Rendering 2024 - Symposium Track

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https://diglib.eg.org/handle/10.2312/3607012

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Browsing Rendering 2024 - Symposium Track by Subject "> Reflectance modeling"

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    High Quality Neural Relighting using Practical Zonal Illumination
    (The Eurographics Association, 2024) Lin, Arvin; Lin, Yiming; Li, Xiaohui; Ghosh, Abhijeet; Haines, Eric; Garces, Elena
    We present a method for high-quality image-based relighting using a practical limited zonal illumination field. Our setup can be implemented with commodity components with no dedicated hardware. We employ a set of desktop monitors to illuminate a subject from a near-hemispherical zone and record One-Light-At-A-Time (OLAT) images from multiple viewpoints. We further extrapolate sampling of incident illumination directions beyond the frontal coverage of the monitors by repeating OLAT captures with the subject rotation in relation to the capture setup. Finally, we train our proposed skip-assisted autoencoder and latent diffusion based generative method to learn a high-quality continuous representation of the reflectance function without requiring explicit alignment of the data captured from various viewpoints. This method enables smooth lighting animation for high-frequency reflectance functions and effectively manages to extend incident lighting beyond the practical capture setup's illumination zone. Compared to state-of-the-art methods, our approach achieves superior image-based relighting results, capturing finer skin pore details and extending to passive performance video relighting.
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    ReflectanceFusion: Diffusion-based text to SVBRDF Generation
    (The Eurographics Association, 2024) Xue, Bowen; Guarnera, Giuseppe Claudio; Zhao, Shuang; Montazeri, Zahra; Haines, Eric; Garces, Elena
    We introduce ReflectanceFusion (Reflectance Diffusion), a new neural text-to-texture model capable of generating high-fidelity SVBRDF maps from textual descriptions. Our method leverages a tandem neural approach, consisting of two modules, to accurately model the distribution of spatially varying reflectance as described by text prompts. Initially, we employ a pre-trained stable diffusion 2 model to generate a latent representation that informs the overall shape of the material and serves as our backbone model. Then, our ReflectanceUNet enables fine-tuning control over the material's physical appearance and generates SVBRDF maps. ReflectanceUNet module is trained on an extensive dataset comprising approximately 200,000 synthetic spatially varying materials. Our generative SVBRDF diffusion model allows for the synthesis of multiple SVBRDF estimates from a single textual input, offering users the possibility to choose the output that best aligns with their requirements. We illustrate our method's versatility by generating SVBRDF maps from a range of textual descriptions, both specific and broad. Our ReflectanceUNet model can integrate optional physical parameters, such as roughness and specularity, enhancing customization. When the backbone module is fixed, the ReflectanceUNet module refines the material, allowing direct edits to its physical attributes. Comparative evaluations demonstrate that ReflectanceFusion achieves better accuracy than existing text-to-material models, such as Text2Mat, while also providing the benefits of editable and relightable SVBRDF maps.