Learning hierarchical poselets for human parsing

We consider the problem of human parsing with part-based models. Most previous work in part-based models only considers rigid parts (e.g. torso, head, half limbs) guided by human anatomy. We argue that this represen-tation of parts is not necessarily appropriate for human parsing. In this paper, we introduce hierarchical poselets – a new representation for human parsing. Hierarchical poselets can be rigid parts, but they can also be parts that cover large portions of human bodies (e.g. torso + left arm). In the extreme case, they can be the whole bod-ies. We develop a structured model to organize poselets in a hierarchical way and learn the model parameters in a max-margin framework. We demonstrate the superior per-formance of our proposed approach on two datasets with aggressive pose variations. 1