## 1 Introduction  
Recently, the development of human pose estimation (HPE) technology has stimulated research in the field of digital fitness. Digital fitness is garnering attention because it allows for exercise guidance remotely through automated systems, even when face-to-face interactions between instructors and learners are challenging. In particular, there has been an active pursuit of research [1; 2; 3; 4; 5; 6; 7] in which artificial intelligence analyzes exercise videos and supports self-training for exercise without communication with a sports instructor. Self-training is a learning method where individuals observe professional athletes' movements and mimic those actions to improve their own sports abilities. Learners observe actual sports players' movements, apply those movements to their own exercise routines, and analyze areas where they fall short. Through this process, they can self-correct and ultimately achieve movements that are similar to those of professional athletes. Systems that support this process automatically compare the movements of professional athletes and learners, and, by highlighting differences in movement, enable the learners to self-correct. Self-training is attracting attention, especially in high-cost sports fields, because it offers a more affordable way to learn. Particularly in the golf lesson market, where high costs were previously required, automated golf pose analysis technology has been introduced through image processing and human pose estimation research. This technology allows learners to self-study the movements of professional golfers, thus making it possible to receive golf lessons at a lower cost. The introduction of such technology has the effect of reducing the cost burden for those starting golf. In automated golf lesson systems, the key feature of self-training is utilizing human pose estimation technology to extract the swing postures of professional golfers and learners and identifying the differences in their movements to provide feedback to the learner. In this process, the swing movements of professional golfers are transformed into embedding vectors, and algorithms are used to detect discrepancies between movements. Conventional golf posture correction studies have extracted the joint positions of golfers through HPE technology and presented the differences in movements to learners by comparing similarities between the embedding vectors of golfer swings represented through a CNN (Convolutional Neural Network). However, there are two problems with this approach. Firstly, as golf swing movements tend to be performed at high speeds, there is a high likelihood of obtaining blurry or ambiguous images. As a result, the accuracy of the extracted coordinates in the pose estimation process may be compromised. Therefore, it is necessary to improve the accuracy of the extracted coordinates through a pose refinement process. Secondly, the conventional image embedding representation method does not contain enough information to compare golf swing movements. The golf swing should consider geometric elements, such as shoulder rotation angle, the angle between the shoulder and elbow, and pelvic angle, as well as the physical differences of the golfer performing the swing. In prior research [2], CNNs have been utilized to embed images of golf swings, facilitating the identification of discrepancies through the computation of similarity against reference embeddings. Nonetheless, this methodology presents problems. The derived similarity might be influenced by extraneous variables, such as background variations in images or disparities in the physical stature of the subjects under comparison. Consequently, the embedding vector's similarity can be affected by factors beyond the mere golf swing dynamics. Furthermore, while the system can detect a frame-wise deviation between two motions, it lacks the precision to identify the specific joints responsible for the discrepancy.