  With the surge in emerging technologies such as Metaverse, spatial computing,
and generative AI, the application of facial style transfer has gained a lot of
interest from researchers as well as startups enthusiasts alike. StyleGAN
methods have paved the way for transfer-learning strategies that could reduce
the dependency on the huge volume of data that is available for the training
process. However, StyleGAN methods have the tendency of overfitting that
results in the introduction of artifacts in the facial images. Studies, such as
DualStyleGAN, proposed the use of multipath networks but they require the
networks to be trained for a specific style rather than generating a fusion of
facial styles at once. In this paper, we propose a FusIon of STyles (FIST)
network for facial images that leverages pre-trained multipath style transfer
networks to eliminate the problem associated with lack of huge data volume in
the training phase along with the fusion of multiple styles at the output. We
leverage pre-trained styleGAN networks with an external style pass that use
residual modulation block instead of a transform coding block. The method also
preserves facial structure, identity, and details via the gated mapping unit
introduced in this study. The aforementioned components enable us to train the
network with very limited amount of data while generating high-quality stylized
images. Our training process adapts curriculum learning strategy to perform
efficient, flexible style and model fusion in the generative space. We perform
extensive experiments to show the superiority of FISTNet in comparison to
existing state-of-the-art methods.
