  For successful semantic editing of real images, it is critical for a GAN
inversion method to find an in-domain latent code that aligns with the domain
of a pre-trained GAN model. Unfortunately, such in-domain latent codes can be
found only for in-range images that align with the training images of a GAN
model. In this paper, we propose BDInvert, a novel GAN inversion approach to
semantic editing of out-of-range images that are geometrically unaligned with
the training images of a GAN model. To find a latent code that is semantically
editable, BDInvert inverts an input out-of-range image into an alternative
latent space than the original latent space. We also propose a regularized
inversion method to find a solution that supports semantic editing in the
alternative space. Our experiments show that BDInvert effectively supports
semantic editing of out-of-range images with geometric transformations.
