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Unconstrained Image generation with high realism is now possible using recentGenerative Adversarial Networks (GANs). However, it is quite challenging togenerate images with a given set of attributes. Recent methods use style-basedGAN models to perform image editing by leveraging the semantic hierarchypresent in the layers of the generator. We present Few-shot Latent-basedAttribute Manipulation and Editing (FLAME), a simple yet effective framework toperform highly controlled image editing by latent space manipulation.Specifically, we estimate linear directions in the latent space (of apre-trained StyleGAN) that controls semantic attributes in the generated image.In contrast to previous methods that either rely on large-scale attributelabeled datasets or attribute classifiers, FLAME uses minimal supervision of afew curated image pairs to estimate disentangled edit directions. FLAME canperform both individual and sequential edits with high precision on a diverseset of images while preserving identity. Further, we propose a novel task ofAttribute Style Manipulation to generate diverse styles for attributes such aseyeglass and hair. We first encode a set of synthetic images of the sameidentity but having different attribute styles in the latent space to estimatean attribute style manifold. Sampling a new latent from this manifold willresult in a new attribute style in the generated image. We propose a novelsampling method to sample latent from the manifold, enabling us to generate adiverse set of attribute styles beyond the styles present in the training set.FLAME can generate diverse attribute styles in a disentangled manner. Weillustrate the superior performance of FLAME against previous image editingmethods by extensive qualitative and quantitative comparisons. FLAME alsogeneralizes well on multiple datasets such as cars and churches.

Everything is There in Latent Space: Attribute Editing and Attribute Style Manipulation by StyleGAN Latent Space Exploration