Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications

The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an unconditional or input-conditional manner. It has enabled the generation of high-resolution photorealistic images and videos,...

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Bibliographic Details
Published in:Proceedings of the IEEE 2021-05, Vol.109 (5), p.839-862
Main Authors: Liu, Ming-Yu, Huang, Xun, Yu, Jiahui, Wang, Ting-Chun, Mallya, Arun
Format: Article
Language:English
Subjects:
Algorithms
Computer vision
Generative adversarial networks
generative adversarial networks (GANs)
Generators
image and video synthesis
Image processing
Image resolution
Image synthesis
Linear programming
Neural networks
neural rendering
Photorealism
Rendering (computer vision)
Synthesis
Training data
Visualization
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Summary:The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an unconditional or input-conditional manner. It has enabled the generation of high-resolution photorealistic images and videos, a task that was challenging or impossible with prior methods. It has also led to the creation of many new applications in content creation. In this article, we provide an overview of GANs with a special focus on algorithms and applications for visual synthesis. We cover several important techniques to stabilize GAN training, which has a reputation for being notoriously difficult. We also discuss its applications to image translation, image processing, video synthesis, and neural rendering.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2021.3049196