Realistic Hair Synthesis with Generative Adversarial Networks

TL;DR

This paper introduces a GAN-based method for realistic hair synthesis that achieves competitive visual quality and faster processing times compared to existing leading methods, evaluated on the FFHQ dataset.

Contribution

A novel GAN approach for real-time hair synthesis that outperforms current methods in speed while maintaining high visual realism.

Findings

Achieves realistic hair synthesis comparable to MichiGAN.

Operates faster than existing methods at 128x128 resolution.

Effective in hair style transfer and reconstruction tasks.

Abstract

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.