FRIDU: Functional Map Refinement with Guided Image Diffusion

dc.contributor.authorRimon, Avigail Cohenen_US
dc.contributor.authorBen-Chen, Mirelaen_US
dc.contributor.authorLitany, Oren_US
dc.contributor.editorAttene, Marcoen_US
dc.contributor.editorSellán, Silviaen_US
dc.date.accessioned2025-06-20T07:40:43Z
dc.date.available2025-06-20T07:40:43Z
dc.date.issued2025
dc.description.abstractWe propose a novel approach for refining a given correspondence map between two shapes. A correspondence map represented as a functional map, namely a change of basis matrix, can be additionally treated as a 2D image. With this perspective, we train an image diffusion model directly in the space of functional maps, enabling it to generate accurate maps conditioned on an inaccurate initial map. The training is done purely in the functional space, and thus is highly efficient. At inference time, we use the pointwise map corresponding to the current functional map as guidance during the diffusion process. The guidance can additionally encourage different functional map objectives, such as orthogonality and commutativity with the Laplace-Beltrami operator. We show that our approach is competitive with state-of-the-art methods of map refinement and that guided diffusion models provide a promising pathway to functional map processing.en_US
dc.description.number5
dc.description.sectionheadersShape Analysis
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70203
dc.identifier.issn1467-8659
dc.identifier.pages15 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70203
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70203
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleFRIDU: Functional Map Refinement with Guided Image Diffusionen_US
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