Generative Fields: Uncovering Hierarchical Feature Control for StyleGAN via Inverted Receptive Fields
This work addresses the limitation of feature control in GANs for image generation, offering a method to directly manipulate features during synthesis, which is incremental but improves upon existing approaches.
The paper tackled the problem of controlling features in StyleGAN-generated images by introducing 'generative fields' to explain hierarchical feature synthesis, resulting in a new editing pipeline that achieves disentangled control without pre-training.
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.