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dc.contributor.authorSutherland, Skylar
dc.contributor.authorEgger, Bernhard
dc.contributor.authorTenenbaum, Joshua
dc.date.accessioned2023-09-21T19:43:21Z
dc.date.available2023-09-21T19:43:21Z
dc.date.issued2023-09-13
dc.identifier.urihttps://hdl.handle.net/1721.1/152192
dc.description.abstractAbstract We propose a method for constructing generative models of 3D objects from a single 3D mesh and improving them through unsupervised low-shot learning from 2D images. Our method produces a 3D morphable model that represents shape and albedo in terms of Gaussian processes. Whereas previous approaches have typically built 3D morphable models from multiple high-quality 3D scans through principal component analysis, we build 3D morphable models from a single scan or template. As we demonstrate in the face domain, these models can be used to infer 3D reconstructions from 2D data (inverse graphics) or 3D data (registration). Specifically, we show that our approach can be used to perform face recognition using only a single 3D template (one scan total, not one per person). We extend our model to a preliminary unsupervised learning framework that enables the learning of the distribution of 3D faces using one 3D template and a small number of 2D images. Our approach is motivated as a potential model for the origins of face perception in human infants, who appear to start with an innate face template and subsequently develop a flexible system for perceiving the 3D structure of any novel face from experience with only 2D images of a relatively small number of familiar faces.en_US
dc.publisherSpringer USen_US
dc.relation.isversionofhttps://doi.org/10.1007/s11263-023-01870-2en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceSpringer USen_US
dc.titleBuilding 3D Generative Models from Minimal Dataen_US
dc.typeArticleen_US
dc.identifier.citationSutherland, Skylar, Egger, Bernhard and Tenenbaum, Joshua. 2023. "Building 3D Generative Models from Minimal Data."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-09-17T03:10:16Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.embargo.termsN
dspace.date.submission2023-09-17T03:10:16Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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