  Existing cross-domain keypoint detection methods always require accessing the
source data during adaptation, which may violate the data privacy law and pose
serious security concerns. Instead, this paper considers a realistic problem
setting called source-free domain adaptive keypoint detection, where only the
well-trained source model is provided to the target domain. For the challenging
problem, we first construct a teacher-student learning baseline by stabilizing
the predictions under data augmentation and network ensembles. Built on this,
we further propose a unified approach, Mixup Augmentation and Progressive
Selection (MAPS), to fully exploit the noisy pseudo labels of unlabeled target
data during training. On the one hand, MAPS regularizes the model to favor
simple linear behavior in-between the target samples via self-mixup
augmentation, preventing the model from over-fitting to noisy predictions. On
the other hand, MAPS employs the self-paced learning paradigm and progressively
selects pseudo-labeled samples from `easy' to `hard' into the training process
to reduce noise accumulation. Results on four keypoint detection datasets show
that MAPS outperforms the baseline and achieves comparable or even better
results in comparison to previous non-source-free counterparts.
