter 9, Video Synthesis. Face reenactment in video synthesis involves transferring the facial expression of the driving video to the face in the target video. This is useful in animation and movie making. Recently, Zakharov et al. proposed a generative model that requires only a few target 2D images. This is done by using facial landmarks as intermediate features, as shown in the following diagram:

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