DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation

Understand

This paper considers the task of articulated human pose estimation of multiple people in real world images.

• We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other.
• This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose.
• We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors.