One of the major challenges in multi-person pose estimation is instance-aware keypoint estimation. Previous methods address this problem by leveraging an off-the-shelf detector, heuristic post-grouping process or explicit instance identification process, hindering further improvements in the inference speed which is an important factor for practical applications. From the statistical point of view, those additional processes for identifying instances are necessary to bypass learning the high-dimensional joint distribution of human keypoints, which is a critical factor for another major challenge, the occlusion scenario. In this work, we propose a novel framework of single-stage instance-aware pose estimation by modeling the joint distribution of human keypoints with a mixture density model, termed as MDPose. Our MDPose estimates the distribution of human keypoints' coordinates using a mixture density model with an instance-aware keypoint head consisting simply of 8 convolutional layers. It is trained by minimizing the negative log-likelihood of the ground truth keypoints. Also, we propose a simple yet effective training strategy, Random Keypoint Grouping (RKG), which significantly alleviates the underflow problem leading to successful learning of relations between keypoints. On OCHuman dataset, which consists of images with highly occluded people, our MDPose achieves state-of-the-art performance by successfully learning the high-dimensional joint distribution of human keypoints. Furthermore, our MDPose shows significant improvement in inference speed with a competitive accuracy on MS COCO, a widely-used human keypoint dataset, thanks to the proposed much simpler single-stage pipeline.

翻译：多人姿态估计的主要挑战之一在于实例感知关键点估计。先前的方法通过利用现成检测器、启发式后分组过程或显式实例识别过程来解决此问题，但阻碍了推理速度（实际应用的重要因素）的进一步提升。从统计学角度来看，这些用于识别实例的额外过程是规避学习人体关键点高维联合分布所必需的，而该分布正是解决另一重大挑战（遮挡场景）的关键因素。本文提出了一种基于混合密度模型建模人体关键点联合分布的单阶段实例感知姿态估计新框架，称为MDPose。我们的MDPose通过仅由8个卷积层组成的实例感知关键点头，利用混合密度模型估计人体关键点坐标的分布，并通过最小化真实关键点的负对数似然进行训练。此外，我们还提出了一种简单而有效的训练策略——随机关键点分组（RKG），该策略显著缓解了数值下溢问题，从而成功学习关键点间关系。在包含高度遮挡人物图像的OCHuman数据集上，我们的MDPose通过成功学习人体关键点的高维联合分布，达到了最先进的性能。此外，得益于所提出的更简化的单阶段流水线，我们的MDPose在广泛使用的人体关键点数据集MS COCO上，在保持竞争性精度的同时，推理速度也获得了显著提升。