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Hace 4 días · Figure 1: We propose ToonAging, which can perform face re-aging and portrait style transfer in a single generation step. Since we adopted an exemplar-based approach, portraits can be transferred to various domains, enabling plausible re-aging progression simultaneously. As previously mentioned, despite various research efforts in both face re ...
Hace 5 días · However, architectures such as StyleGAN provide more degrees of freedom and better disentanglement regarding semantic attributes. One such space is the W-space which is the result of a fully connected MLP applied to the Z-space inputs to map them to a more disentangled space (Karras et al., 2021), characteristic of StyleGAN architectures.
Hace 4 días · Recently, StyleGAN (Karras et al., 2019; Karras et al., 2020) has shown remarkable performance in image generation. StyleGAN converts latent vectors to style vectors through a mapping network consisting of nonlinear fully connected layers, and then uses the style vectors to generate fake images through a synthesis network.
Hace 1 día · With improvements like StyleGAN, GANs now create lifelike, detailed images that greatly enhance the realism of digital content. Furthermore, these advancements expand the use of AI in fields requiring high-fidelity visuals, such as simulation and virtual reality. 6. Enhancing Creative AI Applications
Hace 2 días · Stylegan-xl: Scaling stylegan to large diverse datasets. In ACM SIGGRAPH 2022 Conference Proceedings, SIGGRAPH '22, New York, NY, USA, 2022. Association for Computing Machinery. Google Scholar Digital Library; Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park.
Hace 5 días · Examples models: StyleGAN. 5. Healthcare Applications. Generative models are transforming medical image analysis by enhancing and generating medical images. It can be used to analyze medical images like X-rays or MRIs, potentially aiding in early disease detection or treatment planning.
Hace 4 días · In the StyleGAN2 generator, three convolutional layers are introduced, which are used to further process the data, increase its dimensionality, and generate the final image. In this process, the generator learns how to create increasingly detailed synthetic images from 512-dimensional latent vectors (Hu et al. 2024 ).
