Hairstyle Conversion With GAN Inversion! "LOHO.

3 main points
✔️ Latent Optimization of Hairstyles via Orthogonalization (LOHO), an optimization-based hairstyle transformation method using GAN Inversion
✔️ Improves the quality of the resulting image by performing optimization in two stages
✔️ Achieves higher FID score than existing hairstyle conversion methods

LOHO: Latent Optimization of Hairstyles via Orthogonalization
written by Rohit Saha,Brendan Duke,Florian Shkurti,Graham W. Taylor,Parham Aarabi
(Submitted on 5 Mar 2021 (v1), last revised 10 Mar 2021 (this version, v2))
Comments: Accepted by CVPR 2021
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

code：  

The images used in this article are from the paper or created based on it.

first of all

Several hair transformation methods using deep learning have been studied in the past, but there was a problem that the realism of the resulting image would be degraded if the structure of the source hair and the destination hair were different.

The Latent Optimization of Hairstyles via Orthogonalization (LOHO ) presented in this article generates natural images by using the optimization approach of GAN Inversion.

GAN Inversion

GAN Inversion is a technique for embedding an image into the latent space of a pre-trained GAN model, as opposed to generating an image from latent variables. If this can be done, various operations in the latent space of GAN can be performed on arbitrary images, and images can be easily edited. Another advantage of this technique is that the resulting image is of high quality because the GAN has been pre-trained. There are two approaches to embedding in the latent space, one is using the encoder and the other is the optimization approach, in our method we are using an optimization approach. The following images are the resultant images from the paper.