摘要
This paper presents a survey of image synthesis and editing with Generative Adversarial Networks（GANs）. GANs consist of two deep networks, a generator and a discriminator, which are trained in a competitive way. Due to the power of deep networks and the competitive training manner, GANs are capable of producing reasonable and realistic images, and have shown great capability in many image synthesis and editing applications.This paper surveys recent GAN papers regarding topics including, but not limited to, texture synthesis, image inpainting, image-to-image translation, and image editing.
This paper presents a survey of image synthesis and editing with Generative Adversarial Networks（GANs）. GANs consist of two deep networks, a generator and a discriminator, which are trained in a competitive way. Due to the power of deep networks and the competitive training manner, GANs are capable of producing reasonable and realistic images, and have shown great capability in many image synthesis and editing applications.This paper surveys recent GAN papers regarding topics including, but not limited to, texture synthesis, image inpainting, image-to-image translation, and image editing.
基金
supported by the National Key Technology R&D Program(No.2016YFB1001402)
the National Natural Science Foundation of China(No.61521002)
the Joint NSFC-ISF Research Program(No.61561146393)
Research Grant of Beijing Higher Institution Engineering Research Center and Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology
supported by the EPSRC CDE(No.EP/L016540/1)
作者简介
E-mail： xukun@tsinghua.edu.cn.Xian Wu is a PhD student in the Department of Computer Science and Technology, Tsinghua University. Before that, he received his bachelor degree in the same university in 2015. His research interests include image/video editing and computer vision.;Kun Xu is an associate professor in the Department of Computer Science and Technology, Tsinghua University. Before that, he received his bachelor and PhD degrees from the same university in 2005 and 2009, respectively. His research interests include realistic rendering and image/video editing.;Peter Hall is a professor in the Department of Computer Science at the University of Bath. He is also the director of the Media Technology Research Centre, Bath. He founded vision, video, and graphics network of excellence in the United Kingdom, and has served on the executive committee of the British Machine VisionConference since 2003. He has published works extensively in computer vision, especially where it interfaces with computer graphics. More recently he is developing an interest in robotics.
