<p>Recently, in the training loss, most 2D human pose estimation methods only introduce the position of each human keypoint to guide their training process and neglect that their topology invariance is helpful for localizing them more accurately. Thus, in this paper, we presented a novel 2D human pose estimation method with explicit anatomical keypoints structure constraints, which introduces the topology constraint term consisting of the differences between the distance and direction of the keypoint-to-keypoint and their groundtruth. More importantly, our proposed model can be plugged in the most existing bottom-up or top-down human pose estimation methods and improve their performance. The extensive experiments on the benchmark dataset: COCO keypoint dataset, show that our methods perform favorably against the most existing bottom-up and top-down human pose estimation methods, especially for Lite-HRNet, its AP scores separately raise by 2.9% and 3.3% on COCO val2017 and test-dev2017 datasets.</p>