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DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose Estimation

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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.

Linzhi Huang, Jiahao Liang, Weihong Deng• 2022

Related benchmarks

TaskDatasetResultRank
3D Human Pose EstimationMPI-INF-3DHP (test)
PCK89.5
559
3D Human Pose Estimation3DPW (test)
PA-MPJPE79.3
505
3D Human Pose EstimationHuman3.6M (S9, S11)
Average Error (MPJPE Avg)49.8
94
3D Human Pose EstimationHuman3.6M (S5, S6, S7, S8)
MPJPE52.2
23
3D Pose EstimationHuman3.6M GT 2D poses (Protocol #1)
MPJPE37.9
13
3D Human Pose Estimation3DHP 27-frame (test)
MPJPE (mm)75.4
8
3D Human Pose Estimation3DPW 27-frame (test)
MPJPE87.3
8
3D Human Pose EstimationPartial Human3.6M Target: S6, S7, S8 (S1+S5)
MPJPE47
6
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