An Inception-based Variational Autoencoder for Curves Generation and Interpolation

dc.contributor.authorBarzegar Khalilsaraei, Barzegar Khalilsaraei
dc.contributor.authorAugsdörfer, Ursula
dc.contributor.editorSkala, Václav
dc.date.accessioned2025-07-30T10:17:54Z
dc.date.available2025-07-30T10:17:54Z
dc.date.issued2025
dc.description.abstract-translatedIn this work, we introduce a novel approach for synthesizing new curves by leveraging Variational Autoencoder (VAE) latent space interpolation. Our method encodes existing ordered point sequ ences representing curves into a compact latent representation, enabling smooth and meaningful transitions between different curve shapes. By performing controlled interpolations in the learned latent space, we generate diverse, high-quality smooth curves that maintain structural coherence and geometric consistency. The proposed method is particularly useful for applications in shape design, procedural modeling, and data augmentation in geometric learning.en
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.doihttp://www.doi.org/10.24132/CSRN.2025-25
dc.identifier.issn2464-4617 (Print)
dc.identifier.issn2464-4625 (online)
dc.identifier.urihttp://hdl.handle.net/11025/62235
dc.language.isoenen
dc.publisherVaclav Skala - UNION Agencyen
dc.rights© Vaclav Skala - UNION Agencyen
dc.rights.accessopenAccessen
dc.subjectsyntéza tvarůcs
dc.subjectvariační autoenkodérycs
dc.subjectb-spline křivkycs
dc.subjectgenerativní návrhcs
dc.subject.translatedshape synthesisen
dc.subject.translatedvariational autoencodersen
dc.subject.translatedb-spline curvesen
dc.subject.translatedgenerative designen
dc.titleAn Inception-based Variational Autoencoder for Curves Generation and Interpolationen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.statusPeer revieweden
dc.type.versionpublishedVersionen
local.files.count1*
local.files.size4618504*
local.has.filesyes*

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