In this paper, we propose to perform mutual learning inside the compositional model for human pose estimation. We conduct inference of the human pose in a compositional model which is composed of parts and subparts networks to better capture intrinsic prior knowledge. A macro–micro mutual learning mechanism is put forward to promote the information interaction between human limbs (parts) and joints (subparts). At first, a macro mutual learning module is proposed to conduct the information interaction macroscopically. Features representing the whole human body are leveraged in this case. Secondly, a micro mutual learning module is proposed to promote the information interaction within each limb triplet group to refine corresponding features microcosmically. Combining the macro and micro mutual learning modules promotes the information interaction across different levels. Besides, a novel Balanced Masked Mean Square Error (BMMSE) loss is put forward to solve the positive–negative samples imbalance problem which is more apparent in compositional model. Effectiveness of the method
is evaluated on MPII, LSP and COCO benchmarks. Our algorithm achieves leading positions on the leader board of these three datasets.