Articulated Pose Estimation with Flexible Mixtures of Parts

We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50% while being orders of magnitude faster.

Y. Yang, D. Ramanan. "Articulated Pose Estimation using Flexible Mixtures of Parts" Computer Vision and Pattern Recognition (CVPR) Colorado Springs, Colorado, June 2011.

Filename	Description	Size
README	Description of contents.	2KB
pose-release1.2-basic.tgz	Basic code for detection and pose estimation with pre-trained full-body and upper-body models.	1MB
pose-release1.2-full.tgz	Full code for training and testing, including BUFFY, PARSE, and INRIA image benchmarks.	89MB