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A joint model for 2D and 3D pose estimation from a single image

AuthorsSimo-Serra, Edgar CSIC; Quattoni, Ariadna; Torras, Carme CSIC ORCID ; Moreno-Noguer, Francesc CSIC ORCID
Issue Date2013
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Conference on Computer Vision and Pattern Recognition: 3634-3641 (2013)
AbstractWe introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D poses when the feature detector has performed poorly. In this paper, we address this issue by jointly solving both the 2D detection and the 3D inference problems. For this purpose, we propose a Bayesian framework that integrates a generative model based on latent variables and discriminative 2D part detectors based on HOGs, and perform inference using evolutionary algorithms. Real experimentation demonstrates competitive results, and the ability of our methodology to provide accurate 2D and 3D pose estimations even when the 2D detectors are inaccurate.
DescriptionTrabajo presentado a la CVPR celebrada en Portland del 23 al 28 de junio de 2013.
Publisher version (URL)
Identifiersdoi: 10.1109/CVPR.2013.466
issn: 1063-6919
Appears in Collections:(IRII) Artículos

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