# Photo-realistic Facial Texture Transfer

arXiv: https://arxiv.org/abs/1706.04306 · HTML: https://mlsanity.com/paper/1706.04306/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2017.

## Built on by (most cited first, 3 of 3)

- [DCT-Net: Domain-Calibrated Translation for Portrait Stylization](https://mlsanity.com/paper/2207.02426/) (2022)
- [Cross-Domain Style Mixing for Face Cartoonization](https://mlsanity.com/paper/2205.12450/) (2022)
- [PP-GAN : Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN](https://mlsanity.com/paper/2306.13418/) (2023)

## Abstract (arXiv)

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.