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引用次数: 5

摘要

最近,随着生成对抗网络(GANs)和相应编码器的发展,对真实世界图像的处理得到了高度的阐述,这些编码器将真实世界的图像嵌入到潜在空间中。然而,由于失真和感知之间的权衡,GAN编码器的设计仍然是一个具有挑战性的任务。在本文中,我们指出现有的编码器不仅试图降低兴趣区域(如人脸区域)的失真,而且还试图降低非兴趣区域(如背景图案和障碍物)的失真。然而,现实图像中大多数的无兴趣区域位于out- distribution (OOD),无法通过生成模型进行理想的重构。此外,我们的经验发现,与兴趣区域重叠的非兴趣区域会扭曲兴趣区域的原始特征,例如,与面部区域重叠的麦克风会被反转成白胡子。因此,在保持感知质量的同时降低整个图像的失真是非常具有挑战性的。为了克服这种权衡,我们提出了一种简单而有效的编码器训练方案,称为IntereStyle,它通过关注感兴趣区域来促进编码。兴趣模式引导编码器解开感兴趣和不感兴趣区域的编码。为此,我们对无兴趣区域的信息进行迭代过滤,以调节无兴趣区域的负面影响。我们证明,与现有的最先进的编码器相比,IntereStyle实现了更低的失真和更高的感知质量。特别是该模型对原始图像的特征进行了鲁棒性保存,显示了鲁棒性的图像编辑和样式混合效果。我们将在审查后发布带有预训练模型的代码。
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IntereStyle: Encoding an Interest Region for Robust StyleGAN Inversion
Recently, manipulation of real-world images has been highly elaborated along with the development of Generative Adversarial Networks (GANs) and corresponding encoders, which embed real-world images into the latent space. However, designing encoders of GAN still remains a challenging task due to the trade-off between distortion and perception. In this paper, we point out that the existing encoders try to lower the distortion not only on the interest region, e.g., human facial region but also on the uninterest region, e.g., background patterns and obstacles. However, most uninterest regions in real-world images are located at out-of-distribution (OOD), which are infeasible to be ideally reconstructed by generative models. Moreover, we empirically find that the uninterest region overlapped with the interest region can mangle the original feature of the interest region, e.g., a microphone overlapped with a facial region is inverted into the white beard. As a result, lowering the distortion of the whole image while maintaining the perceptual quality is very challenging. To overcome this trade-off, we propose a simple yet effective encoder training scheme, coined IntereStyle, which facilitates encoding by focusing on the interest region. IntereStyle steers the encoder to disentangle the encodings of the interest and uninterest regions. To this end, we filter the information of the uninterest region iteratively to regulate the negative impact of the uninterest region. We demonstrate that IntereStyle achieves both lower distortion and higher perceptual quality compared to the existing state-of-the-art encoders. Especially, our model robustly conserves features of the original images, which shows the robust image editing and style mixing results. We will release our code with the pre-trained model after the review.
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