Title: Photo-realistic Facial Texture Transfer,
Abstract: Style transfer methods have achieved significant success in recent years with
the use of convolutional neural networks. However, many of these methods
concentrate on artistic style transfer with few constraints on the output image
appearance. We address the challenging problem of transferring face texture
from a style face image to a content face image in a photorealistic manner
without changing the identity of the original content image. Our framework for
face texture transfer (FaceTex) augments the prior work of MRF-CNN with a novel
facial semantic regularization that incorporates a face prior regularization
smoothly suppressing the changes around facial meso-structures (e.g eyes, nose
and mouth) and a facial structure loss function which implicitly preserves the
facial structure so that face texture can be transferred without changing the
original identity. We demonstrate results on face images and compare our
approach with recent state-of-the-art methods. Our results demonstrate superior
texture transfer because of the ability to maintain the identity of the
original face image.