pixel2style2pixel
pixel2style2pixel
**Pixel2Style2Pixel**, or **pSp**, is an image-to-image translation framework that is based on a novel encoder that directly generates a series of style vectors which are fed into a pretrained [StyleGAN](https://paperswithcode.com/method/stylegan) generator, forming the extended $\mathcal{W+}$ latent space. Feature maps are first extracted using a standard feature pyramid over a [ResNet](https://paperswithcode.com/method/resnet) backbone. Then, for each of $18$ target styles, a small mapping network is trained to extract the learned styles from the corresponding feature map, where styles $(0-2)$ are generated from the small feature map, $(3-6)$ from the medium feature map, and $(7-18)$ from the largest feature map. The mapping network, map2style, is a small fully convolutional network, which gradually reduces spatial size using a set of 2-strided convolutions followed by [LeakyReLU](https://paperswithcode.com/method/leaky-relu) activations. Each generated 512 vector, is fed into [StyleGAN](https://paperswithcode.com/method/stylegan), starting from its matching affine transformation, $A$.
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://paperswithcode.com/paper/encoding-in-style-a-stylegan-encoder-for
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation