DANCING LIKE A SUPERSTAR: ACTION GUIDANCE BASED ON POSE ESTIMATION AND CONDITIONAL POSE ALIGNMENT

Action Guidance (AG) aims at scoring how accurate the action is and giving guidance to the learners on how to correct their actions according to the standard instructive videos. AG has plenty of real-world applications such as sports training, rehabilitation treatment, and dance teaching. However, the problem of assessing the accuracy of action has almost no effective solution. In this paper, we describe a two-stage framework for action guidance. Firstly, we estimate the poses in the test video and standard video using person detection method and convolutional pose machines (CPMs). As for action guidance, we propose a network to compute the essential differences between two poses under different sizes, views, and locations. Extensive experiments with real-world and synthetic datasets demonstrate the effectiveness of our framework.