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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

Neural rendering

Rendering is the process of generating photo-realistic images from 2D or 3D computer models. The term neural rendering has recently emerged to describe rendering using a neural network. In traditional 3D rendering, we will need to first create a 3D model with a polygon mesh that describes the object's shape, color, and texture. Then, the lighting and camera position will be set and render the view into a 2D image.

There has been an ongoing research on 3D object generation, but it is still not able to generate satisfying results. We can take advantage of the advancement of GANs by projecting part of the 3D objects into 2D space. We then use GANs to enhance the image in 2D space, for example, to generate a realistic texture using style transfer before projecting that back into the 3D model. The top diagram in the following figure shows the general pipeline of this approach:

Figure 10.12 – Two common frameworks for neural rendering...

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