SVBRDF Diffusion

SVBRDF diffusion models aim to generate or estimate spatially varying bidirectional reflectance distribution functions (SVBRDFs), which describe how light interacts with complex surfaces. Current research focuses on developing generative diffusion models, often employing U-Net architectures, trained on large synthetic datasets of materials to accurately capture the intricate variations in reflectance. These models enable high-fidelity SVBRDF generation from textual descriptions or photographs, offering improved accuracy and control over material properties compared to previous methods. This advances realistic material rendering in computer graphics and facilitates more accurate material analysis in computer vision applications.