  Though GAN (Generative Adversarial Networks) based technique has greatly
advanced the performance of image synthesis and face translation, only few
works available in literature provide region based style encoding and
translation. We propose in this paper a region-wise normalization framework,
for region level face translation. While per-region style is encoded using
available approach, we build a so called RIN (region-wise normalization) block
to individually inject the styles into per-region feature maps and then fuse
them for following convolution and upsampling. Both shape and texture of
different regions can thus be translated to various target styles. A region
matching loss has also been proposed to significantly reduce the inference
between regions during the translation process. Extensive experiments on three
publicly available datasets, i.e. Morph, RaFD and CelebAMask-HQ, suggest that
our approach demonstrate a large improvement over state-of-the-art methods like
StarGAN, SEAN and FUNIT. Our approach has further advantages in precise control
of the regions to be translated. As a result, region level expression changes
and step by step make up can be achieved. The video demo is available at
https://youtu.be/ceRqsbzXAfk.
