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With the excellent disentanglement properties of state-of-the-art generativemodels, image editing has been the dominant approach to control the attributesof synthesised face images. However, these edited results often suffer fromartifacts or incorrect feature rendering, especially when there is a largediscrepancy between the image to be edited and the desired feature set.Therefore, we propose a new approach to mapping the latent vectors of thegenerative model to the scaling factors through solving a set of multivariatelinear equations. The coefficients of the equations are the eigenvectors of theweight parameters of the pre-trained model, which form the basis of a hypercoordinate system. The qualitative and quantitative results both show that theproposed method outperforms the baseline in terms of image diversity. Inaddition, the method is much more time-efficient because you can obtainsynthesised images with desirable features directly from the latent vectors,rather than the former process of editing randomly generated images requiringmany processing steps.

FaceCook: Face Generation Based on Linear Scaling Factors