论文标题
用于图像和视频综合的生成对抗网络：算法和应用程序
Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
论文作者
论文摘要
生成对抗网络（GAN）框架已成为各种图像和视频综合任务的强大工具，允许以无条件或输入条件方式综合视觉内容。它使高分辨率的影像图像和视频能够产生，这项任务在先前的方法中具有挑战性或不可能。它还导致创建了内容创建的许多新应用程序。在本文中，我们提供了gan的概述，特别关注视觉合成算法和应用。我们介绍了几种稳定GAN培训的重要技术，该技术因臭名昭著而闻名。我们还讨论了其在图像翻译，图像处理，视频合成和神经渲染中的应用。
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 paper, 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.
