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dc.rights.licenseopenen_US
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorBELMEKKI, Zakariae
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorGOMEZ, David
IDREF: 154885509
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorREUTER, Patrick
IDREF: 081908210
dc.contributor.authorLI, Jun
dc.contributor.authorMARTIN, Jean-Claude
dc.contributor.authorJENKINS, Karl W.
hal.structure.identifierESTIA INSTITUTE OF TECHNOLOGY
dc.contributor.authorCOUTURE, Nadine
ORCID: 0000-0001-7959-5227
IDREF: 111534275
dc.date.accessioned2024-12-14T12:02:06Z
dc.date.available2024-12-14T12:02:06Z
dc.date.conference2024-11-04
dc.identifier.isbn979-8-4007-0462-8en_US
dc.identifier.urioai:crossref.org:10.1145/3678957.3685712
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/203961
dc.description.abstractEnThere is a rising interest in animating realistic virtual agents for multiple purposes in diferent domains. Such a task requires systems capable of generating complex mental states on par with human emotional complexity. Considering the high representational capacity of Generative Adversarial Networks (GANs), it is only natural to consider them in such applications. In this work, we propose a conditional GAN model for generating sequences of facial expressions of categorical complex emotions. Trained on a scarce and highly imbalanced dataset, the proposed model is able to generate realistic variable-length sequences in a single inference step. These expressions of emotional states, of which there are 24 in total, follow the Facial Actions Coding System (FACS) formatting. In the absence of meaningful objective evaluation methods, we propose a deeplearning-based metric to assess the realism of generated Action Unit (AU) sequences: the Action Unit Fréchet Inception Distance (AUFID). Objective and subjective results validate the realism of our generated samples.
dc.language.isoENen_US
dc.publisherACMen_US
dc.rightsAttribution-ShareAlike 3.0 United States*
dc.rightsAttribution-NonCommercial 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/us/*
dc.sourcecrossref
dc.subject.enGenerative Adversarial Networks
dc.subject.enComplex Emotional States
dc.subject.enSynthetic Action Units
dc.subject.enFACS
dc.title.enGenerating Facial Expression Sequences of Complex Emotions with Generative Adversarial Networks
dc.typeCommunication dans un congrèsen_US
dc.identifier.doi10.1145/3678957.3685712en_US
dc.subject.halSciences de l'ingénieur [physics]en_US
bordeaux.page361-372en_US
bordeaux.hal.laboratoriesESTIA - Rechercheen_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.conference.title26th ACM International Conference on Multimodal Interactionen_US
bordeaux.countrycren_US
bordeaux.title.proceedingICMI '24: Proceedings of the 26th International Conference on Multimodal Interactionen_US
bordeaux.conference.citySan Joseen_US
bordeaux.import.sourcedissemin
hal.identifierhal-04838242
hal.version1
hal.date.transferred2024-12-14T12:02:09Z
hal.proceedingsouien_US
hal.conference.end2024-11-08
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
workflow.import.sourcedissemin
dc.rights.ccCC BY-NC-SAen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.spage=361-372&rft.epage=361-372&rft.au=BELMEKKI,%20Zakariae&GOMEZ,%20David&REUTER,%20Patrick&LI,%20Jun&MARTIN,%20Jean-Claude&rft.isbn=979-8-4007-0462-8&rft.genre=unknown


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