Estimating 3D human pose from a single image suffers from severe ambiguity
since multiple 3D joint configurations may have the same 2D projection. The
state-of-the-art methods often rely on context modeling methods such as
pictorial structure model (PSM) or graph neural network (GNN) to reduce
ambiguity. However, there is no study that rigorously compares them side by
side. So we first present a general formula for context modeling in which both
PSM and GNN are its special cases. By comparing the two methods, we found that
the end-to-end training scheme in GNN and the limb length constraints in PSM
are two complementary factors to improve results. To combine their advantages,
we propose ContextPose based on attention mechanism that allows enforcing soft
limb length constraints in a deep network. The approach effectively reduces the
chance of getting absurd 3D pose estimates with incorrect limb lengths and
achieves state-of-the-art results on two benchmark datasets. More importantly,
the introduction of limb length constraints into deep networks enables the
approach to achieve much better generalization performance.

在通过单个图像估计三维人体姿势时，由于可能存在多个 3D 关节配置具有相同的 2D 投影，因此存在严重的歧义。本文提出了一种通用的公式来进行上下文建模，比较了 PSM 和 GNN 方法，发现二者互补，提出了基于注意机制的 ContextPose 方法来结合它们的优势，有效地减少了使用错误肢体长度产生的荒谬的 3D 姿势估计的机会，并在两个基准数据集上取得了最新的成果。

三维人体姿态估计中的上下文建模：一种统一视角

Context Modeling in 3D Human Pose Estimation: A Unified Perspective

Human pose estimation is a key step to action recognition. We propose a
method of estimating 3D human poses from a single image, which works in
conjunction with an existing 2D pose/joint detector. 3D pose estimation is
challenging because multiple 3D poses may correspond to the same 2D pose after
projection due to the lack of depth information. Moreover, current 2D pose
estimators are usually inaccurate which may cause errors in the 3D estimation.
We address the challenges in three ways: (i) We represent a 3D pose as a linear
combination of a sparse set of bases learned from 3D human skeletons. (ii) We
enforce limb length constraints to eliminate anthropomorphically implausible
skeletons. (iii) We estimate a 3D pose by minimizing the $L_1$-norm error
between the projection of the 3D pose and the corresponding 2D detection. The
$L_1$-norm loss term is robust to inaccurate 2D joint estimations. We use the
alternating direction method (ADM) to solve the optimization problem
efficiently. Our approach outperforms the state-of-the-arts on three benchmark
datasets.

提出了一种在现有 2D 姿态检测器的基础上，从单张图像中估计 3D 人体姿态的方法，通过线性组合稀疏基，实施限制并优化，得出该方法在三个基准数据集上的表现优于现有技术。