We consider the problem of human parsing with partbased models. Most previous work in part-based models only considers rigid parts (e.g. torso, head, half

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Abstract. We show quantitative evidence that a full relational model of the body performs better at upper body parsing than the standard tree model, despite the need to adopt approximate inference and learning procedures. Our method uses an approximate search for inference, and an approximate structure learning method to learn. We compare our method to state of the art methods on our dataset (which depicts a wide range of poses), on the standard Buffy dataset, and on the reduced PASCAL dataset published recently. Our results suggest that the Buffy dataset over emphasizes poses where the arms hang down, and that leads to generalization problems.

We consider the problem of parsing human poses and recognizing their actions in static images 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 representation of parts is not necessarily appropriate. In this paper, we introduce hierarchical poselets-a new representation for modeling the pose configuration of human bodies. 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 bodies. The hierarchical poselets are organized in a hierarchical way via a structured model. Human parsing can be achieved by inferring the optimal labeling of this hierarchical model. The pose information captured by this hierarchical model can also be used as a intermediate representation for other high-level tasks. We demonstrate it in action recognition from static images.

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