  Parsing human body into semantic regions is crucial to human-centric
analysis. In this paper, we propose a segment-based parsing pipeline that
explores human pose information, i.e. the joint location of a human model,
which improves the part proposal, accelerates the inference and regularizes the
parsing process at the same time. Specifically, we first generate part segment
proposals with respect to human joints predicted by a deep model, then part-
specific ranking models are trained for segment selection using both pose-based
features and deep-learned part potential features. Finally, the best ensemble
of the proposed part segments are inferred though an And-Or Graph.
  We evaluate our approach on the popular Penn-Fudan pedestrian parsing
dataset, and demonstrate the effectiveness of using the pose information for
each stage of the parsing pipeline. Finally, we show that our approach yields
superior part segmentation accuracy comparing to the state-of-the-art methods.
