Browsing by Author "Aittala, Miika"

Now showing items 1-6 of 6

    • Appearance-Driven Automatic 3D Model Simplification 

      Hasselgren, Jon; Munkberg, Jacob; Lehtinen, Jaakko; Aittala, Miika; Laine, Samuli (The Eurographics Association, 2021)
      We present a suite of techniques for jointly optimizing triangle meshes and shading models to match the appearance of reference scenes. This capability has a number of uses, including appearance-preserving simplification ...

    • Computational Methods for Capture and Reproduction of Photorealistic Surface Appearance 

      Aittala, Miika (2016-10-28)
      This thesis addresses the problem of capturing and reproducing surface material appearance from real-world examples for use in computer graphics applications. Detailed variation of color, shininess and small-scale shape ...

    • Data-driven Pixel Filter Aware MIP Maps for SVBRDFs 

      Kemppinen, Pauli; Aittala, Miika; Lehtinen, Jaakko (The Eurographics Association, 2023)
      We propose a data-driven approach for generating MIP map pyramids from SVBRDF parameter maps. We learn a latent material representation where linear image downsampling corresponds to linear prefiltering of surface reflectance. ...

    • Flexible SVBRDF Capture with a Multi-Image Deep Network 

      Deschaintre, Valentin; Aittala, Miika; Durand, Fredo; Drettakis, George; Bousseau, Adrien (The Eurographics Association and John Wiley & Sons Ltd., 2019)
      Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures ...

    • MesoGAN: Generative Neural Reflectance Shells 

      Diolatzis, Stavros; Novak, Jan; Rousselle, Fabrice; Granskog, Jonathan; Aittala, Miika; Ramamoorthi, Ravi; Drettakis, George (© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023)
      We introduce MesoGAN, a model for generative 3D neural textures. This new graphics primitive represents mesoscale appearance by combining the strengths of generative adversarial networks (StyleGAN) and volumetric neural ...

    • Video-Based Rendering of Dynamic Stationary Environments from Unsynchronized Inputs 

      Thonat, Theo; Aksoy, Yagiz; Aittala, Miika; Paris, Sylvain; Durand, Fredo; Drettakis, George (The Eurographics Association and John Wiley & Sons Ltd., 2021)
      Image-Based Rendering allows users to easily capture a scene using a single camera and then navigate freely with realistic results. However, the resulting renderings are completely static, and dynamic effects - such as ...