PoseField: an efficient mean-field based method for joint estimation of human pose, segmentation, and depth
Many models have been proposed to estimate human pose and segmentation by leveraging information from several sources. A standard approach is to formulate it in a dual decomposition framework. However, these models generally suffer from the problem of high computational complexity. In this work, we...
Autores principales: | Vineet, V, Sheasby, G, Warrell, J, Torr, PHS |
---|---|
Formato: | Conference item |
Lenguaje: | English |
Publicado: |
Springer
2013
|
Ejemplares similares
-
Simultaneous segmentation and pose estimation of humans using dynamic graph cuts
por: Kohli, P, et al.
Publicado: (2008) -
POSECUT: simultaneous segmentation and 3d pose estimation of humans using dynamic graph-cuts
por: Bray, M, et al.
Publicado: (2006) -
Filter-based mean-field inference for random fields with higher-order terms and product label-spaces
por: Vineet, V, et al.
Publicado: (2012) -
Filter-based mean-field inference for random fields with higher-order terms and product label-spaces
por: Vineet, V, et al.
Publicado: (2014) -
Gaussian process latent variable models for human pose estimation
por: Ek, CH, et al.
Publicado: (2008)