Human Pose Estimation using a Joint Pixel-wise and Part-wise Formulation

Our goal is to detect humans and estimate their 2D pose in single images. In particular, handling cases of partial visibility where some limbs may be occluded or one person is partially occluding another. Two standard, but disparate, approaches have developed in the field: the first is the part based approach for layout type problems, involving optimising an articulated pictorial structure, the second is the pixel based approach for image labelling involving optimising a random field graph defined on the image. Our novel contribution is a formulation for pose estimation which combines these two models in a principled way in one optimisation problem and thereby inherits the advantages of both of them. Inference on this joint model finds the set of instances of persons in an image, the location of their joints, and a pixel-wise body part labelling. We achieve near or state of the art results on standard human pose data sets, and demonstrate the correct estimation for cases of self-occlusion, person overlap and image truncation.