  With the excellent disentanglement properties of state-of-the-art generative
models, image editing has been the dominant approach to control the attributes
of synthesised face images. However, these edited results often suffer from
artifacts or incorrect feature rendering, especially when there is a large
discrepancy between the image to be edited and the desired feature set.
Therefore, we propose a new approach to mapping the latent vectors of the
generative model to the scaling factors through solving a set of multivariate
linear equations. The coefficients of the equations are the eigenvectors of the
weight parameters of the pre-trained model, which form the basis of a hyper
coordinate system. The qualitative and quantitative results both show that the
proposed method outperforms the baseline in terms of image diversity. In
addition, the method is much more time-efficient because you can obtain
synthesised images with desirable features directly from the latent vectors,
rather than the former process of editing randomly generated images requiring
many processing steps.
