论文标题
Stylespace分析：删除样式的控件图像生成
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
论文作者
论文摘要
我们使用在几个不同数据集上预测的模型探索和分析了Stylegan2的潜在风格空间。我们首先表明，thelespace是频道样式参数的空间，比以前的作品探索的其他中级潜在空间要明显更明显。接下来，我们描述了一种发现大量样式通道的方法，每种渠道都以高度局部和分离的方式来控制一个独特的视觉属性。第三，我们提出了一种简单的方法，用于识别使用验证的分类器或少量示例图像来控制特定属性的样式通道。通过这些样式空间控件对视觉属性进行操作表明，与以前的作品中提出的操作相比，它们的分解更好。为了证明这一点，我们利用新提出的属性依赖度度量。最后，我们演示了样式空间控件对操纵真实图像的适用性。我们的发现铺平了通过简单和直观的界面通过简单和直观的界面进行语义有意义且具有良好态度的图像操作的方式。
We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first show that StyleSpace, the space of channel-wise style parameters, is significantly more disentangled than the other intermediate latent spaces explored by previous works. Next, we describe a method for discovering a large collection of style channels, each of which is shown to control a distinct visual attribute in a highly localized and disentangled manner. Third, we propose a simple method for identifying style channels that control a specific attribute, using a pretrained classifier or a small number of example images. Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works. To show this, we make use of a newly proposed Attribute Dependency metric. Finally, we demonstrate the applicability of StyleSpace controls to the manipulation of real images. Our findings pave the way to semantically meaningful and well-disentangled image manipulations via simple and intuitive interfaces.
