FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer

• URL: http://arxiv.org/abs/2307.09020v3
• Date: Tue, 2 Apr 2024 15:46:19 GMT
• Title: FISTNet: FusIon of STyle-path generative Networks for Facial Style Transfer
• Authors: Sunder Ali Khowaja, Lewis Nkenyereye, Ghulam Mujtaba, Ik Hyun Lee, Giancarlo Fortino, Kapal Dev,
• Abstract summary: StyleGAN methods have the tendency of overfitting that results in the introduction of artifacts in the facial images. We propose a FusIon of STyles (FIST) network for facial images that leverages pre-trained multipath style transfer networks.
• Score: 15.308837341075135
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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