DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation
This work addresses generalization issues in 3D human pose estimation for computer vision applications, representing an incremental improvement over existing augmentation techniques.
The paper tackled the problem of poor generalization in 3D human pose estimation due to limited dataset diversity by proposing DH-AUG, a pose augmentation method using a DH forward kinematics model, which improved generalization for video pose estimators and outperformed previous methods for single-frame estimators.
Due to the lack of diversity of datasets, the generalization ability of the pose estimator is poor. To solve this problem, we propose a pose augmentation solution via DH forward kinematics model, which we call DH-AUG. We observe that the previous work is all based on single-frame pose augmentation, if it is directly applied to video pose estimator, there will be several previously ignored problems: (i) angle ambiguity in bone rotation (multiple solutions); (ii) the generated skeleton video lacks movement continuity. To solve these problems, we propose a special generator based on DH forward kinematics model, which is called DH-generator. Extensive experiments demonstrate that DH-AUG can greatly increase the generalization ability of the video pose estimator. In addition, when applied to a single-frame 3D pose estimator, our method outperforms the previous best pose augmentation method. The source code has been released at https://github.com/hlz0606/DH-AUG-DH-Forward-Kinematics-Model-Driven-Augmentation-for-3D-Human-Pose-Estimation.