Estimating 3D Pose via Stochastic Search and Expectation Maximization

Ben Daubney*, Xianghua Xie

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper an approach is described to estimate 3D pose using a part based stochastic method. A proposed representation of the human body is explored defined over joints that employs full conditional models learnt between connected joints. This representation is compared against a popular alternative defined over parts using approximated limb conditionals. It is shown that using full limb conditionals results in a model that is far more representative of the original training data. Furthermore, it is demonstrated that Expectation Maximization is suitable for estimating 31) pose and better convergence is achieved when using full limb conditionals. To demonstrate the efficacy of the proposed method it is applied to the domain of 3D pose estimation using a single monocular image. Quantitative results are provided using the Human Eva dataset which confirm that the proposed method outperforms that of the competing part based model. In this work just a single model is learnt to represent all actions contained in the dataset which is applied to all subjects viewed from differing angles.

Original languageEnglish
Title of host publicationArticulated Motion and Deformable Objects
Subtitle of host publication6th International Conference, AMDO 2010, Port d’Andratx, Mallorca, Spain, July 7-9, 2010. Proceedings
EditorsFJ Perales, RB Fisher
Number of pages11
ISBN (Electronic)978-3-642-14061-7
ISBN (Print)978-3-642-14060-0
Publication statusPublished - 2010
Externally publishedYes
Event6th International Conference on Articulated Motion and Deformable Objects - Port d Andratx, Spain
Duration: 7 Jul 20109 Jul 2010

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference6th International Conference on Articulated Motion and Deformable Objects
CityPort d Andratx


  • 3D Pose Estimation
  • Expectation Maximization
  • Stochastic Search
  • Rigid Joint
  • Loose Limbed

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