Efficient Hair Style Transfer with Generative Adversarial Networks. (arXiv:2210.12524v1 [cs.CV])

Despite the recent success of image generation and style transfer with
Generative Adversarial Networks (GANs), hair synthesis and style transfer
remain challenging due to the shape and style variability of human hair in
in-the-wild conditions. The current state-of-the-art hair synthesis approaches
struggle to maintain global composition of the target style and cannot be used
in real-time applications due to their high running costs on high-resolution
portrait images. Therefore, We propose a novel hairstyle transfer method,
called EHGAN, which reduces computational …

arxiv generative adversarial networks hair networks style transfer transfer