Abstract
StyleGAN has demonstrated the ability of GANs to synthesize highly-realistic faces of imaginary people from random noise. One limitation of GAN-based image generation is the difficulty of controlling the features of the generated image, due to the strong entanglement of the low-dimensional latent space. Previous work that aimed to control StyleGAN with image or text prompts modulated sampling in W latent space, which is more expressive than Z latent space. However, W space still has restricted expressivity since it does not control the feature synthesis directly; also the feature embedding in W space requires a pre-training process to reconstruct the style signal, limiting its application. This paper introduces the concept of "generative fields" to explain the hierarchical feature synthesis in StyleGAN, inspired by the receptive fields of convolution neural networks (CNNs). Additionally, we propose a new image editing pipeline for StyleGAN using generative field theory and the channel-wise style latent space S, utilizing the intrinsic structural feature of CNNs to achieve disentangled control of feature synthesis at synthesis time.
Abstract (translated)
StyleGAN 展示了生成对抗网络（GAN）能够从随机噪声中合成高度逼真的虚构人脸的能力。然而，基于 GAN 的图像生成的一个局限性是难以控制生成图像的特征，这是由于低维潜在空间中的强烈纠缠导致的。先前的工作试图通过使用图像或文本提示来调节 W 潜在空间中的采样以控制 StyleGAN，这种方式比 Z 潜在空间更具表现力。然而，W 空间仍然具有受限的表现力，因为它不能直接控制特征合成；此外，在 W 空间中进行特征嵌入需要一个预训练过程来重建风格信号，这限制了它的应用范围。 本文引入了“生成场”这一概念，用以解释 StyleGAN 中层次化特征合成的过程。该理论受到卷积神经网络（CNN）感受野的启发。此外，我们提出了一种新的基于生成场理论和通道级别的样式潜在空间 S 的 StyleGAN 图像编辑管线。这种方法利用了 CNN 内在结构特性，在图像生成过程中实现了特征合成的解耦控制。
URL
https://arxiv.org/abs/2504.17712
