  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.

Computer Vision and Pattern Recognition