论文标题
Furrygan：高质量的前景感知图像合成
FurryGAN: High Quality Foreground-aware Image Synthesis
论文作者
论文摘要
前景感知的图像合成旨在生成图像及其前景面具。一种常见的方法是将图像制定为前景图像和背景图像的掩盖混合物。这是一个具有挑战性的问题，因为很容易到达琐碎的解决方案，在这些解决方案中，图像淹没了另一个图像，即面具变得完全充满或空虚，并且前景和背景没有有意义的分离。我们将Furrygan提供三个关键组成部分：1）施加前景图像和复合图像是现实的，2）将掩码设计为粗糙和细面膜的组合，以及3）在歧视器中通过辅助掩码预测器引导发电机。我们的方法用非常详细的α面膜产生逼真的图像，这些面膜以完全无监督的方式覆盖头发，皮毛和晶须。
Foreground-aware image synthesis aims to generate images as well as their foreground masks. A common approach is to formulate an image as an masked blending of a foreground image and a background image. It is a challenging problem because it is prone to reach the trivial solution where either image overwhelms the other, i.e., the masks become completely full or empty, and the foreground and background are not meaningfully separated. We present FurryGAN with three key components: 1) imposing both the foreground image and the composite image to be realistic, 2) designing a mask as a combination of coarse and fine masks, and 3) guiding the generator by an auxiliary mask predictor in the discriminator. Our method produces realistic images with remarkably detailed alpha masks which cover hair, fur, and whiskers in a fully unsupervised manner.
