In this paper we consider the problem of human pose estimation from a single
still image. We propose a novel approach where each location in the image votes
for the position of each keypoint using a convolutional neural net. The voting
scheme allows us to utilize information from the whole image, rather than rely
on a sparse set of keypoint locations. Using dense, multi-target votes, not
only produces good keypoint predictions, but also enables us to compute
image-dependent joint keypoint probabilities by looking at consensus voting.
This differs from most previous methods where joint probabilities are learned
from relative keypoint locations and are independent of the image. We finally
combine the keypoints votes and joint probabilities in order to identify the
optimal pose configuration. We show our competitive performance on the MPII
Human Pose and Leeds Sports Pose datasets.

本文提出了一种从单个静态图像中估计人体姿态的新方法，其采用卷积神经网络，使每个图像位置基于密集的多目标投票来预测人体关键点位置，并计算出相应关节的联合概率，最终通过关键点投票和联合概率来确定最优的人体姿态配置，并在 MPII Human Pose 数据集上展示了较为竞争的性能。