  We propose to personalize a human pose estimator given a set of test images
of a person without using any manual annotations. While there is a significant
advancement in human pose estimation, it is still very challenging for a model
to generalize to different unknown environments and unseen persons. Instead of
using a fixed model for every test case, we adapt our pose estimator during
test time to exploit person-specific information. We first train our model on
diverse data with both a supervised and a self-supervised pose estimation
objectives jointly. We use a Transformer model to build a transformation
between the self-supervised keypoints and the supervised keypoints. During test
time, we personalize and adapt our model by fine-tuning with the
self-supervised objective. The pose is then improved by transforming the
updated self-supervised keypoints. We experiment with multiple datasets and
show significant improvements on pose estimations with our self-supervised
personalization.
