  To improve the generalization of 3D human pose estimators, many existing deep
learning based models focus on adding different augmentations to training
poses. However, data augmentation techniques are limited to the "seen" pose
combinations and hard to infer poses with rare "unseen" joint positions. To
address this problem, we present CameraPose, a weakly-supervised framework for
3D human pose estimation from a single image, which can not only be applied on
2D-3D pose pairs but also on 2D alone annotations. By adding a camera parameter
branch, any in-the-wild 2D annotations can be fed into our pipeline to boost
the training diversity and the 3D poses can be implicitly learned by
reprojecting back to 2D. Moreover, CameraPose introduces a refinement network
module with confidence-guided loss to further improve the quality of noisy 2D
keypoints extracted by 2D pose estimators. Experimental results demonstrate
that the CameraPose brings in clear improvements on cross-scenario datasets.
Notably, it outperforms the baseline method by 3mm on the most challenging
dataset 3DPW. In addition, by combining our proposed refinement network module
with existing 3D pose estimators, their performance can be improved in
cross-scenario evaluation.
