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Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains
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GANs can generate photo-realistic images from the domain of their training data. However, those wanting to use them for creative purposes often want to generate imagery from a truly novel domain, a task which GANs are inherently unable to do. It is also desirable to have a level of control so that there is a degree of artistic direction rather than purely curation of random results. Here we present a method for interpolating between generative models of the StyleGAN architecture in a resolution dependent manner. This allows us to generate images from an entirely novel domain and do this with a degree of control over the nature of the output.
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Advancing Facial Stylization through Semantic Preservation Constraint and Pseudo-Paired Supervision
A StyleGAN fine-tuning recipe with semantic preservation and multi-level pseudo-paired supervision yields higher-fidelity facial stylization, plus free multimodal and reference-guided variants.
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