Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion
  and Image Attribute Editing
Hao Li Mengqi Huang Lei Zhang Bo Hu Yi Liu Zhendong Mao
GAN-based image attribute editing firstly leverages GAN Inversion to project
real images into the latent space of GAN and then manipulates corresponding
latent codes. Recent inversion methods mainly utilize additional high-bit
features to improve image details preservation, as low-bit codes cannot
faithfully reconstruct source images, leading to the loss of details. However,
during editing, existing works fail to accurately complement the lost details
and suffer from poor editability. The main reason is they inject all the lost
details indiscriminately at one time, which inherently induces the position and
quantity of details to overfit source images, resulting in inconsistent content
and artifacts in edited images. This work argues that details should be
gradually injected into both the reconstruction and editing process in a
multi-stage coarse-to-fine manner for better detail preservation and high
editability. Therefore, a novel dual-stream framework is proposed to accurately
complement details at each stage. The Reconstruction Stream is employed to
embed coarse-to-fine lost details into residual features and then adaptively
add them to the GAN generator. In the Editing Stream, residual features are
accurately aligned by our Selective Attention mechanism and then injected into
the editing process in a multi-stage manner. Extensive experiments have shown
the superiority of our framework in both reconstruction accuracy and editing
quality compared with existing methods.