High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

CVPR 2018 Ting-Chun Wang • Ming-Yu Liu • Jun-Yan Zhu • Andrew Tao • Jan Kautz • Bryan Catanzaro

We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048x1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures.

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