H-GAN: the power of GANs in your Hands
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http://hdl.handle.net/10045/114586
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Campo DC | Valor | Idioma |
---|---|---|
dc.contributor | 3D Perception Lab | es_ES |
dc.contributor.author | Oprea, Sergiu | - |
dc.contributor.author | Karvounas, Giorgos | - |
dc.contributor.author | Martínez González, Pablo | - |
dc.contributor.author | Kyriazis, Nikolaos | - |
dc.contributor.author | Orts-Escolano, Sergio | - |
dc.contributor.author | Oikonomidis, Iason | - |
dc.contributor.author | Garcia-Garcia, Alberto | - |
dc.contributor.author | Tsoli, Aggeliki | - |
dc.contributor.author | Garcia-Rodriguez, Jose | - |
dc.contributor.author | Argyros, Antonis | - |
dc.contributor.other | Universidad de Alicante. Departamento de Tecnología Informática y Computación | es_ES |
dc.date.accessioned | 2021-04-28T16:34:34Z | - |
dc.date.available | 2021-04-28T16:34:34Z | - |
dc.date.created | 2020 | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://hdl.handle.net/10045/114586 | - |
dc.description.abstract | We present HandGAN (H-GAN), a cycle-consistent adversarial learning approach implementing multi-scale perceptual discriminators. It is designed to translate synthetic images of hands to the real domain. Synthetic hands provide complete ground-truth annotations, yet they are not representative of the target distribution of real-world data. We strive to provide the perfect blend of a realistic hand appearance with synthetic annotations. Relying on image-to-image translation, we improve the appearance of synthetic hands to approximate the statistical distribution underlying a collection of real images of hands. H-GAN tackles not only the cross-domain tone mapping but also structural differences in localized areas such as shading discontinuities. Results are evaluated on a qualitative and quantitative basis improving previous works. Furthermore, we relied on the hand classification task to claim our generated hands are statistically similar to the real domain of hand. | es_ES |
dc.description.sponsorship | Spanish Government PID2019-104818RB-I00 grant for the MoDeaAS project, supported with Feder funds. This work has also been supported by two Spanish national grants for PhD studies, FPU17/00166, and ACIF/2018/197 respectively. | es_ES |
dc.language | eng | es_ES |
dc.rights | © Universitat d'Alacant / Universidad de Alicante. Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) | es_ES |
dc.subject | Synthetic-to-real | es_ES |
dc.subject | Generative adversarial networks | es_ES |
dc.subject | Cycle-consistency | es_ES |
dc.subject | Perceptual discriminator | es_ES |
dc.subject.other | Arquitectura y Tecnología de Computadores | es_ES |
dc.title | H-GAN: the power of GANs in your Hands | es_ES |
dc.type | software | es_ES |
dc.peerreviewed | no | es_ES |
dc.relation.publisherversion | https://arxiv.org/abs/2103.15017 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104818RB-I00 | - |
dc.relation.projectID | info:eu-repo/grantAgreement/MECD//FPU17%2F00166 | - |
dc.rights.holder | Universidad de Alicante | - |
dc.rights.holder | Institute of Computer Science, FORTH, Greece | - |
Aparece en las colecciones: | Registro de Programas de Ordenador y Bases de Datos |
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Archivo | Descripción | Tamaño | Formato | |
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HGAN.pdf | Repositorio H-GAN: the power of GANs in your Hands | 581,63 kB | Adobe PDF | Abrir Vista previa |
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