Collaging Class-specific GANs for Semantic Image Synthesis
Collaging Class-specific GANs for Semantic Image Synthesis
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DOI：
10.1109/iccv48922.2021.01415
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发表时间：
2021-10
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影响因子：
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通讯作者：
Yuheng Li;Yijun Li;Jingwan Lu;Eli Shechtman;Yong Jae Lee;Krishna Kumar Singh
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作者：
Yuheng Li;Yijun Li;Jingwan Lu;Eli Shechtman;Yong Jae Lee;Krishna Kumar Singh
提出了一种新的高分辨率语义图像合成方法。它由一个基本图像生成器和多个特定于类的生成器组成。基本生成器基于分割图生成高质量图像。为了进一步提高不同对象的质量，我们通过单独训练特定类别的模型来创建生成性对抗网络(GANS)库。这有几个好处，包括-每个类别的专用权重；每个模型的中心对齐的数据；来自其他来源的额外训练数据，可能具有更高的分辨率和质量；以及易于操作场景中的特定对象。实验表明，我们的方法可以生成高分辨率的高质量图像，同时通过使用特定于类的生成器来灵活地进行对象级控制。
We propose a new approach for high resolution semantic image synthesis. It consists of one base image generator and multiple class-specific generators. The base generator generates high quality images based on a segmentation map. To further improve the quality of different objects, we create a bank of Generative Adversarial Networks (GANs) by separately training class-specific models. This has several benefits including – dedicated weights for each class; centrally aligned data for each model; additional training data from other sources, potential of higher resolution and quality; and easy manipulation of a specific object in the scene. Experiments show that our approach can generate high quality images in high resolution while having flexibility of object-level control by using class-specific generators.