The research progress of human pose estimation method based on deep learning is comprehensively summarized. On the basis of comparison and analysis of various single-person pose estimation methods, a variety of multi-person pose estimation algorithms are summarized from the top-down and bottom-up approaches. In the top-down approach, the solutions to local area overlap, articulation point confusion, and difficulty in detecting the articulation point of atypical parts of human body are mainly introduced. In the bottom-up approach, the contribution of clustering method to articulation point detection is emphasized. Representative methods to achieve excellent performance on current public datasets are compared and analyzed. The review enables researchers to understand and familiarize themselves with the existing research results in this field, expand research ideas and methods, and look forward to the possible research directions in the future.