Browsing by Author "Granskog, Jonathan"

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    CLIP-based Neural Neighbor Style Transfer for 3D Assets
    (The Eurographics Association, 2023) Mishra, Shailesh; Granskog, Jonathan; Babaei, Vahid; Skouras, Melina
    We present a method for transferring the style from a set of images to the texture of a 3D object. The texture of an asset is optimized with a differentiable renderer and losses using pretrained deep neural networks. More specifically, we utilize a nearest-neighbor feature matching (NNFM) loss with CLIP-ResNet50 that we extend to support multiple style images. We improve color accuracy and artistic control with an extra loss on user-provided or automatically extracted color palettes. Finally, we show that a CLIP-based NNFM loss provides a different appearance over a VGG-based one by focusing more on textural details over geometric shapes. However, we note that user preference is still subjective.
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    MesoGAN: Generative Neural Reflectance Shells
    (© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Diolatzis, Stavros; Novak, Jan; Rousselle, Fabrice; Granskog, Jonathan; Aittala, Miika; Ramamoorthi, Ravi; Drettakis, George; Hauser, Helwig and Alliez, Pierre
    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 field rendering. The primitive can be applied to surfaces as a neural reflectance shell; a thin volumetric layer above the surface with appearance parameters defined by a neural network. To construct the neural shell, we first generate a 2D feature texture using StyleGAN with carefully randomized Fourier features to support arbitrarily sized textures without repeating artefacts. We augment the 2D feature texture with a learned height feature, which aids the neural field renderer in producing volumetric parameters from the 2D texture. To facilitate filtering, and to enable end‐to‐end training within memory constraints of current hardware, we utilize a hierarchical texturing approach and train our model on multi‐scale synthetic datasets of 3D mesoscale structures. We propose one possible approach for conditioning MesoGAN on artistic parameters (e.g. fibre length, density of strands, lighting direction) and demonstrate and discuss integration into physically based renderers.
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    NeRF-Tex: Neural Reflectance Field Textures
    (The Eurographics Association, 2021) Baatz, Hendrik; Granskog, Jonathan; Papas, Marios; Rousselle, Fabrice; Novák, Jan; Bousseau, Adrien and McGuire, Morgan
    We investigate the use of neural fields for modeling diverse mesoscale structures, such as fur, fabric, and grass. Instead of using classical graphics primitives to model the structure, we propose to employ a versatile volumetric primitive represented by a neural reflectance field (NeRF-Tex), which jointly models the geometry of the material and its response to lighting. The NeRF-Tex primitive can be instantiated over a base mesh to ''texture'' it with the desired meso and microscale appearance. We condition the reflectance field on user-defined parameters that control the appearance. A single NeRF texture thus captures an entire space of reflectance fields rather than one specific structure. This increases the gamut of appearances that can be modeled and provides a solution for combating repetitive texturing artifacts. We also demonstrate that NeRF textures naturally facilitate continuous level-of-detail rendering. Our approach unites the versatility and modeling power of neural networks with the artistic control needed for precise modeling of virtual scenes. While all our training data is currently synthetic, our work provides a recipe that can be further extended to extract complex, hard-to-model appearances from real images.