# Photo-realistic Facial Texture Transfer

## TL;DR

This paper introduces FaceTex, a photorealistic face texture transfer framework that preserves identity and facial structure while transferring textures, improving over prior methods by incorporating facial semantic regularization.

## Contribution

The paper proposes a novel facial semantic regularization and structure loss for face texture transfer, enhancing identity preservation and photorealism.

## Key findings

- Superior texture transfer results compared to state-of-the-art methods.
- Effective preservation of facial identity and structure during transfer.
- Demonstrated robustness on various face images.

## 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.