Estimating three-dimensional (3D) human poses from the positions of two-dimensional (2D) joints has shown promising results. However, using 2D joint coordinates as input loses more information than image-based approaches and results in ambiguity. To overcome this problem, we combine bone length and camera parameters with 2D joint coordinates for input. This combination is more discriminative than the 2D joint coordinates in that it can improve the accuracy of the model’s prediction depth and alleviate the ambiguity that comes from projecting 3D coordinates into 2D space. Furthermore, we introduce direction constraints, which can better measure the difference between the ground truth and the output of the proposed model. The experimental results on the Human3.6M show that the method performed better than other state-of-the-art 3D human pose estimation approaches. The code is available at: https://github.com/XTU-PR-LAB/ExtraPose/.

中文翻译：

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使用先验知识估计 3D 人体姿势

从二维 (2D) 关节的位置估计三维 (3D) 人体姿势已显示出有希望的结果。然而，使用 2D 关节坐标作为输入会比基于图像的方法丢失更多信息并导致歧义。为了克服这个问题，我们将骨骼长度和相机参数与 2D 关节坐标结合起来进行输入。这种组合比 2D 关节坐标更具辨别力，因为它可以提高模型预测深度的准确性，并减轻将 3D 坐标投影到 2D 空间所带来的歧义。此外，我们引入了方向约束，可以更好地衡量地面实况与所提出模型的输出之间的差异。在Human3上的实验结果。图 6M 表明该方法的性能优于其他最先进的 3D 人体姿态估计方法。代码位于：https://github.com/XTU-PR-LAB/ExtraPose/。