Most of the existing 3D human pose estimation approaches mainly focus on
predicting 3D positional relationships between the root joint and other human
joints (local motion) instead of the overall trajectory of the human body
(global motion). Despite the great progress achieved by these approaches, they
are not robust to global motion, and lack the ability to accurately predict
local motion with a small movement range. To alleviate these two problems, we
propose a relative information encoding method that yields positional and
temporal enhanced representations. Firstly, we encode positional information by
utilizing relative coordinates of 2D poses to enhance the consistency between
the input and output distribution. The same posture with different absolute 2D
positions can be mapped to a common representation. It is beneficial to resist
the interference of global motion on the prediction results. Second, we encode
temporal information by establishing the connection between the current pose
and other poses of the same person within a period of time. More attention will
be paid to the movement changes before and after the current pose, resulting in
better prediction performance on local motion with a small movement range. The
ablation studies validate the effectiveness of the proposed relative
information encoding method. Besides, we introduce a multi-stage optimization
method to the whole framework to further exploit the positional and temporal
enhanced representations. Our method outperforms state-of-the-art methods on
two public datasets. Code is available at
this https URL

提出了一种相对信息编码方法来加强姿势预测，通过利用 2D 姿势的相对坐标来获得位置信息和建立起时间上同一个人的多个姿势之间的联系来获得时间信息，而多阶段优化方法可以进一步挖掘这些加强的信息编码，并且该方法在两个公共数据集上显著优于现有的预测方法。