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