MatUp: Repurposing Image Upsamplers for SVBRDFs
dc.contributor.author | Gauthier, Alban | en_US |
dc.contributor.author | Kerbl, Bernhard | en_US |
dc.contributor.author | Levallois, Jérémy | en_US |
dc.contributor.author | Faury, Robin | en_US |
dc.contributor.author | Thiery, Jean-Marc | en_US |
dc.contributor.author | Boubekeur, Tamy | en_US |
dc.contributor.editor | Garces, Elena | en_US |
dc.contributor.editor | Haines, Eric | en_US |
dc.date.accessioned | 2024-06-25T10:19:53Z | |
dc.date.available | 2024-06-25T10:19:53Z | |
dc.date.issued | 2024 | |
dc.description.abstract | We 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. | en_US |
dc.description.number | 4 | |
dc.description.sectionheaders | Light and Textures | |
dc.description.seriesinformation | Computer Graphics Forum | |
dc.description.volume | 43 | |
dc.identifier.doi | 10.1111/cgf.15151 | |
dc.identifier.issn | 1467-8659 | |
dc.identifier.pages | 11 pages | |
dc.identifier.uri | https://doi.org/10.1111/cgf.15151 | |
dc.identifier.uri | https://diglib.eg.org/handle/10.1111/cgf15151 | |
dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
dc.subject | CCS Concepts: Computing methodologies → Reflectance modeling; Texturing | |
dc.subject | Computing methodologies → Reflectance modeling | |
dc.subject | Texturing | |
dc.title | MatUp: Repurposing Image Upsamplers for SVBRDFs | en_US |
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