  Occluded Person Re-Identification (ReID) is a metric learning task that
involves matching occluded individuals based on their appearance. While many
studies have tackled occlusions caused by objects, multi-person occlusions
remain less explored. In this work, we identify and address a critical
challenge overlooked by previous occluded ReID methods: the Multi-Person
Ambiguity (MPA) arising when multiple individuals are visible in the same
bounding box, making it impossible to determine the intended ReID target among
the candidates. Inspired by recent work on prompting in vision, we introduce
Keypoint Promptable ReID (KPR), a novel formulation of the ReID problem that
explicitly complements the input bounding box with a set of semantic keypoints
indicating the intended target. Since promptable re-identification is an
unexplored paradigm, existing ReID datasets lack the pixel-level annotations
necessary for prompting. To bridge this gap and foster further research on this
topic, we introduce Occluded-PoseTrack ReID, a novel ReID dataset with
keypoints labels, that features strong inter-person occlusions. Furthermore, we
release custom keypoint labels for four popular ReID benchmarks. Experiments on
person retrieval, but also on pose tracking, demonstrate that our method
systematically surpasses previous state-of-the-art approaches on various
occluded scenarios. Our code, dataset and annotations are available at
https://github.com/VlSomers/keypoint_promptable_reidentification.
