  Existing 3D human pose estimation algorithms trained on distortion-free
datasets suffer performance drop when applied to new scenarios with a specific
camera distortion. In this paper, we propose a simple yet effective model for
3D human pose estimation in video that can quickly adapt to any distortion
environment by utilizing MAML, a representative optimization-based
meta-learning algorithm. We consider a sequence of 2D keypoints in a particular
distortion as a single task of MAML. However, due to the absence of a
large-scale dataset in a distorted environment, we propose an efficient method
to generate synthetic distorted data from undistorted 2D keypoints. For the
evaluation, we assume two practical testing situations depending on whether a
motion capture sensor is available or not. In particular, we propose Inference
Stage Optimization using bone-length symmetry and consistency. Extensive
evaluation shows that our proposed method successfully adapts to various
degrees of distortion in the testing phase and outperforms the existing
state-of-the-art approaches. The proposed method is useful in practice because
it does not require camera calibration and additional computations in a testing
set-up.
