  Image manipulation with StyleGAN has been an increasing concern in recent
years.Recent works have achieved tremendous success in analyzing several
semantic latent spaces to edit the attributes of the generated images.However,
due to the limited semantic and spatial manipulation precision in these latent
spaces, the existing endeavors are defeated in fine-grained StyleGAN image
manipulation, i.e., local attribute translation.To address this issue, we
discover attribute-specific control units, which consist of multiple channels
of feature maps and modulation styles. Specifically, we collaboratively
manipulate the modulation style channels and feature maps in control units
rather than individual ones to obtain the semantic and spatial disentangled
controls. Furthermore, we propose a simple yet effective method to detect the
attribute-specific control units. We move the modulation style along a specific
sparse direction vector and replace the filter-wise styles used to compute the
feature maps to manipulate these control units. We evaluate our proposed method
in various face attribute manipulation tasks. Extensive qualitative and
quantitative results demonstrate that our proposed method performs favorably
against the state-of-the-art methods. The manipulation results of real images
further show the effectiveness of our method.
