Výsledky vyhledávání - "Desjardin, Eric"

Coupling dense point cloud correspondence and template model fitting for 3D human pose and shape reconstruction from a single depth image

Autor: Wang Xiaofang, Boukhayma Adnane, Prevost Stephanie, Desjardin Eric, Loscos Celine, Multon Franck

Publikováno v: International Conference on Interactive Media, Smart Systems and Emerging Technologies (IMET)
International Conference on Interactive Media, Smart Systems and Emerging Technologies (IMET), 2022, Limassol, Cyprus. pp.1-8, ⟨10.1109/IMET54801.2022.9929833⟩

International audience; In this paper, we address the problem of capturing both the shape and the pose of a character using a single depth sensor. Some previous works proposed to fit a parametric generic human template in the depth image, while other

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::837f61fc25bae4457fd093d3e2f0db59
https://hal.inria.fr/hal-03830670

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3D Human Shape and Pose from a Single Depth Image with Deep Dense Correspondence Enabled Model Fitting

Autor: Wang, X, Boukhayma, Adnane, Prevost, Stéphanie, Desjardin, Eric, Loscos, C, Multon, Franck

Publikováno v: Annual Conference of the European Association for Computer Graphics (Eurographics)
Annual Conference of the European Association for Computer Graphics (Eurographics), 2022, Reims, France. pp.1-3
Eurographics 2022-43nd Annual Conference of the European Association for Computer Graphics
Eurographics 2022-43nd Annual Conference of the European Association for Computer Graphics, Apr 2022, Reims, France. pp.1-2, ⟨10.2312/egp.20221008⟩

We propose a two-stage hybrid method, with no initialization, for 3D human shape and pose estimation from a single depth image, combining the benefits of deep learning and optimization. First, a convolutional neural network predicts pixel-wise dense

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37ddd8c6f4aa2e6153f284371ebe5083
https://hal.archives-ouvertes.fr/hal-03777581

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AGAT: Building and evaluating binary partition trees for image segmentation

Autor: Francky Randrianasoa, Jimmy, Kurtz, Camille, Desjardin, Eric, Passat, Nicolas

Publikováno v: SoftwareX, Vol 16, Iss, Pp 100855-(2021)
SoftwareX
SoftwareX, Elsevier, 2021, 16, pp.100855. ⟨10.1016/j.softx.2021.100855⟩

International audience; AGAT is a Java library dedicated to the construction,handling and evaluation of binary partition trees, a hierarchical data structure providing multiscale partitioning of images and, more generally, of valued graphs.On the one

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ef94870f50ee35586497b1c0b0f6d3e6
http://www.sciencedirect.com/science/article/pii/S2352711021001308

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Akademický článek

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Concepts et terminologies de l’imperfection de l’information géographique

Autor: Devillers, Rodolphe, Desjardin, Eric, De Runz, Cyril

Publikováno v: L’imperfection des données géographiques 1 Bases théoriques
Mireille Batton-Hubert; Eric Desjardin; François Pinet. L’imperfection des données géographiques 1 Bases théoriques, ISTE, 2019, 9781784056247

International audience

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c8d445f9d051f15af22433593fa8e9f6
https://hal.archives-ouvertes.fr/hal-02747631

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