Recovery of upper body poses in static images based on joints detection

Recovering human body poses from static images is challenging without prior knowledge of pose, appearance, background and clothing. In this paper, we propose a novel model-based upper poses recovery method via effective joints detection. In our research, three observables are firstly detected: face, skin, and torso. Then the joints are properly initialized according to the observables and some heuristic configuration constraints. Finally the sample-based Markov chain Monte Carlo (MCMC) method is employed to determine the final pose. The main contributions of this paper include a robust torso detector through maximizing a posterior estimation, effective joints initialization, and two continuous likelihood functions developed for effective pose inference. Experiments on 250 real world images show that our method can accurately recover upper body poses from images with a variety of individuals, poses, backgrounds and clothing.