  Occluded person re-identification is a challenging task as human body parts
could be occluded by some obstacles (e.g. trees, cars, and pedestrians) in
certain scenes. Some existing pose-guided methods solve this problem by
aligning body parts according to graph matching, but these graph-based methods
are not intuitive and complicated. Therefore, we propose a transformer-based
Pose-guided Feature Disentangling (PFD) method by utilizing pose information to
clearly disentangle semantic components (e.g. human body or joint parts) and
selectively match non-occluded parts correspondingly. First, Vision Transformer
(ViT) is used to extract the patch features with its strong capability. Second,
to preliminarily disentangle the pose information from patch information, the
matching and distributing mechanism is leveraged in Pose-guided Feature
Aggregation (PFA) module. Third, a set of learnable semantic views are
introduced in transformer decoder to implicitly enhance the disentangled body
part features. However, those semantic views are not guaranteed to be related
to the body without additional supervision. Therefore, Pose-View Matching (PVM)
module is proposed to explicitly match visible body parts and automatically
separate occlusion features. Fourth, to better prevent the interference of
occlusions, we design a Pose-guided Push Loss to emphasize the features of
visible body parts. Extensive experiments over five challenging datasets for
two tasks (occluded and holistic Re-ID) demonstrate that our proposed PFD is
superior promising, which performs favorably against state-of-the-art methods.
Code is available at https://github.com/WangTaoAs/PFD_Net
