Spatial Attention Guided Local Facial Attribute Editing

Facial attribute manipulation has attracted great attention from the public due to its wide range of applications. Aiming to smoothly manipulate the attributes of real facial images, it is critical to search for a proper latent code that aligns with the domain of pre-trained GAN for faithful inversion and controls the transformation within the scope of the attribute for precise editing. Previous methods mainly focused on improving the quality of reconstruction but often ignored the editing effect. To address this issue, we first propose a mapping network to manipulate latent code which is effective for diverse situations, and design a spatial attention network to predict binary mask of the certain attribute which encourages to only alter the relevant region of images and suppress irrelevant changes. In addition, we introduce a novel latent space into the GAN inversion framework which achieves high reconstruction quality especially preserving identity features and retains the ability to edit face attributes. Our methods pave the way to semantically meaningful and disentangled manipulations on both generated images and real images. Ex-perimental results indicate a clear improvement over the cur-rent state-of-the-art methods in various metrics.