Human pose is typically represented by a coordinate vector of body joints or their heatmap
embeddings. While easy for data processing, unrealistic pose estimates are admitted due to …

We propose a direct, regression-based approach to 2D human pose estimation from single
images. We formulate the problem as a sequence prediction task, which we solve using a …

Existing methods detect the keypoints in a non-differentiable way, therefore they can not
directly optimize the position of keypoints through back-propagation. To address this issue …

In computer vision, it is often observed that formulating regression problems as a
classification task often yields better performance. We investigate this curious phenomenon …

In human and hand pose estimation, heatmaps are a crucial intermediate representation for
a body or hand keypoint. Two popular methods to decode the heatmap into a final joint …

For tackling the task of 2D human pose estimation, the great majority of the recent methods
regard this task as a heatmap estimation problem, and optimize the heatmap prediction …

Human pose estimation typically encompasses three categories: heatmap-, regression-, and
integral-based methods. While integral-based methods possess advantages such as end-to …

For monocular RGB-based 3D pose and shape estimation, multiple solutions are often
feasible due to factors like occlusion and truncation. This work presents a multi-hypothesis …

Most recent methods formulate the task of human pose estimation as a heatmap estimation
problem, and use the overall L2 loss computed from the entire heatmap to optimize the …

Multi-person pose estimation aims at localizing the 2D keypoints (or body joints) for all the
people in the image. There are mainly two paradigms to perform this task: top-down and …