论文标题
改进的Stylegan嵌入：好的潜伏在哪里？
Improved StyleGAN Embedding: Where are the Good Latents?
论文作者
论文摘要
Stylegan能够产生与真实照片几乎没有区别的影像图像。找到给定图像的嵌入的反向问题构成了挑战。重建图像的嵌入并不总是适合编辑操作。在本文中，我们解决了找到两个重建图像并支持图像编辑任务的嵌入的问题。首先，我们引入了一个新的归一化空间，以分析重建潜在代码的多样性和质量。这个空间可以帮助回答一个良好的潜在代码位于潜在空间中的问题。其次，我们根据我们的分析提出了一种使用新颖的正则化方法提出的改进的嵌入算法。最后，我们分析了不同嵌入算法的质量。我们将结果与当前的最新方法进行比较，并在重建质量和编辑质量之间取得更好的权衡。
StyleGAN is able to produce photorealistic images that are almost indistinguishable from real photos. The reverse problem of finding an embedding for a given image poses a challenge. Embeddings that reconstruct an image well are not always robust to editing operations. In this paper, we address the problem of finding an embedding that both reconstructs images and also supports image editing tasks. First, we introduce a new normalized space to analyze the diversity and the quality of the reconstructed latent codes. This space can help answer the question of where good latent codes are located in latent space. Second, we propose an improved embedding algorithm using a novel regularization method based on our analysis. Finally, we analyze the quality of different embedding algorithms. We compare our results with the current state-of-the-art methods and achieve a better trade-off between reconstruction quality and editing quality.
