  Human pose estimation is a fundamental yet challenging task in computer
vision, which aims at localizing human anatomical keypoints. However, unlike
human vision that is robust to various data corruptions such as blur and
pixelation, current pose estimators are easily confused by these corruptions.
This work comprehensively studies and addresses this problem by building
rigorous robust benchmarks, termed COCO-C, MPII-C, and OCHuman-C, to evaluate
the weaknesses of current advanced pose estimators, and a new algorithm termed
AdvMix is proposed to improve their robustness in different corruptions. Our
work has several unique benefits. (1) AdvMix is model-agnostic and capable in a
wide-spectrum of pose estimation models. (2) AdvMix consists of adversarial
augmentation and knowledge distillation. Adversarial augmentation contains two
neural network modules that are trained jointly and competitively in an
adversarial manner, where a generator network mixes different corrupted images
to confuse a pose estimator, improving the robustness of the pose estimator by
learning from harder samples. To compensate for the noise patterns by
adversarial augmentation, knowledge distillation is applied to transfer clean
pose structure knowledge to the target pose estimator. (3) Extensive
experiments show that AdvMix significantly increases the robustness of pose
estimations across a wide range of corruptions, while maintaining accuracy on
clean data in various challenging benchmark datasets.
