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Título: | A joint model for 2D and 3D pose estimation from a single image |
Autor: | Simo-Serra, Edgar CSIC; Quattoni, Ariadna; Torras, Carme CSIC ORCID ; Moreno-Noguer, Francesc CSIC ORCID | Fecha de publicación: | 2013 | Editor: | Institute of Electrical and Electronics Engineers | Citación: | IEEE Conference on Computer Vision and Pattern Recognition: 3634-3641 (2013) | Resumen: | We 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. | Descripción: | Trabajo presentado a la CVPR celebrada en Portland del 23 al 28 de junio de 2013. | Versión del editor: | http://dx.doi.org/10.1109/CVPR.2013.466 | URI: | http://hdl.handle.net/10261/96248 | DOI: | 10.1109/CVPR.2013.466 | Identificadores: | doi: 10.1109/CVPR.2013.466 issn: 1063-6919 |
Aparece en las colecciones: | (IRII) Artículos |
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Joint Model for.pdf | 1,73 MB | Adobe PDF | Visualizar/Abrir |
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