高斯过程隐变量模型-运动捕捉MOCAP

Hierarchical GP-LVM Software ============================ This page describes examples of how to use the hierarchical Gaussian process latent variable model Software (HGPLVM). The hierarchical GP-LVM allows you to create hierarchies of Gaussian process models. With the toolbox two hierarchy examples are given below. The HGPLVM software can be downloaded here. Release Information ------------------- Current release is 0.1. As well as downloading the HGPLVM software you need to obtain the toolboxes specified below. | **Toolbox** | **Version** | |----------------------------------------------|-------------| | [NETLAB](/netlab/downloadFiles/vrs3p3) | 3.3 | | [PRIOR](/prior/downloadFiles/vrs0p131) | 0.131 | | [OPTIMI](/optimi/downloadFiles/vrs0p132) | 0.132 | | [DATASETS](/datasets/downloadFiles/vrs0p131) | 0.131 | | [NDLUTIL](/ndlutil/downloadFiles/vrs0p157) | 0.157 | | [MLTOOLS](/mltools/downloadFiles/vrs0p126) | 0.126 | | [MOCAP](/mocap/downloadFiles/vrs0p132) | 0.132 | | [KERN](/kern/downloadFiles/vrs0p166) | 0.166 | | [GP](/gp/downloadFiles/vrs0p12) | 0.12 | | [FGPLVM](/fgplvm/downloadFiles/vrs0p151) | 0.151 | #### Version 0.001 This is preliminary work released as a placeholder. ### Examples Two examples of hierarchical models are provided with the code, the first is an example where two interacting subjects are jointly model. Two subjects from the [CMU Mocap data base](http://mocap.cs.cmu.edu) approach each other and 'high five'. The hierarchy models the subjects separately and jointly. It can be run with the command ```matlab >> demHighFive1 ``` A visualisation of the result, including points that have been propagated through the hierarchy is given below.  *Joint visualisation of the two subjects that 'high five'. The points A, B, C, D, E, F, G and H have been propagated through the hierarchy and are shown on the right. Grey scale visualisations in the latent space have not been shown to keep the smaller plots clear.* A second example involves a subject modelled running and walking. In this case the separate limbs of the subject are split into a hierarchy as shown below.  *Hierarchical decomposition of the skeleton. The limbs and abdomen are leaf nodes, behind which we build a hierarchical structure.* This example can be reconstructed with ```matlab >> demWalkRun1 ``` Results of applying the hierarchical structure to a combined data set of a run and a walk, using two root nodes, one for the run and one for the walk, are shown below.  *Visualisation of a walk and run jointly using hierarchical structures. Again several points have been propagated through the hierarchy.* Page updated on Wed Feb 14 08:50:14 2007