In general, human pose estimation methods are categorized into two approaches
according to their architectures: regression (i.e., heatmap-free) and
heatmap-based methods. The former one directly estimates precise coordinates of
each keypoint using convolutional and fully-connected layers. Although this
approach is able to detect overlapped and dense keypoints, unexpected results
can be obtained by non-existent keypoints in a scene. On the other hand, the
latter one is able to filter the non-existent ones out by utilizing predicted
heatmaps for each keypoint. Nevertheless, it suffers from quantization error
when obtaining the keypoint coordinates from its heatmaps. In addition, unlike
the regression one, it is difficult to distinguish densely placed keypoints in
an image. To this end, we propose a hybrid model for single-stage multi-person
pose estimation, named HybridPose, which mutually overcomes each drawback of
both approaches by maximizing their strengths. Furthermore, we introduce
self-correlation loss to inject spatial dependencies between keypoint
coordinates and their visibility. Therefore, HybridPose is capable of not only
detecting densely placed keypoints, but also filtering the non-existent
keypoints in an image. Experimental results demonstrate that proposed
HybridPose exhibits the keypoints visibility without performance degradation in
terms of the pose estimation accuracy.

本研究提出了一个称为 HybridPose 的混合模型，通过最大化两种方法的优点来互相克服它们的缺点，同时引入自相关损失来注入关键点坐标和可见性之间的空间依赖性，从而在不降低姿势估计准确性的情况下展示了关键点的可见性。

用于单阶段多人姿态估计的混合模型

Hybrid model for Single-Stage Multi-Person Pose Estimation

We introduce HybridPose, a novel 6D object pose estimation approach.
HybridPose utilizes a hybrid intermediate representation to express different
geometric information in the input image, including keypoints, edge vectors,
and symmetry correspondences. Compared to a unitary representation, our hybrid
representation allows pose regression to exploit more and diverse features when
one type of predicted representation is inaccurate (e.g., because of
occlusion). Different intermediate representations used by HybridPose can all
be predicted by the same simple neural network, and outliers in predicted
intermediate representations are filtered by a robust regression module.
Compared to state-of-the-art pose estimation approaches, HybridPose is
comparable in running time and accuracy. For example, on Occlusion Linemod
dataset, our method achieves a prediction speed of 30 fps with a mean ADD(-S)
accuracy of 47.5%, representing a state-of-the-art performance. The
implementation of HybridPose is available at
this https URL

HybridPose 是一种新颖的 6D 物体姿态估计方法，利用混合中间表示来表达输入图像中的不同几何信息，包括关键点，边缘向量和对称性对应关系。