MIMIC-Pose: Implicit Membership Discrimination of Body Joints for Human Pose Estimation

The objective of human pose estimation is to accurately identify the positions of individual body joints for each person seen in an image for human kinematics modelling and analysis. A majority of current deep-learning approaches focus on utilizing feature learning to regress the coordinates of each individual joint. This can be thought of as a constrained point distribution optimization problem in the image plane. However, in the high-dimensional feature space, the feature distribution of joints as a significant constraint and discriminative condition, has not received enough attention yet. Here we propose a novel approach (MIMIC-Pose) that applies an implicit pair-wise keypoint membership constraint using those features to individual joint regression to ensure that the joints of the same person have higher mutual feature similarities compared with those of different people or the image backgrounds. We propose a novel link prediction integrated learning architecture with high-throughput tokens to learn such similarity feature embeddings to improve the prediction accuracy of individual joints in a single forward inference. Experimental results show that our approach can achieve competitive performance with much fewer model parameters and lower computational cost compared to state-of-the-art methods.