43-Issue 7

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/3607047

Browse

Browsing 43-Issue 7 by Subject "Artificial intelligence"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    G-Style: Stylized Gaussian Splatting
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Kovács, Áron Samuel; Hermosilla, Pedro; Raidou, Renata Georgia; Chen, Renjie; Ritschel, Tobias; Whiting, Emily
    We introduce G -Style, a novel algorithm designed to transfer the style of an image onto a 3D scene represented using Gaussian Splatting. Gaussian Splatting is a powerful 3D representation for novel view synthesis, as-compared to other approaches based on Neural Radiance Fields-it provides fast scene renderings and user control over the scene. Recent pre-prints have demonstrated that the style of Gaussian Splatting scenes can be modified using an image exemplar. However, since the scene geometry remains fixed during the stylization process, current solutions fall short of producing satisfactory results. Our algorithm aims to address these limitations by following a three-step process: In a pre-processing step, we remove undesirable Gaussians with large projection areas or highly elongated shapes. Subsequently, we combine several losses carefully designed to preserve different scales of the style in the image, while maintaining as much as possible the integrity of the original scene content. During the stylization process and following the original design of Gaussian Splatting, we split Gaussians where additional detail is necessary within our scene by tracking the gradient of the stylized color. Our experiments demonstrate that G -Style generates high-quality stylizations within just a few minutes, outperforming existing methods both qualitatively and quantitatively
  • Thumbnail Image
    Item
    P-Hologen: An End-to-End Generative Framework for Phase-Only Holograms
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Park, JooHyun; Jeon, YuJin; Kim, HuiYong; Baek, SeungHwan; Kang, HyeongYeop; Chen, Renjie; Ritschel, Tobias; Whiting, Emily
    Holography stands at the forefront of visual technology, offering immersive, three-dimensional visualizations through the manipulation of light wave amplitude and phase. Although generative models have been extensively explored in the image domain, their application to holograms remains relatively underexplored due to the inherent complexity of phase learning. Exploiting generative models for holograms offers exciting opportunities for advancing innovation and creativity, such as semantic-aware hologram generation and editing. Currently, the most viable approach for utilizing generative models in the hologram domain involves integrating an image-based generative model with an image-to-hologram conversion model, which comes at the cost of increased computational complexity and inefficiency. To tackle this problem, we introduce P-Hologen, the first endto- end generative framework designed for phase-only holograms (POHs). P-Hologen employs vector quantized variational autoencoders to capture the complex distributions of POHs. It also integrates the angular spectrum method into the training process, constructing latent spaces for complex phase data using strategies from the image processing domain. Extensive experiments demonstrate that P-Hologen achieves superior quality and computational efficiency compared to the existing methods. Furthermore, our model generates high-quality unseen, diverse holographic content from its learned latent space without requiring pre-existing images. Our work paves the way for new applications and methodologies in holographic content creation, opening a new era in the exploration of generative holographic content. The code for our paper is publicly available on https://github.com/james0223/P-Hologen.