  Pre-training is playing an increasingly important role in learning generic
feature representation for Person Re-identification (ReID). We argue that a
high-quality ReID representation should have three properties, namely,
multi-level awareness, occlusion robustness, and cross-region invariance. To
this end, we propose a simple yet effective pre-training framework, namely
PersonMAE, which involves two core designs into masked autoencoders to better
serve the task of Person Re-ID. 1) PersonMAE generates two regions from the
given image with RegionA as the input and \textit{RegionB} as the prediction
target. RegionA is corrupted with block-wise masking to mimic common occlusion
in ReID and its remaining visible parts are fed into the encoder. 2) Then
PersonMAE aims to predict the whole RegionB at both pixel level and semantic
feature level. It encourages its pre-trained feature representations with the
three properties mentioned above. These properties make PersonMAE compatible
with downstream Person ReID tasks, leading to state-of-the-art performance on
four downstream ReID tasks, i.e., supervised (holistic and occluded setting),
and unsupervised (UDA and USL setting). Notably, on the commonly adopted
supervised setting, PersonMAE with ViT-B backbone achieves 79.8% and 69.5% mAP
on the MSMT17 and OccDuke datasets, surpassing the previous state-of-the-art by
a large margin of +8.0 mAP, and +5.3 mAP, respectively.
