HSSAN: hair synthesis with style-guided spatially adaptive normalization on generative adversarial network

Hair synthesis plays a crucial role in generating facial images, but the complex textures and varied shapes of hair create obstacles in creating genuine images of hair on photographs utilizing generative adversarial networks. This research paper proposes an inventive normalization technique, HSSAN (Hair Style-Guided Spatially Adaptive Normalization), that incorporates four connected phases, each set exclusively for hair feature attributes, and uses them to improve the generator to generate hairstyle transfer images. The hair synthesizer generator utilizes several HSSAN residual blocks in the network framework, while the input modules comprise only an appearance module and a background module. Furthermore, a regularized loss function is introduced to regulate the style vector. Through the network, realistic hair generation images can be generated. We employed the FFHQ dataset to perform our experiments and observed that our methodology generates hair images surpassing existing generative adversarial network-based methods in terms of visual realism and Fréchet Inception Distance.