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是实现实例感知的关键点估计。以往的方法通过采用现成的检测器、启发式后处理分组或显式实例识别过程来解决这一问题，从而阻碍了推理速度的进一步提升，而推理速度是实际应用中的重要因素。从统计学角度来看，这些额外的实例识别过程是必要的，目的是绕过学习人体关键点的高维联合分布（这是遮挡场景这一重大挑战的关键因素）。本文提出了一种新颖的单阶段实例感知姿态估计框架，通过混合密度模型对人体关键点的联合分布进行建模，称之为MDPose。我们的MDPose使用由仅8个卷积层构成的实例感知关键点头部，通过混合密度模型估计人体关键点坐标的分布。它通过最小化真实关键点的负对数似然进行训练。此外，我们提出了一种简单而有效的训练策略——随机关键点分组（RKG），该策略显著缓解了下溢问题，从而成功学习了关键点之间的关系。在由高度遮挡人体图像组成的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单的单阶段流水线，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，推理速度显著提升。