Computer Vision News - August 2023

these challenges, data-driven approaches that leverage machine learning may provide viable solutions. Nevertheless, designing and training machine learning models that meet all these competing requirements remains a challenging task, requiring careful consideration of trade-offs between quality and efficiency. In my thesis, we propose novel learning-based solutions to address several key challenges in physically-based rendering and material digitization. Our approaches leverage various forms of neural networks to introduce innovative algorithms for radiance encoding, digital material generation, edition, and estimation. First, we present a visual attribute transfer framework for digital materials that can effectively generalize to new illumination conditions and geometric distortions. We showcase a use-case of this method for high-resolution material acquisition using a custom device. Additionally, we propose a generative model capable of synthesizing tileable textures from a single input image, which helps improve the quality of material rendering. Building upon recent work in neural fields, we also introduce a material representation that accurately encodes material reflectance while offering powerful editing and propagation capabilities. In addition to reflectance, we present a novel method for global illumination encoding that leverages carefully designed generative models to achieve significantly faster sampling than previous work. Finally, we propose two innovative methods for low-cost material digitization. With flatbed scanners as our capture device, we present a generative model that can provide high-resolution material reflectance estimations using a single image as input, while introducing an uncertainty quantification algorithm that increases its reliability and efficiency. Additionally, we present a novel method for digitizing fabric mechanical properties using depth images as input, which we extend with a perceptually-validated drape similarity metric. Overall, the contributions of this thesis represent significant advances in the fields of radiance encoding and digital material acquisition and edition, enhancing the quality, scalability, and efficiency of physically-based rendering pipelines. 9 Carlos Rodríguez - Pardo Computer Vision News