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2D-3D pose consistency-based conditional random fields for 3D human pose estimation

机译：基于2D-3D姿态一致性的3D人体姿态估计条件随机场

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摘要

This study considers the 3D human pose estimation problem in a single RGB image by proposing a conditional random field (CRF) model over 2D poses, in which the 3D pose is obtained as a byproduct of the inference process. The unary term of the proposed CRF model is defined based on a powerful heat-map regression network, which has been proposed for 2D human pose estimation. This study also presents a regression network for lifting the 2D pose to 3D pose and proposes the prior term based on the consistency between the estimated 3D pose and the 2D pose. To obtain the approximate solution of the proposed CRF model, the N-best strategy is adopted. The proposed inference algorithm can be viewed as sequential processes of bottom-up generation of 2D and 3D pose proposals from the input 2D image based on deep networks and top-down verification of such proposals by checking their consistencies. To evaluate the proposed method, we use two large-scale datasets: Human3.6M and HumanEva. Experimental results show that the proposed method achieves the state-of-the-art 3D human pose estimation performance.
机译：这项研究通过在2D姿势上提出条件随机场（CRF）模型来考虑单个RGB图像中的3D人体姿势估计问题，其中3D姿势是推理过程的副产品。基于强大的热图回归网络定义了所提出的CRF模型的一元术语，该网络已提出用于二维人体姿势估计。这项研究还提出了将2D姿势提升为3D姿势的回归网络，并基于估计的3D姿势和2D姿势之间的一致性提出了先前的术语。为了获得所提出的CRF模型的近似解，采用了N最佳策略。可以将所提出的推理算法看作是基于深度网络从输入2D图像由下而上生成2D和3D姿态建议的顺序过程，以及通过检查其一致性自上而下进行验证的顺序过程。为了评估所提出的方法，我们使用两个大型数据集：Human3.6M和HumanEva。实验结果表明，该方法可以达到最新的3D人体姿势估计性能。

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