We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.

Style Image (a) (b) (c) (d) (e) (f) Figure 1: Face image editing controlled via style images and segmentation masks. a) source images. b) reconstruction of the source image; segmentation mask shown as small inset. c-f) four separate edits; we show the image that provides new style information on top and show the part of the segmentation mask that gets edited as small inset. The results of the successive edits are shown in row two and three. The four edits change hair, mouth and eyes, skin tone, and background, respectively.

Figure 1: (a) and (b): input images; (c): the "two-face" generated by naively copying the left half from (a) and the right half from (b); (d): the "two-face" generated by our Image2StyleGAN++ framework.