Learning stick-figure models using nonparametric Bayesian priors over trees

We present a fully probabilistic stick-figure model that uses a nonparametric Bayesian distribution over trees for its structure prior. Sticks are represented by nodes in a tree in such a way that their parameter distributions are probabilistically centered around their parent-node. This prior enables the inference procedures to learn multiple explanations for motion-capture data, each of which could be trees of different depth and path lengths. Thus, the algorithm can automatically determine a reasonable distribution over the number of sticks in a given dataset and their hierarchical relationships. We provide experimental results on several motion-capture datasets, demonstrating the model's ability to recover plausible stick-figure structure, and also the model's robust behavior when faced with occlusion.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Edward Meeds, David A. Ross, Richard S. Zemel, Sam T. Roweis
• Keywords: Computer Vision, CVPR 2008, Motion-capture Datasets, Nonparametric Bayesian Distribution, Parameter Distributions, Plausible Stick-figure Structure, Probabilistic Stick-figure Model