Gauthier, AlbanKerbl, BernhardLevallois, JérémyFaury, RobinThiery, Jean-MarcBoubekeur, TamyGarces, ElenaHaines, Eric2024-06-252024-06-2520241467-8659https://doi.org/10.1111/cgf.15151https://diglib.eg.org/handle/10.1111/cgf15151We propose MATUP, an upsampling filter for material super-resolution. Our method takes as input a low-resolution SVBRDF and upscales its maps so that their rendering under various lighting conditions fits upsampled renderings inferred in the radiance domain with pre-trained RGB upsamplers. We formulate our local filter as a compact Multilayer Perceptron (MLP), which acts on a small window of the input SVBRDF and is optimized using a data-fitting loss defined over upsampled radiance at various locations. This optimization is entirely performed at the scale of a single, independent material. Doing so, MATUP leverages the reconstruction capabilities acquired over large collections of natural images by pre-trained RGB models and provides regularization over self-similar structures. In particular, our light-weight neural filter avoids retraining complex architectures from scratch or accessing any large collection of low/high resolution material pairs - which do not actually exist at the scale RGB upsamplers are trained with. As a result, MATUP provides fine and coherent details in the upscaled material maps, as shown in the extensive evaluation we provide.CCS Concepts: Computing methodologies → Reflectance modeling; TexturingComputing methodologies → Reflectance modelingTexturing10.1111/cgf.1515111 pages