  Recent successes in generative modeling have accelerated studies on this
subject and attracted the attention of researchers. One of the most important
methods used to achieve this success is Generative Adversarial Networks (GANs).
It has many application areas such as; virtual reality (VR), augmented reality
(AR), super resolution, image enhancement. Despite the recent advances in hair
synthesis and style transfer using deep learning and generative modelling, due
to the complex nature of hair still contains unsolved challenges. The methods
proposed in the literature to solve this problem generally focus on making
high-quality hair edits on images. In this thesis, a generative adversarial
network method is proposed to solve the hair synthesis problem. While
developing this method, it is aimed to achieve real-time hair synthesis while
achieving visual outputs that compete with the best methods in the literature.
The proposed method was trained with the FFHQ dataset and then its results in
hair style transfer and hair reconstruction tasks were evaluated. The results
obtained in these tasks and the operating time of the method were compared with
MichiGAN, one of the best methods in the literature. The comparison was made at
a resolution of 128x128. As a result of the comparison, it has been shown that
the proposed method achieves competitive results with MichiGAN in terms of
realistic hair synthesis, and performs better in terms of operating time.
